diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..86fb5fd Binary files /dev/null and b/.DS_Store differ diff --git a/1 PA Decline/.DS_Store b/1 PA Decline/.DS_Store new file mode 100644 index 0000000..d7f241e Binary files /dev/null and b/1 PA Decline/.DS_Store differ diff --git a/1 PA Decline/00master.R b/1 PA Decline/00master.R new file mode 100644 index 0000000..2db076a --- /dev/null +++ b/1 PA Decline/00master.R @@ -0,0 +1,252 @@ +## +## Master script +## +## Based on the assignment work from the ISL-course +## +## Generation 2 - 02.december.2022 +## Code preparation for analysis on Denmarks Statistics server with enriched data set. +## +## Analysis plan: +## Table 1 +## Figure 1: Sankey plot (drop & hop colored) +## Table 2: Linear regression model of pase_6~. +## Table 3: Elastic net prediction models of drop and hop. Performance measures referenced in text. +## +## A Rmarkdown file could be created to write the initial report with main results. +## This code is a bit of a mess, as it is the result of several iterations. It works however. +## + +## ==================================================================== +# Step 0: Primary outcome +## ==================================================================== + +# Script to run as hop and drop + +pout <- "drop" # Drop to first quartile + +# decl_rel +# decl_abs +# drop +# hop + +## ==================================================================== +## Data +## ==================================================================== + + +# setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/") + +source(here::here("1 PA Decline/data_set.R")) +# Loading data-set from USB, to not store on computer + +source(here::here("1 PA Decline/data_format.R")) + +## ==================================================================== +## +## Baseline - by PASE group +## +## ==================================================================== + +ts_q <- X_tbl |> + select(vars) |> + mutate(pase_0_cut = factor(quantile_cut(pase_0, groups = 4)[[1]],ordered = TRUE)) |> + select(-pase_6,-pase_0) |> + tbl_summary(missing = "no", + by="pase_0_cut", + value = list(where(is.factor) ~ "2"), + type = list(mrs_0 ~ "categorical", + all_continuous() ~ "continuous2"), + statistic = list(all_continuous() ~ c("{N_nonmiss}", + "{median} ({p25}, {p75})", + "{min}, {max}", + "{mean} ({sd})")) +) |> + add_overall() |> + add_n () + +ts_q + +tbl_one_rtf <- file("table1.RTF", "w") +writeLines(ts_q%>%as_gt()%>%as_rtf(), tbl_one_rtf) +close(tbl_one_rtf) + +## ==================================================================== +# Drops and hops +## ==================================================================== + +# TRUEs are patients dropping +table(X_tbl$pase_0_cut!="1"&X_tbl$pase_6_cut=="1")/nrow(X_tbl[X_tbl$pase_0_cut!="1",]) + +# TRUEs are percentage of patients inactive before stroke being more active after +table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_0_cut=="1",]) + +# TRUEs are percentage of patients being more active after that were inactive before stroke +table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_6_cut!="1",]) + +# Difference between hop/no-hop +t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"]) + +summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +# Stationary low +t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"]) + +boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"]) + +## ==================================================================== +# Sankey plot +## ==================================================================== + +# source("sankey.R") +# p_delta + +## ==================================================================== +# Six months PASE: Bivariate and multivariate analyses +## ==================================================================== + +dta_lmreg <- X_tbl |> + select(vars) |> + mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) + +Hmisc::label(dta_lmreg$mrs_0) <- "Pre-stroke mRS >0" + +uv_reg <- tbl_uvregression(data=dta_lmreg, + method=lm, + y="pase_6", + show_single_row = where(is.factor), + estimate_fun = ~style_sigfig(.x,digits = 3), + pvalue_fun = ~style_pvalue(.x, digits = 3) +) + +mu_reg <- dta_lmreg |> + lm(formula=pase_6~.,data=_) |> + tbl_regression(show_single_row = where(is.factor), + estimate_fun = ~style_sigfig(.x,digits = 3), + pvalue_fun = ~style_pvalue(.x, digits = 3) + )|> + add_n() + +tbl_merge(list(uv_reg,mu_reg)) + + +## ==================================================================== +## +## Data variance +## +## Illustrating principal components. +## +## ==================================================================== + + +# source("PCA.R") +# +# +# pca22 +# ggsave("pc_plot.png",width = 18, height = 12, dpi = 300, limitsize = TRUE, units = "cm") + + +## ==================================================================== +## +## Models +## +## ==================================================================== + + +# source("assign_full.R") + +ls <- list() +for (i in c("drop","hop")){ + pout <- i + source("data_format.R") + source("regularisation_steps.R") +} + +# Loop to run regularised model on both drop and hop. +# Saved in list for printing and exporting the plot. + +## ==================================================================== +# Step 1: data merge +## ==================================================================== +tbl<-merge(ls$drop$RegularisedCoefs$'_data',ls$hop$RegularisedCoefs$'_data',by="name",all.x=T, sort=F) + +## ==================================================================== +# Step 2: table +## ==================================================================== +com_coef_tbl<-tbl%>% + gt()%>% + fmt_number( + columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric + rows = everything(), + decimals = 3)%>% + tab_spanner( + label = "DROP", + columns = 2:5 + )%>% + tab_spanner( + label = "HOP", + columns = 6:9 + )%>% + tab_header( + title = "Model coefficients", + subtitle = "Combined table of both full and regularised model coefficients" + ) + + +# paste0("Regularised model, (a=", +# best_alph, +# ", l=", +# round(best_lamb,3), +# ")") + +com_coef_tbl + +## ==================================================================== +# Step 3: export +## ==================================================================== +com_coef_rtf <- file("table2.RTF", "w") +writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf) +close(com_coef_rtf) + + + +## ==================================================================== +## +## Model performance +## +## Table with performance meassures for the two different models. +## +## ==================================================================== + +## ==================================================================== +# Step 1: data set +## ==================================================================== + +tbl<-data.frame(Meassure=c(names(ls$drop$ConfusionMatrx$byClass),"Mean AUC"), + "Drop"=round(c(ls$drop$ConfusionMatrx$byClass,ls$drop$AUROC["Mean"]),3), + "Hop"=round(c(ls$hop$ConfusionMatrx$byClass,ls$hop$AUROC["Mean"]),3)) + +## ==================================================================== +# Step 2: table +## ==================================================================== +tbl_perf<-tbl%>% + gt()%>% + tab_header( + title = "Performance meassures", + subtitle = "Combined table of both drop and hop" + ) + +tbl_perf + +## ==================================================================== +# Step 3: export +## ==================================================================== +tbl_perf_rtf <- file("table3.RTF", "w") +writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf) +close(tbl_perf_rtf) + + + diff --git a/1 PA Decline/ESOC2023.qmd b/1 PA Decline/ESOC2023.qmd new file mode 100644 index 0000000..3e9c75b --- /dev/null +++ b/1 PA Decline/ESOC2023.qmd @@ -0,0 +1,42 @@ +--- +title: "ESOC2023" +format: html +editor: visual +--- + +```{r} +library(plotly) + +df + +fig <- plot_ly( + type = "sankey", + orientation = "h", + + node = list( + label = c("A1", "A2", "B1", "B2", "C1", "C2"), + color = c("blue", "blue", "blue", "blue", "blue", "blue"), + pad = 15, + thickness = 20, + line = list( + color = "black", + width = 0.5 + ) + ), + + link = list( + source = c(0,1,0,2,3,3), + target = c(2,3,3,4,4,5), + value = c(8,4,2,8,4,2) + ) + ) +fig <- fig %>% layout( + title = "Basic Sankey Diagram", + font = list( + size = 10 + ) +) + +fig + +``` diff --git a/1 PA Decline/Fra DDV/.DS_Store b/1 PA Decline/Fra DDV/.DS_Store new file mode 100644 index 0000000..6f49129 Binary files /dev/null and b/1 PA Decline/Fra DDV/.DS_Store differ diff --git a/1 PA Decline/Fra DDV/240620/.DS_Store b/1 PA Decline/Fra DDV/240620/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/1 PA Decline/Fra DDV/240620/.DS_Store differ diff --git a/1 PA Decline/Fra DDV/240620/calibration.png b/1 PA Decline/Fra DDV/240620/calibration.png new file mode 100755 index 0000000..1520121 Binary files /dev/null and b/1 PA Decline/Fra DDV/240620/calibration.png differ diff --git a/1 PA Decline/Fra DDV/240620/calibration_imp.png b/1 PA Decline/Fra DDV/240620/calibration_imp.png new file mode 100755 index 0000000..03c1c2b Binary files /dev/null and b/1 PA Decline/Fra DDV/240620/calibration_imp.png differ diff --git a/1 PA Decline/Fra DDV/240620/pa_change_analyses.docx b/1 PA Decline/Fra DDV/240620/pa_change_analyses.docx new file mode 100755 index 0000000..a67915a Binary files /dev/null and b/1 PA Decline/Fra DDV/240620/pa_change_analyses.docx differ diff --git a/1 PA Decline/Fra DDV/240620/pa_change_tbl1.docx b/1 PA Decline/Fra DDV/240620/pa_change_tbl1.docx new file mode 100755 index 0000000..4be0389 Binary files /dev/null and b/1 PA Decline/Fra DDV/240620/pa_change_tbl1.docx differ diff --git a/1 PA Decline/Fra DDV/240624/.DS_Store b/1 PA Decline/Fra DDV/240624/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/1 PA Decline/Fra DDV/240624/.DS_Store differ diff --git a/1 PA Decline/Fra DDV/240624/calibration_imp.png b/1 PA Decline/Fra DDV/240624/calibration_imp.png new file mode 100644 index 0000000..ec258ed Binary files /dev/null and b/1 PA Decline/Fra DDV/240624/calibration_imp.png differ diff --git a/1 PA Decline/Fra DDV/240624/pa_change_analyses.docx b/1 PA Decline/Fra DDV/240624/pa_change_analyses.docx new file mode 100644 index 0000000..a52430c Binary files /dev/null and b/1 PA Decline/Fra DDV/240624/pa_change_analyses.docx differ diff --git a/1 PA Decline/Fra DDV/241002/.DS_Store b/1 PA Decline/Fra DDV/241002/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/1 PA Decline/Fra DDV/241002/.DS_Store differ diff --git a/1 PA Decline/Fra DDV/241002/excluded.R b/1 PA Decline/Fra DDV/241002/excluded.R new file mode 100755 index 0000000..b036bf5 --- /dev/null +++ b/1 PA Decline/Fra DDV/241002/excluded.R @@ -0,0 +1,9 @@ +targets::tar_read(df_all_data_formatted) |> + get_vars(c("clin","lifestyle","ses", "assess.pred")) |> + dplyr::mutate(exclude=ifelse(is.na(pase_0)|is.na(pase_4),"Excluded","Included"))|> + dplyr::select(-pase_0,-pase_4) |> + dplyr::select(exclude,soc_status_nowork, fam_indk_hl, edu_level_hl)|> + gtsummary::tbl_summary(by=exclude) |> + gtsummary::add_p() |> + fix_labels() |> + mask_micro_summary(micro.n = 5) diff --git a/1 PA Decline/Fra DDV/241002/minimal_perf_all.docx b/1 PA Decline/Fra DDV/241002/minimal_perf_all.docx new file mode 100755 index 0000000..0c34411 Binary files /dev/null and b/1 PA Decline/Fra DDV/241002/minimal_perf_all.docx differ diff --git a/1 PA Decline/Fra DDV/241002/mmrm.docx b/1 PA Decline/Fra DDV/241002/mmrm.docx new file mode 100644 index 0000000..78b50ae Binary files /dev/null and b/1 PA Decline/Fra DDV/241002/mmrm.docx differ diff --git a/1 PA Decline/Fra DDV/241002/mmrm_female.docx b/1 PA Decline/Fra DDV/241002/mmrm_female.docx new file mode 100755 index 0000000..a71a782 Binary files /dev/null and b/1 PA Decline/Fra DDV/241002/mmrm_female.docx differ diff --git a/1 PA Decline/Fra DDV/241002/mmrm_male.docx b/1 PA Decline/Fra DDV/241002/mmrm_male.docx new file mode 100755 index 0000000..f9a6b80 Binary files /dev/null and b/1 PA Decline/Fra DDV/241002/mmrm_male.docx differ diff --git a/1 PA Decline/Fra DDV/241002/pa_change_missings.docx b/1 PA Decline/Fra DDV/241002/pa_change_missings.docx new file mode 100755 index 0000000..14326e3 Binary files /dev/null and b/1 PA Decline/Fra DDV/241002/pa_change_missings.docx differ diff --git a/1 PA Decline/Fra DDV/241002/~$mmrm.docx b/1 PA Decline/Fra DDV/241002/~$mmrm.docx new file mode 100644 index 0000000..b367b80 Binary files /dev/null and b/1 PA Decline/Fra DDV/241002/~$mmrm.docx differ diff --git a/1 PA Decline/Fra DDV/calibration_imp.xlsx b/1 PA Decline/Fra DDV/calibration_imp.xlsx new file mode 100644 index 0000000..1c18d1a Binary files /dev/null and b/1 PA Decline/Fra DDV/calibration_imp.xlsx differ diff --git a/1 PA Decline/Fra DDV/functions200411.R b/1 PA Decline/Fra DDV/functions200411.R new file mode 100755 index 0000000..69cc30e --- /dev/null +++ b/1 PA Decline/Fra DDV/functions200411.R @@ -0,0 +1,2627 @@ +# pop <- haven::read_sas(here::here("E:/rawdata/709203/Population/pop_talos.sas7bdat")) + +# sst <- list.files(here::here("E:/rawdata/709203/Eksterne data"), pattern = "*.sas7bdat", full.names = TRUE) |> +# purrr::map(haven::read_sas) + + + +#' Read all sas files in folder to list +#' +#' @param path folder path +#' +#' @return list +sas2list <- function(path) { + ls <- list.files(here::here(path), pattern = "*.sas7bdat", full.names = TRUE) |> + purrr::map(haven::read_sas) + names(ls) <- list.files(here::here(path), pattern = "*.sas7bdat") |> + gsub(".sas7bdat", "", x = _) |> + toupper() + ls +} + + +#' Flatten multilevel list +#' +#' @param paths character vector of folder paths +#' +#' @return flattened list +flatmultiread <- function(paths) { + paths |> + purrr::map(sas2list) |> + purrr::list_flatten() +} + +# ls <- targets::tar_read(reg_data) + +# Vectors are kept for compatibility. Calling functions can be done from within other functions. So much easier! + +date_cutter <- function() as.Date("2023-01-01") +date.cut <- date_cutter() # The earliest date will define the overall date cut + +censor_cutter <- function() 8.5 +censor.cut <- censor_cutter() # years of maximum follow up, due to small numbers + +vasc.diags <- c("I21", "I61", "I63", "I64", "G45", "K28") + +#' Extract deaths from Dødsårsagsregiseret +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(reg_data) |> get_deaths() +get_deaths <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + + + # vasc.death.tilg <- mapply(ls$DAR_T_DODSAARSAG_2[, "C_DODTILGRUNDL_ACME"], + # FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + # i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + # i_3 %in% diags.vasc # Rowvise matching + # } + # ) + + vasc.death.tilg <- ls$DAR_T_DODSAARSAG_2[["C_DODTILGRUNDL_ACME"]] |> substr(1, 3) %in% diags.vasc + + + vasc.death.any <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DODTILGRUNDL_ACME", "C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + # + vasc.death.other <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + + diag.either <- xor(vasc.death.other, vasc.death.tilg) + diag.both <- vasc.death.other & vasc.death.tilg + + vasc.death.diags <- + apply( + mapply( + ls$DAR_T_DODSAARSAG_2[, c( + "C_DODTILGRUNDL_ACME", + "C_DOD_1A", + "C_DOD_1B", + "C_DOD_1C", + "C_DOD_1D" + )], + FUN = function(i) { + # mapply inside apply call to handle rowvise matching in in matrix + substr(i, 1, 3) # Substr only the 3 first characters to match by group + } + ), + 1, + paste, + collapse = "," + ) + + deaths.vasc <- + ls$DAR_T_DODSAARSAG_2 |> + dplyr::select(K_CPR, D_STATDATO) |> + dplyr::filter(vasc.death.tilg) + + df.death.all <- ls$CPR3_T_PERSON |> + dplyr::filter(C_STATUS == 90) |> # People migrating are filtered (n ~ 1) + dplyr::select(c( + "V_PNR", + "D_STATUS_HEN_START" + )) |> + dplyr::left_join(deaths.vasc, by = c("V_PNR" = "K_CPR")) |> + dplyr::mutate(vasc_death = !is.na(D_STATDATO)) |> + dplyr::transmute( + PNR = V_PNR, + death_date = D_STATUS_HEN_START, + vasc_death = vasc_death + ) + + df.death.all |> dplyr::filter(death_date < max.date) +} + + +#' Title +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' ls <- targets::tar_read(reg_list) +get_events <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + ident.vars <- toupper(c("_recnum", "_cpr")) + + df.vasc.events.lpr <- ls$LPR_T_DIAG |> + dplyr::mutate(dia.f = substr(C_DIAG, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # C_DIAGTYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars), + "C_DIAG", + "C_DIAGTYPE" + ) |> + dplyr::left_join( + ls$LPR_T_ADM |> dplyr::select( + tidyselect::ends_with(ident.vars), + "D_INDDTO", + "C_INDM", + "D_UDDTO", + "C_UDM", + "C_SGH", + "C_AFD", + "C_ADIAG" + ), + by = c("V_RECNUM" = "K_RECNUM") + ) + + ## LPR-F - LPR 3 + + ident.vars.lpr3 <- toupper(c("cpr", "_kontakt", "DW_EK_FORLOEB")) + + df.vasc.events.lpr3 <- ls$LPR_F_DIAGNOSER |> + dplyr::mutate(dia.f = substr(DIAGNOSEKODE, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # DIAGNOSETYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars.lpr3), + "DIAGNOSEKODE", + "DIAGNOSETYPE" + ) |> + dplyr::left_join( + ls$LPR_F_KONTAKTER |> dplyr::select( + ends_with(ident.vars.lpr3), + "DATO_START", + "DATO_SLUT", + "PRIORITET" + ), + by = c("DW_EK_KONTAKT") + ) |> + dplyr::mutate(PRIORITET = as.character((PRIORITET == "ATA1") + 1)) # If ATA1 then 1, if not (ATA3) then 2, cowboy coding + + df.vasc.events <- dplyr::full_join(df.vasc.events.lpr, df.vasc.events.lpr3, by = c( + "V_CPR" = "CPR", + "C_DIAG" = "DIAGNOSEKODE", + "C_DIAGTYPE" = "DIAGNOSETYPE", + "D_INDDTO" = "DATO_START", + "D_UDDTO" = "DATO_SLUT", + "C_INDM" = "PRIORITET" + )) |> + dplyr::mutate(date.event = D_INDDTO) + + df.vasc.events |> dplyr::filter(date.event < max.date) +} + + +# deaths <- targets::tar_read(df_deaths) +# events <- targets::tar_read(df_events) +# clinical <- targets::tar_read(pop_df) + + +#' Filter only truly considered events +#' +#' @param data +#' +#' @return tibble +define_events <- function(data) { + data |> dplyr::filter( + C_DIAGTYPE == "A", # Primary diagnosis + C_INDM == "1", # Acutely admitted + difftime(date.event, rdate, units = "days") > 5 # More than five (5) days after randomisation/primary stroke + ) +} + +#' The big merger and filter of events +#' +#' @param ls list of events, deaths and clinical +#' +#' @return tibble +merge_events <- function(ls) { + df.events <- dplyr::full_join( + purrr::pluck(ls, "events"), + purrr::pluck(ls, "deaths") |> + dplyr::mutate( + # These are just added to ease later filtering + C_DIAGTYPE = "A", + C_INDM = "1" + ), # Ads diagtype=A, C_INDM=1 for easier sorting later + by = c( + "V_CPR" = "PNR", + "date.event" = "death_date", + "C_DIAGTYPE", + "C_INDM" + ) + ) |> + dplyr::full_join(dplyr::select(purrr::pluck(ls, "clinical"), c("PNR", "rdate", "enddate")), + by = c("V_CPR" = "PNR") + ) |> + dplyr::arrange(date.event) |> # Sort by event date + dplyr::mutate(event.type = dplyr::if_else( + is.na(C_DIAG), + dplyr::if_else(vasc_death, "death.vasc", "death.other"), + substr(C_DIAG, 1, 4) + )) + + df.events |> + dplyr::group_split(V_CPR) |> # Splits by CPR + purrr::map(define_events) |> # Custom function to specify criteria for events + purrr::discard(\(x) nrow(x) == 0) |> # Discard empty elements + purrr::modify(\(x) x[1, ]) |> # Select first event + purrr::list_rbind() |> + dplyr::transmute( + CPR = V_CPR, + date.event, + event.type + ) +} + + + + +#' Count number of prescriptions of given ATC group for each CPR +#' +#' @param atc.code atc group +#' @param data dataset from LMS +#' +#' @return tibble +count_treat <- function(atc.code, data) { + data |> + dplyr::filter(grepl(atc.code, ATC)) |> + dplyr::count(CPR) |> + dplyr::filter(n > 1) +} + + +#' Get LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_lms <- function(ls, max.date, atc.tbl) { + ls$LMS_EPIKUR |> + dplyr::filter(grepl(atc.tbl[1], ATC)) |> + dplyr::left_join(ls$LMS_LAEGEMIDDELOPLYSNINGER) |> + dplyr::filter(as.Date(ACTDATE) < max.date) +} + +#' Get count of treated patients from LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_treated <- function(ls, + max.date = date.cut, + atc.tbl = c( + atc.antidep = "N06A", + atc.ssri = "N06AB" + )) { + df <- atc.tbl |> + purrr::map(count_treat, get_lms(ls, max.date, atc.tbl)) |> + purrr::reduce(dplyr::full_join, by = "CPR") + colnames(df) <- c("CPR", paste0("n.", names(atc.tbl))) + df +} + +#' BMI calc, drops +#' +#' @param w weight in kg +#' @param h height in cm +#' @param data data set +#' +#' @return tibble +bmi_calc <- function(data, drop = TRUE) { + # After inspection, both h+w are missing if any is missing + out <- data |> dplyr::mutate(reg_bmi = suppressWarnings(as.numeric(dplyr::if_else(reg_vaegt == "NA", reg_vaegt_anslaaet, reg_vaegt)) / ((as.numeric(reg_hojde) / 100)^2))) + + if (drop) { + out <- out |> + dplyr::select(-dplyr::all_of(c("reg_vaegt", "reg_vaegt_anslaaet", "reg_hojde"))) + } + out +} + +is_equal <- function(data, test) { + data == test +} + +#' Load clinical population data +#' +#' @return tibble +#' @examples +#' get_clinical() |> colnames() +#' +get_clinical <- function() { + sas2list("E:/rawdata/709203/Population")[[2]] |> + correct_na() |> + dplyr::mutate( + reg_smoker = dplyr::case_match( + reg_rygning, "1" ~ TRUE, + c("2", "3", "4") ~ FALSE, + "9" ~ NA + ), + # Living alone defined as not together with somebody + reg_alone = dplyr::case_match( + reg_civil, "1" ~ FALSE, + c("2", "3") ~ TRUE, + "9" ~ NA + ), + reg_more_alc = dplyr::case_match( + reg_alkohol, "1" ~ FALSE, + "2" ~ TRUE, + "9" ~ NA + ), + reg_female = sex == "Kvinde", + dplyr::across( + .cols = c( + "reg_hyperten", + "reg_diabetes", + "reg_atriefli", + "reg_perifer_arteriel", + "reg_tidl_tci", + "reg_ami" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + "2" ~ FALSE, + "9" ~ NA + ) + ), + dplyr::across( + .cols = c( + "reg_trombolyse", + "reg_trombektomi" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + c("3","4") ~ FALSE, + "9" ~ NA + ) + ), + reg_any_perf = reg_trombolyse | reg_trombektomi + ) |> + bmi_calc() +} + +# get_clinical() |> pragmatic_imputation() |> skimr::skim() + +# get_clinical <- function() { +# sas2list("E:/rawdata/709203/Population")[[2]] |> +# dplyr::mutate( +# reg_smoker = reg_rygning == 1, +# reg_cohabiting = reg_civil == 1, +# reg_more_alc = reg_alkohol == 2, +# reg_female = sex == "Kvinde", +# dplyr::across(.cols = c("reg_hyperten", "reg_diabetes", "reg_atriefli", "reg_perifer_arteriel", "reg_tidl_tci", "reg_ami", "reg_trombolyse", "reg_trombektomi"), ~ .x == 1), +# reg_any_perf = reg_trombolyse | reg_trombektomi +# ) |> +# bmi_calc() +# } + +pragmatic_imputation <- function(data, vec=c("reg_smoker","reg_cohabiting","reg_more_alc","reg_hyperten", "reg_diabetes", "reg_atriefli", "reg_perifer_arteriel", "reg_tidl_tci", "reg_ami", "reg_trombolyse", "reg_trombektomi","reg_any_perf")) { + # Assumes, if not TRUE, then FALSE (gets rid of NAs) + data |> dplyr::mutate(dplyr::across(.cols = tidyselect::any_of(vec), ~dplyr::if_else(.x,TRUE,FALSE,missing = FALSE))) +} + +#' Load all registry tables to list +#' +#' @return list +get_reg_ls <- function() { + flatmultiread(c("E:/rawdata/709203/Eksterne data", "E:/rawdata/709203/Grunddata")) +} + + +#' Definition of relevant variables from DST tables +#' +#' @return +define_dst_vars <- function() { + list( + bef = c("PNR", "FAMILIE_ID"), + faik = c("FAMILIE_ID", "FAMAEKVIVADISP_13", "FAMSOCIOGRUP_13"), + ras = c("PNR", "SOC_STATUS_KODE"), + uddf = c("PNR", "HFAUDD") + ) +} + +#' Simple wrapper of dplyr::select +#' +#' @param data +#' @param vars +#' +#' @return +select_vars <- function(data, vars) { + data |> dplyr::select({{ vars }}) +} + +#' Subset DST tables to only include relvant variables. +#' +#' @param ls List of all registry tables +#' +#' @return +get_dst_tables <- function(ls) { + dst_vars <- define_dst_vars() + dst_tbl <- toupper(names(dst_vars)) + ls.all <- purrr::map(seq_along(dst_vars), function(i) { + ls.reg <- ls[grepl(paste0("^", dst_tbl[i]), names(ls))] |> purrr::map(select_vars, vars = dst_vars[[i]]) + names(ls.reg) <- paste0("y", stringr::str_extract(names(ls.reg), "[0-9]{4}")) + ls.reg + }) + names(ls.all) <- dst_tbl + ls.all +} + +#' Wrapper to generate string matching pattern for stringr::str_detect() +#' +#' @param data +#' +#' @return +match_str <- function(data) { + paste0("[", paste0(data, collapse = ","), "]") +} + +#' Wrapper to generate string matching pattern for grepl() +#' +#' @param data character vector +#' +#' @return +match_str_grepl <- function(data) { + paste0("(", paste0(data, collapse = "|"), ")") +} + +# get_dst_tables(ls) + +#' Generate sequence of previous N length +#' +#' @param data numeric vector of length 1 +#' +#' @return +#' +#' @examples +#' last5y(10) +#' last5y(c(10, 6, 3)) +lastNy <- function(data, n = 5) { + paste0("y", seq((data - n), data) - 1) +} + +#' Filter PNR (cpr) across list elements +#' +#' @param data list of tibbles to pass through +#' @param index index number (PNR/CPR) +#' +#' @return tibble +filterCPRacross <- function(data, index) { + data |> + purrr::map(function(i) { + i[i$PNR == index, ] + }) |> + purrr::list_rbind() +} + +#' Summarise data from last 5 years prior to inclusion +#' +#' @param data list with +#' @param v.median variables to get median +#' @param v.latest variables to get latest +#' @param v.mean variables to get mean +#' +#' @return tibble +previousNyears <- function(data, data.clin, n.years = 5, v.median = NULL, v.latest = c("FAMSOCIOGRUP_13", "SOC_STATUS_KODE"), v.mean = c("FAMAEKVIVADISP_13")) { + df.cpryear <- data.clin |> dplyr::transmute( + CPR = PNR, + year = as.numeric(format(as.Date(rdate), "%Y")) + ) + + seqs <- purrr::map(df.cpryear$year, lastNy, n = n.years) + + seq_along(seqs) |> + purrr::map(function(i) { + df <- data[c(seqs[[i]])] |> + filterCPRacross(index = df.cpryear$CPR[i]) |> + dplyr::group_by(PNR) |> + dplyr::summarise( + dplyr::across(tidyselect::any_of(v.latest), \(x) tail(x, n = 1), .names = "{.col}.latest"), + dplyr::across(tidyselect::any_of(v.mean), \(x) mean(x, na.rm = TRUE), .names = "{.col}.{n.years}.mean"), + dplyr::across(tidyselect::any_of(v.median), \(x) median(x, na.rm = TRUE), .names = "{.col}.{n.years}.median") + ) + }) |> + purrr::list_rbind() +} + + +#' Extract relevant and summarised data from BAF and FAIK +#' +#' @param data list of dst data tables +#' +#' @return tibble +#' +#' @examples +#' get_reg_ls() |> +#' get_dst_tables() |> +#' get_beffaikras() +get_beffaikras <- function(data, clin.data = get_clinical()) { + data <- data[stringr::str_detect(match_str(c("BEF", "FAIK", "RAS")), names(data))] |> purrr::list_flatten() + + years <- stringr::str_extract(names(data), "y[0-9]{4}") + + years[duplicated(years)] |> + purrr::map(grep, years) |> + purrr::map(function(i) { + data[c(i)] |> purrr::reduce(dplyr::full_join) + }) |> + purrr::set_names(years[duplicated(years)]) |> + previousNyears(data.clin = clin.data) + + ## BEF + ## # Befolkningsoversigt. Data skal bruges for at kunne udtrække husstandsindkomst. + ## FAIK + ## # Familieindkomst. Familie id skal flættes med ID fra BEF for hvert år for at tage hensyn til evt skifte i status. + ## FAMAEKVIVADISP_13 er relevante variabel for ækvivaleret indkomst + ## Der findes også familiesocioøkonomisk status. Gør som Sine. Be done with it! +} + +#' Title +#' +#' @return +#' @export +#' +#' @examples +#' data <- read_edu_level() +#' data |> dplyr::count(ISCED) +read_edu_level <- function() { + haven::read_dta("E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l4_k.dta") |> + dplyr::transmute( + HFAUDD = start, + ISCED = AUDD_LEVEL_L1L4_K + ) +} + +# haven::read_dta( +# "E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l3_k.dta") |> +# dplyr::count(AUDD_LEVEL_L1L3_K) + +# ls <- get_reg_ls() |> get_dst_tables() + +get_uddf <- function(ls) { + ls |> + purrr::pluck("UDDF") |> + purrr::pluck(1) |> + dplyr::mutate(HFAUDD = as.character(HFAUDD)) |> + dplyr::left_join(read_edu_level()) |> + dplyr::group_by(PNR) |> + dplyr::summarise(ISCED = max(ISCED), .groups = "keep") |> + dplyr::mutate( + ISCED = as.numeric(ISCED), + ISCED_lvl = dplyr::case_match(ISCED, 0:2 ~ "low", + 3:4 ~ "medium", + 5:9 ~ "high", + .default = NA + ), + ISCED_bin = dplyr::case_match(ISCED, 0:3 ~ "low", + 4:9 ~ "high", + .default = NA + ) + ) +} + + +#' Collects all relevant variables from DST tables +#' +#' @param ls list of all registry tables +#' @param df.clin clinical data set +#' +#' @return tibble +#' @examples +#' get_reg_ls() |> get_dst(df.clin = get_clinical()) +get_dst <- function(ls, df.clin) { + ls_dst <- get_dst_tables(ls) + ls_dst |> + ## BEFxFAIKxRAS + get_beffaikras(clin.data = df.clin) |> + dplyr::full_join( + ## UDDF + get_uddf(ls_dst) + ) +} + +#' Eases pipe renaming of columns +#' +#' @param data tibble, list or other object, for which names() makes sense. see ?setNames +#' @param prefix prefix to add +#' @param exclude names not to modify +#' @param new.names character vector of all new names +#' +#' @return object of same class as data +#' +#' @examples +#' set_colnames(data = mtcars, prefix = "WOW", exclude = "mpg") +set_colnames <- function(data, new.names = NULL, prefix = NULL, exclude = c("PNR", "CPR"), prefix.sep = "_") { + if (is.null(new.names)) { + nms <- names(data) + } else { + nms <- new.names + } + + if (is.null(prefix)) { + nms.mod <- nms + } else { + nms.mod <- paste(prefix, nms, sep = prefix.sep) + } + + setNames( + object = data, + nm = dplyr::if_else(stringr::str_detect(match_str(exclude), nms), + nms, + nms.mod + ) + ) +} + + +#' Store of variable names for data sub-setting +#' +#' @return list +#' @examples +#' define_variables() +define_variables <- function() { + list( + clin = c( + "age", + "reg_female", + "nihss_0", + "reg_trombolyse", + "reg_trombektomi", + # "rtreat", + "rtreat_placebo"), + lifestyle=c( + "pase_0", + "pase_4", + "reg_alone", + "reg_bmi", + # "reg_hojde", + # "reg_vaegt_alt", + "reg_smoker", + "reg_more_alc", + "reg_hyperten", + "reg_diabetes", + "reg_tidl_tci", + "reg_atriefli", + "reg_ami", + "reg_perifer_arteriel"), + ses=c( + # "soc_status", + # "soc_status_work", + "soc_status_nowork", + # "fam_indk", + "fam_indk_hl", + # "fam_indk_high", + # "fam_indk_low", + # "edu_level", + # "edu_high", + # "edu_low", + "edu_level_hl" + ), + assess.events = c( + "who_4", + "mdi_4", + "mrs_4_above1", + "mfi_gen_4", + "time", + "status", + "event.include" + ), + assess.pred = c( + "who_0", + "mrs_0_above0" + ), + extra = c( + "soc_status", + "pase_0", + "pase_4" + ) + ) +} + +#' Get var names in vector from group names. Possibility to keep all vars for as log as possible. Can be supplied to `gtsummary` functions +#' +#' @param groups vector of group names. See names(define_variables()) for options +#' +#' @return +#' @export +#' +#' @examples +get_var_vec <- function(v.groups){ + define_variables()[{{ v.groups }}] |> purrr::list_c() +} + +#' SUbsets dataset based on variable group names as defined +#' +#' @param vector character vector of category names +#' +#' @return character vector +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> get_vars(c("universal", "events")) +get_vars <- function(data, vars.groups) { + data |> dplyr::select(tidyselect::all_of(get_var_vec(vars.groups))) +} + + + +#' Collect all relevant data for the events analysis data set +#' +#' @param ls ls of tibbles +#' +#' @return tibble +collectall <- function(ls) { + purrr::pluck(ls, "clinical") |> + dplyr::left_join(purrr::pluck(ls, "all_events") |> set_colnames(prefix = "event"), by = c("PNR" = "CPR")) |> + dplyr::left_join(purrr::pluck(ls, "dst") |> set_colnames(prefix = "dst"), by = "PNR") +} + + +## Formatting for analysis + +#' Function to cut and group PASE data +#' +#' @param data data set including pase_0 and _4 +#' +#' @return tibble +pase_cutter <- function(data, pase.rev = TRUE, drop.pase = FALSE, drop.nas=FALSE) { + data.classes <- class(data) + if ("mids" %in% data.classes) { + data <- data |> mice::complete(action = "long", include = TRUE) + } + + data <- data |> + dplyr::mutate(dplyr::across(.cols = c("pase_0", "pase_4"), \(i) { + cut(x = i, breaks = quantile(pase_0, na.rm = TRUE), labels = 1:4, include.lowest = TRUE) + }, .names = "{.col}_quartile")) |> + dplyr::mutate(pase_change = factor(dplyr::case_when( + pase_0_quartile == 1 & pase_4_quartile == 1 ~ "Persistently low", + pase_0_quartile %in% 2:4 & + pase_4_quartile %in% 2:4 ~ "Persistently high", + pase_0_quartile == 1 & + pase_4_quartile %in% 2:4 ~ "Increase", + pase_0_quartile %in% 2:4 & + pase_4_quartile == 1 ~ "Decrease" + ), ordered = FALSE), + pase_change=factor(pase_change,levels=c("Increase", "Persistently low", "Decrease", "Persistently high"))) + + if (drop.pase) { + data <- data |> dplyr::select(-tidyselect::all_of(c("pase_0_quartile", "pase_4_quartile", "pase_0", "pase_4"))) + } + + + if (pase.rev) { + data <- data |> dplyr::mutate( + pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Persistently low", "Increase")) + ) + } + + if (drop.nas) { + data <- data |> + dplyr::filter(!is.na(pase_change)) + } + + + if ("mids" %in% data.classes) { + data |> mice::as.mids() + } else { + data + } +} + +# as.Date(data$event_date.event) +define_status_time <- function(data) { + data |> dplyr::mutate(dplyr::across(c("rdate", "enddate", "event_date.event"), ~ as.Date(.x)), + time = difftime(dplyr::if_else(is.na(event_date.event), date_cutter(), event_date.event), enddate) |> lubridate::time_length("years"), + status = as.integer(!is.na(event_event.type)), + time = dplyr::if_else(time > censor_cutter(), censor_cutter(), time), + status = dplyr::if_else(time > censor_cutter(), FALSE, status), + # status= dplyr::if_else(status,1,0), + event.include = time > 0 + ) +} + +# as.integer(c(TRUE,FALSE)) + +#' Grouping soc status +#' +#' @param data tibble +#' +#' @return tibble +group_soc_status <- function(data) { + data |> + dplyr::mutate( + soc_status = factor(dplyr::case_when( + soc_status < 200 ~ "work", + soc_status == 200 ~ "off", + # only ~4 in the data set off work + soc_status >= 200 ~ "outside" + )), + soc_status_work = soc_status == "work", + soc_status_nowork = !soc_status_work + ) +} + +#' Correction of character NA +#' +#' @param data tibble +#' @param char.missing character vector of entries to consider as NA +#' +#' @return tibble +correct_na <- function(data, char.missing = "NA") { + data |> dplyr::mutate(dplyr::across(dplyr::where(is.character), ~ dplyr::na_if(.x, char.missing))) +} + +#' Formatting the complete data set +#' +#' @param data the merged raw data set +#' +#' @return tibble +#' @examples +#' ds <- targets::tar_read(df_all_data) |> +#' data_formatting() |> +#' subset_df("mdi") +#' ds |> skimr::skim() +#' ds |> View() +data_formatting <- function(data) { + to_logical <- grep(match_str_grepl(c("missings", "incompletes")), names(data)) + + suppressWarnings( + data |> + correct_na() |> + dplyr::mutate(dplyr::across(all_of(to_logical), ~ .x == "TRUE")) |> + dplyr::mutate( + # This uses the work-corrected score + # pase_0 = dplyr::if_else(pase_score_missings_w_0 | is.na(talos_pase10_0), NA, pase_score_sum_w_0), + # pase_4 = dplyr::if_else(pase_score_missings_w_4 | is.na(talos_pase10_4), NA, pase_score_sum_w_4), + # Below is the plain PASE scor used according to the manual used with TALOS + pase_0 = dplyr::if_else(pase_score_missings_0, NA,pase_score_sum_0), + pase_4 = dplyr::if_else(pase_score_missings_4, NA,pase_score_sum_4), + who_0 = as.numeric(talos_who07_0), + who_4 = as.numeric(talos_who07_4), + mrs_0 = factor(substr(talos_mrs01_0, 1, 1), ordered = FALSE), + mrs_0_above0 = (as.numeric(mrs_0) - 1) > 0, + mrs_4 = factor(substr(talos_mrs01_4, 1, 1), ordered = FALSE), + mrs_4_above1 = (as.numeric(mrs_4) - 1) > 1, + mdi_4 = as.numeric(talos_mdi12_4), + mfi_gen_4 = as.numeric(talos_mfi_gen_4), + nihss_0 = as.numeric(talos_nihss16_0), + soc_status = dst_SOC_STATUS_KODE.latest, + fam_indk = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 3), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "medium", "high"), + include.lowest = TRUE + ), + fam_indk_bin = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 2), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "high"), + include.lowest = TRUE + ), + fam_indk_hl=forcats::fct_rev(fam_indk), + fam_indk_high = dplyr::if_else(fam_indk_bin=="high",TRUE,FALSE), + fam_indk_low = dplyr::if_else(fam_indk_bin=="low",TRUE,FALSE), + edu_level = factor(dst_ISCED_lvl, ordered = FALSE, levels = c("low", "medium", "high")), + edu_high = dplyr::if_else(dst_ISCED_bin=="high",TRUE,FALSE), + edu_low = dplyr::if_else(dst_ISCED_lvl=="low",TRUE,FALSE), + edu_level_hl=forcats::fct_rev(edu_level), + rtreat_placebo=rtreat=="Placebo" + ) |> + group_soc_status() |> + define_status_time() + ) +} + + +#' Title +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> +#' events_ready() |> +#' View() +events_ready <- function(data,v.groups=c("clin","lifestyle","ses", "assess.events")) { + data |> + get_vars(vars.groups = v.groups) |> + dplyr::filter(event.include) |> + dplyr::select(-tidyselect::all_of("event.include"))# |> + # labelling_data() +} + + +#' Title +#' +#' @param date +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' +#' data |> +#' prediction_ready() |> +#' View() +prediction_ready <- function(data) { + data |> + get_vars(c("clin","lifestyle","ses", "assess.pred"))|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4))#|> + # labelling_data() +} + +## Data inspection and exploration +## +## +#' Title +#' +#' @param data +#' @param subdf +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data) |> +#' subset_df() |> +#' View() +subset_df <- function(data, subdf = "pase") { + data[grepl(paste0("^(", paste("PNR", subdf, paste0("talos_", subdf), sep = "|"), ")"), names(data))] +} + +#' Imputation as a function, includes "pragmatic imputation" +#' +#' @param data +#' @param outcome.vars +#' @param ignore +#' @param pragmatic.reg +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_ready() +#' data |> labelling_data() |> fun_impute() +fun_impute <- function(data, outcome.vars = c("status", "time"), ignore = NULL,pragmatic.reg=TRUE,pase.mod=FALSE) { + # data |> mice::md.pattern() + if (pragmatic.reg){ + data <- data |> pragmatic_imputation() + } + + ## Excluding entries with missing outcome measures + data <- data |> + dplyr::filter(!dplyr::if_any(tidyselect::all_of(c(outcome.vars)), ~ is.na(.x))) + + init <- data |> + mice::mice(maxit = 0) + + meth <- init$method + meth[ignore] <- "" + + pred <- init$predictorMatrix + pred[, c(outcome.vars)] <- 0 + + # data_out <- data |> mice::futuremice( + # pred = pred, + # method = meth, + # print = FALSE, + # parallelseed = 8123, + # use.logical = FALSE, + # maxit = 20, + # m = 10 + # ) + + data_out <- data |> mice::mice( + pred = pred, + method = meth, + print = FALSE, + seed = 8123, + maxit = 20, + m = 10 + ) + + if (pase.mod){ + data_out <- data_out |> pase_cutter_mids() + } + + data_out + + # lattice::densityplot(imp_data) + # Regarding EVENTS + # + # On inspection/eye-balling densityplots looks reasonable with the current settings + # +} + +#' Function to cut PASE in mids object +#' +#' @param data mids object +#' +#' @return mids object +#' @export +#' +pase_cutter_mids <- function(data){ +data |> + mice::complete(action = "long", include = TRUE) |> + pase_cutter(drop.pase = TRUE, drop.nas = TRUE)|> + mice::as.mids() + } + + +#' Completes events data set, option to impute +#' +#' @param data +#' @param impute +#' +#' @return mids or tibble +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset() |> +#' targets::tar_read(df_all_data_formatted) |> +events_dataset <- function(data, impute = TRUE) { + data <- data |> events_ready() + if (impute) { + data |> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = TRUE) + } else { + data |> + dplyr::select(-tidyselect::all_of("reg_bmi")) |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + } +} + +#' Title +#' +#' @param data +#' @param all.vars +#' @param outcome.var +#' @param use.strata +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_ready() |> subset_df("pase") +#' data <- targets::tar_read(df_all_data_formatted) |> events_dataset(FALSE) +#' data |> cox_regression() +cox_regression <- function(data, all.vars = TRUE,outcome.var="pase_change",use.strata=TRUE) { + if ("mids" %in% class(data)) { + nms <- names(data$data) + } else { + nms <- names(data) + data <- data |> + labelling_data() + # BMI meassure is excluded from non-imputed dataset + # data <- data |> dplyr::select(-tidyselect::all_of(c("reg_bmi"))) + } + + vars <- nms[!nms %in% c("time", "status", outcome.var)] + + form.prefix <- "survival::Surv(time, status) ~" + + if (use.strata) { + reg.form <- glue::glue("{form.prefix} strata({outcome.var})") + } else { + reg.form <- glue::glue("{form.prefix} {outcome.var}") + } + + if (all.vars) reg.form <- paste0(reg.form, " + ", paste(vars, collapse = " + ")) + + require(survival) + out <- with(data, survival::coxph( + as.formula(reg.form) + )) + + out$call$formula <- as.formula(reg.form) + + out +} + + +#' Wrapper to print summary table with extended info +#' +#' @param data formatted and subset data set +#' @param by.var stratify by +#' +#' @return +#' @examples +#' targets::tar_read(df_pred_data)|>print_table_summary(by="reg_female") +print_table_summary <- function(data, by.var = "pase_change") { + data |> + labelling_data() |> + # pase_cutter(drop.pase = TRUE) |> + gtsummary::tbl_summary( + missing = "ifany", + by = tidyselect::all_of(by.var), + value = list(where(is.logical) ~ TRUE)#, + # type = list(gtsummary::all_continuous() ~ "continuous2"), + # statistic = list(gtsummary::all_continuous() ~ c( + # # "{N_nonmiss} ({p_nonmiss}%)", + # "{median} ({p25}, {p75})", + # # "{min}, {max}", + # "{mean} ({sd})"#, + # # "{N_miss} ({p_miss}%)" + # )#, + # gtsummary::all_categorical() ~ c( + # "{N_obs} ({p_nonmiss}%)"#, + # # "{N_miss} ({p_miss})" + # ) + # ) + ) |> + gtsummary::add_overall() |> + gtsummary::add_n() |> + gtsummary::add_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' targets::tar_read(df_all_data_formatted) |> events_tblone() +events_tblone <- function(data) { + data |> + events_ready() |> + pase_cutter(drop.pase = TRUE) |> + dplyr::select(-tidyselect::all_of(c("status", "time"))) |> + dplyr::filter(!is.na(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data)|> +#' dplyr::transmute(stRoke::quantile_cut(pase_0,4,group.names = 1:4),soc_status_work,fam_indk,edu_level) |> +#' summary_tblone() +summary_tblone <- function(data,by=names(data)[1]) { + # data <- targets::tar_read(df_pred_data) + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + # pase_cutter(drop.pase = TRUE) |> + # dplyr::filter(!is.na(pase_change)) |> + # dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary(by.var = by) +} + +# +#' Summaries of DST data for PASE quartiles at 0 and 4 +#' +#' @param data +#' @param vars +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> sum_pase_tables() +sum_pase_tables <- function(data,vars=c("pase_0","pase_4")){ + vars |> lapply(function(.x){ + dplyr::tibble(stRoke::quantile_cut(data[[.x]],y=data[["pase_0"]],4,group.names = 1:4), + dplyr::select(data,soc_status_nowork,fam_indk_hl,edu_level_hl)) |> + summary_tblone() + }) +} + + +#' Creating a truthful stratified table for predictions +#' +#' @param data data frame +#' +#' @return list +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> true_pred_sum_plot() +true_pred_sum_plot <- function(data){ + true_sum <- data |> + pase_cutter(drop.pase = TRUE) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) + + list(true_sum,true_sum |> (function(.x){ + split(.x,.x$pase_change %in% c("Persistently low","Increase")) + })() |> purrr::map(function(.y){.y |> dplyr::mutate(pase_change=factor(pase_change))})) |> + purrr::list_flatten() |> purrr::map(summary_tblone,by="pase_change") |> + gtsummary::tbl_merge() +} + +#' Get quick summary of missing vs non-missing for each given variable +#' +#' @param data data set +#' @param var variable to summarise over +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> dplyr::select(soc_status_work,fam_indk,edu_level) |> who_is_missing() +#' targets::tar_read(df_pred_data) |> who_is_missing(var="reg_bmi") +who_is_missing <- function(data, var = "edu_level") { + data |> + dplyr::mutate(log = factor(c("non-missing","missing")[is.na(data[[var]])+1])) |> + dplyr::select(log, tidyselect::everything(),-tidyselect::all_of(var)) |> + summary_tblone() |> gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data) |> preds_tblone() +preds_tblone <- function(data) { + # data <- targets::tar_read(df_pred_data) + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + pase_cutter(drop.pase = TRUE) |> + dplyr::filter(!is.na(pase_change)) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' gt <- targets::tar_read(ls_pred_summary)[[1]] +#' gt |> add_var_groups_gt() +#' +#' # For this to work, the function would need to handle labels and levels +#' gt <- targets::tar_read(tbl_pred_summary)|> gtsummary::as_gt() +#' gt |> add_var_groups_gt() +add_var_groups_gt <- function(gt){ + b.cols <- names(gt$`_data`) + + if (b.cols[[1]]!="variable"){ + # Flag to indicate if format is native gt or not. Simple assumption + # class(gt) gt is not enough + labels <- gt$`_data`[[1]] + group.var <- names(gt$`_data`[[1]]) + } else { + labels <- gt$`_data`[["label"]][gt$`_data`[["row_type"]]=="label"] + group.var <- gt$`_data`[["variable"]] + } + + groups <- matrix(ncol=length(labels)) |> + data.frame() |> + setNames(ifelse(labels=="","unknown_var",labels)) |> + tibble::tibble() |> groups_in_ds(labels = TRUE) + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + labels.all <- group.labels |> purrr::imap(function(.x,.y){ + c(.x,groups[[.y]][["label"]]) + }) |> purrr::list_c() + + for (i in rev(seq_along(group.labels))){ + gt <- gt |> gt::tab_row_group(label=gt::md(glue::glue("*{group.labels[[i]]}*")), + rows=which(group.var %in% groups[[names(group.labels)[[i]]]][["var"]])) + + } + + gt +} + + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' tbl <- targets::tar_read(ls_pred_summary)[[1]] +add_var_groups_pre_calc <- function(data,b.cols){ + + groups <- data |> groups_in_ds() + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + t0 <- data.frame(matrix(ncol=length(b.cols))) |> + setNames(b.cols) |> + tibble::tibble() + list(ext = group.labels |> purrr::imap(function(.x,.y){ + t0 |> dplyr::mutate( + variable=.y, + val_label=.x, + row_type="group", + label=.x + ) + }) |> dplyr::bind_rows(), + lvls = group.labels |> purrr::imap(function(.x,.y){ + c(.y,groups[[.y]][["var"]]) + }) |> purrr::list_c() + ) +} + +#' Adds variable grouping and formatting to gtsummary tables +#' +#' @param tbl +#' +#' @return +#' @export +#' +#' @examples +#' +#' tbl <- targets::tar_read(tbl_pred_summary) +#' targets::tar_read(tbl_pred_summary) |> add_var_groups() +#' +add_var_groups <- function(tbl, + pre_ls=add_var_groups_pre_calc(tbl$inputs$data, + names(tbl$table_body))){ + + + tbl |> gtsummary::modify_table_body( + ~.x |> dplyr::bind_rows(pre_ls[["ext"]]) |> + dplyr::arrange(factor(variable,levels=pre_ls[["lvls"]])) + ) |> + gtsummary::modify_table_styling(columns=label, + rows= row_type%in%"level",text_format = "indent2") |> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"label",text_format = "indent")|> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"group",text_format = c("italic")) +} + +#' Functionalised character vector of all labels +#' +#' @return +#' @export +#' +#' @examples +var_labels <- function(){ + c( + age = "Age", + reg_female = "Female sex", + reg_bmi = "Body mass index", + reg_smoker = "Current smoker", + reg_alone = "Living alone", + reg_more_alc = "High alcohol consumption", + reg_hyperten = "Hypertension", + reg_diabetes = "Diabetes", + reg_atriefli = "Atrial fibrillation", + reg_perifer_arteriel = "Peripheral arterial disease", + reg_tidl_tci = "Previous TIA", + reg_ami = "Previous MI", + reg_trombolyse = "Treated with IVT", + reg_trombektomi = "Treated with EVT", + # reg_any_perf, + # rtreat = "Study group allocation", + rtreat_placebo = "Placebo trial treatment", + pase_0 = "Pre-stroke PASE score", + pase_4 = "6 months post-stroke PASE score", + # pase_change, + nihss_0 = "Admission NIHSS", + # soc_status, + soc_status_work = "Employed", + soc_status_nowork = "Not employed", + fam_indk = "Family income group", + fam_indk_hl = "Lower family income", + fam_indk_high = "Higher family income", + fam_indk_low = "Lower family income", + edu_level = "Educational level group", + edu_level_hl = "Lower educational level", + edu_high = "Higher educational level", + edu_low = "Low educational level", + who_4 = "WHO-5 score 6 months post-stroke", + mdi_4 = "MDI score 6 months post-stroke", + mrs_4_above1 = "mRS > 1 at 6 months post-stroke", + mfi_gen_4 = "General fatigue (MFI domain) 6 months post-stroke", + time = "Time", + status = "Status", + event.include = "Include event", + who_0 = "Pre-stroke WHO-5 score", + mrs_0_above0 = "Pre-stroke mRS > 0", + pase_change = "PA change group" + ) +} + + +group_labels <- function(data){ + c("clin" = "Clinical data", + "lifestyle" = "Lifestyle and chronic diseases", + "ses" = "Socio-economic factors", + "assess.events" = "Assessments", + "assess.pred" = "Assessments", + "extra" = "extras") +} + +rev_naming <- function(x){ + setNames(names(x),x) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +groups_in_ds <- function(data, labels=FALSE){ + groups <- define_variables() |> purrr::imap(function(.x,.y){ + tibble::tibble(group=.y,var=.x) + }) |> + dplyr::bind_rows() + + if (labels){ + matching <- subset_named_labels(names(data), + rev_naming(var_labels())) + }else { + matching <- names(data) + } + groups[match(matching,groups[["var"]]),] |> + (\(.x){ + .x |> dplyr::mutate(group=factor(group,levels=unique(.x[["group"]]))) + })() |> + cbind( + tibble::tibble( + label=labelling_data(data) |> labelled::var_label() |> purrr::list_c() + ) + )|> + (\(.x){ + split(.x,.x[["group"]]) + })() +} + + +#' Subset labels +#' +#' @param data +#' @param labels.raw +#' +#' @return character vector +#' @export +#' +subset_named_labels <- function(data,labels.raw){ + labels.raw[match(data,names(labels.raw))] +} + +#' Assign labels to data.frame or tibble +#' +#' @param data +#' @param labels +#' +#' @return +#' @export +#' +#' @examples +assign_labels <- function(data,labels){ + # data |> labelled::set_variable_labels(labels) + + labelled::var_label(data) <- labels + + data +} + +#' Flexible labelling using labelled for nicer tables +#' +#' @param data data set +#' +#' @return +#' @export labelled data.frame/tibble +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' data <- data |> dplyr::mutate(test="test") +#' data |> labelling_data() |> labelled::var_label() +labelling_data <- function(data,label.list=var_labels()){ + + labs <- subset_named_labels(names(data),label.list) + labs[is.na(labs)] <- names(data)[is.na(labs)] + + data |> assign_labels(labels = labs) +} + + + +#' Print regression table +#' +#' @param data cox regression ready data set +#' +#' @return gtsummary tbl_regression list object +#' @examples +#' targets::tar_read(df_all_data_formatted) |> show_table_regression() +#' targets::tar_read(df_all_data_formatted) |> show_table_regression(use.mice=TRUE) +show_table_regression <- function(data, use.mice=FALSE, by.var="pase_change") { + data |> + events_dataset(impute = use.mice) |> + cox_regression(all.vars = TRUE, use.strata = FALSE,outcome.var = by.var) |> + gtsummary::tbl_regression(exponentiate = TRUE, add_estimate_to_reference_rows = TRUE) |> + # gtsummary::add_n() |> + gtsummary::bold_p() +} + +#' Splitting df to list by PA trajectory +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_ls_split <- function(data, excluded.vars = "reg_bmi"){ + data |> + pase_cutter(drop.pase = TRUE) |> + dplyr::group_split(pase_split = pase_change %in% c("Increase", "Persistently low")) |> + setNames(c("drop", "hop")) |> + purrr::map2(.y = c("Decrease", "Increase"), .f = \(x, y){ + x |> + dplyr::mutate(pase_bin = pase_change == y) |> + dplyr::select(-tidyselect::all_of(c(excluded.vars, c("pase_change", "pase_split")))) |> + na.omit() + }) +} + +#' Run regularisation steps for split data set +#' +#' @param data selected data set +#' +#' @return list +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' targets::tar_read(df_pred_data) |> pred_models() +pred_models <- function(data, excludes = "reg_bmi") { + ls <- data |> + pred_ls_split(excluded.vars = excludes) |> + purrr::map(regularisation_steps) + + class(ls) <- c("regular_list", class(ls)) + ls +} + +cross_mean_median_exp_table <- function(data) { + nms <- paste0("v", seq_len(ncol(data))) + + cross_calcs <- data |> + as.data.frame() |> + setNames(nms) |> + dplyr::rowwise() |> + dplyr::transmute( + median = median(dplyr::c_across(tidyselect::all_of(nms))), + medianOR = exp(median), + mean = mean(dplyr::c_across(tidyselect::all_of(nms))), + meanOR = exp(mean) + ) + + dplyr::tibble(names = rownames(data), cross_calcs) |> + dplyr::select(-tidyselect::all_of(c("mean","median"))) +} + + +gather_coefs_step1 <- function(data) { + data |> + list3levelpluck(lvl1 = "model", lvl2 = "B") |> + purrr::map(purrr::reduce, cbind) +} + +gather_coefs <- function(data) { + # imputed.list <- "mids_regular_list" %in% class(data) + + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(gather_coefs_step1) |> + purrr::map(purrr::reduce, cbind) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> gather_coefs_step1() + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map(cross_mean_median_exp_table) |> + purrr::reduce(dplyr::full_join, by = "names", suffix = paste0("_", names(data))) +} + + +#' Merge and print model coefficients. Pools datafrom mids analyses. +#' +#' @param data list +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(ls_pred_models) +#' targets::tar_read(ls_pred_models) |> print_pred_coefs() +#' targets::tar_read(ls_pred_mids_reg) |> print_pred_coefs() |> add_var_groups_gt() +print_pred_coefs <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + # nms <- names(data) + + if ("mids_regular_list" %in% class(data)) { + type.table <- "Pooled regularised models" + } else if ("regular_list" %in% class(data)) { + type.table <- "Single regularised model" + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + merged_tbl <- data |> + gather_coefs() |> + ## Leaving out the intercept + (function(.x) .x[-1,])() + + sel_mean_med <- colnames(merged_tbl)[!grepl(pattern = "OR",colnames(merged_tbl))][-1] + sel_or <- colnames(merged_tbl)[grepl(pattern = "OR",colnames(merged_tbl))] + + news <- subset_named_labels(merged_tbl$names,var_labels()) + + merged_tbl <- merged_tbl |> dplyr::mutate(names=dplyr::if_else(is.na(news),names,news)) + + gt_merged_tbl <- merged_tbl|> + gt::gt() |> + gt::fmt_number(decimals = 5) + + merged_tbl_log <- merged_tbl |> dplyr::mutate(dplyr::across(tidyselect::all_of(sel_or), ~.x!=1), + dplyr::across(tidyselect::all_of(sel_mean_med), ~.x!=0)) + + for (j in colnames(merged_tbl)[-1]) { + + i <- merged_tbl_log[[j]] + + gt_merged_tbl <- gt_merged_tbl |> gt::tab_style(style = list( + gt::cell_text(weight="bold") + ), + locations = gt::cells_body( + columns=j, + rows = i + ) + )} + + for (i in names(data)) { + gt_merged_tbl <- gt_merged_tbl |> + gt::tab_spanner(label = i, columns = tidyselect::ends_with(i)) + } + + gt_merged_tbl |> gt::tab_spanner( + label = type.table, + columns = -1 + ) +} + +#' Calculates confusionMatrix from contingency tables. Pools if object class is . +#' +#' @param data +#' +#' @return list +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_table_cfm() +#' targets::tar_read(ls_pred_models) |> multi_table_cfm() +multi_table_cfm <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + if ("mids_regular_list" %in% class(data)) { + data <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + # purrr::map(\(y) y |> purrr::pluck("model") |> purrr::pluck("cMatTest"))|> + list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") |> + # All tables are add together + purrr::reduce(\(i, j) i + j) + }) + } else if ("regular_list" %in% class(data)) { + data <- data |> list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data |> + purrr::map(caret::confusionMatrix) +} + +#' Collect and summarise auc meassures. Pools if "mids_regular_list" object +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_auc_summary() +#' targets::tar_read(ls_pred_models) |> multi_auc_summary() +multi_auc_summary <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + # All tables are add together + purrr::reduce(c) + }) + } else if ("regular_list" %in% class(data)) { + data_step <- data |> + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + purrr::map(c) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step |> + purrr::map(summary) +} + +#' Map and 2 level recursive purrr::pluck to ease regular_list subsetting +#' +#' @param data +#' @param lvl1 +#' @param lvl2 +#' +#' @return +#' @export +#' +#' @examples +list3levelpluck <- function(data, lvl1 = "model", lvl2 = "cMatTest") { + data |> purrr::map(\(y) y |> + purrr::pluck(lvl1) |> + purrr::pluck(lvl2)) +} + +#' Plot performance curve from glmnet regularisation +#' +#' @param data list of cvs.glmnet objects +#' +#' @return ggplot list object +#' @export +#' +#' @examples +plot_roc_curve <- function(data, title.text) { + ggplot2::ggplot() + + purrr::map(data, function(i) { + ggplot2::geom_step(data = i, ggplot2::aes(x = FPR, y = TPR)) + }) + + ggplot2::coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) + + ggplot2::geom_abline() + + ggplot2::theme_bw() + + ggplot2::ggtitle(title.text) +} + +roc_gather_step <- function(x) { + with(x, glmnet::roc.glmnet(cvs[[1]]$fit.preval, newy = y1)[match(bestL, lambdas)]) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_roc_plot() +#' targets::tar_read(ls_pred_models) |> multi_roc_plot() +multi_roc_plot <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, roc_gather_step) |> + purrr::map(purrr::list_flatten) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> purrr::map(roc_gather_step) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map2(.y = names(data), plot_roc_curve) |> + patchwork::wrap_plots() +} + +#' Title +#' +#' @param tuning.param +#' @param data +#' +#' @return +#' @export +#' +#' @examples +multi_tuning_gather <- function(tuning.param = "bestA", data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, \(x) x |> purrr::pluck(tuning.param)) |> + purrr::map(purrr::reduce, c) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::pluck, tuning.param) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> purrr::map(summary) +} + + +#' Tidied tuning summary call +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> tuning_summary() +#' targets::tar_read(ls_pred_models) |> tuning_summary() +tuning_summary <- function(data) { + c(ALPHA = "bestA", LAMBDA = "bestL") |> purrr::map(\(x) x |> multi_tuning_gather(data = data)) +} + +#' Apply regularisation steps to MIDS object, output arranged by grouping +#' +#' @param data mids object from mice package +#' +#' @return list +#' +#' @examples +#' targets::tar_read(df_pred_mids) |> mids_regularisation() +mids_regularisation <- function(data) { + ls <- data |> + mice::complete(action = "long") |> + dplyr::group_split(.imp) |> + purrr::modify(\(x){ + x |> dplyr::select(-tidyselect::all_of(c(".imp", ".id"))) + }) |> + purrr::map(pred_models) + + nms <- ls |> + purrr::map(names) |> + unique() |> + purrr::reduce(c) + + # As a consequence of the above code each "set" of analyses are together. + # Here the same group analyses are subset and grouped + ls_n <- purrr::map(nms, function(i) { + ls |> purrr::map(purrr::pluck, i) + }) |> + setNames(nms) + + # Special class is applied to ease future handling + class(ls_n) <- c("mids_regular_list", class(ls_n)) + ls_n +} + +#' A collection of all the summary functions to be applied to list of +#' pred_models() output +#' +#' @param data list of data +#' +#' @return list +#' @export +#' +multi_summary <- function(data){ + list( "coefTable" = print_pred_coefs(data) |> gt::fmt_number(n_sigfig = 4) |> add_var_groups_gt(), + "confusionMatrices" = multi_table_cfm(data), + "summaryAUC" = multi_auc_summary(data), + "rocPlots" = multi_roc_plot(data), + "tuningSummaries" = tuning_summary(data)) +} + +# funs <-list( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +# multi_summary <- plyr::each( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +#' Subset multiple elements from list +#' +#' @param data list +#' @param indices numeric or character vector +#' +#' @return list +#' @examples +#' targets::tar_read(ls_pred_summary)$confusionMatrices |> purrr::map(list_subset) +list_subset <- function(data,indices=c("overall","byClass")){ + data[indices] +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_summary) |> print_model_resutls() +print_model_resutls <- function(data){ + par(mfrow=c(1,2)) + + list(data$coefTable, + invisible(data$confusionMatrices |> purrr::map(\(x) x |> purrr::pluck("table") |> fourfoldplot())), + data$confusionMatrices |> purrr::map(list_subset), + data$summaryAUC, + data$tuningSummaries + ) +} + + +#' Classic logistic regression on prediction covariates +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' targets::tar_read(df_pred_data) |> pred_log_reg() +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_log_reg <- function(data){ + data |> pred_ls_split() |> + purrr::map(\(x) { + gtsummary::tbl_regression(glm(pase_bin~.,family = binomial,data = x), + exponentiate= TRUE)|> + gtsummary::bold_p() + } + ) |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() } + +#' Classic linear regression on 6 months PASE score. Uni and multi. +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' data <- targets::tar_read(df_pred_data) +#' data <- targets::tar_read(df_pred_mids) +#' targets::tar_read(df_pred_data) |> pred_lin_reg() +#' targets::tar_read(df_pred_mids) |> pred_lin_reg() +pred_lin_reg <- function(data){ + + # list("tbl_regression-str:ref_row_text"="Reference") |> + # gtsummary::set_gtsummary_theme() + + if ("mids" %in% class(data)){ + cols <- names(data$data) + + } else { + cols <- names(data) + data <- data |> + labelling_data() + } + + vars <- cols[cols!="pase_4"] + + formula_pase <- paste("pase_4",paste(vars,collapse = "+"),sep="~" ) + + # multi <- with(data=data,lm(pase_4~.)) |> + # gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE)|> + # gtsummary::bold_p() |> gtsummary::add_n() + + if (!"mids" %in% class(data)){ + ls <- list("Univariate"=data |> + gtsummary::tbl_uvregression(method=lm, show_single_row = dplyr::where(is.logical), + y=pase_4, + add_estimate_to_reference_rows = TRUE)|> + gtsummary::bold_p(), + "Multivariate (no BMI)"=lm(pase_4~.,data=dplyr::select(data,-reg_bmi)) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + gtsummary::bold_p() |> gtsummary::add_n(), + "Multivariate (ALL)"= lm(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + gtsummary::bold_p() |> gtsummary::add_n() + ) + + } else { + ls <- list("Multivariate (ALL)"= suppressWarnings(mice::lm.mids(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + gtsummary::bold_p() |> gtsummary::add_n())) + } + + ls |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() +} + +#' Simple standard plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() |> plot_survival() +plot_survival <- function(data){ + data |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit(linetype_aes = TRUE, size = 0.8) + + ggsurvfit::add_confidence_interval() + + ggsurvfit::add_risktable( + risktable_stats = c("n.risk", "cum.event"), + stats_label = list(cum.event = "Cumulative Observed Events", + n.risk = "Number at Risk"), + theme = + list( + ggsurvfit::theme_risktable_default(axis.text.y.size = 11, + plot.title.size = 11), + ggplot2::theme(plot.title = ggplot2::element_text(face = "bold")) + ) + ) + + ggplot2::scale_y_continuous( + limits = c(0, 1), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0)) +} + + +#' Smooth tidy survfit object +#' +#' @param data survfit object +#' +#' @return tibble +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() +#' data |> ggsurvfit::survfit2(robust=TRUE) |> +#' ggsurvfit::tidy_survfit(type="survival") |> +#' dplyr::group_split(strata) |> +#' purrr::map(smooth_col) +smooth_col <- function(data){ + smoothed <- lapply(c("estimate","conf.high","conf.low"),function(i){ + stats::predict(mgcv::gam(data=data,formula = as.formula(glue::glue("{i}~s(time,bs='cs')")))) |> + as.data.frame()|> + setNames(glue::glue("{i}_smooth")) + }) |> purrr::list_cbind() + + dplyr::tibble(data, + smoothed) + +} + +#' Prepare cox regression for smooth survival plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() |> smooth_cox_data() +smooth_cox_data <- function(data){ + data |> + ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(smooth_col) |> + purrr::list_rbind() +} + +#' Plot smooth survival plot +#' +#' @param data df from cox regression +#' +#' @return ggplot list object +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression(use.strata=TRUE) +#' data |> plot_survival_smooth() +plot_survival_smooth <- function(data){ + if ("mira" %in% class(data)) stop("Only plots non-imputed survival data") + + n.level <- length(data$xlevels[[1]]) + + # data |> + # ggsurvfit::survfit2() |> + # ggsurvfit::tidy_survfit() + + if (data |> ggsurvfit::survfit2() |> purrr::pluck("n") |> length() ==1 ){ + ds <- data |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() + p <- ds |> + ggplot2::ggplot(ggplot2::aes(x=time, y=estimate))+ + ggplot2::geom_smooth(se=TRUE, method="loess", formula = "y~x", linewidth=2, color="grey10") + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high) + + } else { + ds <- data |> + smooth_cox_data() + p <- ds |> + ggplot2::ggplot()+ + ggplot2::geom_line(ggplot2::aes(x=time, y=estimate_smooth, color=strata, linetype=strata), linewidth=2)+ + ggplot2::geom_ribbon(ggplot2::aes(x=time, ymin=conf.low_smooth,ymax=conf.high_smooth, fill=strata), alpha=.2) + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high_smooth) + + } + p+ + ggplot2::scale_y_continuous(limits = c(0,1.02), + breaks = seq(0,1,.25), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0))+ + ggplot2::scale_fill_manual(values=viridisLite::turbo(n=n.level,direction = 1))+ + ggplot2::scale_color_manual(values=viridisLite::turbo(n=n.level,direction = 1))+ + ggplot2::theme_minimal()+ + ggplot2::theme(axis.title.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + # axis.text = ggplot2::element_blank(), + # legend.position = "none", + panel.grid.minor.y = ggplot2::element_blank(), + panel.grid.major.y = ggplot2::element_line(color="grey45",linewidth = 1)) + +} + +cluster_rank <- function(data){ + data |> cox_regression(outcome.var = "clust",use.strata = TRUE) |> ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(\(x){ + min(x[["estimate"]]) + }) |> purrr::list_c() |> rank() |> rev() +} + +cox_relevel <- function(data){ + data |> dplyr::mutate(clust=factor(clust,levels=cluster_rank(data))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +complete_preds_data <- function(data){ + data |> events_ready() |> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = FALSE) |> + mice::complete() |> + dplyr::filter((!is.na(pase_0)&!is.na(pase_4))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam <- targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam |>print_table_summary(by.var = "kam_grp") +#' data_kam |> cox_regression(outcome.var="kam_grp")|> plot_survival_smooth() +#' data_kam |> cox_regression(outcome.var="kam_grp",use.strata = FALSE)|> gtsummary::tbl_regression(exponentiate = TRUE, add_estimate_to_reference_rows = TRUE) |> gtsummary::bold_p() +#' targets::tar_read(df_events_complete) |> kamila_cluster(n.clusters=3) +kamila_cluster <- function(data, n.clusters=3,include.out=FALSE){ + # An index number could be added to later join pack. Of input a complete data set from imputation and pooling?? + data_orig <- data + + + if (!include.out){ + data <- data |> + dplyr::select(-tidyselect::one_of(c("time","status"))) + } + + + catInd <- data |> lapply(\(x) is.character(x)|is.logical(x)) |> purrr::list_c() + conInd <- data |> lapply(\(x) is.numeric(x)|is.integer(x)) |> purrr::list_c() + + catVars <- data[,catInd] + catVars <- catVars |> lapply(factor) |> dplyr::bind_cols() |> as.data.frame() + conVars <- data[,conInd] |> scale()|> as.data.frame() + + if (is.null(n.clusters)){ + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = 2:7, numInit = 10, + calcNumClust = "ps" + ) + }else { + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = n.clusters, numInit = 10) + } + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kamila_cluster",class(ls)) + + ls + +} + + +#' VarSelLCM wrapper +#' +#' @param data complete dataset with no missings +#' @param n.clusters number of clusters (if length 1, n is fixed, in n>1 given clusters are tested) +#' @param include.out flag to include outcome variables or not +#' @param memb.out output data frame with final membership or not (then outputs standard model output) +#' +#' @return list with VarSelLCM output and original dataset with cluster appended +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> lcm_cluster() +lcm_cluster <- function(data, n.clusters=3, include.out=FALSE, var.sel=FALSE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(-c("time", "status")) + } + + + set.seed(5432) + + out <- data |> + dplyr::mutate(dplyr::across(where(is.logical)|where(is.character),~factor(.x))) |> + as.data.frame() |> + VarSelLCM::VarSelCluster( + gvals=n.clusters, + crit.varsel="BIC", + vbleSelec = var.sel, + nbcores = round(parallel::detectCores()*.8) + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("lcm_cluster",class(ls)) + + ls + +} + +#' Kmeans clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> kmeans_cluster() +#' data |> kmeans_cluster(n.clusters=3) +kmeans_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + out <- data |> + dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + dplyr::across(where(is.factor),~as.numeric(.x))) |> + stats::kmeans( + centers=n.clusters + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kmeans_cluster",class(ls)) + + ls + +} + +#' dbscan clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> dbscan_cluster() +#' data |> dbscan_cluster(n.clusters=3) +dbscan_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + data <- data |> na.omit() |> dplyr::mutate(rtreat=rtreat!="Placebo", + dplyr::across(dplyr::everything(), as.numeric)) + + + ## This plot indicates that eps should be set around 60, but at this value everything is one cluster. + dbscan::kNNdistplot(data,k = 5) + + ## Performing hierachical clustering, it is clear, that the algorithm is not able to seperate clusters. + hds <- dbscan::hdbscan(data,minPts = 5) + + plot(hds,show_flat = TRUE) + + ## Clustering with set eps value and minPts + ds <- dbscan::dbscan(data,eps = 25,minPts = 2) + + ds[["cluster"]] + + ## dbscan is not an interesting approach, apparently + + # + # + # + # + # out <- data |> + # dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + # dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + # dplyr::across(where(is.factor),~as.numeric(.x))) |> + # stats::kmeans( + # centers=n.clusters + # ) + # + # ls <- list("out"=out,"data_orig"=data_orig) + # + # class(ls) <- c("kmeans_cluster",class(ls)) + # + # ls + +} + +#' Title +#' +#' @param ls +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' ls <- data |> lcm_cluster() +#' ls |> final_membership() +final_membership <- function(ls){ + cls <- class(ls) + if ("kamila_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$finalMemb), + ls$data_orig) + + } else if ("lcm_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out@partitions@zMAP), + ls$data_orig) + + } else if ("kmeans_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$cluster), + ls$data_orig) + + } else stop("Class not recognised") + +} + + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> get_clusters() +#' data |> get_clusters(n.cl=2:7) +get_clusters <- function(data,n.cl=4,rm.out=TRUE){ + set.seed(1123) + + if (length(n.cl)>1){ + list( + # "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } else { + list( + "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } + +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(list_pred_clusters) +#' data |> final_clusters() +final_clusters <- function(data,new.levels=NULL){ + out <- data |> lapply(final_membership) |> lapply(labelling_data) + + if (is.null(new.levels)){ + out + } else { + out |> + purrr::map2(relevels,function(x,y){ + # x$clust <- factor(factor(x$clust,levels=y),labels=1:4) + x$clust <- factor(x$clust,levels=y) + x #|> + # dplyr::filter(clust %in% range(as.numeric(clust))) |> + # dplyr::mutate(clust=factor(clust)) + }) + } + + } + + + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_summary_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> + # print_table_summary(by.var = by) + gtsummary::tbl_summary(by=by) |> + gtsummary::add_p() |> gtsummary::bold_p() + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Easy cox regression tbl for uniform results +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +cox2tbl <- function(data,by="clust"){ +data|> cox_regression(outcome.var = by,use.strata = FALSE,all.vars = FALSE) |> + gtsummary::tbl_regression(exponentiate =TRUE) |> gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_cox_reg_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> cox2tbl(by=by) + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +wrapped_surv_plot <- function(data,by="clust"){ +data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = by,use.strata = TRUE,all.vars = FALSE) |> + plot_survival_smooth()+ggplot2::labs(color="Cluster",fill="Cluster",linetype="Cluster") + } + ) |> (\(x){ + x |> patchwork::wrap_plots(ncol=1) + patchwork::plot_annotation(tag_levels = list(names(x))) + })() +} + + +# Ranking by most events +relevel_by_rank <- function(data){ + ## Assigning clusters to each dataset +data <- targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = NULL) + +## Calculating cox regressions and ranking by the final point on the survival plot +relevels <- data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = "clust",use.strata = TRUE,all.vars = FALSE) |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() |> + (\(x){ + split(x,x[["strata"]]) |> + purrr::map(function(.y){ + .y[["estimate"]][nrow(.y)] + }) + })() |> purrr::reduce(c) |> rank() + } + ) + +#3 Assigning the new, ranked levels +targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = relevels) +} + +## TODO +## Verify definitions +## Do remaining documentation of functions +## +## +## How does elastic net work with imputed dataset? +## Functionalise to allow for imputed and non-imputed (both analyses) - in both cases with and without BMI - include department of inclusion to investigate reason of missing BMI data +## +## tidymodels does not allow pmm in mice. Thy're out! diff --git a/1 PA Decline/Fra DDV/functions240418.R b/1 PA Decline/Fra DDV/functions240418.R new file mode 100644 index 0000000..b9a1267 --- /dev/null +++ b/1 PA Decline/Fra DDV/functions240418.R @@ -0,0 +1,2688 @@ +# pop <- haven::read_sas(here::here("E:/rawdata/709203/Population/pop_talos.sas7bdat")) + +# sst <- list.files(here::here("E:/rawdata/709203/Eksterne data"), pattern = "*.sas7bdat", full.names = TRUE) |> +# purrr::map(haven::read_sas) + + + +#' Read all sas files in folder to list +#' +#' @param path folder path +#' +#' @return list +sas2list <- function(path) { + ls <- list.files(here::here(path), pattern = "*.sas7bdat", full.names = TRUE) |> + purrr::map(haven::read_sas) + names(ls) <- list.files(here::here(path), pattern = "*.sas7bdat") |> + gsub(".sas7bdat", "", x = _) |> + toupper() + ls +} + + +#' Flatten multilevel list +#' +#' @param paths character vector of folder paths +#' +#' @return flattened list +flatmultiread <- function(paths) { + paths |> + purrr::map(sas2list) |> + purrr::list_flatten() +} + +# ls <- targets::tar_read(reg_data) + +# Vectors are kept for compatibility. Calling functions can be done from within other functions. So much easier! + +date_cutter <- function() as.Date("2023-01-01") +date.cut <- date_cutter() # The earliest date will define the overall date cut + +censor_cutter <- function() 8.5 +censor.cut <- censor_cutter() # years of maximum follow up, due to small numbers + +vasc.diags <- c("I21", "I61", "I63", "I64", "G45", "K28") + +#' Extract deaths from Dødsårsagsregiseret +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(reg_data) |> get_deaths() +get_deaths <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + + + # vasc.death.tilg <- mapply(ls$DAR_T_DODSAARSAG_2[, "C_DODTILGRUNDL_ACME"], + # FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + # i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + # i_3 %in% diags.vasc # Rowvise matching + # } + # ) + + vasc.death.tilg <- ls$DAR_T_DODSAARSAG_2[["C_DODTILGRUNDL_ACME"]] |> substr(1, 3) %in% diags.vasc + + + vasc.death.any <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DODTILGRUNDL_ACME", "C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + # + vasc.death.other <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + + diag.either <- xor(vasc.death.other, vasc.death.tilg) + diag.both <- vasc.death.other & vasc.death.tilg + + vasc.death.diags <- + apply( + mapply( + ls$DAR_T_DODSAARSAG_2[, c( + "C_DODTILGRUNDL_ACME", + "C_DOD_1A", + "C_DOD_1B", + "C_DOD_1C", + "C_DOD_1D" + )], + FUN = function(i) { + # mapply inside apply call to handle rowvise matching in in matrix + substr(i, 1, 3) # Substr only the 3 first characters to match by group + } + ), + 1, + paste, + collapse = "," + ) + + deaths.vasc <- + ls$DAR_T_DODSAARSAG_2 |> + dplyr::select(K_CPR, D_STATDATO) |> + dplyr::filter(vasc.death.tilg) + + df.death.all <- ls$CPR3_T_PERSON |> + dplyr::filter(C_STATUS == 90) |> # People migrating are filtered (n ~ 1) + dplyr::select(c( + "V_PNR", + "D_STATUS_HEN_START" + )) |> + dplyr::left_join(deaths.vasc, by = c("V_PNR" = "K_CPR")) |> + dplyr::mutate(vasc_death = !is.na(D_STATDATO)) |> + dplyr::transmute( + PNR = V_PNR, + death_date = D_STATUS_HEN_START, + vasc_death = vasc_death + ) + + df.death.all |> dplyr::filter(death_date < max.date) +} + + +#' Title +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' ls <- targets::tar_read(reg_list) +get_events <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + ident.vars <- toupper(c("_recnum", "_cpr")) + + df.vasc.events.lpr <- ls$LPR_T_DIAG |> + dplyr::mutate(dia.f = substr(C_DIAG, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # C_DIAGTYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars), + "C_DIAG", + "C_DIAGTYPE" + ) |> + dplyr::left_join( + ls$LPR_T_ADM |> dplyr::select( + tidyselect::ends_with(ident.vars), + "D_INDDTO", + "C_INDM", + "D_UDDTO", + "C_UDM", + "C_SGH", + "C_AFD", + "C_ADIAG" + ), + by = c("V_RECNUM" = "K_RECNUM") + ) + + ## LPR-F - LPR 3 + + ident.vars.lpr3 <- toupper(c("cpr", "_kontakt", "DW_EK_FORLOEB")) + + df.vasc.events.lpr3 <- ls$LPR_F_DIAGNOSER |> + dplyr::mutate(dia.f = substr(DIAGNOSEKODE, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # DIAGNOSETYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars.lpr3), + "DIAGNOSEKODE", + "DIAGNOSETYPE" + ) |> + dplyr::left_join( + ls$LPR_F_KONTAKTER |> dplyr::select( + ends_with(ident.vars.lpr3), + "DATO_START", + "DATO_SLUT", + "PRIORITET" + ), + by = c("DW_EK_KONTAKT") + ) |> + dplyr::mutate(PRIORITET = as.character((PRIORITET == "ATA1") + 1)) # If ATA1 then 1, if not (ATA3) then 2, cowboy coding + + df.vasc.events <- dplyr::full_join(df.vasc.events.lpr, df.vasc.events.lpr3, by = c( + "V_CPR" = "CPR", + "C_DIAG" = "DIAGNOSEKODE", + "C_DIAGTYPE" = "DIAGNOSETYPE", + "D_INDDTO" = "DATO_START", + "D_UDDTO" = "DATO_SLUT", + "C_INDM" = "PRIORITET" + )) |> + dplyr::mutate(date.event = D_INDDTO) + + df.vasc.events |> dplyr::filter(date.event < max.date) +} + + +# deaths <- targets::tar_read(df_deaths) +# events <- targets::tar_read(df_events) +# clinical <- targets::tar_read(pop_df) + + +#' Filter only truly considered events +#' +#' @param data +#' +#' @return tibble +define_events <- function(data) { + data |> dplyr::filter( + C_DIAGTYPE == "A", # Primary diagnosis + C_INDM == "1", # Acutely admitted + difftime(date.event, rdate, units = "days") > 5 # More than five (5) days after randomisation/primary stroke + ) +} + +#' The big merger and filter of events +#' +#' @param ls list of events, deaths and clinical +#' +#' @return tibble +merge_events <- function(ls) { + df.events <- dplyr::full_join( + purrr::pluck(ls, "events"), + purrr::pluck(ls, "deaths") |> + dplyr::mutate( + # These are just added to ease later filtering + C_DIAGTYPE = "A", + C_INDM = "1" + ), # Ads diagtype=A, C_INDM=1 for easier sorting later + by = c( + "V_CPR" = "PNR", + "date.event" = "death_date", + "C_DIAGTYPE", + "C_INDM" + ) + ) |> + dplyr::full_join(dplyr::select(purrr::pluck(ls, "clinical"), c("PNR", "rdate", "enddate")), + by = c("V_CPR" = "PNR") + ) |> + dplyr::arrange(date.event) |> # Sort by event date + dplyr::mutate(event.type = dplyr::if_else( + is.na(C_DIAG), + dplyr::if_else(vasc_death, "death.vasc", "death.other"), + substr(C_DIAG, 1, 4) + )) + + df.events |> + dplyr::group_split(V_CPR) |> # Splits by CPR + purrr::map(define_events) |> # Custom function to specify criteria for events + purrr::discard(\(x) nrow(x) == 0) |> # Discard empty elements + purrr::modify(\(x) x[1, ]) |> # Select first event + purrr::list_rbind() |> + dplyr::transmute( + CPR = V_CPR, + date.event, + event.type + ) +} + + + + +#' Count number of prescriptions of given ATC group for each CPR +#' +#' @param atc.code atc group +#' @param data dataset from LMS +#' +#' @return tibble +count_treat <- function(atc.code, data) { + data |> + dplyr::filter(grepl(atc.code, ATC)) |> + dplyr::count(CPR) |> + dplyr::filter(n > 1) +} + + +#' Get LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_lms <- function(ls, max.date, atc.tbl) { + ls$LMS_EPIKUR |> + dplyr::filter(grepl(atc.tbl[1], ATC)) |> + dplyr::left_join(ls$LMS_LAEGEMIDDELOPLYSNINGER) |> + dplyr::filter(as.Date(ACTDATE) < max.date) +} + +#' Get count of treated patients from LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_treated <- function(ls, + max.date = date.cut, + atc.tbl = c( + atc.antidep = "N06A", + atc.ssri = "N06AB" + )) { + df <- atc.tbl |> + purrr::map(count_treat, get_lms(ls, max.date, atc.tbl)) |> + purrr::reduce(dplyr::full_join, by = "CPR") + colnames(df) <- c("CPR", paste0("n.", names(atc.tbl))) + df +} + +#' BMI calc, drops +#' +#' @param w weight in kg +#' @param h height in cm +#' @param data data set +#' +#' @return tibble +bmi_calc <- function(data, drop = TRUE) { + # After inspection, both h+w are missing if any is missing + out <- data |> dplyr::mutate(reg_bmi = suppressWarnings(as.numeric(dplyr::if_else(reg_vaegt == "NA", reg_vaegt_anslaaet, reg_vaegt)) / ((as.numeric(reg_hojde) / 100)^2))) + + if (drop) { + out <- out |> + dplyr::select(-dplyr::all_of(c("reg_vaegt", "reg_vaegt_anslaaet", "reg_hojde"))) + } + out +} + +is_equal <- function(data, test) { + data == test +} + +#' Load clinical population data +#' +#' @return tibble +#' @examples +#' get_clinical() |> colnames() +#' +get_clinical <- function() { + sas2list("E:/rawdata/709203/Population")[[2]] |> + correct_na() |> + dplyr::mutate( + reg_smoker = dplyr::case_match( + reg_rygning, "1" ~ TRUE, + c("2", "3", "4") ~ FALSE, + "9" ~ NA + ), + # Living alone defined as not together with somebody + reg_alone = dplyr::case_match( + reg_civil, "1" ~ FALSE, + c("2", "3") ~ TRUE, + "9" ~ NA + ), + reg_more_alc = dplyr::case_match( + reg_alkohol, "1" ~ FALSE, + "2" ~ TRUE, + "9" ~ NA + ), + reg_female = sex == "Kvinde", + dplyr::across( + .cols = c( + "reg_hyperten", + "reg_diabetes", + "reg_atriefli", + "reg_perifer_arteriel", + "reg_tidl_tci", + "reg_ami" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + "2" ~ FALSE, + "9" ~ NA + ) + ), + dplyr::across( + .cols = c( + "reg_trombolyse", + "reg_trombektomi" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + c("3","4") ~ FALSE, + "9" ~ NA + ) + ), + reg_any_perf = reg_trombolyse | reg_trombektomi + ) |> + bmi_calc() +} + +# get_clinical() |> pragmatic_imputation() |> skimr::skim() + +# get_clinical <- function() { +# sas2list("E:/rawdata/709203/Population")[[2]] |> +# dplyr::mutate( +# reg_smoker = reg_rygning == 1, +# reg_cohabiting = reg_civil == 1, +# reg_more_alc = reg_alkohol == 2, +# reg_female = sex == "Kvinde", +# dplyr::across(.cols = c("reg_hyperten", "reg_diabetes", "reg_atriefli", "reg_perifer_arteriel", "reg_tidl_tci", "reg_ami", "reg_trombolyse", "reg_trombektomi"), ~ .x == 1), +# reg_any_perf = reg_trombolyse | reg_trombektomi +# ) |> +# bmi_calc() +# } + +pragmatic_imputation <- function(data, vec=c("reg_smoker","reg_cohabiting","reg_more_alc","reg_hyperten", "reg_diabetes", "reg_atriefli", "reg_perifer_arteriel", "reg_tidl_tci", "reg_ami", "reg_trombolyse", "reg_trombektomi","reg_any_perf")) { + # Assumes, if not TRUE, then FALSE (gets rid of NAs) + data |> dplyr::mutate(dplyr::across(.cols = tidyselect::any_of(vec), ~dplyr::if_else(.x,TRUE,FALSE,missing = FALSE))) +} + +#' Load all registry tables to list +#' +#' @return list +get_reg_ls <- function() { + flatmultiread(c("E:/rawdata/709203/Eksterne data", "E:/rawdata/709203/Grunddata")) +} + + +#' Definition of relevant variables from DST tables +#' +#' @return +define_dst_vars <- function() { + list( + bef = c("PNR", "FAMILIE_ID"), + faik = c("FAMILIE_ID", "FAMAEKVIVADISP_13", "FAMSOCIOGRUP_13"), + ras = c("PNR", "SOC_STATUS_KODE"), + uddf = c("PNR", "HFAUDD") + ) +} + +#' Simple wrapper of dplyr::select +#' +#' @param data +#' @param vars +#' +#' @return +select_vars <- function(data, vars) { + data |> dplyr::select({{ vars }}) +} + +#' Subset DST tables to only include relvant variables. +#' +#' @param ls List of all registry tables +#' +#' @return +get_dst_tables <- function(ls) { + dst_vars <- define_dst_vars() + dst_tbl <- toupper(names(dst_vars)) + ls.all <- purrr::map(seq_along(dst_vars), function(i) { + ls.reg <- ls[grepl(paste0("^", dst_tbl[i]), names(ls))] |> purrr::map(select_vars, vars = dst_vars[[i]]) + names(ls.reg) <- paste0("y", stringr::str_extract(names(ls.reg), "[0-9]{4}")) + ls.reg + }) + names(ls.all) <- dst_tbl + ls.all +} + +#' Wrapper to generate string matching pattern for stringr::str_detect() +#' +#' @param data +#' +#' @return +match_str <- function(data) { + paste0("[", paste0(data, collapse = ","), "]") +} + +#' Wrapper to generate string matching pattern for grepl() +#' +#' @param data character vector +#' +#' @return +match_str_grepl <- function(data) { + paste0("(", paste0(data, collapse = "|"), ")") +} + +# get_dst_tables(ls) + +#' Generate sequence of previous N length +#' +#' @param data numeric vector of length 1 +#' +#' @return +#' +#' @examples +#' last5y(10) +#' last5y(c(10, 6, 3)) +lastNy <- function(data, n = 5) { + paste0("y", seq((data - n), data) - 1) +} + +#' Filter PNR (cpr) across list elements +#' +#' @param data list of tibbles to pass through +#' @param index index number (PNR/CPR) +#' +#' @return tibble +filterCPRacross <- function(data, index) { + data |> + purrr::map(function(i) { + i[i$PNR == index, ] + }) |> + purrr::list_rbind() +} + +#' Summarise data from last 5 years prior to inclusion +#' +#' @param data list with +#' @param v.median variables to get median +#' @param v.latest variables to get latest +#' @param v.mean variables to get mean +#' +#' @return tibble +previousNyears <- function(data, data.clin, n.years = 5, v.median = NULL, v.latest = c("FAMSOCIOGRUP_13", "SOC_STATUS_KODE"), v.mean = c("FAMAEKVIVADISP_13")) { + df.cpryear <- data.clin |> dplyr::transmute( + CPR = PNR, + year = as.numeric(format(as.Date(rdate), "%Y")) + ) + + seqs <- purrr::map(df.cpryear$year, lastNy, n = n.years) + + seq_along(seqs) |> + purrr::map(function(i) { + df <- data[c(seqs[[i]])] |> + filterCPRacross(index = df.cpryear$CPR[i]) |> + dplyr::group_by(PNR) |> + dplyr::summarise( + dplyr::across(tidyselect::any_of(v.latest), \(x) tail(x, n = 1), .names = "{.col}.latest"), + dplyr::across(tidyselect::any_of(v.mean), \(x) mean(x, na.rm = TRUE), .names = "{.col}.{n.years}.mean"), + dplyr::across(tidyselect::any_of(v.median), \(x) median(x, na.rm = TRUE), .names = "{.col}.{n.years}.median") + ) + }) |> + purrr::list_rbind() +} + + +#' Extract relevant and summarised data from BAF and FAIK +#' +#' @param data list of dst data tables +#' +#' @return tibble +#' +#' @examples +#' get_reg_ls() |> +#' get_dst_tables() |> +#' get_beffaikras() +get_beffaikras <- function(data, clin.data = get_clinical()) { + data <- data[stringr::str_detect(match_str(c("BEF", "FAIK", "RAS")), names(data))] |> purrr::list_flatten() + + years <- stringr::str_extract(names(data), "y[0-9]{4}") + + years[duplicated(years)] |> + purrr::map(grep, years) |> + purrr::map(function(i) { + data[c(i)] |> purrr::reduce(dplyr::full_join) + }) |> + purrr::set_names(years[duplicated(years)]) |> + previousNyears(data.clin = clin.data) + + ## BEF + ## # Befolkningsoversigt. Data skal bruges for at kunne udtrække husstandsindkomst. + ## FAIK + ## # Familieindkomst. Familie id skal flættes med ID fra BEF for hvert år for at tage hensyn til evt skifte i status. + ## FAMAEKVIVADISP_13 er relevante variabel for ækvivaleret indkomst + ## Der findes også familiesocioøkonomisk status. Gør som Sine. Be done with it! +} + +#' Title +#' +#' @return +#' @export +#' +#' @examples +#' data <- read_edu_level() +#' data |> dplyr::count(ISCED) +read_edu_level <- function() { + haven::read_dta("E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l4_k.dta") |> + dplyr::transmute( + HFAUDD = start, + ISCED = AUDD_LEVEL_L1L4_K + ) +} + +# haven::read_dta( +# "E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l3_k.dta") |> +# dplyr::count(AUDD_LEVEL_L1L3_K) + +# ls <- get_reg_ls() |> get_dst_tables() + +get_uddf <- function(ls) { + ls |> + purrr::pluck("UDDF") |> + purrr::pluck(1) |> + dplyr::mutate(HFAUDD = as.character(HFAUDD)) |> + dplyr::left_join(read_edu_level()) |> + dplyr::group_by(PNR) |> + dplyr::summarise(ISCED = max(ISCED), .groups = "keep") |> + dplyr::mutate( + ISCED = as.numeric(ISCED), + ISCED_lvl = dplyr::case_match(ISCED, 0:2 ~ "low", + 3:4 ~ "medium", + 5:9 ~ "high", + .default = NA + ), + ISCED_bin = dplyr::case_match(ISCED, 0:3 ~ "low", + 4:9 ~ "high", + .default = NA + ) + ) +} + + +#' Collects all relevant variables from DST tables +#' +#' @param ls list of all registry tables +#' @param df.clin clinical data set +#' +#' @return tibble +#' @examples +#' get_reg_ls() |> get_dst(df.clin = get_clinical()) +get_dst <- function(ls, df.clin) { + ls_dst <- get_dst_tables(ls) + ls_dst |> + ## BEFxFAIKxRAS + get_beffaikras(clin.data = df.clin) |> + dplyr::full_join( + ## UDDF + get_uddf(ls_dst) + ) +} + +#' Eases pipe renaming of columns +#' +#' @param data tibble, list or other object, for which names() makes sense. see ?setNames +#' @param prefix prefix to add +#' @param exclude names not to modify +#' @param new.names character vector of all new names +#' +#' @return object of same class as data +#' +#' @examples +#' set_colnames(data = mtcars, prefix = "WOW", exclude = "mpg") +set_colnames <- function(data, new.names = NULL, prefix = NULL, exclude = c("PNR", "CPR"), prefix.sep = "_") { + if (is.null(new.names)) { + nms <- names(data) + } else { + nms <- new.names + } + + if (is.null(prefix)) { + nms.mod <- nms + } else { + nms.mod <- paste(prefix, nms, sep = prefix.sep) + } + + setNames( + object = data, + nm = dplyr::if_else(stringr::str_detect(match_str(exclude), nms), + nms, + nms.mod + ) + ) +} + + +#' Store of variable names for data sub-setting +#' +#' @return list +#' @examples +#' define_variables() +define_variables <- function() { + list( + clin = c( + "age", + "reg_female", + "nihss_0", + "reg_trombolyse", + "reg_trombektomi", + # "rtreat", + "rtreat_placebo"), + lifestyle=c( + "pase_0", + "pase_4", + "reg_alone", + "reg_bmi", + # "reg_hojde", + # "reg_vaegt_alt", + "reg_smoker", + "reg_more_alc", + "reg_hyperten", + "reg_diabetes", + "reg_tidl_tci", + "reg_atriefli", + "reg_ami", + "reg_perifer_arteriel"), + ses=c( + # "soc_status", + # "soc_status_work", + "soc_status_nowork", + # "fam_indk", + "fam_indk_hl", + # "fam_indk_high", + # "fam_indk_low", + # "edu_level", + # "edu_high", + # "edu_low", + "edu_level_hl" + ), + assess.events = c( + "who_4", + "mdi_4", + "mrs_4_above1", + "mfi_gen_4", + "time", + "status", + "event.include" + ), + assess.pred = c( + "who_0", + "mrs_0_above0" + ), + extra = c( + "soc_status", + "pase_0", + "pase_4" + ) + ) +} + +#' Get var names in vector from group names. Possibility to keep all vars for as log as possible. Can be supplied to `gtsummary` functions +#' +#' @param groups vector of group names. See names(define_variables()) for options +#' +#' @return +#' @export +#' +#' @examples +get_var_vec <- function(v.groups){ + define_variables()[{{ v.groups }}] |> purrr::list_c() +} + +#' SUbsets dataset based on variable group names as defined +#' +#' @param vector character vector of category names +#' +#' @return character vector +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> get_vars(c("universal", "events")) +get_vars <- function(data, vars.groups) { + data |> dplyr::select(tidyselect::all_of(get_var_vec(vars.groups))) +} + + + +#' Collect all relevant data for the events analysis data set +#' +#' @param ls ls of tibbles +#' +#' @return tibble +collectall <- function(ls) { + purrr::pluck(ls, "clinical") |> + dplyr::left_join(purrr::pluck(ls, "all_events") |> set_colnames(prefix = "event"), by = c("PNR" = "CPR")) |> + dplyr::left_join(purrr::pluck(ls, "dst") |> set_colnames(prefix = "dst"), by = "PNR") +} + + +## Formatting for analysis + +#' Function to cut and group PASE data +#' +#' @param data data set including pase_0 and _4 +#' +#' @return tibble +pase_cutter <- function(data, pase.rev = TRUE, drop.pase = FALSE, drop.nas=FALSE) { + data.classes <- class(data) + if ("mids" %in% data.classes) { + data <- data |> mice::complete(action = "long", include = TRUE) + } + + data <- data |> + dplyr::mutate(dplyr::across(.cols = c("pase_0", "pase_4"), \(i) { + cut(x = i, breaks = quantile(pase_0, na.rm = TRUE), labels = 1:4, include.lowest = TRUE) + }, .names = "{.col}_quartile")) |> + dplyr::mutate(pase_change = factor(dplyr::case_when( + pase_0_quartile == 1 & pase_4_quartile == 1 ~ "Low-low", + pase_0_quartile %in% 2:4 & + pase_4_quartile %in% 2:4 ~ "High-high", + pase_0_quartile == 1 & + pase_4_quartile %in% 2:4 ~ "Increase", + pase_0_quartile %in% 2:4 & + pase_4_quartile == 1 ~ "Decrease" + ), ordered = FALSE), + pase_change=factor(pase_change,levels=c("Increase", "Low-low", "Decrease", "High-high"))) + + if (drop.pase) { + data <- data |> dplyr::select(-tidyselect::all_of(c("pase_0_quartile", "pase_4_quartile", "pase_0", "pase_4"))) + } + + + if (pase.rev) { + data <- data |> dplyr::mutate( + pase_change = factor(pase_change, levels = c("High-high", "Decrease", "Low-low", "Increase")) + ) + } + + if (drop.nas) { + data <- data |> + dplyr::filter(!is.na(pase_change)) + } + + + if ("mids" %in% data.classes) { + data |> mice::as.mids() + } else { + data + } +} + +# as.Date(data$event_date.event) +define_status_time <- function(data) { + data |> dplyr::mutate(dplyr::across(c("rdate", "enddate", "event_date.event"), ~ as.Date(.x)), + time = difftime(dplyr::if_else(is.na(event_date.event), date_cutter(), event_date.event), enddate) |> lubridate::time_length("years"), + status = as.integer(!is.na(event_event.type)), + time = dplyr::if_else(time > censor_cutter(), censor_cutter(), time), + status = dplyr::if_else(time > censor_cutter(), FALSE, status), + # status= dplyr::if_else(status,1,0), + event.include = time > 0 + ) +} + +# as.integer(c(TRUE,FALSE)) + +#' Grouping soc status +#' +#' @param data tibble +#' +#' @return tibble +group_soc_status <- function(data) { + data |> + dplyr::mutate( + soc_status = factor(dplyr::case_when( + soc_status < 200 ~ "work", + soc_status == 200 ~ "off", + # only ~4 in the data set off work + soc_status >= 200 ~ "outside" + )), + soc_status_work = soc_status == "work", + soc_status_nowork = !soc_status_work + ) +} + +#' Correction of character NA +#' +#' @param data tibble +#' @param char.missing character vector of entries to consider as NA +#' +#' @return tibble +correct_na <- function(data, char.missing = "NA") { + data |> dplyr::mutate(dplyr::across(dplyr::where(is.character), ~ dplyr::na_if(.x, char.missing))) +} + +#' Formatting the complete data set +#' +#' @param data the merged raw data set +#' +#' @return tibble +#' @examples +#' ds <- targets::tar_read(df_all_data) |> +#' data_formatting() |> +#' subset_df("mdi") +#' ds |> skimr::skim() +#' ds |> View() +data_formatting <- function(data) { + to_logical <- grep(match_str_grepl(c("missings", "incompletes")), names(data)) + + suppressWarnings( + data |> + correct_na() |> + dplyr::mutate(dplyr::across(all_of(to_logical), ~ .x == "TRUE")) |> + dplyr::mutate( + # This uses the work-corrected score + # pase_0 = dplyr::if_else(pase_score_missings_w_0 | is.na(talos_pase10_0), NA, pase_score_sum_w_0), + # pase_4 = dplyr::if_else(pase_score_missings_w_4 | is.na(talos_pase10_4), NA, pase_score_sum_w_4), + # Below is the plain PASE scor used according to the manual used with TALOS + pase_0 = dplyr::if_else(pase_score_missings_0, NA,pase_score_sum_0), + pase_4 = dplyr::if_else(pase_score_missings_4, NA,pase_score_sum_4), + who_0 = as.numeric(talos_who07_0), + who_4 = as.numeric(talos_who07_4), + mrs_0 = factor(substr(talos_mrs01_0, 1, 1), ordered = FALSE), + mrs_0_above0 = (as.numeric(mrs_0) - 1) > 0, + mrs_4 = factor(substr(talos_mrs01_4, 1, 1), ordered = FALSE), + mrs_4_above1 = (as.numeric(mrs_4) - 1) > 1, + mdi_4 = as.numeric(talos_mdi12_4), + mfi_gen_4 = as.numeric(talos_mfi_gen_4), + nihss_0 = as.numeric(talos_nihss16_0), + soc_status = dst_SOC_STATUS_KODE.latest, + fam_indk = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 3), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "medium", "high"), + include.lowest = TRUE + ), + fam_indk_bin = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 2), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "high"), + include.lowest = TRUE + ), + fam_indk_hl=forcats::fct_rev(fam_indk), + fam_indk_high = dplyr::if_else(fam_indk_bin=="high",TRUE,FALSE), + fam_indk_low = dplyr::if_else(fam_indk_bin=="low",TRUE,FALSE), + edu_level = factor(dst_ISCED_lvl, ordered = FALSE, levels = c("low", "medium", "high")), + edu_high = dplyr::if_else(dst_ISCED_bin=="high",TRUE,FALSE), + edu_low = dplyr::if_else(dst_ISCED_lvl=="low",TRUE,FALSE), + edu_level_hl=forcats::fct_rev(edu_level), + rtreat_placebo=rtreat=="Placebo" + ) |> + group_soc_status() |> + define_status_time() + ) +} + + +#' Title +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> +#' events_ready() |> +#' View() +events_ready <- function(data,v.groups=c("clin","lifestyle","ses", "assess.events")) { + data |> + get_vars(vars.groups = v.groups) |> + dplyr::filter(event.include) |> + dplyr::select(-tidyselect::all_of("event.include"))# |> + # labelling_data() +} + + +#' Title +#' +#' @param date +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' +#' data |> +#' prediction_ready() |> +#' View() +prediction_ready <- function(data) { + data |> + get_vars(c("clin","lifestyle","ses", "assess.pred"))|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4))#|> + # labelling_data() +} + +## Data inspection and exploration +## +## +#' Title +#' +#' @param data +#' @param subdf +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data) |> +#' subset_df() |> +#' View() +subset_df <- function(data, subdf = "pase") { + data[grepl(paste0("^(", paste("PNR", subdf, paste0("talos_", subdf), sep = "|"), ")"), names(data))] +} + +#' Imputation as a function, includes "pragmatic imputation" +#' +#' @param data +#' @param outcome.vars +#' @param ignore +#' @param pragmatic.reg +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_ready() +#' data |> labelling_data() |> fun_impute() +fun_impute <- function(data, outcome.vars = c("status", "time"), ignore = NULL,pragmatic.reg=TRUE,pase.mod=FALSE) { + # data |> mice::md.pattern() + if (pragmatic.reg){ + data <- data |> pragmatic_imputation() + } + + ## Excluding entries with missing outcome measures + data <- data |> + dplyr::filter(!dplyr::if_any(tidyselect::all_of(c(outcome.vars)), ~ is.na(.x))) + + init <- data |> + mice::mice(maxit = 0) + + meth <- init$method + meth[ignore] <- "" + + pred <- init$predictorMatrix + pred[, c(outcome.vars)] <- 0 + + # data_out <- data |> mice::futuremice( + # pred = pred, + # method = meth, + # print = FALSE, + # parallelseed = 8123, + # use.logical = FALSE, + # maxit = 20, + # m = 10 + # ) + + data_out <- data |> mice::mice( + pred = pred, + method = meth, + print = FALSE, + seed = 8123, + maxit = 20, + m = 10 + ) + + if (pase.mod){ + data_out <- data_out |> pase_cutter_mids() + } + + data_out + + # lattice::densityplot(imp_data) + # Regarding EVENTS + # + # On inspection/eye-balling densityplots looks reasonable with the current settings + # +} + +#' Function to cut PASE in mids object +#' +#' @param data mids object +#' +#' @return mids object +#' @export +#' +pase_cutter_mids <- function(data){ +data |> + mice::complete(action = "long", include = TRUE) |> + pase_cutter(drop.pase = TRUE, drop.nas = TRUE)|> + mice::as.mids() + } + + +#' Completes events data set, option to impute +#' +#' @param data +#' @param impute +#' +#' @return mids or tibble +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset() |> +#' targets::tar_read(df_all_data_formatted) |> +events_dataset <- function(data, impute = TRUE) { + data <- data |> events_ready() + if (impute) { + data |> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = TRUE) + } else { + data |> + dplyr::select(-tidyselect::all_of("reg_bmi")) |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + } +} + +#' Title +#' +#' @param data +#' @param all.vars +#' @param outcome.var +#' @param use.strata +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_ready() |> subset_df("pase") +#' data <- targets::tar_read(df_all_data_formatted) |> events_dataset(FALSE) +#' data |> cox_regression() +cox_regression <- function(data, all.vars = TRUE,outcome.var="pase_change",use.strata=TRUE) { + if ("mids" %in% class(data)) { + nms <- names(data$data) + } else { + nms <- names(data) + data <- data |> + labelling_data() + # BMI meassure is excluded from non-imputed dataset + # data <- data |> dplyr::select(-tidyselect::all_of(c("reg_bmi"))) + } + + vars <- nms[!nms %in% c("time", "status", outcome.var)] + + form.prefix <- "survival::Surv(time, status) ~" + + if (use.strata) { + reg.form <- glue::glue("{form.prefix} strata({outcome.var})") + } else { + reg.form <- glue::glue("{form.prefix} {outcome.var}") + } + + if (all.vars) reg.form <- paste0(reg.form, " + ", paste(vars, collapse = " + ")) + + require(survival) + out <- with(data, survival::coxph( + as.formula(reg.form) + )) + + out$call$formula <- as.formula(reg.form) + + out +} + + +#' Wrapper to print summary table with extended info +#' +#' @param data formatted and subset data set +#' @param by.var stratify by +#' +#' @return +#' @examples +#' targets::tar_read(df_pred_data)|>print_table_summary(by="reg_female") +print_table_summary <- function(data, by.var = "pase_change") { + data |> + labelling_data() |> + # pase_cutter(drop.pase = TRUE) |> + gtsummary::tbl_summary( + missing = "no", + by = tidyselect::all_of(by.var), + value = list(where(is.logical) ~ TRUE), + type = list(gtsummary::all_continuous() ~ "continuous2"), + statistic = list(gtsummary::all_continuous() ~ c( + # "{N_nonmiss} ({p_nonmiss}%)", + "{median} ({p25}, {p75})", + # "{min}, {max}", + "{mean} ({sd})"#, + # "{N_miss} ({p_miss}%)" + )#, + # gtsummary::all_categorical() ~ c( + # "{N_obs} ({p_nonmiss}%)"#, + # # "{N_miss} ({p_miss})" + # ) + ) + ) |> + gtsummary::add_overall() |> + gtsummary::add_n() #|> + # gtsummary::add_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' targets::tar_read(df_all_data_formatted) |> events_tblone() +events_tblone <- function(data) { + data |> + events_ready() |> + pase_cutter(drop.pase = TRUE) |> + dplyr::select(-tidyselect::all_of(c("status", "time"))) |> + dplyr::filter(!is.na(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data)|> +#' dplyr::transmute(stRoke::quantile_cut(pase_0,4,group.names = 1:4),soc_status_work,fam_indk,edu_level) |> +#' summary_tblone() +summary_tblone <- function(data,by=names(data)[1]) { + # data <- targets::tar_read(df_pred_data) + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + # pase_cutter(drop.pase = TRUE) |> + # dplyr::filter(!is.na(pase_change)) |> + # dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary(by.var = by) +} + +# +#' Summaries of DST data for PASE quartiles at 0 and 4 +#' +#' @param data +#' @param vars +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> sum_pase_tables() +sum_pase_tables <- function(data,vars=c("pase_0","pase_4")){ + vars |> lapply(function(.x){ + dplyr::tibble(stRoke::quantile_cut(data[[.x]],y=data[["pase_0"]],4,group.names = 1:4), + dplyr::select(data,soc_status_nowork,fam_indk_hl,edu_level_hl)) |> + summary_tblone() + }) +} + + +#' Creating a truthful stratified table for predictions +#' +#' @param data data frame +#' +#' @return list +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> true_pred_sum_plot() +true_pred_sum_plot <- function(data){ + true_sum <- data |> + pase_cutter(drop.pase = TRUE) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) + + list(true_sum,true_sum |> (function(.x){ + split(.x,.x$pase_change %in% c("Low-low","Increase")) + })() |> purrr::map(function(.y){.y |> dplyr::mutate(pase_change=factor(pase_change))})) |> + purrr::list_flatten() |> purrr::map(summary_tblone,by="pase_change") |> + gtsummary::tbl_merge() +} + +#' Get quick summary of missing vs non-missing for each given variable +#' +#' @param data data set +#' @param var variable to summarise over +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> dplyr::select(soc_status_work,fam_indk,edu_level) |> who_is_missing() +#' targets::tar_read(df_pred_data) |> who_is_missing(var="reg_bmi") +who_is_missing <- function(data, var = "edu_level") { + data |> + dplyr::mutate(log = factor(c("non-missing","missing")[is.na(data[[var]])+1])) |> + dplyr::select(log, tidyselect::everything(),-tidyselect::all_of(var)) |> + summary_tblone() #|> gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data) |> preds_tblone() +preds_tblone <- function(data) { + # data <- targets::tar_read(df_pred_data) + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + pase_cutter(drop.pase = TRUE) |> + dplyr::filter(!is.na(pase_change)) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' gt <- targets::tar_read(ls_pred_summary)[[1]] +#' gt |> add_var_groups_gt() +#' +#' # For this to work, the function would need to handle labels and levels +#' gt <- targets::tar_read(tbl_pred_summary)|> gtsummary::as_gt() +#' gt |> add_var_groups_gt() +add_var_groups_gt <- function(gt){ + # gt <- sex_ls |> purrr::pluck(2) |> gtsummary::as_gt() + # gt <- fix_labels(gt) + cls <- class(gt) + + b.cols <- names(gt$`_data`) + + if (b.cols[[1]]!="variable"){ + # Flag to indicate if format is native gt or not. Simple assumption + # class(gt) gt is not enough + labels <- gt$`_data`[[1]] + group.var <- names(gt$`_data`[[1]]) + } else { + labels <- gt$`_data`[["label"]][gt$`_data`[["row_type"]]=="label"] + group.var <- gt$`_data`[["variable"]] + } + + + + groups <- matrix(ncol=length(labels)) |> + data.frame() |> + setNames(ifelse(labels=="","unknown_var",labels)) |> + tibble::as_tibble() |> groups_in_ds(labels = TRUE) + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + labels.all <- group.labels |> purrr::imap(function(.x,.y){ + c(.x,groups[[.y]][["label"]]) + }) |> purrr::list_c() + + for (i in rev(seq_along(group.labels))){ + gt <- gt |> gt::tab_row_group(label=gt::md(glue::glue("*{group.labels[[i]]}*")), + rows=which(group.var %in% groups[[names(group.labels)[[i]]]][["var"]])) + + } + + class(gt) <- cls + gt +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(tbl_preds_lin_imp_reg) +#' data |> fix_labels() +fix_labels <- function(data){ + cls <- class(data) + data[[1]][["variable"]][data[[1]][["row_type"]]=="label"] |> + subset_named_labels(var_labels()) |> + unname() -> data[[1]][["label"]][data[[1]][["row_type"]]=="label"] + class(data) <- cls + data +} + + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' tbl <- targets::tar_read(ls_pred_summary)[[1]] +add_var_groups_pre_calc <- function(data,b.cols){ + + groups <- data |> groups_in_ds() + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + t0 <- data.frame(matrix(ncol=length(b.cols))) |> + setNames(b.cols) |> + tibble::tibble() + list(ext = group.labels |> purrr::imap(function(.x,.y){ + t0 |> dplyr::mutate( + variable=.y, + val_label=.x, + row_type="group", + label=.x + ) + }) |> dplyr::bind_rows(), + lvls = group.labels |> purrr::imap(function(.x,.y){ + c(.y,groups[[.y]][["var"]]) + }) |> purrr::list_c() + ) +} + +#' Adds variable grouping and formatting to gtsummary tables +#' +#' @param tbl +#' +#' @return +#' @export +#' +#' @examples +#' +#' tbl <- targets::tar_read(tbl_pred_summary) +#' targets::tar_read(tbl_pred_summary) |> add_var_groups() +#' +add_var_groups <- function(tbl, + pre_ls=add_var_groups_pre_calc(tbl$inputs$data, + names(tbl$table_body))){ + + + tbl |> gtsummary::modify_table_body( + ~.x |> dplyr::bind_rows(pre_ls[["ext"]]) |> + dplyr::arrange(factor(variable,levels=pre_ls[["lvls"]])) + ) |> + gtsummary::modify_table_styling(columns=label, + rows= row_type%in%"level",text_format = "indent2") |> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"label",text_format = "indent")|> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"group",text_format = c("italic")) +} + +#' Functionalised character vector of all labels +#' +#' @return +#' @export +#' +#' @examples +var_labels <- function(){ + c( + age = "Age", + reg_female = "Female sex", + reg_bmi = "Body mass index", + reg_smoker = "Current smoker", + reg_alone = "Living alone", + reg_more_alc = "High alcohol consumption", + reg_hyperten = "Hypertension", + reg_diabetes = "Diabetes", + reg_atriefli = "Atrial fibrillation", + reg_perifer_arteriel = "Peripheral arterial disease", + reg_tidl_tci = "Previous TIA", + reg_ami = "Previous MI", + reg_trombolyse = "Treated with IVT", + reg_trombektomi = "Treated with EVT", + # reg_any_perf, + # rtreat = "Study group allocation", + rtreat_placebo = "Placebo trial treatment", + pase_0 = "Pre-stroke PASE score", + pase_4 = "6 months post-stroke PASE score", + # pase_change, + nihss_0 = "Admission NIHSS", + # soc_status, + soc_status_work = "Employed", + soc_status_nowork = "Not employed", + fam_indk = "Family income group", + fam_indk_hl = "Lower family income", + fam_indk_high = "Higher family income", + fam_indk_low = "Lower family income", + edu_level = "Educational level group", + edu_level_hl = "Lower educational level", + edu_high = "Higher educational level", + edu_low = "Low educational level", + who_4 = "WHO-5 score 6 months post-stroke", + mdi_4 = "MDI score 6 months post-stroke", + mrs_4_above1 = "mRS > 1 at 6 months post-stroke", + mfi_gen_4 = "General fatigue (MFI domain) 6 months post-stroke", + time = "Time", + status = "Status", + event.include = "Include event", + who_0 = "Pre-stroke WHO-5 score", + mrs_0_above0 = "Pre-stroke mRS > 0" + ) +} + + +group_labels <- function(data){ + c("clin" = "Clinical data", + "lifestyle" = "Lifestyle and chronic diseases", + "ses" = "Socio-economic factors", + "assess.events" = "Assessments", + "assess.pred" = "Assessments", + "extra" = "extras") +} + +rev_naming <- function(x){ + setNames(names(x),x) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +groups_in_ds <- function(data, labels=FALSE){ + groups <- define_variables() |> purrr::imap(function(.x,.y){ + tibble::tibble(group=.y,var=.x) + }) |> + dplyr::bind_rows() + + if (labels){ + matching <- subset_named_labels(names(data), + rev_naming(var_labels())) + }else { + matching <- names(data) + } + groups[match(matching,groups[["var"]]),] |> + (\(.x){ + .x |> dplyr::mutate(group=factor(group,levels=unique(.x[["group"]]))) + })() |> + cbind( + tibble::tibble( + label=labelling_data(data) |> labelled::var_label() |> purrr::list_c() + ) + )|> + (\(.x){ + split(.x,.x[["group"]]) + })() +} + + +#' Subset labels +#' +#' @param data +#' @param labels.raw +#' +#' @return character vector +#' @export +#' +subset_named_labels <- function(data,labels.raw){ + labels.raw[match(data,names(labels.raw))] +} + +#' Assign labels to data.frame or tibble +#' +#' @param data +#' @param labels +#' +#' @return +#' @export +#' +#' @examples +assign_labels <- function(data,labels){ + # data |> labelled::set_variable_labels(labels) + + labelled::var_label(data) <- labels + + data +} + +#' Flexible labelling using labelled for nicer tables +#' +#' @param data data set +#' +#' @return +#' @export labelled data.frame/tibble +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' data <- data |> dplyr::mutate(test="test") +#' data |> labelling_data() |> labelled::var_label() +labelling_data <- function(data,label.list=var_labels()){ + + labs <- subset_named_labels(names(data),label.list) + labs[is.na(labs)] <- names(data)[is.na(labs)] + + data |> assign_labels(labels = labs) +} + + + +#' Print regression table +#' +#' @param data cox regression ready data set +#' +#' @return gtsummary tbl_regression list object +#' @examples +#' targets::tar_read(df_all_data_formatted) |> show_table_regression() +#' targets::tar_read(df_all_data_formatted) |> show_table_regression(use.mice=TRUE) +show_table_regression <- function(data, use.mice=FALSE, by.var="pase_change") { + data |> + events_dataset(impute = use.mice) |> + cox_regression(all.vars = TRUE, use.strata = FALSE,outcome.var = by.var) |> + gtsummary::tbl_regression(exponentiate = TRUE, add_estimate_to_reference_rows = TRUE) #|> + # gtsummary::add_n() |> + # gtsummary::bold_p() +} + +#' Splitting df to list by PA trajectory +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_ls_split <- function(data, excluded.vars = "reg_bmi"){ + data |> + pase_cutter(drop.pase = FALSE) |> + dplyr::select(-tidyselect::all_of(c("pase_4","pase_0_quartile","pase_4_quartile"))) |> + dplyr::group_split(pase_split = pase_change %in% c("Increase", "Low-low")) |> + setNames(c("drop", "hop")) |> + purrr::map2(.y = c("Decrease", "Increase"), .f = \(x, y){ + x |> + dplyr::mutate(pase_bin = pase_change == y) |> + dplyr::select(-tidyselect::all_of(c(excluded.vars, c("pase_change", "pase_split")))) |> + na.omit() + }) +} + +#' Run regularisation steps for split data set +#' +#' @param data selected data set +#' +#' @return list +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' targets::tar_read(df_pred_data) |> pred_models() +pred_models <- function(data, excludes = "reg_bmi") { + ls <- data |> + pred_ls_split(excluded.vars = excludes) |> + purrr::map(regularisation_steps) + + class(ls) <- c("regular_list", class(ls)) + ls +} + +cross_mean_median_exp_table <- function(data) { + nms <- paste0("v", seq_len(ncol(data))) + + cross_calcs <- data |> + as.data.frame() |> + setNames(nms) |> + dplyr::rowwise() |> + dplyr::transmute( + median = median(dplyr::c_across(tidyselect::all_of(nms))), + medianOR = exp(median), + mean = mean(dplyr::c_across(tidyselect::all_of(nms))), + meanOR = exp(mean) + ) + + dplyr::tibble(names = rownames(data), cross_calcs) |> + dplyr::select(-tidyselect::all_of(c("mean","median"))) +} + + +gather_coefs_step1 <- function(data) { + data |> + list3levelpluck(lvl1 = "model", lvl2 = "B") |> + purrr::map(purrr::reduce, cbind) +} + +gather_coefs <- function(data) { + # imputed.list <- "mids_regular_list" %in% class(data) + + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(gather_coefs_step1) |> + purrr::map(purrr::reduce, cbind) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> gather_coefs_step1() + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map(cross_mean_median_exp_table) |> + purrr::reduce(dplyr::full_join, by = "names", suffix = paste0("_", names(data))) +} + + +#' Merge and print model coefficients. Pools datafrom mids analyses. +#' +#' @param data list +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(ls_pred_models) +#' targets::tar_read(ls_pred_models) |> print_pred_coefs() +#' targets::tar_read(ls_pred_mids_reg) |> print_pred_coefs() |> add_var_groups_gt() +print_pred_coefs <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + # nms <- names(data) + + if ("mids_regular_list" %in% class(data)) { + type.table <- "Pooled regularised models" + } else if ("regular_list" %in% class(data)) { + type.table <- "Single regularised model" + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + merged_tbl <- data |> + gather_coefs() |> + ## Leaving out the intercept + (function(.x) .x[-1,])() + + sel_mean_med <- colnames(merged_tbl)[!grepl(pattern = "OR",colnames(merged_tbl))][-1] + sel_or <- colnames(merged_tbl)[grepl(pattern = "OR",colnames(merged_tbl))] + + news <- subset_named_labels(merged_tbl$names,var_labels()) + + merged_tbl <- merged_tbl |> dplyr::mutate(names=dplyr::if_else(is.na(news),names,news)) + + gt_merged_tbl <- merged_tbl|> + gt::gt() |> + gt::fmt_number(decimals = 5) + + merged_tbl_log <- merged_tbl |> dplyr::mutate(dplyr::across(tidyselect::all_of(sel_or), ~.x!=1), + dplyr::across(tidyselect::all_of(sel_mean_med), ~.x!=0)) + + for (j in colnames(merged_tbl)[-1]) { + + i <- merged_tbl_log[[j]] + + gt_merged_tbl <- gt_merged_tbl |> gt::tab_style(style = list( + gt::cell_text(weight="bold") + ), + locations = gt::cells_body( + columns=j, + rows = i + ) + )} + + for (i in names(data)) { + gt_merged_tbl <- gt_merged_tbl |> + gt::tab_spanner(label = i, columns = tidyselect::ends_with(i)) + } + + gt_merged_tbl |> gt::tab_spanner( + label = type.table, + columns = -1 + ) +} + +#' Calculates confusionMatrix from contingency tables. Pools if object class is . +#' +#' @param data +#' +#' @return list +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_table_cfm() +#' targets::tar_read(ls_pred_models) |> multi_table_cfm() +multi_table_cfm <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + if ("mids_regular_list" %in% class(data)) { + data <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + # purrr::map(\(y) y |> purrr::pluck("model") |> purrr::pluck("cMatTest"))|> + list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") |> + # All tables are add together + purrr::reduce(\(i, j) i + j) + }) + } else if ("regular_list" %in% class(data)) { + data <- data |> list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data |> + purrr::map(caret::confusionMatrix) +} + +#' Collect and summarise auc meassures. Pools if "mids_regular_list" object +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_auc_summary() +#' targets::tar_read(ls_pred_models) |> multi_auc_summary() +multi_auc_summary <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + # All tables are add together + purrr::reduce(c) + }) + } else if ("regular_list" %in% class(data)) { + data_step <- data |> + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + purrr::map(c) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step |> + purrr::map(summary) +} + +#' Map and 2 level recursive purrr::pluck to ease regular_list subsetting +#' +#' @param data +#' @param lvl1 +#' @param lvl2 +#' +#' @return +#' @export +#' +#' @examples +list3levelpluck <- function(data, lvl1 = "model", lvl2 = "cMatTest") { + data |> purrr::map(\(y) y |> + purrr::pluck(lvl1) |> + purrr::pluck(lvl2)) +} + +#' Plot performance curve from glmnet regularisation +#' +#' @param data list of cvs.glmnet objects +#' +#' @return ggplot list object +#' @export +#' +#' @examples +plot_roc_curve <- function(data, title.text) { + ggplot2::ggplot() + + purrr::map(data, function(i) { + ggplot2::geom_step(data = i, ggplot2::aes(x = FPR, y = TPR)) + }) + + ggplot2::coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) + + ggplot2::geom_abline() + + ggplot2::theme_bw() + + ggplot2::ggtitle(title.text) +} + +roc_gather_step <- function(x) { + with(x, glmnet::roc.glmnet(cvs[[1]]$fit.preval, newy = y1)[match(bestL, lambdas)]) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_roc_plot() +#' targets::tar_read(ls_pred_models) |> multi_roc_plot() +multi_roc_plot <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, roc_gather_step) |> + purrr::map(purrr::list_flatten) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> purrr::map(roc_gather_step) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map2(.y = names(data), plot_roc_curve) |> + patchwork::wrap_plots() +} + +#' Title +#' +#' @param tuning.param +#' @param data +#' +#' @return +#' @export +#' +#' @examples +multi_tuning_gather <- function(tuning.param = "bestA", data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, \(x) x |> purrr::pluck(tuning.param)) |> + purrr::map(purrr::reduce, c) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::pluck, tuning.param) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> purrr::map(summary) +} + + +#' Tidied tuning summary call +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> tuning_summary() +#' targets::tar_read(ls_pred_models) |> tuning_summary() +tuning_summary <- function(data) { + c(ALPHA = "bestA", LAMBDA = "bestL") |> purrr::map(\(x) x |> multi_tuning_gather(data = data)) +} + +#' Apply regularisation steps to MIDS object, output arranged by grouping +#' +#' @param data mids object from mice package +#' +#' @return list +#' +#' @examples +#' targets::tar_read(df_pred_mids) |> mids_regularisation() +mids_regularisation <- function(data) { + ls <- data |> + mice::complete(action = "long") |> + dplyr::group_split(.imp) |> + purrr::modify(\(x){ + x |> dplyr::select(-tidyselect::all_of(c(".imp", ".id"))) + }) |> + purrr::map(pred_models) + + nms <- ls |> + purrr::map(names) |> + unique() |> + purrr::reduce(c) + + # As a consequence of the above code each "set" of analyses are together. + # Here the same group analyses are subset and grouped + ls_n <- purrr::map(nms, function(i) { + ls |> purrr::map(purrr::pluck, i) + }) |> + setNames(nms) + + # Special class is applied to ease future handling + class(ls_n) <- c("mids_regular_list", class(ls_n)) + ls_n +} + +#' A collection of all the summary functions to be applied to list of +#' pred_models() output +#' +#' @param data list of data +#' +#' @return list +#' @export +#' +multi_summary <- function(data){ + list( "coefTable" = print_pred_coefs(data) |> + gt::fmt_number(n_sigfig = 3) |> + fix_labels() #|> add_var_groups_gt() + , + "confusionMatrices" = multi_table_cfm(data), + "summaryAUC" = multi_auc_summary(data), + "rocPlots" = multi_roc_plot(data), + "tuningSummaries" = tuning_summary(data)) +} + +# funs <-list( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +# multi_summary <- plyr::each( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +#' Subset multiple elements from list +#' +#' @param data list +#' @param indices numeric or character vector +#' +#' @return list +#' @examples +#' targets::tar_read(ls_pred_summary)$confusionMatrices |> purrr::map(list_subset) +list_subset <- function(data,indices=c("overall","byClass")){ + data[indices] +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_summary) |> print_model_resutls() +print_model_resutls <- function(data){ + par(mfrow=c(1,2)) + + list(data$coefTable, + invisible(data$confusionMatrices |> purrr::map(\(x) x |> purrr::pluck("table"))), + data$confusionMatrices |> purrr::map(list_subset), + data$summaryAUC, + data$tuningSummaries + ) +} + + +#' Classic logistic regression on prediction covariates +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' targets::tar_read(df_pred_data) |> pred_log_reg() +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_log_reg <- function(data){ + data |> pred_ls_split() |> + purrr::map(\(x) { + gtsummary::tbl_regression(glm(pase_bin~.,family = binomial,data = x), + exponentiate= TRUE)#|> + # gtsummary::bold_p() + } + ) |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() } + +#' Small wrapper to format CI with square brackets +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +square_ci <- function(data){ + gsub("(-?\\d*\\.?\\d*)(, )(-?\\d*\\.?\\d*)", + "\\[\\1; \\3\\]",data)} + +#' Apply CI formatting across gtsummary table including merged tables +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +fix_ci <- function(data){ + data |> gtsummary::modify_table_body(~ .x |> + dplyr::mutate(dplyr::across(dplyr::starts_with("ci"), + function(.y){square_ci(.y)}))) +} + +#' Classic linear regression on 6 months PASE score. Uni and multi. +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' data <- targets::tar_read(df_pred_data) +#' data <- targets::tar_read(df_pred_mids) +#' targets::tar_read(df_pred_data) |> pred_lin_reg() +#' targets::tar_read(df_pred_mids) |> pred_lin_reg() +pred_lin_reg <- function(data){ + + # list("tbl_regression-str:ref_row_text"="Reference") |> + # gtsummary::set_gtsummary_theme() + + if ("mids" %in% class(data)){ + cols <- names(data$data) + + } else { + cols <- names(data) + data <- data |> + labelling_data() + } + + vars <- cols[cols!="pase_4"] + + formula_pase <- paste("pase_4",paste(vars,collapse = "+"),sep="~" ) + + # multi <- with(data=data,lm(pase_4~.)) |> + # gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE)|> + # gtsummary::bold_p() |> gtsummary::add_n() + + if (!"mids" %in% class(data)){ + ls <- list("Univariate"=data |> + gtsummary::tbl_uvregression(method=lm, show_single_row = dplyr::where(is.logical), + y=pase_4, + add_estimate_to_reference_rows = TRUE,pvalue_fun = NULL)#|> gtsummary::bold_p() + , + "Multivariate (no BMI)"=lm(pase_4~.,data=dplyr::select(data,-reg_bmi)) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n() + , + "Multivariate (ALL)"= lm(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n() + ) + + } else { + ls <- list("Multivariate (ALL)"= suppressWarnings(mice::lm.mids(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n())) + } + + ls |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() |> + fix_ci() +} + + +#' Simple standard plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() |> plot_survival() +plot_survival <- function(data){ + data |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit(linetype_aes = TRUE, size = 0.8) + + ggsurvfit::add_confidence_interval() + + ggsurvfit::add_risktable( + risktable_stats = c("n.risk", "cum.event"), + stats_label = list(cum.event = "Cumulative Observed Events", + n.risk = "Number at Risk"), + theme = + list( + ggsurvfit::theme_risktable_default(axis.text.y.size = 11, + plot.title.size = 11), + ggplot2::theme(plot.title = ggplot2::element_text(face = "bold")) + ) + ) + + ggplot2::scale_y_continuous( + limits = c(0, 1), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0)) +} + + +#' Smooth tidy survfit object +#' +#' @param data survfit object +#' +#' @return tibble +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() +#' data |> ggsurvfit::survfit2(robust=TRUE) |> +#' ggsurvfit::tidy_survfit(type="survival") |> +#' dplyr::group_split(strata) |> +#' purrr::map(smooth_col) +smooth_col <- function(data){ + smoothed <- lapply(c("estimate","conf.high","conf.low"),function(i){ + stats::predict(mgcv::gam(data=data,formula = as.formula(glue::glue("{i}~s(time,bs='cs')")))) |> + as.data.frame()|> + setNames(glue::glue("{i}_smooth")) + }) |> purrr::list_cbind() + + dplyr::tibble(data, + smoothed) + +} + +#' Prepare cox regression for smooth survival plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() |> smooth_cox_data() +smooth_cox_data <- function(data){ + data |> + ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(smooth_col) |> + purrr::list_rbind() +} + +#' Plot smooth survival plot +#' +#' @param data df from cox regression +#' +#' @return ggplot list object +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression(use.strata=TRUE) +#' data |> plot_survival_smooth() +plot_survival_smooth <- function(data){ + if ("mira" %in% class(data)) stop("Only plots non-imputed survival data") + + n.level <- length(data$xlevels[[1]]) + + # data |> + # ggsurvfit::survfit2() |> + # ggsurvfit::tidy_survfit() + + if (data |> ggsurvfit::survfit2() |> purrr::pluck("n") |> length() ==1 ){ + ds <- data |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() + p <- ds |> + ggplot2::ggplot(ggplot2::aes(x=time, y=estimate))+ + ggplot2::geom_smooth(se=TRUE, method="loess", formula = "y~x", linewidth=2, color="grey10") + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high) + + } else { + ds <- data |> + smooth_cox_data() + p <- ds |> + ggplot2::ggplot()+ + ggplot2::geom_line(ggplot2::aes(x=time, y=estimate_smooth, color=strata, linetype=strata), linewidth=2)+ + ggplot2::geom_ribbon(ggplot2::aes(x=time, ymin=conf.low_smooth,ymax=conf.high_smooth, fill=strata), alpha=.2) + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high_smooth) + + } + p+ + ggplot2::scale_y_continuous(limits = c(0,1.02), + breaks = seq(0,1,.25), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0))+ + ggplot2::scale_fill_manual(values=viridisLite::turbo(n=n.level,direction = 1))+ + ggplot2::scale_color_manual(values=viridisLite::turbo(n=n.level,direction = 1))+ + ggplot2::theme_minimal()+ + ggplot2::theme(axis.title.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + # axis.text = ggplot2::element_blank(), + # legend.position = "none", + panel.grid.minor.y = ggplot2::element_blank(), + panel.grid.major.y = ggplot2::element_line(color="grey45",linewidth = 1)) + +} + +cluster_rank <- function(data){ + data |> cox_regression(outcome.var = "clust",use.strata = TRUE) |> ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(\(x){ + min(x[["estimate"]]) + }) |> purrr::list_c() |> rank() |> rev() +} + +cox_relevel <- function(data){ + data |> dplyr::mutate(clust=factor(clust,levels=cluster_rank(data))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +complete_preds_data <- function(data){ + data |> events_ready() |> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = FALSE) |> + mice::complete() |> + dplyr::filter((!is.na(pase_0)&!is.na(pase_4))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam <- targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam |>print_table_summary(by.var = "kam_grp") +#' data_kam |> cox_regression(outcome.var="kam_grp")|> plot_survival_smooth() +#' data_kam |> cox_regression(outcome.var="kam_grp",use.strata = FALSE)|> gtsummary::tbl_regression(exponentiate = TRUE, add_estimate_to_reference_rows = TRUE) |> gtsummary::bold_p() +#' targets::tar_read(df_events_complete) |> kamila_cluster(n.clusters=3) +kamila_cluster <- function(data, n.clusters=3,include.out=FALSE){ + # An index number could be added to later join pack. Of input a complete data set from imputation and pooling?? + data_orig <- data + + + if (!include.out){ + data <- data |> + dplyr::select(-tidyselect::one_of(c("time","status"))) + } + + + catInd <- data |> lapply(\(x) is.character(x)|is.logical(x)) |> purrr::list_c() + conInd <- data |> lapply(\(x) is.numeric(x)|is.integer(x)) |> purrr::list_c() + + catVars <- data[,catInd] + catVars <- catVars |> lapply(factor) |> dplyr::bind_cols() |> as.data.frame() + conVars <- data[,conInd] |> scale()|> as.data.frame() + + if (is.null(n.clusters)){ + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = 2:7, numInit = 10, + calcNumClust = "ps" + ) + }else { + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = n.clusters, numInit = 10) + } + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kamila_cluster",class(ls)) + + ls + +} + + +#' VarSelLCM wrapper +#' +#' @param data complete dataset with no missings +#' @param n.clusters number of clusters (if length 1, n is fixed, in n>1 given clusters are tested) +#' @param include.out flag to include outcome variables or not +#' @param memb.out output data frame with final membership or not (then outputs standard model output) +#' +#' @return list with VarSelLCM output and original dataset with cluster appended +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> lcm_cluster() +lcm_cluster <- function(data, n.clusters=3, include.out=FALSE, var.sel=FALSE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(-c("time", "status")) + } + + + set.seed(5432) + + out <- data |> + dplyr::mutate(dplyr::across(where(is.logical)|where(is.character),~factor(.x))) |> + as.data.frame() |> + VarSelLCM::VarSelCluster( + gvals=n.clusters, + crit.varsel="BIC", + vbleSelec = var.sel, + nbcores = round(parallel::detectCores()*.8) + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("lcm_cluster",class(ls)) + + ls + +} + +#' Kmeans clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> kmeans_cluster() +#' data |> kmeans_cluster(n.clusters=3) +kmeans_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + out <- data |> + dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + dplyr::across(where(is.factor),~as.numeric(.x))) |> + stats::kmeans( + centers=n.clusters + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kmeans_cluster",class(ls)) + + ls + +} + +#' dbscan clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> dbscan_cluster() +#' data |> dbscan_cluster(n.clusters=3) +dbscan_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + data <- data |> na.omit() |> dplyr::mutate(rtreat=rtreat!="Placebo", + dplyr::across(dplyr::everything(), as.numeric)) + + + ## This plot indicates that eps should be set around 60, but at this value everything is one cluster. + dbscan::kNNdistplot(data,k = 5) + + ## Performing hierachical clustering, it is clear, that the algorithm is not able to seperate clusters. + hds <- dbscan::hdbscan(data,minPts = 5) + + plot(hds,show_flat = TRUE) + + ## Clustering with set eps value and minPts + ds <- dbscan::dbscan(data,eps = 25,minPts = 2) + + ds[["cluster"]] + + ## dbscan is not an interesting approach, apparently + + # + # + # + # + # out <- data |> + # dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + # dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + # dplyr::across(where(is.factor),~as.numeric(.x))) |> + # stats::kmeans( + # centers=n.clusters + # ) + # + # ls <- list("out"=out,"data_orig"=data_orig) + # + # class(ls) <- c("kmeans_cluster",class(ls)) + # + # ls + +} + +#' Title +#' +#' @param ls +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' ls <- data |> lcm_cluster() +#' ls |> final_membership() +final_membership <- function(ls){ + cls <- class(ls) + if ("kamila_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$finalMemb), + ls$data_orig) + + } else if ("lcm_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out@partitions@zMAP), + ls$data_orig) + + } else if ("kmeans_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$cluster), + ls$data_orig) + + } else stop("Class not recognised") + +} + + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> get_clusters() +#' data |> get_clusters(n.cl=2:7) +get_clusters <- function(data,n.cl=4,rm.out=TRUE){ + set.seed(1123) + + if (length(n.cl)>1){ + list( + # "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } else { + list( + "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } + +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(list_pred_clusters) +#' data |> final_clusters() +final_clusters <- function(data,new.levels=NULL){ + out <- data |> lapply(final_membership) |> lapply(labelling_data) + + if (is.null(new.levels)){ + out + } else { + out |> + purrr::map2(relevels,function(x,y){ + # x$clust <- factor(factor(x$clust,levels=y),labels=1:4) + x$clust <- factor(x$clust,levels=y) + x #|> + # dplyr::filter(clust %in% range(as.numeric(clust))) |> + # dplyr::mutate(clust=factor(clust)) + }) + } + + } + + + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_summary_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> + # print_table_summary(by.var = by) + gtsummary::tbl_summary(by=by) #|> + # gtsummary::add_p() |> gtsummary::bold_p() + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Easy cox regression tbl for uniform results +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +cox2tbl <- function(data,by="clust"){ +data|> cox_regression(outcome.var = by,use.strata = FALSE,all.vars = FALSE) |> + gtsummary::tbl_regression(exponentiate =TRUE) |> gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_cox_reg_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> cox2tbl(by=by) + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +wrapped_surv_plot <- function(data,by="clust"){ +data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = by,use.strata = TRUE,all.vars = FALSE) |> + plot_survival_smooth()+ggplot2::labs(color="Cluster",fill="Cluster",linetype="Cluster") + } + ) |> (\(x){ + x |> patchwork::wrap_plots(ncol=1) + patchwork::plot_annotation(tag_levels = list(names(x))) + })() +} + + +# Ranking by most events +relevel_by_rank <- function(data){ + ## Assigning clusters to each dataset +data <- targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = NULL) + +## Calculating cox regressions and ranking by the final point on the survival plot +relevels <- data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = "clust",use.strata = TRUE,all.vars = FALSE) |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() |> + (\(x){ + split(x,x[["strata"]]) |> + purrr::map(function(.y){ + .y[["estimate"]][nrow(.y)] + }) + })() |> purrr::reduce(c) |> rank() + } + ) + +#3 Assigning the new, ranked levels +targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = relevels) +} + +## TODO +## Verify definitions +## Do remaining documentation of functions +## +## +## How does elastic net work with imputed dataset? +## Functionalise to allow for imputed and non-imputed (both analyses) - in both cases with and without BMI - include department of inclusion to investigate reason of missing BMI data +## +## tidymodels does not allow pmm in mice. Thy're out! diff --git a/1 PA Decline/Fra DDV/pa_change_analyses240411.docx b/1 PA Decline/Fra DDV/pa_change_analyses240411.docx new file mode 100755 index 0000000..6883b1f Binary files /dev/null and b/1 PA Decline/Fra DDV/pa_change_analyses240411.docx differ diff --git a/1 PA Decline/Fra DDV/pa_change_analyses240411_coefs.docx b/1 PA Decline/Fra DDV/pa_change_analyses240411_coefs.docx new file mode 100644 index 0000000..d9ee49d Binary files /dev/null and b/1 PA Decline/Fra DDV/pa_change_analyses240411_coefs.docx differ diff --git a/1 PA Decline/Fra DDV/pa_change_analyses240418.docx b/1 PA Decline/Fra DDV/pa_change_analyses240418.docx new file mode 100644 index 0000000..5f8de11 Binary files /dev/null and b/1 PA Decline/Fra DDV/pa_change_analyses240418.docx differ diff --git a/1 PA Decline/Fra DDV/pa_change_tbl1240411.docx b/1 PA Decline/Fra DDV/pa_change_tbl1240411.docx new file mode 100755 index 0000000..07e10bb Binary files /dev/null and b/1 PA Decline/Fra DDV/pa_change_tbl1240411.docx differ diff --git a/1 PA Decline/Til DDV/00master.R b/1 PA Decline/Til DDV/00master.R new file mode 100644 index 0000000..41dee5d --- /dev/null +++ b/1 PA Decline/Til DDV/00master.R @@ -0,0 +1,251 @@ +## +## Master script +## +## Based on the assignment work from the ISL-course +## +## Generation 2 - 02.december.2022 +## Code preparation for analysis on Denmarks Statistics server with enriched data set. +## +## Analysis plan: +## Table 1 +## Figure 1: Sankey plot (drop & hop colored) +## Table 2: Linear regression model of pase_6~. +## Table 3: Elastic net prediction models of drop and hop. Performance measures referenced in text. +## +## A Rmarkdown file could be created to write the initial report with main results. +## This code is a bit of a mess, as it is the result of several iterations. It works however. +## + +## ==================================================================== +# Step 0: Primary outcome +## ==================================================================== + +# Script to run as hop and drop + +pout <- "drop" # Drop to first quartile + +# decl_rel +# decl_abs +# drop +# hop + +## ==================================================================== +## Data +## ==================================================================== + + +setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/") + +source("data_set.R") + +source("data_format.R") + +## ==================================================================== +## +## Baseline - by PASE group +## +## ==================================================================== + +ts_q <- X_tbl |> + select(vars) |> + mutate(pase_0_cut = factor(quantile_cut(pase_0, groups = 4)[[1]],ordered = TRUE)) |> + select(-pase_6,-pase_0) |> + tbl_summary(missing = "no", + by="pase_0_cut", + value = list(where(is.factor) ~ "2"), + type = list(mrs_0 ~ "categorical", + all_continuous() ~ "continuous2"), + statistic = list(all_continuous() ~ c("{N_nonmiss}", + "{median} ({p25}, {p75})", + "{min}, {max}", + "{mean} ({sd})")) +) |> + add_overall() |> + add_n () + +ts_q + +tbl_one_rtf <- file("table1.RTF", "w") +writeLines(ts_q%>%as_gt()%>%as_rtf(), tbl_one_rtf) +close(tbl_one_rtf) + +## ==================================================================== +# Drops and hops +## ==================================================================== + +# TRUEs are patients dropping +table(X_tbl$pase_0_cut!="1"&X_tbl$pase_6_cut=="1")/nrow(X_tbl[X_tbl$pase_0_cut!="1",]) + +# TRUEs are percentage of patients inactive before stroke being more active after +table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_0_cut=="1",]) + +# TRUEs are percentage of patients being more active after that were inactive before stroke +table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_6_cut!="1",]) + +# Difference between hop/no-hop +t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"]) + +summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +# Stationary low +t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"]) + +boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"]) + +## ==================================================================== +# Sankey plot +## ==================================================================== + +# source("sankey.R") +# p_delta + +## ==================================================================== +# Six months PASE: Bivariate and multivariate analyses +## ==================================================================== + +dta_lmreg <- X_tbl |> + select(vars) |> + mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) + +Hmisc::label(dta_lmreg$mrs_0) <- "Pre-stroke mRS >0" + +uv_reg <- tbl_uvregression(data=dta_lmreg, + method=lm, + y="pase_6", + show_single_row = where(is.factor), + estimate_fun = ~style_sigfig(.x,digits = 3), + pvalue_fun = ~style_pvalue(.x, digits = 3) +) + +mu_reg <- dta_lmreg |> + lm(formula=pase_6~.,data=_) |> + tbl_regression(show_single_row = where(is.factor), + estimate_fun = ~style_sigfig(.x,digits = 3), + pvalue_fun = ~style_pvalue(.x, digits = 3) + )|> + add_n() + +tbl_merge(list(uv_reg,mu_reg)) + + +## ==================================================================== +## +## Data variance +## +## Illustrating principal components. +## +## ==================================================================== + + +# source("PCA.R") +# +# +# pca22 +# ggsave("pc_plot.png",width = 18, height = 12, dpi = 300, limitsize = TRUE, units = "cm") + + +## ==================================================================== +## +## Models +## +## ==================================================================== + + +# source("assign_full.R") + +ls <- list() +for (i in c("drop","hop")){ + pout <- i + source("data_format.R") + source("regularisation_steps.R") +} + +# Loop to run regularised model on both drop and hop. +# Saved in list for printing and exporting the plot. + +## ==================================================================== +# Step 1: data merge +## ==================================================================== +tbl<-merge(ls$drop$RegularisedCoefs$'_data',ls$hop$RegularisedCoefs$'_data',by="name",all.x=T, sort=F) + +## ==================================================================== +# Step 2: table +## ==================================================================== +com_coef_tbl<-tbl%>% + gt()%>% + fmt_number( + columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric + rows = everything(), + decimals = 3)%>% + tab_spanner( + label = "DROP", + columns = 2:5 + )%>% + tab_spanner( + label = "HOP", + columns = 6:9 + )%>% + tab_header( + title = "Model coefficients", + subtitle = "Combined table of both full and regularised model coefficients" + ) + + +# paste0("Regularised model, (a=", +# best_alph, +# ", l=", +# round(best_lamb,3), +# ")") + +com_coef_tbl + +## ==================================================================== +# Step 3: export +## ==================================================================== +com_coef_rtf <- file("table2.RTF", "w") +writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf) +close(com_coef_rtf) + + + +## ==================================================================== +## +## Model performance +## +## Table with performance meassures for the two different models. +## +## ==================================================================== + +## ==================================================================== +# Step 1: data set +## ==================================================================== + +tbl<-data.frame(Meassure=c(names(ls$drop$ConfusionMatrx$byClass),"Mean AUC"), + "Drop"=round(c(ls$drop$ConfusionMatrx$byClass,ls$drop$AUROC["Mean"]),3), + "Hop"=round(c(ls$hop$ConfusionMatrx$byClass,ls$hop$AUROC["Mean"]),3)) + +## ==================================================================== +# Step 2: table +## ==================================================================== +tbl_perf<-tbl%>% + gt()%>% + tab_header( + title = "Performance meassures", + subtitle = "Combined table of both drop and hop" + ) + +tbl_perf + +## ==================================================================== +# Step 3: export +## ==================================================================== +tbl_perf_rtf <- file("table3.RTF", "w") +writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf) +close(tbl_perf_rtf) + + + diff --git a/1 PA Decline/Til DDV/data_format.R b/1 PA Decline/Til DDV/data_format.R new file mode 100644 index 0000000..e5668af --- /dev/null +++ b/1 PA Decline/Til DDV/data_format.R @@ -0,0 +1,50 @@ +## Article 1 outcome group definition script +## To be enriched from Statistics Denmark +## +## Based on the ItMLiHSmar2022 course + +library(Hmisc) +library(dplyr) +library(daDoctoR) +library(tidyselect) + +# Setting final primary output from "pout" +if (pout=="drop"){ + X_tbl <- X_tbl|> + mutate(group=pase_drop_fac) + + # print(quantile(as.numeric(X_tbl$pase_0))) + # print(quantile(as.numeric(X_tbl$pase_6))) + # print(summary(X_tbl$pase_0_cut)) + + X_tbl_f <- X_tbl|> + filter(pase_0_cut!=1)|> + select(-starts_with("pase_")) +} + +if (pout=="hop"){ + X_tbl <- X_tbl|> + mutate(group=pase_hop_fac) + + # print(quantile(as.numeric(X_tbl$pase_0))) + # print(quantile(as.numeric(X_tbl$pase_6))) + # print(summary(X_tbl$pase_0_cut)) + + X_tbl_f <- X_tbl|> + filter(pase_6_cut!=1)|> + select(-starts_with("pase_")) +} + +# Dropping non-complete for analysis +Xy <- X_tbl_f|> + na.omit()|> # Keeping only complete observations + select(-c(tci) # Left out of model as no present in drop-group + )|> + mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) # Sets binary mRS 0 to include in glmnet, 0 or above + +label(Xy) = as.list(var.labels[match(names(Xy), names(var.labels))]) + +X<-dplyr::select(Xy,-c(group, -starts_with("pase_")) # Exclude primary outcome + ) +y<-Xy$group + diff --git a/1 PA Decline/Til DDV/data_set.R b/1 PA Decline/Til DDV/data_set.R new file mode 100644 index 0000000..72948eb --- /dev/null +++ b/1 PA Decline/Til DDV/data_set.R @@ -0,0 +1,193 @@ +## Article 1 data set definition +## To be enriched from Statistics Denmark +## +## Based on the ItMLiHSmar2022 course + +library(Hmisc) +library(dplyr) +library(daDoctoR) +library(tidyverse) +library(patchwork) +library(caret) +library(glmnet) +library(leaps) +library(pROC) +library(gt) +library(gtsummary) +library(glue) +# library(ggdendro) +library(corrplot) + +## ==================================================================== +# Step 2: Selection +## ==================================================================== + + +export<-export[,c("pase_0", + "age", + "sex", + "civil", + "smoke_ever", + "smoker", + "rtreat", + "alc", + "afli", + "hypertension", + "diabetes", + "mrs_0", + "nihss_c", + "thrombolysis", + "pad", + "thrombechtomy", + "ami", + "tci", + "pase_6")] + +## ==================================================================== +# Step 3: Formatting variables +## ==================================================================== + +export$diabetes[is.na(export$diabetes)]<-"no" +export$diabetes[is.na(export$hypertension)]<-"no" +export$thrombolysis[is.na(export$thrombolysis)]<-"no" +export$thrombechtomy[is.na(export$thrombechtomy)]<-"no" +export$pad[is.na(export$pad)]<-"no" +export$ami[is.na(export$ami)]<-"no" +# export$smoker_prev <- ifelse(export$smoker=="3","yes","no") +export$smoker <- ifelse(export$smoker=="1","yes","no") +export$smoker[is.na(export$smoker)] <- "no" +# export$mrs_0[export$mrs_0==3]<-NA + +dta <- export %>% + # as_tibble()%>% + mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received + male_sex= factor(ifelse(sex=="female","no","yes")), + # smoke_ever=factor(ifelse(smoke_ever=="never","no","yes")), + civil=factor(ifelse(civil=="partner","no","yes")), # Sets "yes" for not-cohabiting + rtreat=factor(ifelse(rtreat=="Placebo","no","yes")), # "Yes" receives active treatment + alc=factor(ifelse(alc=="more","yes","no")), # Yes for more than guideline + pase_0=as.numeric(pase_0), + pase_6=as.numeric(pase_6), + across(c("diabetes", + "hypertension", + "smoker", + "afli", + "pad", + "ami", + "tci", + "mrs_0"),as.factor), + across(c("nihss_c", + "age"),as.numeric ) + )%>% + select(-c(sex)) + + +## ==================================================================== +# Step 4: Defining outcome +## ==================================================================== + +## Changed to step 7 +## This is to perform proper quantile split based on actually included. + +## ==================================================================== +# Step 5: Ordering variables +## ==================================================================== + +vars <- c("age", + "male_sex", + "civil", + "pase_0", + "smoker", + "alc", + "afli", + "hypertension", + "diabetes", + "pad", + "ami", + "tci", + "mrs_0", + "nihss_c", + "any_rep", + "rtreat", + "pase_6") + +dta<-dta[vars] + +## ==================================================================== +# Step 6: Labeling +## ==================================================================== + +var.labels = c(age="Age", + male_sex="Male", + civil="Living alone", + pase_0="Pre-stroke PASE score", + pase_6="Six month PASE score", + smoker="Daily or occasinally smoking", + alc="More alcohol than recommendation", + afli="AFIB", + hypertension="Hypertension", + diabetes="Diabetes", + pad="PAD", + ami="Previous MI", + tci="Previous TIA", + mrs_0="Pre-stroke mRS [-1]", + nihss_c="Acute NIHSS score", + thrombolysis="Acute thrombolysis", + thrombechtomy="Acute thrombechtomy", + any_rep="Any reperfusion therapy", + rtreat="Active trial treatment", + pase_drop_fac="PASE first quartile drop F", + pase_hop_fac="PASE first quartile hop F", + pase_0_cut="PASE 0 quartiles", + pase_6_cut="PASE 6 quartiles") + + + +## ==================================================================== +# Step 7: final data export +## ==================================================================== + +data_summary<-summary(dta) + +# Saving "old" factorised variables +sel<-sapply(dta,is.factor) +# Reformatting factors as 1/2 for analysis +dta<-dta |> + mutate(across(where(is.factor), as.numeric))|> # Turning factors into 1(no) or 2(yes) for model. Numbered alphabetically. + mutate(across(matches(colnames(dta)[sel]), as.factor), + across(starts_with("pase_"), as.numeric)) + +# Filtering out non-PASE +X_tbl<-dta |> + filter(!is.na(pase_0),!is.na(pase_6)) + +nrow(X_tbl) + +# Defining possible outcome meassures. Keeping in df for characterisation +X_tbl <- X_tbl|> + mutate(## Relative decline + pase_diff=(pase_0-pase_6), + pase_decl_rel = pase_diff/pase_0*100, + # pase_decl_rel_fac=factor(ifelse(pase_decl_rel>=rel_dif,"yes","no")), + ## Absolute decline + # pase_decl_abs_fac=factor(ifelse(pase_diff>=abs_dif,"yes","no")), + ## Drop + pase_0_cut=quantile_cut(as.numeric(pase_0), + groups=4, + group.names = c(as.character(1:4)), + y=as.numeric(pase_0), + ordered.f = TRUE, + inc.outs = TRUE, + detail.lst=FALSE), + pase_6_cut=quantile_cut(as.numeric(pase_6), + groups=4, + group.names = c(as.character(1:4)), + y=as.numeric(pase_0), + ordered.f = TRUE, + inc.outs = TRUE, + detail.lst=FALSE), + pase_drop_fac=factor(ifelse(pase_6_cut==1&pase_0_cut!=1,"yes","no")), + pase_hop_fac=factor(ifelse(pase_6_cut!=1&pase_0_cut==1,"yes","no"))) + +Hmisc::label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))]) + diff --git a/1 PA Decline/Til DDV/regular_fun.R b/1 PA Decline/Til DDV/regular_fun.R new file mode 100644 index 0000000..1b457b0 --- /dev/null +++ b/1 PA Decline/Til DDV/regular_fun.R @@ -0,0 +1,117 @@ +## ItMLiHSmar2022 +## regular_fun.R, child script +## Regularisation model building function +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + +regular_fun<-function(X,y,K,lambdas,alpha){ +n<-nrow(X) +set.seed(321) + +# Using caret function to ensure both levels represented in all folds +c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) + +B<-yhatTestProbKeep<-list() +accTrain<-accTest<-err_train<-err_test<-auc_train<-auc_test<-matrix(nrow = K,ncol = length(lambdas)) + +catinfo<-levels(y) + +cMatTrain<-cMatTest<-table(true=factor(c(0,0),levels=catinfo),pred=factor(c(0,0),levels=catinfo)) + + +## Iterate over partitions +for (idx1 in 1:K){ + + # Status + cat('Processing fold', idx1, 'of', K,'\n') + + # idx1=1 + # Get training- and test sets + I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE + I_test = !I_train + + Xtrain = X[I_train,] + ytrain = y[I_train] + Xtest = X[I_test,] + ytest = y[I_test] + + + ## Model matrices for glmnet + ## Using the complicated approach not to include first level. + # Xmat.train<-model.matrix(~ .-1, data=Xtrain, + # contrasts.arg = lapply(Xtrain[,sapply(Xtrain, is.factor)], + # contrasts, contrasts=T)) + # Xmat.test<-model.matrix(~ .-1, data=Xtest, + # contrasts.arg = lapply(Xtest[,sapply(Xtest, is.factor)], + # contrasts, contrasts=T)) + + # Xmat.train<-model.matrix(~.-1,Xtrain) + # Xmat.test<-model.matrix(~.-1,Xtest) + + # Weights + ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain))) + # ytest_weight<-as.vector(1 / (table(ytest)[ytest] / length(ytest))) + + # Fit regularized linear regression model + mod<-glmnet(Xtrain, ytrain, + alpha = alpha, ## Alpha = 1 for lasso + lambda = lambdas, ## Setting lambdas + standardize = TRUE, ## Scales and centers + weights = ytrain_weight, + family = "binomial" + ) + + # Keep coefficients for plot + B[[idx1]] <- as.matrix(coef(mod)) + + # Iterate over regularization strengths to compute training- and test + # errors for individual regularization strengths. + for (idx2 in 1:length(lambdas)){ + # idx2=1 + + # Predict + yhatTrainProb<-predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtrain), + type = "response" + ) + + yhatTestProb<-predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtest), + type = "response" + ) + + # Compute training and test error + yhatTrain = round(yhatTrainProb) + yhatTest = round(yhatTestProb) + + # Make predictions categorical again (instead of 0/1 coding) + yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + + # Evaluate classifier performance + # Accuracy + # accTrain[idx1,idx2] <- sum(yhatTrainCat==ytrain)/length(ytrain) + # accTest [idx1,idx2] <- sum(yhatTestCat==ytest)/length(ytest) + # # + # # Error rate + # err_train[idx1,idx2] = 1 - accTrain[idx1,idx2] + # err_test [idx1,idx2] = 1 - accTest[idx1,idx2] + + # AUROC + suppressMessages( + auc_train[idx1,idx2]<-auc(ytrain, yhatTrainCat)) + suppressMessages( + auc_test [idx1,idx2]<-auc(ytest, yhatTestCat)) + + # Compute confusion matrices + cMatTrain = cMatTrain + table(true=ytrain,pred=yhatTrainCat) + cMatTest = cMatTest + table(true=ytest,pred=yhatTestCat) + } +} +ls<-list(mod=mod,B=B,auc_train=auc_train,auc_test=auc_test,cMatTrain=cMatTrain,cMatTest=cMatTest) +return(ls) +} diff --git a/1 PA Decline/Til DDV/regularisation_steps.R b/1 PA Decline/Til DDV/regularisation_steps.R new file mode 100644 index 0000000..bacd072 --- /dev/null +++ b/1 PA Decline/Til DDV/regularisation_steps.R @@ -0,0 +1,150 @@ +## ItMLiHSmar2022 +## regularisation_steps.R, child script +## Regularised model building and analysation for assignment +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + +## ==================================================================== +## Step 0: data import and wrangling +## ==================================================================== + +setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/") + +# source("data_format.R") +y1<-factor(as.integer(y)-1) ## Outcome is required to be factor of 0 or 1. + + +## ==================================================================== +## Step 1: settings +## ==================================================================== + +## Folds +K=10 +set.seed(3) +c<-caret::createFolds(y=y, + k = K, + list = FALSE, + returnTrain = TRUE) # Foldids for alpha tuning + +## Defining tuning parameters +lambdas=2^seq(-10, 5, 1) +alphas<-seq(0,1,.1) + +## Weights for models +weighted=TRUE +if (weighted == TRUE) { + wght<-as.vector(1 - (table(y)[y] / length(y))) +} else { + wght <- rep(1, nrow(y)) +} + + +## Standardise numeric +## Centered and + + + +## ==================================================================== +## Step 2: all cross validations for each alpha +## ==================================================================== + +library(furrr) +library(purrr) +library(doMC) +registerDoMC(cores=6) + +# Nested CVs with analysis for all lambdas for each alpha +# +set.seed(3) +cvs <- future_map(alphas, function(a){ + cv.glmnet(model.matrix(~.-1,X), + y1, + weights = wght, + lambda=lambdas, + type.measure = "deviance", # This is standard measure and recommended for tuning + foldid = c, # Per recommendation the folds are kept for alpha optimisation + alpha=a, + standardize=TRUE, + family=quasibinomial, + keep=TRUE) # Same as binomial, but not as picky +}) + +## ==================================================================== +# Step 3: optimum lambda for each alpha +## ==================================================================== + + +# For each alpha, lambda is chosen for the lowest meassure (deviance) +each_alpha <- sapply(seq_along(alphas), function(id) { + each_cv <- cvs[[id]] + alpha_val <- alphas[id] + index_lmin <- match(each_cv$lambda.min, + each_cv$lambda) + c(lamb = each_cv$lambda.min, + alph = alpha_val, + cvm = each_cv$cvm[index_lmin]) +}) + +# Best lambda +best_lamb <- min(each_alpha["lamb", ]) + +# Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",] +best_alph <- each_alpha["alph",][each_alpha["cvm",]==min(each_alpha["cvm",] + [each_alpha["lamb",] %in% best_lamb])] + +## https://stackoverflow.com/questions/42007313/plot-an-roc-curve-in-r-with-ggplot2 +p_roc<-roc.glmnet(cvs[[1]]$fit.preval, newy = y)[[match(best_alph,alphas)]]|> # Plots performance from model with best alpha + ggplot(aes(FPR,TPR)) + + geom_step() + + coord_cartesian(xlim=c(0,1), ylim=c(0,1)) + + geom_abline()+ + theme_bw() + +## ==================================================================== +# Step 4: Creating the final model +## ==================================================================== + +source("regular_fun.R") # Custom function +optimised_model<-regular_fun(X,y1,K,lambdas=best_lamb,alpha=best_alph) +# With lambda and alpha specified, the function is just a k-fold cross-validation wrapper, +# but keeps model performance figures from each fold. + +list2env(optimised_model,.GlobalEnv) +# Function outputs a list, which is unwrapped to Env. +# See source script for reference. + +## ==================================================================== +# Step 5: creating table of coefficients for inference +## ==================================================================== + +Bmatrix<-matrix(unlist(B),ncol=10) +Bmedian<-apply(Bmatrix,1,median) +Bmean<-apply(Bmatrix,1,mean) + +reg_coef_tbl<-tibble( + name = c("Intercept",Hmisc::label(X)), + medianX = round(Bmedian,5), + ORmed = round(exp(Bmedian),5), + meanX = round(Bmean,5), + ORmea = round(exp(Bmean),5))%>% + # arrange(desc(abs(medianX)))%>% + gt() + +## ==================================================================== +# Step 6: plotting predictive performance +## ==================================================================== + +reg_cfm<-confusionMatrix(cMatTest) +reg_auc_sum<-summary(auc_test[,1]) + +## ==================================================================== +# Step 7: Packing list to save in loop +## ==================================================================== + +ls[[i]] <- list("RegularisedCoefs"=reg_coef_tbl, + "bestA"=best_alph, + "bestL"=best_lamb, + "ConfusionMatrx"=reg_cfm, + "AUROC"=reg_auc_sum) diff --git a/1 PA Decline/Til DDV/standardise.R b/1 PA Decline/Til DDV/standardise.R new file mode 100644 index 0000000..2521e6d --- /dev/null +++ b/1 PA Decline/Til DDV/standardise.R @@ -0,0 +1,41 @@ +## ItMLiHSmar2022 +## standardise.R, child script +## Data standardisation, returns list +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk + +standardise<-function(train,test,type){ + # From: + # https://datascience.stackexchange.com/questions/13971/standardization-normalization-test-data-in-r + + sel<-sapply(Xtrain,is.numeric) # Deciding which to stadardise (only numeric) + cnm<-colnames(Xtrain) # Saving column names for ordering + + # Subsetting + + ## Data to treat + train.tr<-train[,sel] + test.tr<-test[,sel] + + ## Data to save + train.sv<-train[,!sel] + test.sv<-test[,!sel] + + # Calculate mean and SD of train data + trainMean <- sapply(train.tr,mean) + trainSd <- sapply(train.tr,sd) + + if (type=="c"){ + ## centered + norm.trainData<-sweep(train.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise + norm.testData<-sweep(test.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise + } + + if (type=="cs"){ + ## centered AND scaled (Z-score standardisation) + norm.trainData<-sweep(sweep(train.tr, 2L, trainMean), 2, trainSd, "/") + norm.testData<-sweep(sweep(test.tr, 2L, trainMean), 2, trainSd, "/") + } + return(list(XtrainSt=cbind(norm.trainData,train.sv)[,cnm], # Reordering columns to original + XtestSt=cbind(norm.testData,test.sv)[,cnm])) +} + diff --git a/1 PA Decline/archive/.DS_Store b/1 PA Decline/archive/.DS_Store new file mode 100644 index 0000000..ada98ef Binary files /dev/null and b/1 PA Decline/archive/.DS_Store differ diff --git a/1 PA Decline/archive/dataset.R b/1 PA Decline/archive/dataset.R new file mode 100644 index 0000000..b49348d --- /dev/null +++ b/1 PA Decline/archive/dataset.R @@ -0,0 +1,98 @@ +# Data +## Import from previous work +dta<-read.csv("/Volumes/Data/exercise/source/background.csv",na.strings = c("NA","","unknown"),colClasses = "character") + +## Cleaning and enhancing +dta$pase_drop<-factor(ifelse((dta$pase_0_q=="q_2"|dta$pase_0_q=="q_3"|dta$pase_0_q=="q_4")&dta$pase_06_q=="q_1","yes","no"),levels = c("no","yes")) +dta$pase_drop[is.na(dta$pase_6)]<-NA +dta$pase_drop[is.na(dta$pase_0)]<-NA + +## Selection of data set and formatting +library(dplyr) +dta_f<-dta %>% filter(pase_0_q != "q_1" & !is.na(pase_drop)) + + +variable_names<-c("age","sex","weight","height", + "bmi", + "smoke_ever", + "civil", + "diabetes", + "hypertension", + "pad", + "afli", + "ami", + "tci", + "nihss_0", + "thrombolysis", + "thrombechtomy", + "rep_any","pase_0_q","pase_drop") + + +library(daDoctoR) +dta2<-dta_f[,variable_names] + +dta2<-col_num(c("age","weight","height","bmi","nihss_0"),dta2) +dta2<-col_fact(c("sex","smoke_ever","civil","diabetes", "hypertension","pad", "afli", "ami", "tci","thrombolysis", "thrombechtomy","rep_any","pase_0_q","pase_drop"),dta2) + +## Partitioning +library(caret) +set.seed(100) + +## Step 1: Get row numbers for the training data +trainRowNumbers <- createDataPartition(dta2$pase_drop, p=0.8, list=FALSE) + +## Step 2: Create the training dataset +trainData <- dta2[trainRowNumbers,] + +## Step 3: Create the test dataset +testData <- dta2[-trainRowNumbers,] +y_test = testData[,"pase_drop"] + +# Store X and Y for later use. +x = trainData %>% select(!matches("pase_drop")) +y = trainData[,"pase_drop"] + +# Normalization and dummy binaries + +# One-Hot Encoding +# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories. +dummies_model <- dummyVars(pase_drop ~ ., data=trainData) + +# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat. +trainData_mat <- predict(dummies_model, newdata = trainData) + +# # Convert to dataframe +trainData <- data.frame(trainData_mat) + +# # See the structure of the new dataset +str(trainData) + +dummies_model <- dummyVars(pase_drop ~ ., data=testData) +testData_mat <- predict(dummies_model, newdata = testData) +testData <- data.frame(testData_mat) +preProcess_range_model <- preProcess(testData, method='range') +testData <- predict(preProcess_range_model, newdata = testData) +testData$pase_drop<-y_test + +# Imputation + +library(RANN) # required for knnInpute +preProcess_missingdata_model <- preProcess(trainData, method='knnImpute') +# preProcess_missingdata_model + +trainData <- predict(preProcess_missingdata_model, newdata = trainData) # Giver fejl?? +anyNA(trainData) + +# skimr::skim(trainData) +# skimr::skim(x) + +preProcess_range_model <- preProcess(trainData, method='range') +trainData <- predict(preProcess_range_model, newdata = trainData) + +# Append the Y variable +trainData$pase_drop <- y + + +# Export +write.csv(trainData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",row.names = FALSE) +write.csv(testData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",row.names = FALSE) diff --git a/1 PA Decline/archive/generation_1/.DS_Store b/1 PA Decline/archive/generation_1/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/1 PA Decline/archive/generation_1/.DS_Store differ diff --git a/1 PA Decline/archive/generation_1/00master.R b/1 PA Decline/archive/generation_1/00master.R new file mode 100644 index 0000000..729c79d --- /dev/null +++ b/1 PA Decline/archive/generation_1/00master.R @@ -0,0 +1,409 @@ +## +## Master script +## +## Based on the assignment work from the ISL-course +## +## +## + +## ==================================================================== +# Step 0: Primary outcome +## ==================================================================== + +# Difs +rel_dif <- 20 # 20 % difference +abs_dif <- 20 # 20 point diff + +# pout <- "diff" +# +# Note:: By increasing the relative decline, the sensitivity increases and specificity declines. +# This fact is an argument against over fitting. The reason being the nature of the clinical data and the fact, that predicting PA is difficult (!) +# +pout <- "drop" # Drop to first quartile + +# decl_rel +# decl_abs +# drop +# hop + + +## ==================================================================== +## Data +## ==================================================================== + + +setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/") + +source("data_set.R") +# Loading data-set from USB, to not store on computer + +source("data_format.R") + +## ==================================================================== +# Libraries +## ==================================================================== + + +library(tidyverse) +library(glue) +library(patchwork) +# library(ggdendro) +library(corrplot) +library(gt) +library(gtsummary) + + +## ==================================================================== +## +## Baseline +## +## ==================================================================== + + +## ==================================================================== +# Step 0: labels +## ==================================================================== + + +lbs<-var.labels[match(colnames(X_tbl), + names(var.labels))] + +ls<-lapply(1:ncol(X_tbl),function(x){ + as.formula(paste0(names(lbs)[x],"~","\"",lbs[x],"\"")) +}) + +ts<-tbl_summary(X_tbl|>filter(pase_0_cut!="1"), + by = "group", + missing = "no", + # label = ls[-length(ls)], ## Removing the last, as this is output + value = list(where(is.factor) ~ "2"), + type = list(mrs_0 ~ "categorical"), + statistic = list(all_continuous() ~ "{median} ({p25};{p75}) [{min},{max}]") +)%>% + add_overall() %>% + add_n()%>% + as_gt() + +ts + +ts_rtf <- file("table1.RTF", "w") +writeLines(ts%>%as_rtf(), ts_rtf) +close(ts_rtf) + + + +## ==================================================================== +# Step 1: labels +## ==================================================================== +lbs<-var.labels[match(colnames(X_tbl_f), names(var.labels))] + +ls<-lapply(1:ncol(X_tbl_f),function(x){ + as.formula(paste0(names(lbs)[x],"~","\"",lbs[x],"\"")) +}) + +## ==================================================================== +# Step 2: table - edited +## ==================================================================== + +ts_e<-tbl_summary(X_tbl, + missing = "no", + value = list(where(is.factor) ~ "2"), + type = list(mrs_0 ~ "categorical", + mrs_1 ~ "categorical"), + statistic = list(all_continuous() ~ "{median} ({p25};{p75}) [{min},{max}]") +)%>% + as_gt() + +ts_e + +## ==================================================================== +# Step 3: table export +## ==================================================================== + +ts_rtf <- file("table1_overall.RTF", "w") +writeLines(ts%>%as_rtf(), ts_rtf) +close(ts_rtf) + +## ==================================================================== +# Baseline table - by PASE group +## ==================================================================== + +ts_q <- X_tbl |> + select(vars) |> + mutate(pase_0_cut = factor(quantile_cut(pase_0, groups = 4)[[1]],ordered = TRUE)) |> + select(-pase_6,-pase_0) |> + tbl_summary(missing = "no", + by="pase_0_cut", + value = list(where(is.factor) ~ "2"), + type = list(mrs_0 ~ "categorical"), + statistic = list(all_continuous() ~ "{median} ({p25};{p75}) [{min},{max}]") +) |> + add_overall() |> + add_n () + +ts_q + +## ==================================================================== +# Drops and hops +## ==================================================================== + +# TRUEs are patients dropping +table(X_tbl$pase_0_cut!="1"&X_tbl$pase_6_cut=="1")/nrow(X_tbl[X_tbl$pase_0_cut!="1",]) + +# TRUEs are percentage of patients inactive before stroke being more active after +table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_0_cut=="1",]) + +# TRUEs are percentage of patients being more active after that were inactive before stroke +table(X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1")/nrow(X_tbl[X_tbl$pase_6_cut!="1",]) + +# Difference between hop/no-hop +t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"]) + +summary(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut!="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"]) + +# Stationary low +t.test(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"]) + +boxplot(X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_0"],X_tbl[X_tbl$pase_0_cut=="1"&X_tbl$pase_6_cut=="1","pase_6"]) + +## ==================================================================== +# Sankey plot +## ==================================================================== + +if (pout=="drop"){ + source("sankey.R") + p_delta +} + + +## ==================================================================== +# Six months PASE: Bivariate and multivariate analyses +## ==================================================================== + +dta_lmreg <- X_tbl |> + select(vars) |> + mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) + +Hmisc::label(dta_lmreg$mrs_0) <- "Pre-stroke mRS >0" + +uv_reg <- tbl_uvregression(data=dta_lmreg, + method=lm, + y="pase_6", + show_single_row = where(is.factor), + estimate_fun = ~style_sigfig(.x,digits = 3), + pvalue_fun = ~style_pvalue(.x, digits = 3) +) + +mu_reg <- dta_lmreg |> + lm(formula=pase_6~.,data=_) |> + tbl_regression(show_single_row = where(is.factor), + estimate_fun = ~style_sigfig(.x,digits = 3), + pvalue_fun = ~style_pvalue(.x, digits = 3) + )|> + add_n() + +tbl_merge(list(uv_reg,mu_reg)) + + +## ==================================================================== +## +## Data variance +## +## Illustrating principal components. +## +## ==================================================================== + + +# source("PCA.R") +# +# +# pca22 +# ggsave("pc_plot.png",width = 18, height = 12, dpi = 300, limitsize = TRUE, units = "cm") + + +## ==================================================================== +## +## Models +## +## ==================================================================== + + +source("assign_full.R") +source("regularisation_steps.R") + +## ==================================================================== +# Step 1: data merge +## ==================================================================== +tbl<-merge(reg_coef_tbl$'_data',full_coef_tbl$'_data',by="name",all.x=T, sort=F) + +## ==================================================================== +# Step 2: table +## ==================================================================== +com_coef_tbl<-tbl%>% + gt()%>% + fmt_number( + columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric + rows = everything(), + decimals = 3)%>% + tab_spanner( + label = "Full model", + columns = 6:8 + )%>% + tab_spanner( + label = paste0("Regularised model, (a=", + best_alph, + ", l=", + round(best_lamb,3), + ")"), + columns = 2:5 + )%>% + tab_header( + title = "Model coefficients", + subtitle = "Combined table of both full and regularised model coefficients" + ) + +com_coef_tbl + +## ==================================================================== +# Step 3: export +## ==================================================================== +com_coef_rtf <- file("table2.RTF", "w") +writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf) +close(com_coef_rtf) + + + +## ==================================================================== +## +## Model performance +## +## Table with performance meassures for the two different models. +## +## ==================================================================== + +# ROC curve of best model + +p_roc +ggsave("roc_plot.png",width = 12, height = 12, dpi = 300, limitsize = TRUE, units = "cm") + + +## ==================================================================== +# Step 1: data set +## ==================================================================== +tbl<-data.frame(Meassure=c(names(full_cfm$byClass),"Mean AUC"), + "Regularised model"=round(c(reg_cfm$byClass,reg_auc_sum["Mean"]),3), + "Full model"=round(c(full_cfm$byClass,full_auc_sum["Mean"]),3)) + +## ==================================================================== +# Step 2: table +## ==================================================================== +tbl_perf<-tbl%>% + gt()%>% + tab_header( + title = "Performance meassures", + subtitle = "Combined table of both full and regularised performance meassures" + ) + +tbl_perf + +## ==================================================================== +# Step 3: export +## ==================================================================== +tbl_perf_rtf <- file("table3.RTF", "w") +writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf) +close(tbl_perf_rtf) +# + +## ==================================================================== +## +## Secondary analysis +## +## ==================================================================== + +Xy<-dta_s +X<-dta_s|>select(-group) +y<-dta_s$group + + +source("assign_full.R") +source("regularisation_steps.R") + +## ==================================================================== +# Step 1: data merge +## ==================================================================== +tbl<-merge(reg_coef_tbl$'_data',full_coef_tbl$'_data',by="name",all.x=T, sort=F) + +## ==================================================================== +# Step 2: table +## ==================================================================== +com_coef_tbl<-tbl%>% + gt()%>% + fmt_number( + columns=colnames(tbl)[sapply(tbl,is.numeric)], ## Selecting all numeric + rows = everything(), + decimals = 3)%>% + tab_spanner( + label = "Full model", + columns = 6:8 + )%>% + tab_spanner( + label = paste0("Regularised model, (a=", + best_alph, + ", l=", + round(best_lamb,3), + ")"), + columns = 2:5 + )%>% + tab_header( + title = "Model coefficients", + subtitle = "Combined table of both full and regularised model coefficients" + ) + +com_coef_tbl + +## ==================================================================== +# Step 3: export +## ==================================================================== +com_coef_rtf <- file("table2_sec.RTF", "w") +writeLines(com_coef_tbl%>%as_rtf(), com_coef_rtf) +close(com_coef_rtf) + + +## ==================================================================== +## +## Model performance +## +## Table with performance meassures for the two different models. +## +## ==================================================================== + + +## ==================================================================== +# Step 1: data set +## ==================================================================== +tbl<-data.frame(Meassure=c(names(full_cfm$byClass),"Mean AUC"), + "Regularised model"=round(c(reg_cfm$byClass,reg_auc_sum["Mean"]),3), + "Full model"=round(c(full_cfm$byClass,full_auc_sum["Mean"]),3)) + +## ==================================================================== +# Step 2: table +## ==================================================================== +tbl_perf<-tbl%>% + gt()%>% + tab_header( + title = "Performance meassures", + subtitle = "Combined table of both full and regularised performance meassures" + ) + +tbl_perf + +## ==================================================================== +# Step 3: export +## ==================================================================== +tbl_perf_rtf <- file("table3_sec.RTF", "w") +writeLines(tbl_perf%>%as_rtf(), tbl_perf_rtf) +close(tbl_perf_rtf) diff --git a/1 PA Decline/archive/generation_1/PCA.R b/1 PA Decline/archive/generation_1/PCA.R new file mode 100644 index 0000000..c659cb0 --- /dev/null +++ b/1 PA Decline/archive/generation_1/PCA.R @@ -0,0 +1,74 @@ +## ItMLiHSmar2022 +## PCA.R, child script +## Principal components analysis for data visualisation +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk + + +## ==================================================================== +## Step 0: data wrangling +## ==================================================================== +# source("data_format.R") + +X1<-X %>% dplyr::mutate(across(where(is.factor), as.numeric)) + +pc.out<-prcomp(X1, center=TRUE, scale = TRUE) + +pc.sum<-summary(pc.out) + +## ==================================================================== +## Step 1: plotting +## ==================================================================== +Xy$group<-factor(Xy$group,labels = c("No decline", "Decline")) + +library(ggfortify) +ppc12 <- autoplot(pc.out, + data = Xy, + x=1, + y=2, + colour = 'group')+ + labs(title = "PC1 and PC2", + colour = "Outcome") +ppc13 <- autoplot(pc.out, + data = Xy, + x=1, + y=3, + colour = 'group')+ + labs(title = "PC1 and PC3", + colour = "Outcome") +ppc23 <- autoplot(pc.out, + data = Xy, + x=2, + y=3, + colour = 'group')+ + labs(title = "PC2 and PC3", + colour = "Outcome") + +# Scree plot +pscr<-tibble(x=1:dim(pc.sum$importance)[2], + Proportion=pc.sum$importance[2,], + Cumulative=pc.sum$importance[3,])%>% + pivot_longer(cols=-x)%>% + ggplot(aes(x=x,y=value,color=name))+ + geom_line()+ + geom_point()+ + ylim(0,1)+ + labs(title = "Scree plot", + color= "Variance")+ + ylab("Variance")+ + xlab("Principal components") + +## ==================================================================== +## Step 2: merge plots +## ==================================================================== +library(patchwork) +pca22<-ppc12+ + theme(legend.position="none")+ + ppc13+ + ppc23+theme(legend.position="none")+ + pscr+ + plot_layout(ncol=2)+ + plot_annotation(title = 'Principal component visualisation', + tag_levels = "A") + +# pca22 + diff --git a/1 PA Decline/archive/generation_1/assign_full.R b/1 PA Decline/archive/generation_1/assign_full.R new file mode 100644 index 0000000..d6e4b2f --- /dev/null +++ b/1 PA Decline/archive/generation_1/assign_full.R @@ -0,0 +1,141 @@ +## ItMLiHSmar2022 +## assign_full.R, child script +## Full model building and analysation for assignment +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk + +## ==================================================================== +## Step 0: data import and wrangling +## ==================================================================== + +# source("data_format.R") + +## ==================================================================== +## Step 1: settings +## ==================================================================== +K<-10 +n<-nrow(X) +set.seed(321) + +# Using caret function to ensure both levels represented in all folds +c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) + +B<-list() +auc_train<-auc_test<-c() + +catinfo<-levels(y) + +cMatTrain<-cMatTest<-table(factor(c(0,0),levels=catinfo),factor(c(0,0),levels=catinfo)) + +## ==================================================================== +## Step 2: cross validation +## ==================================================================== + +set.seed(321) +## Iterate over partitions +for (idx1 in 1:K){ + + # Status + cat('Processing fold', idx1, 'of', K,'\n') + + # idx1=1 + # Get training- and test sets + I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE + I_test = !I_train + + Xtrain = X[I_train,] + ytrain = y[I_train] + Xtest = X[I_test,] + ytest = y[I_test] + + # Z-score standardisation + source("standardise.R") + list2env(standardise(Xtrain,Xtest,type="cs"),.GlobalEnv) + ## Outputs XtrainSt and XtestSt + ## Standardised by centering and scaling + + + ## Model matrices for glmnet + # Xmat.train<-model.matrix(~.-1,XtrainSt) + # Xmat.test<-model.matrix(~.-1,XtestSt) + + # Weights + ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain))) + + # Fit regularized linear regression model + mod<-glm(ytrain~., + data=XtrainSt, + weights = ytrain_weight, + family = stats::quasibinomial(link = "logit")) + + # Keep coefficients for plot + B[[idx1]] <- mod + + # Predict + yhatTrainProb<-predict(mod, + newdata = XtrainSt, + type = "response" + ) + + yhatTestProb<-predict(mod, + newdata = XtestSt, + type = "response" + ) + + # Compute training and test error + yhatTrain = round(yhatTrainProb) + yhatTest = round(yhatTestProb) + + # Make predictions categorical again (instead of 0/1 coding) + yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + # Compute confusion matrices + cMatTrain = cMatTrain + table(ytrain,yhatTrainCat) + cMatTest = cMatTest + table(ytest,yhatTestCat) + + # AUROC + suppressMessages( + auc_train[idx1]<-auc(ytrain, yhatTrainCat)) + suppressMessages( + auc_test [idx1]<-auc(ytest, yhatTestCat)) +} + +## ==================================================================== +# Step 3: creating table of coefficients for inference +## ==================================================================== + +confs<-lapply(1:K, function(x){ + cs<-exp(confint(B[[x]])) + lo<-cs[,1] + hi<-cs[,2] + return(list(lo=lo,hi=hi)) +}) + +coefs<-apply(Reduce('cbind', lapply(B,"[[", "coefficients")),1,mean) + +unlist(strsplit(names(B[[1]]$coefficients),2)) + +var.labels<-c(var.labels,'(Intercept)'="Intercept") + +ds<-tibble(name=var.labels[match(unlist(strsplit(names(coefs),2)), names(var.labels))], + coefs=coefs, + OR=round(exp(coefs),3), + CIs=paste0("(", + round(apply(Reduce('cbind', lapply(confs,"[[", "lo")),1,mean),3), + ",", + round(apply(Reduce('cbind', lapply(confs,"[[", "hi")),1,mean),3), + ")") + ) + +# +full_coef_tbl<-ds%>% + gt(rowname_col = list(age~"Age")) +# +full_coef_tbl + +## ==================================================================== +# 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==================================================================== +# Step 1: Loading libraries +## ==================================================================== + +library(Hmisc) +library(dplyr) +library(daDoctoR) +library(tidyselect) + +## ==================================================================== +# Step 2: Loading data +## ==================================================================== + +# rm(list = ls()) # Clear +# setwd("/Users/au301842/Library/CloudStorage/OneDrive-Personligt/Research/ISLcourse/") +# dta<-read.csv("/Users/au301842/Library/CloudStorage/OneDrive-Personligt/Research/ISLcourse/assigndata.csv") + +## ==================================================================== +# Step 3: Formatting variables +## ==================================================================== + +dta <- export %>% + # as_tibble()%>% + mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received + male_sex= factor(ifelse(sex=="female","no","yes")), + # smoke_ever=factor(ifelse(smoke_ever=="never","no","yes")), + civil=factor(ifelse(civil=="partner","no","yes")), # Sets "yes" for not-cohabiting + rtreat=factor(ifelse(rtreat=="Placebo","no","yes")), # "Yes" receives active treatment + alc=factor(ifelse(alc=="more","yes","no")), # Yes for more than guideline + pase_0=as.numeric(pase_0), + pase_6=as.numeric(pase_6), + across(c("diabetes", + "hypertension", + "smoker", + # "smoker_prev", + "afli", + "pad", + "ami", + "tci", + "mrs_0", + "mrs_1"),as.factor), + across(c("nihss_c", + "age", + "mdi_1", # For "enriched" analysis + "who5_score_1", + "mfi_gen_1", + "mfi_phys_1", + "mfi_act_1", + "mfi_mot_1", + "mfi_men_1"),as.numeric ) + )%>% + select(-c(sex)) + + +## ==================================================================== +# Step 4: Defining outcome +## ==================================================================== + +## Changed to step 7 +## This is to perform proper quantile split based on actually included. + +## ==================================================================== +# Step 5: Ordering variables +## ==================================================================== + +vars <- c("age", + "male_sex", + "civil", + "pase_0", + "smoker", + "alc", + "afli", + "hypertension", + "diabetes", + "pad", + "ami", + "tci", + "mrs_0", + "nihss_c", + "any_rep", + "rtreat", + "pase_6") + +dta<-select(dta,c(vars, + "mrs_1", + "mfi_gen_1", + "mfi_phys_1", + "mfi_act_1", + "mfi_mot_1", + "mfi_men_1", + "mdi_1", + "who5_score_1" + )) + +## ==================================================================== +# Step 6: Labeling +## ==================================================================== + +var.labels = c(age="Age", + male_sex="Male", + civil="Living alone", + pase_0="Pre-stroke PASE score", + pase_6="Six month PASE score", + smoker="Daily or occasinally smoking", + # smoker_prev="Previous habbit of smoking", + alc="More alcohol than recommendation", + afli="AFIB", + hypertension="Hypertension", + diabetes="Diabetes", + pad="PAD", + ami="Previous MI", + tci="Previous TIA", + mrs_0="Pre-stroke mRS [-1]", + nihss_c="Acute NIHSS score", + thrombolysis="Acute thrombolysis", + thrombechtomy="Acute thrombechtomy", + any_rep="Any reperfusion therapy", + rtreat="Active trial treatment", + mrs_1="One month mRS [-1]", + mfi_gen_1="One month MFI (General fatigue)", + mfi_phys_1="One month MFI (Physical fatigue)", + mfi_act_1="One month MFI (Reduced activity)", + mfi_mot_1="One month MFI (Reduced motivation)", + mfi_men_1="One month MFI (Mental fatigue)", + mdi_1="One month MDI", + who5_score_1="One month WHO5", + pase_decl_rel_fac="PASE score difference, relative F", + pase_decl_abs_fac="PASE score difference, absolute F", + pase_drop_fac="PASE first quartile drop F", + pase_hop_fac="PASE first quartile hop F", + pase_diff="PASE absolute decline", + pase_decl_rel="PASE relative decline", + pase_0_cut="PASE 0 quartiles", + pase_6_cut="PASE 6 quartiles") + +## Labelling based on outcome flag +if (pout=="decl_rel"|pout=="decl_abs"){ + var.labels = c(var.labels,group="PASE decline")} +if (pout=="drop"){ + var.labels = c(var.labels,group="PASE drop")} + +## ==================================================================== +# Step 7: final data export +## ==================================================================== + +data_summary<-summary(dta) + +# Saving "old" factorised variables +sel<-sapply(dta,is.factor) +# Reformatting factors as 1/2 for analysis +dta<-dta |> + mutate(across(where(is.factor), as.numeric))|> # Turning factors into 1(no) or 2(yes) for model. Numbered alphabetically. + mutate(across(matches(colnames(dta)[sel]), as.factor), + across(starts_with("pase_"), as.numeric)) + +# Filtering out non-PASE +X_tbl<-dta |> + filter(!is.na(pase_0),!is.na(pase_6)) + +nrow(X_tbl) + +# Defining possible outcome meassures. Keeping in df for characterisation +X_tbl <- X_tbl|> + mutate(## Relative decline + pase_diff=(pase_0-pase_6), + pase_decl_rel = pase_diff/pase_0*100, + pase_decl_rel_fac=factor(ifelse(pase_decl_rel>=rel_dif,"yes","no")), + ## Absolute decline + pase_decl_abs_fac=factor(ifelse(pase_diff>=abs_dif,"yes","no")), + ## Drop + pase_0_cut=quantile_cut(as.numeric(pase_0), + groups=4, + group.names = c(as.character(1:4)), + y=as.numeric(pase_0), + ordered.f = TRUE, + inc.outs = TRUE, + detail.lst=FALSE), + pase_6_cut=quantile_cut(as.numeric(pase_6), + groups=4, + group.names = c(as.character(1:4)), + y=as.numeric(pase_0), + ordered.f = TRUE, + inc.outs = TRUE, + detail.lst=FALSE), + pase_drop_fac=factor(ifelse(pase_6_cut==1&pase_0_cut!=1,"yes","no")), + pase_hop_fac=factor(ifelse(pase_6_cut!=1&pase_0_cut==1,"yes","no"))) + +Hmisc::label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))]) + +# Setting final primary output from "pout" +if (pout=="decl_rel"){ + X_tbl <- X_tbl|> + mutate(group=pase_decl_rel_fac) + + X_tbl_f <- X_tbl|> + filter(pase_0!=0)|> + select(-starts_with("pase_")) +} + +if (pout=="decl_abs"){ + X_tbl <- X_tbl|> + mutate(group=pase_decl_rel_fac) + + X_tbl_f <- X_tbl|> + filter(pase_0>=abs_dif)|> + select(-starts_with("pase_")) +} + +if (pout=="drop"){ + X_tbl <- X_tbl|> + mutate(group=pase_drop_fac) + + # print(quantile(as.numeric(X_tbl$pase_0))) + # print(quantile(as.numeric(X_tbl$pase_6))) + # print(summary(X_tbl$pase_0_cut)) + + X_tbl_f <- X_tbl|> + filter(pase_0_cut!=1)|> + select(-starts_with("pase_")) +} + +if (pout=="hop"){ + X_tbl <- X_tbl|> + mutate(group=pase_hop_fac) + + # print(quantile(as.numeric(X_tbl$pase_0))) + # print(quantile(as.numeric(X_tbl$pase_6))) + # print(summary(X_tbl$pase_0_cut)) + + X_tbl_f <- X_tbl|> + filter(pase_6_cut!=1)|> + select(-starts_with("pase_")) +} + +# Excluding one month measures for primary analysis and setting df for table one +X_tbl_f <- X_tbl_f |> + select(-c(who5_score_1, + mdi_1, + mrs_1, + starts_with("mfi_"))) # Left out of model as no present in drop-group + +# Dropping non-complete for analysis +Xy <- X_tbl_f|> + na.omit()|> # Keeping only complete observations + select(-c(tci) # Left out of model as no present in drop-group + )|> + mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) # Sets binary mRS 0 to include in glmnet, 0 or above + +label(Xy) = as.list(var.labels[match(names(Xy), names(var.labels))]) + +X<-dplyr::select(Xy,-c(group, -starts_with("pase_")) # Exclude primary outcome + ) +y<-Xy$group + + +## ==================================================================== +# Secondary analysis +## ==================================================================== + + +dta_s<-X_tbl|> + select(-c(tci), + -starts_with("pase_"))|> + na.omit()|> + mutate(mrs_0=factor(ifelse(mrs_0==1,1,2)),# Sets binary mRS 0 to include in glmnet, 0 or above + mrs_1=factor(ifelse(mrs_1==1,1,2)))# Sets binary mRS 1 to include in glmnet, 0 or above + +label(dta_s) = as.list(var.labels[match(names(dta_s), names(var.labels))]) + +## ==================================================================== +# Step 8: Loading rest of libraries +## ==================================================================== + +library(tidyverse) +library(patchwork) +library(caret) +library(glmnet) +library(leaps) +library(pROC) +library(gt) +library(gtsummary) +library(dplyr) diff --git a/1 PA Decline/archive/generation_1/data_set.R b/1 PA Decline/archive/generation_1/data_set.R new file mode 100644 index 0000000..f7455fc --- /dev/null +++ b/1 PA Decline/archive/generation_1/data_set.R @@ -0,0 +1,49 @@ +## ItMLiHS assignment data set + +export<-read.csv("/Volumes/Data 1/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown")) + +export<-export[,c("pase_0", + "age", + "sex", + "civil", + "smoke_ever", + "smoker", + "rtreat", + "alc", + "afli", + "hypertension", + "diabetes", + "mrs_0", + "nihss_c", + "thrombolysis", + "pad", + "thrombechtomy", + "ami", + "tci", + "pase_6", + "mrs_1", + "mfi_gen_1", + "mfi_phys_1", + "mfi_act_1", + "mfi_mot_1", + "mfi_men_1", + "mdi_1", + "who5_score_1")] + +export$diabetes[is.na(export$diabetes)]<-"no" +export$diabetes[is.na(export$hypertension)]<-"no" +export$thrombolysis[is.na(export$thrombolysis)]<-"no" +export$thrombechtomy[is.na(export$thrombechtomy)]<-"no" +export$pad[is.na(export$pad)]<-"no" +export$ami[is.na(export$ami)]<-"no" +# export$smoker_prev <- ifelse(export$smoker=="3","yes","no") +export$smoker <- ifelse(export$smoker=="1","yes","no") +export$smoker[is.na(export$smoker)] <- "no" +# export$mrs_0[export$mrs_0==3]<-NA + + + +# export<-na.omit(export) + +export +# write.csv(export,"/Users/au301842/Library/CloudStorage/OneDrive-Personligt/Research/ISLcourse/assigndata.csv",row.names = FALSE) diff --git a/1 PA Decline/archive/generation_1/dataset_redcap.R b/1 PA Decline/archive/generation_1/dataset_redcap.R new file mode 100644 index 0000000..1b0bf62 --- /dev/null +++ b/1 PA Decline/archive/generation_1/dataset_redcap.R @@ -0,0 +1,138 @@ +# Data +## Import from previous work +# dta<-read.csv("/Volumes/Data/exercise/source/background.csv",na.strings = c("NA","","unknown"),colClasses = "character") + +library(REDCapR) +library(lubridate) +library(dplyr) +library(daDoctoR) + +# source("https://raw.githubusercontent.com/agdamsbo/daDoctoR/master/R/dob_extract_cpr_function.R") + +dta <- redcap_read_oneshot( + redcap_uri = "https://redcap.au.dk/api/", + token = read.csv("/Users/au301842/talos_redcap_token.csv",header = FALSE)[[1]], + fields = c("talos_basis02a", #Indlæggelsesdato + "cpr", + "talos_nihss16_0", #Akut NIHSS + "basis_kon", + "reg_hojde", #Alle fra "reg(ister/DAP)" + "reg_vaegt", + "reg_vaegt_anslaaet", + "reg_rygning", + "reg_alkohol", + "reg_civil", + "reg_bolig", + "reg_diabetes", + "reg_hyperten", + "reg_perifer_arteriel", + "reg_atriefli", + "reg_ami", + "reg_tidl_tci", + "reg_trombolyse", + "reg_trombektomi", + "rtreat" #Trial treatment + ) +)$data |> + mutate(age=time_length(talos_basis02a-dob_extract_cpr(cpr), + unit="year") + )|> + select(!c("cpr")) + + + + +## Cleaning and enhancing +dta$pase_drop<-factor(ifelse((dta$pase_0_q=="q_2"|dta$pase_0_q=="q_3"|dta$pase_0_q=="q_4")&dta$pase_06_q=="q_1","yes","no"),levels = c("no","yes")) +dta$pase_drop[is.na(dta$pase_6)]<-NA +dta$pase_drop[is.na(dta$pase_0)]<-NA + +## Selection of data set and formatting +library(dplyr) +dta_f<-dta %>% filter(pase_0_q != "q_1" & !is.na(pase_drop)) + + +variable_names<-c("age","sex","weight","height", + "bmi", + "smoke_ever", + "civil", + "diabetes", + "hypertension", + "pad", + "afli", + "ami", + "tci", + "nihss_0", + "thrombolysis", + "thrombechtomy", + "rep_any","pase_0_q","pase_drop") + + +library(daDoctoR) +dta2<-dta_f[,variable_names] + +dta2<-col_num(c("age","weight","height","bmi","nihss_0"),dta2) +dta2<-col_fact(c("sex","smoke_ever","civil","diabetes", "hypertension","pad", "afli", "ami", "tci","thrombolysis", "thrombechtomy","rep_any","pase_0_q","pase_drop"),dta2) + +## Partitioning +library(caret) +set.seed(100) + +## Step 1: Get row numbers for the training data +trainRowNumbers <- createDataPartition(dta2$pase_drop, p=0.8, list=FALSE) + +## Step 2: Create the training dataset +trainData <- dta2[trainRowNumbers,] + +## Step 3: Create the test dataset +testData <- dta2[-trainRowNumbers,] +y_test = testData[,"pase_drop"] + +# Store X and Y for later use. +x = trainData %>% select(!matches("pase_drop")) +y = trainData[,"pase_drop"] + +# Normalization and dummy binaries + +# One-Hot Encoding +# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories. +dummies_model <- dummyVars(pase_drop ~ ., data=trainData) + +# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat. +trainData_mat <- predict(dummies_model, newdata = trainData) + +# # Convert to dataframe +trainData <- data.frame(trainData_mat) + +# # See the structure of the new dataset +str(trainData) + +dummies_model <- dummyVars(pase_drop ~ ., data=testData) +testData_mat <- predict(dummies_model, newdata = testData) +testData <- data.frame(testData_mat) +preProcess_range_model <- preProcess(testData, method='range') +testData <- predict(preProcess_range_model, newdata = testData) +testData$pase_drop<-y_test + +# Imputation + +library(RANN) # required for knnInpute +preProcess_missingdata_model <- preProcess(trainData, method='knnImpute') +# preProcess_missingdata_model + +trainData <- predict(preProcess_missingdata_model, newdata = trainData) # Giver fejl?? +anyNA(trainData) + +# skimr::skim(trainData) +# skimr::skim(x) + +preProcess_range_model <- preProcess(trainData, method='range') +trainData <- predict(preProcess_range_model, newdata = trainData) + +# Append the Y variable +trainData$pase_drop <- y + + +# Export +write.csv(trainData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",row.names = FALSE) +write.csv(testData,"/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",row.names = FALSE) diff --git a/1 PA Decline/archive/generation_1/pc_plot.png b/1 PA Decline/archive/generation_1/pc_plot.png new file mode 100644 index 0000000..a3757f5 Binary files /dev/null and b/1 PA Decline/archive/generation_1/pc_plot.png differ diff --git a/1 PA Decline/archive/generation_1/regular_fun.R b/1 PA Decline/archive/generation_1/regular_fun.R new file mode 100644 index 0000000..1b457b0 --- /dev/null +++ b/1 PA Decline/archive/generation_1/regular_fun.R @@ -0,0 +1,117 @@ +## ItMLiHSmar2022 +## regular_fun.R, child script +## Regularisation model building function +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + +regular_fun<-function(X,y,K,lambdas,alpha){ +n<-nrow(X) +set.seed(321) + +# Using caret function to ensure both levels represented in all folds +c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) + +B<-yhatTestProbKeep<-list() +accTrain<-accTest<-err_train<-err_test<-auc_train<-auc_test<-matrix(nrow = K,ncol = length(lambdas)) + +catinfo<-levels(y) + +cMatTrain<-cMatTest<-table(true=factor(c(0,0),levels=catinfo),pred=factor(c(0,0),levels=catinfo)) + + +## Iterate over partitions +for (idx1 in 1:K){ + + # Status + cat('Processing fold', idx1, 'of', K,'\n') + + # idx1=1 + # Get training- and test sets + I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE + I_test = !I_train + + Xtrain = X[I_train,] + ytrain = y[I_train] + Xtest = X[I_test,] + ytest = y[I_test] + + + ## Model matrices for glmnet + ## Using the complicated approach not to include first level. + # Xmat.train<-model.matrix(~ .-1, data=Xtrain, + # contrasts.arg = lapply(Xtrain[,sapply(Xtrain, is.factor)], + # contrasts, contrasts=T)) + # Xmat.test<-model.matrix(~ .-1, data=Xtest, + # contrasts.arg = lapply(Xtest[,sapply(Xtest, is.factor)], + # contrasts, contrasts=T)) + + # Xmat.train<-model.matrix(~.-1,Xtrain) + # Xmat.test<-model.matrix(~.-1,Xtest) + + # Weights + ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain))) + # ytest_weight<-as.vector(1 / (table(ytest)[ytest] / length(ytest))) + + # Fit regularized linear regression model + mod<-glmnet(Xtrain, ytrain, + alpha = alpha, ## Alpha = 1 for lasso + lambda = lambdas, ## Setting lambdas + standardize = TRUE, ## Scales and centers + weights = ytrain_weight, + family = "binomial" + ) + + # Keep coefficients for plot + B[[idx1]] <- as.matrix(coef(mod)) + + # Iterate over regularization strengths to compute training- and test + # errors for individual regularization strengths. + for (idx2 in 1:length(lambdas)){ + # idx2=1 + + # Predict + yhatTrainProb<-predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtrain), + type = "response" + ) + + yhatTestProb<-predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtest), + type = "response" + ) + + # Compute training and test error + yhatTrain = round(yhatTrainProb) + yhatTest = round(yhatTestProb) + + # Make predictions categorical again (instead of 0/1 coding) + yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + + # Evaluate classifier performance + # Accuracy + # accTrain[idx1,idx2] <- sum(yhatTrainCat==ytrain)/length(ytrain) + # accTest [idx1,idx2] <- sum(yhatTestCat==ytest)/length(ytest) + # # + # # Error rate + # err_train[idx1,idx2] = 1 - accTrain[idx1,idx2] + # err_test [idx1,idx2] = 1 - accTest[idx1,idx2] + + # AUROC + suppressMessages( + auc_train[idx1,idx2]<-auc(ytrain, yhatTrainCat)) + suppressMessages( + auc_test [idx1,idx2]<-auc(ytest, yhatTestCat)) + + # Compute confusion matrices + cMatTrain = cMatTrain + table(true=ytrain,pred=yhatTrainCat) + cMatTest = cMatTest + table(true=ytest,pred=yhatTestCat) + } +} +ls<-list(mod=mod,B=B,auc_train=auc_train,auc_test=auc_test,cMatTrain=cMatTrain,cMatTest=cMatTest) +return(ls) +} diff --git a/1 PA Decline/archive/generation_1/regularisation_steps.R b/1 PA Decline/archive/generation_1/regularisation_steps.R new file mode 100644 index 0000000..faf9a23 --- /dev/null +++ b/1 PA Decline/archive/generation_1/regularisation_steps.R @@ -0,0 +1,142 @@ +## ItMLiHSmar2022 +## regularisation_steps.R, child script +## Regularised model building and analysation for assignment +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + +## ==================================================================== +## Step 0: data import and wrangling +## ==================================================================== + +setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/") + +# source("data_format.R") +y1<-factor(as.integer(y)-1) ## Outcome is required to be factor of 0 or 1. + + +## ==================================================================== +## Step 1: settings +## ==================================================================== + +## Folds +K=10 +set.seed(3) +c<-caret::createFolds(y=y, + k = K, + list = FALSE, + returnTrain = TRUE) # Foldids for alpha tuning + +## Defining tuning parameters +lambdas=2^seq(-10, 5, 1) +alphas<-seq(0,1,.1) + +## Weights for models +weighted=TRUE +if (weighted == TRUE) { + wght<-as.vector(1 - (table(y)[y] / length(y))) +} else { + wght <- rep(1, nrow(y)) +} + + +## Standardise numeric +## Centered and + + + +## ==================================================================== +## Step 2: all cross validations for each alpha +## ==================================================================== + +library(furrr) +library(purrr) +library(doMC) +registerDoMC(cores=6) + +# Nested CVs with analysis for all lambdas for each alpha +# +set.seed(3) +cvs <- future_map(alphas, function(a){ + cv.glmnet(model.matrix(~.-1,X), + y1, + weights = wght, + lambda=lambdas, + type.measure = "deviance", # This is standard measure and recommended for tuning + foldid = c, # Per recommendation the folds are kept for alpha optimisation + alpha=a, + standardize=TRUE, + family=quasibinomial, + keep=TRUE) # Same as binomial, but not as picky +}) + +## ==================================================================== +# Step 3: optimum lambda for each alpha +## ==================================================================== + + +# For each alpha, lambda is chosen for the lowest meassure (deviance) +each_alpha <- sapply(seq_along(alphas), function(id) { + each_cv <- cvs[[id]] + alpha_val <- alphas[id] + index_lmin <- match(each_cv$lambda.min, + each_cv$lambda) + c(lamb = each_cv$lambda.min, + alph = alpha_val, + cvm = each_cv$cvm[index_lmin]) +}) + +# Best lambda +best_lamb <- min(each_alpha["lamb", ]) + +# Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",] +best_alph <- each_alpha["alph",][each_alpha["cvm",]==min(each_alpha["cvm",] + [each_alpha["lamb",] %in% best_lamb])] + +## https://stackoverflow.com/questions/42007313/plot-an-roc-curve-in-r-with-ggplot2 +p_roc<-roc.glmnet(cvs[[1]]$fit.preval, newy = y)[[match(best_alph,alphas)]]|> # Plots performance from model with best alpha + ggplot(aes(FPR,TPR)) + + geom_step() + + coord_cartesian(xlim=c(0,1), ylim=c(0,1)) + + geom_abline()+ + theme_bw() + +## ==================================================================== +# Step 4: Creating the final model +## ==================================================================== + +source("regular_fun.R") # Custom function +optimised_model<-regular_fun(X,y1,K,lambdas=best_lamb,alpha=best_alph) +# With lambda and alpha specified, the function is just a k-fold cross-validation wrapper, +# but keeps model performance figures from each fold. + +list2env(optimised_model,.GlobalEnv) +# Function outputs a list, which is unwrapped to Env. +# See source script for reference. + +## ==================================================================== +# Step 5: creating table of coefficients for inference +## ==================================================================== + +Bmatrix<-matrix(unlist(B),ncol=10) +Bmedian<-apply(Bmatrix,1,median) +Bmean<-apply(Bmatrix,1,mean) + +reg_coef_tbl<-tibble( + name = c("Intercept",Hmisc::label(X)), + medianX = round(Bmedian,5), + ORmed = round(exp(Bmedian),5), + meanX = round(Bmean,5), + ORmea = round(exp(Bmean),5))%>% + # arrange(desc(abs(medianX)))%>% + gt() + +reg_coef_tbl + +## ==================================================================== +# Step 6: plotting predictive performance +## ==================================================================== + +reg_cfm<-confusionMatrix(cMatTest) +reg_auc_sum<-summary(auc_test[,1]) diff --git a/1 PA Decline/archive/generation_1/roc_plot.png b/1 PA Decline/archive/generation_1/roc_plot.png new file mode 100644 index 0000000..b1e51b9 Binary files /dev/null and b/1 PA Decline/archive/generation_1/roc_plot.png differ diff --git a/1 PA Decline/archive/generation_1/sankey.R b/1 PA Decline/archive/generation_1/sankey.R new file mode 100644 index 0000000..ad6ea82 --- /dev/null +++ b/1 PA Decline/archive/generation_1/sankey.R @@ -0,0 +1,72 @@ +# +# Sankey plot of quartile movement for drops +# + +gth<-X_tbl[,c("pase_0_cut","pase_6_cut","pase_drop_fac")] + +gth$pase_drop_fac <- factor(ifelse(gth$pase_0_cut=="1", + "low", + gth$pase_drop_fac), + labels = c("no","yes","low")) # Tried and tried to do vectorised, but failed. Matrices acting up.. + +# Visuals - sankey +# https://stackoverflow.com/questions/50395027/beautifying-sankey-alluvial-visualization-using-r + + +## Painting +# LOOK AT THIS GREAT FUNCTION!! Wide pivot format. Includes factor for possible quartile-colouring. + +df<-data.frame(gth %>% count(pase_0_cut,pase_6_cut,pase_drop_fac)) + +lbs0<-c(paste0("1st\n(n=",sum(df$n[df[1]=="1"]),")"), + paste0("2nd\n(n=",sum(df$n[df[1]=="2"]),")"), + paste0("3rd\n(n=",sum(df$n[df[1]=="3"]),")"), + paste0("4th\n(n=",sum(df$n[df[1]=="4"]),")")) + +lbs6<-c(paste0("1st\n(n=",sum(df$n[df[2]=="1"]),")"), + paste0("2nd\n(n=",sum(df$n[df[2]=="2"]),")"), + paste0("3rd\n(n=",sum(df$n[df[2]=="3"]),")"), + paste0("4th\n(n=",sum(df$n[df[2]=="4"]),")")) + +df[1:2] <- as_factor(df[1:2]) + +levels(df[,1])<-lbs0[1:length(levels(df[,1]))] +levels(df[,2])<-lbs6[1:length(levels(df[,2]))] + +df[,3]<-factor(df[,3],levels=c("low","no","yes")) + + +lows <- "grey80" # grey +drops <- "#990033" # Midtrød +nos <- "grey50" +nas <- "grey90" +border<- "#66c1a3" +box <- "#7fccb2" + +cls <- c(lows,nos,drops) +alpha <- 0.7 + +library(ggalluvial) +(p_delta<-ggplot(df,aes(y = n, axis1 = pase_0_cut, axis2 = pase_6_cut)) + + geom_alluvium(aes(fill = pase_drop_fac, color=pase_drop_fac), width = 1/10, alpha = alpha, knot.pos = 0.3)+ + geom_stratum(width = 1/6, fill = box, color = border) + + geom_text(stat = "stratum", aes(label=after_stat(stratum))) + + scale_x_continuous(breaks = 1:2, labels = c("Pre-stroke\nPASE score\nquartiles", "Six months\nPASE score\nquartiles")) + + scale_fill_manual(values = cls) + + scale_color_manual(values = cls) + + scale_y_reverse() + # Easy solution to flip y-axis + labs(title="Change in physical activity") + + ylab("Quartiles")+ + theme_minimal() + + theme(legend.position = "none", + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.y = element_blank(), + axis.title.y = element_blank(), + axis.text.x = element_text(size = 14, face = "bold"), + plot.title = element_text(hjust = 0.5, size = 20, face = "bold"))) + +ggsave("sankey.png", plot = last_plot(), device = NULL, path = NULL, + scale = 1, width = 120, height = 200, dpi = 450, limitsize = TRUE, + units = "mm") + \ No newline at end of file diff --git a/1 PA Decline/archive/generation_1/sankey.png b/1 PA Decline/archive/generation_1/sankey.png new file mode 100644 index 0000000..aabb3ec Binary files /dev/null and b/1 PA Decline/archive/generation_1/sankey.png differ diff --git a/1 PA Decline/archive/generation_1/standardise.R b/1 PA Decline/archive/generation_1/standardise.R new file mode 100644 index 0000000..2521e6d --- /dev/null +++ b/1 PA Decline/archive/generation_1/standardise.R @@ -0,0 +1,41 @@ +## ItMLiHSmar2022 +## standardise.R, child script +## Data standardisation, returns list +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk + +standardise<-function(train,test,type){ + # From: + # https://datascience.stackexchange.com/questions/13971/standardization-normalization-test-data-in-r + + sel<-sapply(Xtrain,is.numeric) # Deciding which to stadardise (only numeric) + cnm<-colnames(Xtrain) # Saving column names for ordering + + # Subsetting + + ## Data to treat + train.tr<-train[,sel] + test.tr<-test[,sel] + + ## Data to save + train.sv<-train[,!sel] + test.sv<-test[,!sel] + + # Calculate mean and SD of train data + trainMean <- sapply(train.tr,mean) + trainSd <- sapply(train.tr,sd) + + if (type=="c"){ + ## centered + norm.trainData<-sweep(train.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise + norm.testData<-sweep(test.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise + } + + if (type=="cs"){ + ## centered AND scaled (Z-score standardisation) + norm.trainData<-sweep(sweep(train.tr, 2L, trainMean), 2, trainSd, "/") + norm.testData<-sweep(sweep(test.tr, 2L, trainMean), 2, trainSd, "/") + } + return(list(XtrainSt=cbind(norm.trainData,train.sv)[,cnm], # Reordering columns to original + XtestSt=cbind(norm.testData,test.sv)[,cnm])) +} + diff --git a/1 PA Decline/archive/generation_1/table1.RTF b/1 PA Decline/archive/generation_1/table1.RTF new file mode 100644 index 0000000..6ec9f0e --- /dev/null +++ b/1 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\clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5616 +\intbl {\f0 {\f0\fs20 0 (0%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7488 +\intbl {\f0 {\f0\fs20 0 (0%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0 (0%)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 {\super \i 1}Median (25%;75%) [Minimum,Maximum]; n (%)}}\cell + +\row + +} diff --git a/1 PA Decline/archive/generation_1/table2.RTF b/1 PA Decline/archive/generation_1/table2.RTF new file mode 100644 index 0000000..d0a3072 --- /dev/null +++ b/1 PA Decline/archive/generation_1/table2.RTF @@ -0,0 +1,491 @@ +{\rtf\ansi\ansicpg1252{\fonttbl{\f0\froman\fcharset0\fprq0 Courier New;}{\f1\froman\fcharset0\fprq0 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occasinally smoking}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340 +\intbl {\f0 {\f0\fs20 0.156}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510 +\intbl {\f0 {\f0\fs20 1.169}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680 +\intbl {\f0 {\f0\fs20 0.161}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850 +\intbl {\f0 {\f0\fs20 1.175}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020 +\intbl {\f0 {\f0\fs20 0.546}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190 +\intbl {\f0 {\f0\fs20 1.727}}\cell + +\pard\plain\uc0\qr\clvertalc 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1.311}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 (0.608,2.904)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170 +\intbl {\f0 {\f0\fs20 Acute NIHSS score}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340 +\intbl {\f0 {\f0\fs20 0.033}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510 +\intbl {\f0 {\f0\fs20 1.034}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680 +\intbl {\f0 {\f0\fs20 0.037}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850 +\intbl {\f0 {\f0\fs20 1.037}}\cell + 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\clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 (0.897,2.445)}}\cell + +\row + +} diff --git a/1 PA Decline/archive/generation_1/table2_sec.RTF b/1 PA Decline/archive/generation_1/table2_sec.RTF new file mode 100644 index 0000000..b2419c3 --- /dev/null +++ b/1 PA Decline/archive/generation_1/table2_sec.RTF @@ -0,0 +1,715 @@ +{\rtf\ansi\ansicpg1252{\fonttbl{\f0\froman\fcharset0\fprq0 Courier New;}{\f1\froman\fcharset0\fprq0 Times;}}{\colortbl;\red51\green51\blue51;\red211\green211\blue211;} + +\paperw12240\paperh15840\widowctrl\ftnbj\fet0\sectd\linex0 +\lndscpsxn +\margl1440\margr1440\margt1440\margb1440 +\headery720\footery720\fs20 + +\trowd\trrh0\trhdr + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0\cf1 {\f0\fs20 Model coefficients} {\f0\fs20\i\super } \line {\f0\fs20 Combined table of both full and regularised model coefficients} {\f0\fs20\i\super }}\cell + +\row + 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\clpadfr3\clpadr85 \cellx7020 +\intbl {\f0 {\f0\fs20 0.093}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190 +\intbl {\f0 {\f0\fs20 1.097}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 (0.853,1.42)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1170 +\intbl {\f0 {\f0\fs20 One month MDI}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2340 +\intbl {\f0 {\f0\fs20 0.027}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510 +\intbl {\f0 {\f0\fs20 1.028}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680 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0.000}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3510 +\intbl {\f0 {\f0\fs20 1.000}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4680 +\intbl {\f0 {\f0\fs20 0.000}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5850 +\intbl {\f0 {\f0\fs20 1.000}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx7020 +\intbl {\f0 {\f0\fs20 -0.108}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8190 +\intbl {\f0 {\f0\fs20 0.898}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 (0.653,1.239)}}\cell + +\row + +} diff --git a/1 PA Decline/archive/generation_1/table3.RTF b/1 PA Decline/archive/generation_1/table3.RTF new file mode 100644 index 0000000..3a9caa1 --- /dev/null +++ b/1 PA Decline/archive/generation_1/table3.RTF @@ -0,0 +1,184 @@ +{\rtf\ansi\ansicpg1252{\fonttbl{\f0\froman\fcharset0\fprq0 Courier New;}{\f1\froman\fcharset0\fprq0 Times;}}{\colortbl;\red51\green51\blue51;\red211\green211\blue211;} + +\paperw12240\paperh15840\widowctrl\ftnbj\fet0\sectd\linex0 +\lndscpsxn +\margl1440\margr1440\margt1440\margb1440 +\headery720\footery720\fs20 + +\trowd\trrh0\trhdr + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0\cf1 {\f0\fs20 Performance meassures} {\f0\fs20\i\super } \line {\f0\fs20 Combined table of both full and regularised performance meassures} {\f0\fs20\i\super }}\cell + +\row + +\trowd\trrh0\trhdr + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 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+\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Pos Pred Value}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.646}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.672}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Neg Pred Value}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.500}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.516}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Precision}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.646}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.672}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Recall}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.890}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.896}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 F1}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.749}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.768}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Prevalence}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.626}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.646}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Detection Rate}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.557}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.579}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Detection Prevalence}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.862}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.862}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Balanced Accuracy}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.537}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.549}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx3120 +\intbl {\f0 {\f0\fs20 Mean AUC}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6240 +\intbl {\f0 {\f0\fs20 0.577}}\cell + +\pard\plain\uc0\qr\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9360 +\intbl {\f0 {\f0\fs20 0.594}}\cell + +\row + +} diff --git a/1 PA Decline/archive/prediction exercise.R b/1 PA Decline/archive/prediction exercise.R new file mode 100644 index 0000000..fd04c04 --- /dev/null +++ b/1 PA Decline/archive/prediction exercise.R @@ -0,0 +1,142 @@ +# https://www.machinelearningplus.com/machine-learning/caret-package/ + +# install.packages(c('caret', 'skimr', 'RANN', 'randomForest', 'fastAdaboost', 'gbm', 'xgboost', 'caretEnsemble', 'C50', 'earth')) + +# Load the caret package +library(caret) + +# Import dataset +orange <- read.csv('https://raw.githubusercontent.com/selva86/datasets/master/orange_juice_withmissing.csv') + +# Structure of the dataframe +str(orange) + +# See top 6 rows and 10 columns +head(orange[, 1:10]) + +# Create the training and test datasets +set.seed(100) + +# Step 1: Get row numbers for the training data +trainRowNumbers <- createDataPartition(orange$Purchase, p=0.8, list=FALSE) + +# Step 2: Create the training dataset +trainData <- orange[trainRowNumbers,] + +# Step 3: Create the test dataset +testData <- orange[-trainRowNumbers,] + +# Store X and Y for later use. +x = trainData[, 2:18] +y = trainData$Purchase + +library(skimr) +skimmed <- skim(trainData) +skimmed + +# Create the knn imputation model on the training data +preProcess_missingdata_model <- preProcess(trainData, method='knnImpute') +preProcess_missingdata_model + +# Use the imputation model to predict the values of missing data points +library(RANN) # required for knnInpute +trainData <- predict(preProcess_missingdata_model, newdata = trainData) +anyNA(trainData) + +# One-Hot Encoding +# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories. +dummies_model <- dummyVars(Purchase ~ ., data=trainData) + +# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat. +trainData_mat <- predict(dummies_model, newdata = trainData) + +# # Convert to dataframe +trainData <- data.frame(trainData_mat) + +# # See the structure of the new dataset +str(trainData) + + +preProcess_range_model <- preProcess(trainData, method='range') +trainData <- predict(preProcess_range_model, newdata = trainData) + +# Append the Y variable +trainData$Purchase <- y + +apply(trainData[, 1:10], 2, FUN=function(x){c('min'=min(x), 'max'=max(x))}) + + +featurePlot(x=trainData[,1:18], + y=factor(trainData$Purchase), + plot="box", + strip=strip.custom(par.strip.text=list(cex=.7)), + scales = list(x = list(relation="free"), + y = list(relation="free"))) + +featurePlot(x=trainData[,1:18], + y=factor(trainData$Purchase), + plot="density", + strip=strip.custom(par.strip.text=list(cex=.7)), + scales = list(x = list(relation="free"), + y = list(relation="free"))) + +# 5 + +set.seed(100) +options(warn=-1) + +subsets <- c(1:5, 10, 15, 18) + +ctrl <- rfeControl(functions = rfFuncs, + method = "repeatedcv", + repeats = 5, + verbose = FALSE) + +lmProfile <- rfe(x=trainData[, 1:18], y=factor(trainData$Purchase), + sizes = subsets, + rfeControl = ctrl) + +lmProfile + + +# See available algorithms in caret +modelnames <- dput(names(getModelInfo())) +# modelnames <- paste(names(getModelInfo()), collapse=', ') +modelnames + + +# Set the seed for reproducibility +set.seed(100) + +# Train the model using randomForest and predict on the training data itself. +model_mars = train(Purchase ~ ., data=trainData, method='earth') +fitted <- predict(model_mars) + +model_mars + + +plot(model_mars, main="Model Accuracies with MARS") + +varimp_mars <- varImp(model_mars) +plot(varimp_mars, main="Variable Importance with MARS") + + +## 6.4 + +# Step 1: Impute missing values +testData2 <- predict(preProcess_missingdata_model, testData) + +# Step 2: Create one-hot encodings (dummy variables) +testData3 <- predict(dummies_model, testData2) + +# Step 3: Transform the features to range between 0 and 1 +testData4 <- predict(preProcess_range_model, testData3) + +# View +head(testData4[, 1:10]) + +predicted <- predict(model_mars, testData4) +head(predicted) + +# Compute the confusion matrix +confusionMatrix(reference = factor(testData$Purchase), data = predicted, mode='everything', positive='MM') diff --git a/1 PA Decline/archive/predictive_model.Rmd b/1 PA Decline/archive/predictive_model.Rmd new file mode 100644 index 0000000..b774729 --- /dev/null +++ b/1 PA Decline/archive/predictive_model.Rmd @@ -0,0 +1,101 @@ +--- +title: "predictive_model" +output: pdf_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +# Data +```{r} +library(caret) +library(pROC) +library(daDoctoR) +library(dplyr) +``` + +Import +```{r} +trainData<-read.csv("/Users/au301842/PhysicalActivityandStrokeOutcome/data/trainData.csv",) +testData<-read.csv("/Users/au301842/PhysicalActivityandStrokeOutcome/data/testData.csv",) +``` + + +# Prediction +Inspiration: https://stackoverflow.com/questions/30366143/how-to-compute-roc-and-auc-under-roc-after-training-using-caret-in-r and https://www.machinelearningplus.com/machine-learning/caret-package/ + +## Early visualisation + +```{r} +featurePlot(x = trainData %>% select(!matches("pase_drop")), + y = factor(trainData$pase_drop), + plot = "box", + strip=strip.custom(par.strip.text=list(cex=.7)), + scales = list(x = list(relation="free"), + y = list(relation="free"))) + +featurePlot(x = trainData %>% select(!matches("pase_drop")), + y = factor(trainData$pase_drop), + plot = "density", + strip=strip.custom(par.strip.text=list(cex=.7)), + scales = list(x = list(relation="free"), + y = list(relation="free"))) +``` + + +```{r} +subsets <- c(1:10, 15, 18,33) + +ctrl <- rfeControl(functions = rfFuncs, + method = "repeatedcv", + repeats = 5, + verbose = FALSE) + +lmProfile <- rfe(x = trainData %>% select(!matches("pase_drop")), + y = trainData$pase_drop, + sizes = subsets, + rfeControl = ctrl) + +lmProfile +``` + + +```{r} +set.seed(1000) + +forest.model <- train(pase_drop ~., trainData) + +result.predicted.prob <- predict(forest.model, testData, type="prob") # Prediction + +result.roc <- roc(factor(testData$pase_drop), result.predicted.prob$no) # Draw ROC curve. + +plot(result.roc, print.thres="best", print.thres.best.method="closest.topleft") + +result.coords <- coords(result.roc, "best", best.method="closest.topleft", ret=c("threshold", "accuracy")) +print(result.coords)#to get threshold and accuracy +``` + +```{r} +library(MLeval) + +myTrainingControl <- trainControl(method = "cv", + number = 10, + savePredictions = TRUE, + classProbs = TRUE, + verboseIter = TRUE) + +randomForestFit = train(x = trainData[,1:32], + y = as.factor(trainData$pase_drop), + method = "rf", + trControl = myTrainingControl, + preProcess = c("center","scale"), + ntree = 50) + +x <- evalm(randomForestFit) + +x$roc + +x$stdres +``` + diff --git a/1 PA Decline/archive/regularised model.R b/1 PA Decline/archive/regularised model.R new file mode 100644 index 0000000..c1015ce --- /dev/null +++ b/1 PA Decline/archive/regularised model.R @@ -0,0 +1,6 @@ +## +## Regularisation +## +## + + diff --git a/1 PA Decline/article ready.docx b/1 PA Decline/article ready.docx new file mode 100644 index 0000000..ab90991 Binary files /dev/null and b/1 PA Decline/article ready.docx differ diff --git a/1 PA Decline/article ready.html b/1 PA Decline/article ready.html new file mode 100644 index 0000000..2b01493 --- /dev/null +++ b/1 PA Decline/article ready.html @@ -0,0 +1,1379 @@ + + + + + + + + + +Article A: Final renders + + + + + + + + + + + + + + + + + + + +
+ +
+ +
+
+

Article A: Final renders

+
+ + + +
+ + + + +
+ + + +
+ + +
+
# ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv"))
+
+
+
# ds |> finalfit::missing_plot()
+
+

On hanlding missings: https://finalfit.org/articles/missing.html

+
+
source(here::here("1 PA Decline/dst import.R"))
+ls_tbl <- docx2ds(
+  path = "/Users/au301842/Library/CloudStorage/OneDrive-Personal/Research/PhD/1 Change in PA/Manuscript/Manuscript_Change in PA_v2_0.docx"
+)
+
+
Content types in the current document are as follows:
+
+
+
[1] "paragraph"  "table cell"
+
+
+
Warning: The `value` argument of `names<-()` must have the same length as `x` as of
+tibble 3.0.0.
+
+
+
Warning: The `value` argument of `names<-()` can't be empty as of tibble 3.0.0.
+
+
ls <- ls_tbl |> purrr::pluck(2) |> 
+  (\(.x){
+    .x[-c(1:3,nrow(.x)),]
+  })()
+
+n <- 0
+for (i in seq_len(nrow(ls))){
+  if (ls[[2]][i]!=""){
+    n[i]<-n[length(n)]+1
+  } else {
+    n[i] <- n[length(n)]
+  }
+}
+
+split(ls,n) |> lapply(function(.x){
+  
+  if ("  TRUE" %in% .x[[1]]){
+    cbind(.x[1,1:2],.x[3,3:5],.x[1,6],.x[3,7:9])
+  } else {
+    .x
+  }
+}) |> dplyr::bind_rows() |> 
+  gt::gt()
+
+
New names:
+• `` -> `...1`
+• `` -> `...2`
+• `` -> `...3`
+• `` -> `...4`
+• `` -> `...5`
+• `` -> `...6`
+• `` -> `...7`
+• `` -> `...8`
+• `` -> `...9`
+
+
+
New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+New names:
+• `` -> `...1`
+• `` -> `...2`
+• `` -> `...3`
+• `` -> `...4`
+• `` -> `...5`
+• `` -> `...6`
+• `` -> `...7`
+• `` -> `...8`
+• `` -> `...9`
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
...1...2...3...4...5...6...7...8...9
Age523-2.4-3.0, -1.9<0.001423-0.90-1.7, -0.120.025
Female sex523-38-53, -22<0.001423-3.5-19, 120.7
Body mass index3581.80.14, 3.50.034----
Current smoker507-18-34, -1.90.029423-5.8-21, 9.50.5
Living with somebody5174630, 61<0.00142310-5.6, 260.2
Alchohol consumption5105.6-21, 320.74234.8-19, 280.7
Hypertension519-32-47, -17<0.001423-3.0-17, 110.7
Diabetes517-19-43, 5.00.12423-16-38, 6.40.2
Atrial fibrilation5181.3-19, 22>0.9423212.0, 400.030
Periferal arterial disease511-38-76, 0.700.054423-9.0-45, 270.6
Previous TIA517-2.8-51, 46>0.94239.7-32, 510.6
Previous MI5172.8-25, 310.84233.8-23, 310.8
Treated with IVT50914-2.3, 290.0944235.9-9.2, 210.4
Treated with EVT510-9.5-39, 200.5423-3.6-35, 280.8
Study group allocation523423
Aktiv (Citalopram)0.00Reference0.00Reference
Placebo7.2-8.0, 220.42.7-11, 160.7
Pre-stroke PASE score5230.510.44, 0.59<0.0014230.330.23, 0.43<0.001
Acute NIHSS513-3.7-5.4, -2.0<0.001423-2.3-4.2, -0.350.021
Employed5237056, 84<0.00142313-5.4, 310.2
Family income group523423
low0.00Reference0.00Reference
medium3922, 57<0.00115-2.7, 320.10
high7962, 97<0.001309.8, 500.004
Educational level group474423
low0.00Reference0.00Reference
medium2811, 460.0011.6-14, 170.8
high4624, 67<0.0016.9-14, 270.5
Pre-stroke WHO-5 score5210.870.49, 1.3<0.0014230.620.27, 0.97<0.001
Pre-stroke mRS > 0523-58-77, -38<0.001423-16-34, 2.50.091
+ +
+
+
+ +
+ + +
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!important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:rgba(0,0,0,0) !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme=dark]{--bs-navbar-color: #fdfefe;--bs-navbar-hover-color: rgba(253, 254, 255, 0.8);--bs-navbar-disabled-color: rgba(253, 254, 254, 0.75);--bs-navbar-active-color: #fdfeff;--bs-navbar-brand-color: #fdfefe;--bs-navbar-brand-hover-color: #fdfeff;--bs-navbar-toggler-border-color: rgba(253, 254, 254, 0);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfefe' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme=dark] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='%23fdfefe' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: 0.5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: 1px;--bs-card-border-color: rgba(0, 0, 0, 0.175);--bs-card-border-radius: 0.375rem;--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(0.375rem - 1px);--bs-card-cap-padding-y: 0.5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(33, 37, 41, 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: #ffffff;--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: 0.75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-0.5*var(--bs-card-title-spacer-y));margin-bottom:0;color:var(--bs-card-subtitle-color)}.card-text:last-child{margin-bottom:0}.card-link+.card-link{margin-left:var(--bs-card-spacer-x)}.card-header{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);margin-bottom:0;color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-bottom:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-header:first-child{border-radius:var(--bs-card-inner-border-radius) var(--bs-card-inner-border-radius) 0 0}.card-footer{padding:var(--bs-card-cap-padding-y) var(--bs-card-cap-padding-x);color:var(--bs-card-cap-color);background-color:var(--bs-card-cap-bg);border-top:var(--bs-card-border-width) solid var(--bs-card-border-color)}.card-footer:last-child{border-radius:0 0 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.nav-link.active{background-color:var(--bs-card-bg);border-bottom-color:var(--bs-card-bg)}.card-header-pills{margin-right:calc(-0.5*var(--bs-card-cap-padding-x));margin-left:calc(-0.5*var(--bs-card-cap-padding-x))}.card-img-overlay{position:absolute;top:0;right:0;bottom:0;left:0;padding:var(--bs-card-img-overlay-padding);border-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-top,.card-img-bottom{width:100%}.card-img,.card-img-top{border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card-img,.card-img-bottom{border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card-group>.card{margin-bottom:var(--bs-card-group-margin)}@media(min-width: 576px){.card-group{display:flex;display:-webkit-flex;flex-flow:row wrap;-webkit-flex-flow:row wrap}.card-group>.card{flex:1 0 0%;-webkit-flex:1 0 0%;margin-bottom:0}.card-group>.card+.card{margin-left:0;border-left:0}.card-group>.card:not(:last-child){border-top-right-radius:0;border-bottom-right-radius:0}.card-group>.card:not(:last-child) .card-img-top,.card-group>.card:not(:last-child) .card-header{border-top-right-radius:0}.card-group>.card:not(:last-child) .card-img-bottom,.card-group>.card:not(:last-child) .card-footer{border-bottom-right-radius:0}.card-group>.card:not(:first-child){border-top-left-radius:0;border-bottom-left-radius:0}.card-group>.card:not(:first-child) .card-img-top,.card-group>.card:not(:first-child) .card-header{border-top-left-radius:0}.card-group>.card:not(:first-child) .card-img-bottom,.card-group>.card:not(:first-child) .card-footer{border-bottom-left-radius:0}}.accordion{--bs-accordion-color: #212529;--bs-accordion-bg: #ffffff;--bs-accordion-transition: color 0.15s ease-in-out, background-color 0.15s ease-in-out, border-color 0.15s ease-in-out, box-shadow 0.15s ease-in-out, border-radius 0.15s 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.708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #86b7fe;--bs-accordion-btn-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: #052c65;--bs-accordion-active-bg: #cfe2ff}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media(prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1*var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media(prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme=dark] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%236ea8fe'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: rgba(33, 37, 41, 0.75);--bs-breadcrumb-item-padding-x: 0.5rem;--bs-breadcrumb-item-active-color: rgba(33, 37, 41, 0.75);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, ">") /* rtl: var(--bs-breadcrumb-divider, ">") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: 0.75rem;--bs-pagination-padding-y: 0.375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: #0d6efd;--bs-pagination-bg: #ffffff;--bs-pagination-border-width: 1px;--bs-pagination-border-color: #dee2e6;--bs-pagination-border-radius: 0.375rem;--bs-pagination-hover-color: #0a58ca;--bs-pagination-hover-bg: #f8f9fa;--bs-pagination-hover-border-color: #dee2e6;--bs-pagination-focus-color: #0a58ca;--bs-pagination-focus-bg: #e9ecef;--bs-pagination-focus-box-shadow: 0 0 0 0.25rem rgba(13, 110, 253, 0.25);--bs-pagination-active-color: #ffffff;--bs-pagination-active-bg: #0d6efd;--bs-pagination-active-border-color: #0d6efd;--bs-pagination-disabled-color: rgba(33, 37, 41, 0.75);--bs-pagination-disabled-bg: #e9ecef;--bs-pagination-disabled-border-color: #dee2e6;display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color .15s ease-in-out,background-color .15s ease-in-out,border-color .15s ease-in-out,box-shadow .15s ease-in-out}@media(prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(1px*-1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: 0.75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: 0.5rem}.pagination-sm{--bs-pagination-padding-x: 0.5rem;--bs-pagination-padding-y: 0.25rem;--bs-pagination-font-size:0.875rem;--bs-pagination-border-radius: 0.25rem}.badge{--bs-badge-padding-x: 0.65em;--bs-badge-padding-y: 0.35em;--bs-badge-font-size:0.75em;--bs-badge-font-weight: 700;--bs-badge-color: #ffffff;--bs-badge-border-radius: 0.375rem;display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: 1px solid var(--bs-alert-border-color);--bs-alert-border-radius: 0.375rem;--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:0.75rem;--bs-progress-bg: #e9ecef;--bs-progress-border-radius: 0.375rem;--bs-progress-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.075);--bs-progress-bar-color: #ffffff;--bs-progress-bar-bg: #0d6efd;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media(prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media(prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: #212529;--bs-list-group-bg: #ffffff;--bs-list-group-border-color: #dee2e6;--bs-list-group-border-width: 1px;--bs-list-group-border-radius: 0.375rem;--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: 0.5rem;--bs-list-group-action-color: rgba(33, 37, 41, 0.75);--bs-list-group-action-hover-color: #000;--bs-list-group-action-hover-bg: #f8f9fa;--bs-list-group-action-active-color: #212529;--bs-list-group-action-active-bg: #e9ecef;--bs-list-group-disabled-color: rgba(33, 37, 41, 0.75);--bs-list-group-disabled-bg: #ffffff;--bs-list-group-active-color: #ffffff;--bs-list-group-active-bg: #0d6efd;--bs-list-group-active-border-color: 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". 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var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: 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var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' 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.value-box-grid{grid-template-columns:var(---bslib-value-box-showcase-w-fs, 1fr) auto}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-showcase{grid-area:left}.bslib-value-box.showcase-left-center:not([data-fill-screen=true]) .value-box-grid .value-box-area{grid-area:right}.bslib-value-box.showcase-bottom .value-box-grid{grid-template-columns:1fr;grid-template-rows:1fr var(---bslib-value-box-showcase-h, auto);grid-template-areas:"top" "bottom";overflow:hidden}.bslib-value-box.showcase-bottom .value-box-grid .value-box-showcase{grid-area:bottom;padding:0;margin:0}.bslib-value-box.showcase-bottom .value-box-grid .value-box-area{grid-area:top}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid{grid-template-rows:1fr var(---bslib-value-box-showcase-h-fs, 2fr)}.bslib-value-box.showcase-bottom[data-full-screen=true] .value-box-grid .value-box-showcase{padding:1rem}[data-bs-theme=dark] .bslib-value-box{--bslib-value-box-shadow: 0 0.5rem 1rem rgb(0 0 0 / 50%)}@media(min-width: 576px){.nav:not(.nav-hidden){display:flex !important;display:-webkit-flex !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column){float:none !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.bslib-nav-spacer{margin-left:auto !important}.nav:not(.nav-hidden):not(.nav-stacked):not(.flex-column)>.form-inline{margin-top:auto;margin-bottom:auto}.nav:not(.nav-hidden).nav-stacked{flex-direction:column;-webkit-flex-direction:column;height:100%}.nav:not(.nav-hidden).nav-stacked>.bslib-nav-spacer{margin-top:auto !important}}.bslib-card{overflow:auto}.bslib-card .card-body+.card-body{padding-top:0}.bslib-card .card-body{overflow:auto}.bslib-card .card-body p{margin-top:0}.bslib-card .card-body p:last-child{margin-bottom:0}.bslib-card .card-body{max-height:var(--bslib-card-body-max-height, none)}.bslib-card[data-full-screen=true]>.card-body{max-height:var(--bslib-card-body-max-height-full-screen, none)}.bslib-card 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1)}.bslib-full-screen-exit svg{margin-left:.5rem;font-size:1.5rem}#bslib-full-screen-overlay{position:fixed;inset:0;background-color:rgba(var(--bs-body-color-rgb), 0.6);backdrop-filter:blur(2px);-webkit-backdrop-filter:blur(2px);z-index:1069;animation:bslib-full-screen-overlay-enter 400ms cubic-bezier(0.6, 0.02, 0.65, 1) forwards}@keyframes bslib-full-screen-overlay-enter{0%{opacity:0}100%{opacity:1}}.bslib-grid{display:grid !important;gap:var(--bslib-spacer, 1rem);height:var(--bslib-grid-height)}.bslib-grid.grid{grid-template-columns:repeat(var(--bs-columns, 12), minmax(0, 1fr));grid-template-rows:unset;grid-auto-rows:var(--bslib-grid--row-heights);--bslib-grid--row-heights--xs: unset;--bslib-grid--row-heights--sm: unset;--bslib-grid--row-heights--md: unset;--bslib-grid--row-heights--lg: unset;--bslib-grid--row-heights--xl: unset;--bslib-grid--row-heights--xxl: unset}.bslib-grid.grid.bslib-grid--row-heights--xs{--bslib-grid--row-heights: 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this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const 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Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C=0,S=L?"width":"height",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var I=Ve($),N=[];if(o&&N.push(D[x]<=0),a&&N.push(D[$]<=0,D[I]<=0),N.every((function(t){return t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var 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m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu 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e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
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")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get 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e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const 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t=e.state,n=e.name,r=t.rects.reference,o=t.rects.popper,i=t.modifiersData.preventOverflow,a=J(t,{elementContext:"reference"}),s=J(t,{altBoundary:!0}),f=ve(a,r),c=ve(s,o,i),p=ye(f),u=ye(c);t.modifiersData[n]={referenceClippingOffsets:f,popperEscapeOffsets:c,isReferenceHidden:p,hasPopperEscaped:u},t.attributes.popper=Object.assign({},t.attributes.popper,{"data-popper-reference-hidden":p,"data-popper-escaped":u})}},be=Z({defaultModifiers:[ee,te,oe,ie]}),xe=[ee,te,oe,ie,ae,le,he,me,ge],we=Z({defaultModifiers:xe});e.applyStyles=ie,e.arrow=me,e.computeStyles=oe,e.createPopper=we,e.createPopperLite=be,e.defaultModifiers=xe,e.detectOverflow=J,e.eventListeners=ee,e.flip=le,e.hide=ge,e.offset=ae,e.popperGenerator=Z,e.popperOffsets=te,e.preventOverflow=he,Object.defineProperty(e,"__esModule",{value:!0})})); + diff --git a/1 PA Decline/article ready_files/libs/quarto-html/quarto-syntax-highlighting.css b/1 PA Decline/article ready_files/libs/quarto-html/quarto-syntax-highlighting.css new file mode 100644 index 0000000..d9fd98f --- /dev/null +++ b/1 PA Decline/article ready_files/libs/quarto-html/quarto-syntax-highlighting.css @@ -0,0 +1,203 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-ot-color: #003B4F; + --quarto-hl-at-color: #657422; + --quarto-hl-ss-color: #20794D; + --quarto-hl-an-color: #5E5E5E; + --quarto-hl-fu-color: #4758AB; + --quarto-hl-st-color: #20794D; + --quarto-hl-cf-color: #003B4F; + --quarto-hl-op-color: #5E5E5E; + --quarto-hl-er-color: #AD0000; + --quarto-hl-bn-color: #AD0000; + --quarto-hl-al-color: #AD0000; + --quarto-hl-va-color: #111111; + --quarto-hl-bu-color: inherit; + --quarto-hl-ex-color: inherit; + --quarto-hl-pp-color: #AD0000; + --quarto-hl-in-color: #5E5E5E; + --quarto-hl-vs-color: #20794D; + --quarto-hl-wa-color: #5E5E5E; + --quarto-hl-do-color: #5E5E5E; + --quarto-hl-im-color: #00769E; + --quarto-hl-ch-color: #20794D; + --quarto-hl-dt-color: #AD0000; + --quarto-hl-fl-color: #AD0000; + --quarto-hl-co-color: #5E5E5E; + --quarto-hl-cv-color: #5E5E5E; + --quarto-hl-cn-color: #8f5902; + --quarto-hl-sc-color: #5E5E5E; + --quarto-hl-dv-color: #AD0000; + --quarto-hl-kw-color: #003B4F; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +pre > code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; + font-style: inherit; +} + +code span.at { + color: #657422; + font-style: inherit; +} + +code span.ss { + color: #20794D; + font-style: inherit; +} + +code span.an { + color: #5E5E5E; + font-style: inherit; +} + +code span.fu { + color: #4758AB; + font-style: inherit; +} + +code span.st { + color: #20794D; + font-style: inherit; +} + +code span.cf { + color: #003B4F; + font-style: inherit; +} + +code span.op { + color: #5E5E5E; + font-style: inherit; +} + +code span.er { + color: #AD0000; + font-style: inherit; +} + +code span.bn { + color: #AD0000; + font-style: inherit; +} + +code span.al { + color: #AD0000; + font-style: inherit; +} + +code span.va { + color: #111111; + font-style: inherit; +} + +code span.bu { + font-style: inherit; +} + +code span.ex { + font-style: inherit; +} + +code span.pp { + color: #AD0000; + font-style: inherit; +} + +code span.in { + color: #5E5E5E; + font-style: inherit; +} + +code span.vs { + color: #20794D; + font-style: inherit; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; + font-style: inherit; +} + +code span.ch { + color: #20794D; + font-style: inherit; +} + +code span.dt { + color: #AD0000; + font-style: inherit; +} + +code span.fl { + color: #AD0000; + font-style: inherit; +} + +code span.co { + color: #5E5E5E; + font-style: inherit; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; + font-style: inherit; +} + +code span.sc { + color: #5E5E5E; + font-style: inherit; +} + +code span.dv { + color: #AD0000; + font-style: inherit; +} + +code span.kw { + color: #003B4F; + font-style: inherit; +} + +.prevent-inlining { + content: " { + // Find any conflicting margin elements and add margins to the + // top to prevent overlap + const marginChildren = window.document.querySelectorAll( + ".column-margin.column-container > *, .margin-caption, .aside" + ); + + let lastBottom = 0; + for (const marginChild of marginChildren) { + if (marginChild.offsetParent !== null) { + // clear the top margin so we recompute it + marginChild.style.marginTop = null; + const top = marginChild.getBoundingClientRect().top + window.scrollY; + if (top < lastBottom) { + const marginChildStyle = window.getComputedStyle(marginChild); + const marginBottom = parseFloat(marginChildStyle["marginBottom"]); + const margin = lastBottom - top + marginBottom; + marginChild.style.marginTop = `${margin}px`; + } + const styles = window.getComputedStyle(marginChild); + const marginTop = parseFloat(styles["marginTop"]); + lastBottom = top + marginChild.getBoundingClientRect().height + marginTop; + } + } +}; + +window.document.addEventListener("DOMContentLoaded", function (_event) { + // Recompute the position of margin elements anytime the body size changes + if (window.ResizeObserver) { + const resizeObserver = new window.ResizeObserver( + throttle(() => { + layoutMarginEls(); + if ( + window.document.body.getBoundingClientRect().width < 990 && + isReaderMode() + ) { + quartoToggleReader(); + } + }, 50) + ); + resizeObserver.observe(window.document.body); + } + + const tocEl = window.document.querySelector('nav.toc-active[role="doc-toc"]'); + const sidebarEl = window.document.getElementById("quarto-sidebar"); + const leftTocEl = window.document.getElementById("quarto-sidebar-toc-left"); + const marginSidebarEl = window.document.getElementById( + "quarto-margin-sidebar" + ); + // function to determine whether the element has a previous sibling that is active + const prevSiblingIsActiveLink = (el) => { + const sibling = el.previousElementSibling; + if (sibling && sibling.tagName === "A") { + return sibling.classList.contains("active"); + } else { + return false; + } + }; + + // fire slideEnter for bootstrap tab activations (for htmlwidget resize behavior) + function fireSlideEnter(e) { + const event = window.document.createEvent("Event"); + event.initEvent("slideenter", true, true); + window.document.dispatchEvent(event); + } + const tabs = window.document.querySelectorAll('a[data-bs-toggle="tab"]'); + tabs.forEach((tab) => { + tab.addEventListener("shown.bs.tab", fireSlideEnter); + }); + + // fire slideEnter for tabby tab activations (for htmlwidget resize behavior) + document.addEventListener("tabby", fireSlideEnter, false); + + // Track scrolling and mark TOC links as active + // get table of contents and sidebar (bail if we don't have at least one) + const tocLinks = tocEl + ? [...tocEl.querySelectorAll("a[data-scroll-target]")] + : []; + const makeActive = (link) => tocLinks[link].classList.add("active"); + const removeActive = (link) => tocLinks[link].classList.remove("active"); + const removeAllActive = () => + [...Array(tocLinks.length).keys()].forEach((link) => removeActive(link)); + + // activate the anchor for a section associated with this TOC entry + tocLinks.forEach((link) => { + link.addEventListener("click", () => { + if (link.href.indexOf("#") !== -1) { + const anchor = link.href.split("#")[1]; + const heading = window.document.querySelector( + `[data-anchor-id=${anchor}]` + ); + if (heading) { + // Add the class + heading.classList.add("reveal-anchorjs-link"); + + // function to show the anchor + const handleMouseout = () => { + heading.classList.remove("reveal-anchorjs-link"); + heading.removeEventListener("mouseout", handleMouseout); + }; + + // add a function to clear the anchor when the user mouses out of it + heading.addEventListener("mouseout", handleMouseout); + } + } + }); + }); + + const sections = tocLinks.map((link) => { + const target = link.getAttribute("data-scroll-target"); + if (target.startsWith("#")) { + return window.document.getElementById(decodeURI(`${target.slice(1)}`)); + } else { + return window.document.querySelector(decodeURI(`${target}`)); + } + }); + + const sectionMargin = 200; + let currentActive = 0; + // track whether we've initialized state the first time + let init = false; + + const updateActiveLink = () => { + // The index from bottom to top (e.g. reversed list) + let sectionIndex = -1; + if ( + window.innerHeight + window.pageYOffset >= + window.document.body.offsetHeight + ) { + sectionIndex = 0; + } else { + sectionIndex = [...sections].reverse().findIndex((section) => { + if (section) { + return window.pageYOffset >= section.offsetTop - sectionMargin; + } else { + return false; + } + }); + } + if (sectionIndex > -1) { + const current = sections.length - sectionIndex - 1; + if (current !== currentActive) { + removeAllActive(); + currentActive = current; + makeActive(current); + if (init) { + window.dispatchEvent(sectionChanged); + } + init = true; + } + } + }; + + const inHiddenRegion = (top, bottom, hiddenRegions) => { + for (const region of hiddenRegions) { + if (top <= region.bottom && bottom >= region.top) { + return true; + } + } + return false; + }; + + const categorySelector = "header.quarto-title-block .quarto-category"; + const activateCategories = (href) => { + // Find any categories + // Surround them with a link pointing back to: + // #category=Authoring + try { + const categoryEls = window.document.querySelectorAll(categorySelector); + for (const categoryEl of categoryEls) { + const categoryText = categoryEl.textContent; + if (categoryText) { + const link = `${href}#category=${encodeURIComponent(categoryText)}`; + const linkEl = window.document.createElement("a"); + linkEl.setAttribute("href", link); + for (const child of categoryEl.childNodes) { + linkEl.append(child); + } + categoryEl.appendChild(linkEl); + } + } + } catch { + // Ignore errors + } + }; + function hasTitleCategories() { + return window.document.querySelector(categorySelector) !== null; + } + + function offsetRelativeUrl(url) { + const offset = getMeta("quarto:offset"); + return offset ? offset + url : url; + } + + function offsetAbsoluteUrl(url) { + const offset = getMeta("quarto:offset"); + const baseUrl = new URL(offset, window.location); + + const projRelativeUrl = url.replace(baseUrl, ""); + if (projRelativeUrl.startsWith("/")) { + return projRelativeUrl; + } else { + return "/" + projRelativeUrl; + } + } + + // read a meta tag value + function getMeta(metaName) { + const metas = window.document.getElementsByTagName("meta"); + for (let i = 0; i < metas.length; i++) { + if (metas[i].getAttribute("name") === metaName) { + return metas[i].getAttribute("content"); + } + } + return ""; + } + + async function findAndActivateCategories() { + const currentPagePath = offsetAbsoluteUrl(window.location.href); + const response = await fetch(offsetRelativeUrl("listings.json")); + if (response.status == 200) { + return response.json().then(function (listingPaths) { + const listingHrefs = []; + for (const listingPath of listingPaths) { + const pathWithoutLeadingSlash = listingPath.listing.substring(1); + for (const item of listingPath.items) { + if ( + item === currentPagePath || + item === currentPagePath + "index.html" + ) { + // Resolve this path against the offset to be sure + // we already are using the correct path to the listing + // (this adjusts the listing urls to be rooted against + // whatever root the page is actually running against) + const relative = offsetRelativeUrl(pathWithoutLeadingSlash); + const baseUrl = window.location; + const resolvedPath = new URL(relative, baseUrl); + listingHrefs.push(resolvedPath.pathname); + break; + } + } + } + + // Look up the tree for a nearby linting and use that if we find one + const nearestListing = findNearestParentListing( + offsetAbsoluteUrl(window.location.pathname), + listingHrefs + ); + if (nearestListing) { + activateCategories(nearestListing); + } else { + // See if the referrer is a listing page for this item + const referredRelativePath = offsetAbsoluteUrl(document.referrer); + const referrerListing = listingHrefs.find((listingHref) => { + const isListingReferrer = + listingHref === referredRelativePath || + listingHref === referredRelativePath + "index.html"; + return isListingReferrer; + }); + + if (referrerListing) { + // Try to use the referrer if possible + activateCategories(referrerListing); + } else if (listingHrefs.length > 0) { + // Otherwise, just fall back to the first listing + activateCategories(listingHrefs[0]); + } + } + }); + } + } + if (hasTitleCategories()) { + findAndActivateCategories(); + } + + const findNearestParentListing = (href, listingHrefs) => { + if (!href || !listingHrefs) { + return undefined; + } + // Look up the tree for a nearby linting and use that if we find one + const relativeParts = href.substring(1).split("/"); + while (relativeParts.length > 0) { + const path = relativeParts.join("/"); + for (const listingHref of listingHrefs) { + if (listingHref.startsWith(path)) { + return listingHref; + } + } + relativeParts.pop(); + } + + return undefined; + }; + + const manageSidebarVisiblity = (el, placeholderDescriptor) => { + let isVisible = true; + let elRect; + + return (hiddenRegions) => { + if (el === null) { + return; + } + + // Find the last element of the TOC + const lastChildEl = el.lastElementChild; + + if (lastChildEl) { + // Converts the sidebar to a menu + const convertToMenu = () => { + for (const child of el.children) { + child.style.opacity = 0; + child.style.overflow = "hidden"; + } + + nexttick(() => { + const toggleContainer = window.document.createElement("div"); + toggleContainer.style.width = "100%"; + toggleContainer.classList.add("zindex-over-content"); + toggleContainer.classList.add("quarto-sidebar-toggle"); + toggleContainer.classList.add("headroom-target"); // Marks this to be managed by headeroom + toggleContainer.id = placeholderDescriptor.id; + toggleContainer.style.position = "fixed"; + + const toggleIcon = window.document.createElement("i"); + toggleIcon.classList.add("quarto-sidebar-toggle-icon"); + toggleIcon.classList.add("bi"); + toggleIcon.classList.add("bi-caret-down-fill"); + + const toggleTitle = window.document.createElement("div"); + const titleEl = window.document.body.querySelector( + placeholderDescriptor.titleSelector + ); + if (titleEl) { + toggleTitle.append( + titleEl.textContent || titleEl.innerText, + toggleIcon + ); + } + toggleTitle.classList.add("zindex-over-content"); + toggleTitle.classList.add("quarto-sidebar-toggle-title"); + toggleContainer.append(toggleTitle); + + const toggleContents = window.document.createElement("div"); + toggleContents.classList = el.classList; + toggleContents.classList.add("zindex-over-content"); + toggleContents.classList.add("quarto-sidebar-toggle-contents"); + for (const child of el.children) { + if (child.id === "toc-title") { + continue; + } + + const clone = child.cloneNode(true); + clone.style.opacity = 1; + clone.style.display = null; + toggleContents.append(clone); + } + toggleContents.style.height = "0px"; + const positionToggle = () => { + // position the element (top left of parent, same width as parent) + if (!elRect) { + elRect = el.getBoundingClientRect(); + } + toggleContainer.style.left = `${elRect.left}px`; + toggleContainer.style.top = `${elRect.top}px`; + toggleContainer.style.width = `${elRect.width}px`; + }; + positionToggle(); + + toggleContainer.append(toggleContents); + el.parentElement.prepend(toggleContainer); + + // Process clicks + let tocShowing = false; + // Allow the caller to control whether this is dismissed + // when it is clicked (e.g. sidebar navigation supports + // opening and closing the nav tree, so don't dismiss on click) + const clickEl = placeholderDescriptor.dismissOnClick + ? toggleContainer + : toggleTitle; + + const closeToggle = () => { + if (tocShowing) { + toggleContainer.classList.remove("expanded"); + toggleContents.style.height = "0px"; + tocShowing = false; + } + }; + + // Get rid of any expanded toggle if the user scrolls + window.document.addEventListener( + "scroll", + throttle(() => { + closeToggle(); + }, 50) + ); + + // Handle positioning of the toggle + window.addEventListener( + "resize", + throttle(() => { + elRect = undefined; + positionToggle(); + }, 50) + ); + + window.addEventListener("quarto-hrChanged", () => { + elRect = undefined; + }); + + // Process the click + clickEl.onclick = () => { + if (!tocShowing) { + toggleContainer.classList.add("expanded"); + toggleContents.style.height = null; + tocShowing = true; + } else { + closeToggle(); + } + }; + }); + }; + + // Converts a sidebar from a menu back to a sidebar + const convertToSidebar = () => { + for (const child of el.children) { + child.style.opacity = 1; + child.style.overflow = null; + } + + const placeholderEl = window.document.getElementById( + placeholderDescriptor.id + ); + if (placeholderEl) { + placeholderEl.remove(); + } + + el.classList.remove("rollup"); + }; + + if (isReaderMode()) { + convertToMenu(); + isVisible = false; + } else { + // Find the top and bottom o the element that is being managed + const elTop = el.offsetTop; + const elBottom = + elTop + lastChildEl.offsetTop + lastChildEl.offsetHeight; + + if (!isVisible) { + // If the element is current not visible reveal if there are + // no conflicts with overlay regions + if (!inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToSidebar(); + isVisible = true; + } + } else { + // If the element is visible, hide it if it conflicts with overlay regions + // and insert a placeholder toggle (or if we're in reader mode) + if (inHiddenRegion(elTop, elBottom, hiddenRegions)) { + convertToMenu(); + isVisible = false; + } + } + } + } + }; + }; + + const tabEls = document.querySelectorAll('a[data-bs-toggle="tab"]'); + for (const tabEl of tabEls) { + const id = tabEl.getAttribute("data-bs-target"); + if (id) { + const columnEl = document.querySelector( + `${id} .column-margin, .tabset-margin-content` + ); + if (columnEl) + tabEl.addEventListener("shown.bs.tab", function (event) { + const el = event.srcElement; + if (el) { + const visibleCls = `${el.id}-margin-content`; + // walk up until we find a parent tabset + let panelTabsetEl = el.parentElement; + while (panelTabsetEl) { + if (panelTabsetEl.classList.contains("panel-tabset")) { + break; + } + panelTabsetEl = panelTabsetEl.parentElement; + } + + if (panelTabsetEl) { + const prevSib = panelTabsetEl.previousElementSibling; + if ( + prevSib && + prevSib.classList.contains("tabset-margin-container") + ) { + const childNodes = prevSib.querySelectorAll( + ".tabset-margin-content" + ); + for (const childEl of childNodes) { + if (childEl.classList.contains(visibleCls)) { + childEl.classList.remove("collapse"); + } else { + childEl.classList.add("collapse"); + } + } + } + } + } + + layoutMarginEls(); + }); + } + } + + // Manage the visibility of the toc and the sidebar + const marginScrollVisibility = manageSidebarVisiblity(marginSidebarEl, { + id: "quarto-toc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + const sidebarScrollVisiblity = manageSidebarVisiblity(sidebarEl, { + id: "quarto-sidebarnav-toggle", + titleSelector: ".title", + dismissOnClick: false, + }); + let tocLeftScrollVisibility; + if (leftTocEl) { + tocLeftScrollVisibility = manageSidebarVisiblity(leftTocEl, { + id: "quarto-lefttoc-toggle", + titleSelector: "#toc-title", + dismissOnClick: true, + }); + } + + // Find the first element that uses formatting in special columns + const conflictingEls = window.document.body.querySelectorAll( + '[class^="column-"], [class*=" column-"], aside, [class*="margin-caption"], [class*=" margin-caption"], [class*="margin-ref"], [class*=" margin-ref"]' + ); + + // Filter all the possibly conflicting elements into ones + // the do conflict on the left or ride side + const arrConflictingEls = Array.from(conflictingEls); + const leftSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return false; + } + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + className.startsWith("column-") && + !className.endsWith("right") && + !className.endsWith("container") && + className !== "column-margin" + ); + }); + }); + const rightSideConflictEls = arrConflictingEls.filter((el) => { + if (el.tagName === "ASIDE") { + return true; + } + + const hasMarginCaption = Array.from(el.classList).find((className) => { + return className == "margin-caption"; + }); + if (hasMarginCaption) { + return true; + } + + return Array.from(el.classList).find((className) => { + return ( + className !== "column-body" && + !className.endsWith("container") && + className.startsWith("column-") && + !className.endsWith("left") + ); + }); + }); + + const kOverlapPaddingSize = 10; + function toRegions(els) { + return els.map((el) => { + const boundRect = el.getBoundingClientRect(); + const top = + boundRect.top + + document.documentElement.scrollTop - + kOverlapPaddingSize; + return { + top, + bottom: top + el.scrollHeight + 2 * kOverlapPaddingSize, + }; + }); + } + + let hasObserved = false; + const visibleItemObserver = (els) => { + let visibleElements = [...els]; + const intersectionObserver = new IntersectionObserver( + (entries, _observer) => { + entries.forEach((entry) => { + if (entry.isIntersecting) { + if (visibleElements.indexOf(entry.target) === -1) { + visibleElements.push(entry.target); + } + } else { + visibleElements = visibleElements.filter((visibleEntry) => { + return visibleEntry !== entry; + }); + } + }); + + if (!hasObserved) { + hideOverlappedSidebars(); + } + hasObserved = true; + }, + {} + ); + els.forEach((el) => { + intersectionObserver.observe(el); + }); + + return { + getVisibleEntries: () => { + return visibleElements; + }, + }; + }; + + const rightElementObserver = visibleItemObserver(rightSideConflictEls); + const leftElementObserver = visibleItemObserver(leftSideConflictEls); + + const hideOverlappedSidebars = () => { + marginScrollVisibility(toRegions(rightElementObserver.getVisibleEntries())); + sidebarScrollVisiblity(toRegions(leftElementObserver.getVisibleEntries())); + if (tocLeftScrollVisibility) { + tocLeftScrollVisibility( + toRegions(leftElementObserver.getVisibleEntries()) + ); + } + }; + + window.quartoToggleReader = () => { + // Applies a slow class (or removes it) + // to update the transition speed + const slowTransition = (slow) => { + const manageTransition = (id, slow) => { + const el = document.getElementById(id); + if (el) { + if (slow) { + el.classList.add("slow"); + } else { + el.classList.remove("slow"); + } + } + }; + + manageTransition("TOC", slow); + manageTransition("quarto-sidebar", slow); + }; + const readerMode = !isReaderMode(); + setReaderModeValue(readerMode); + + // If we're entering reader mode, slow the transition + if (readerMode) { + slowTransition(readerMode); + } + highlightReaderToggle(readerMode); + hideOverlappedSidebars(); + + // If we're exiting reader mode, restore the non-slow transition + if (!readerMode) { + slowTransition(!readerMode); + } + }; + + const highlightReaderToggle = (readerMode) => { + const els = document.querySelectorAll(".quarto-reader-toggle"); + if (els) { + els.forEach((el) => { + if (readerMode) { + el.classList.add("reader"); + } else { + el.classList.remove("reader"); + } + }); + } + }; + + const setReaderModeValue = (val) => { + if (window.location.protocol !== "file:") { + window.localStorage.setItem("quarto-reader-mode", val); + } else { + localReaderMode = val; + } + }; + + const isReaderMode = () => { + if (window.location.protocol !== "file:") { + return window.localStorage.getItem("quarto-reader-mode") === "true"; + } else { + return localReaderMode; + } + }; + let localReaderMode = null; + + const tocOpenDepthStr = tocEl?.getAttribute("data-toc-expanded"); + const tocOpenDepth = tocOpenDepthStr ? Number(tocOpenDepthStr) : 1; + + // Walk the TOC and collapse/expand nodes + // Nodes are expanded if: + // - they are top level + // - they have children that are 'active' links + // - they are directly below an link that is 'active' + const walk = (el, depth) => { + // Tick depth when we enter a UL + if (el.tagName === "UL") { + depth = depth + 1; + } + + // It this is active link + let isActiveNode = false; + if (el.tagName === "A" && el.classList.contains("active")) { + isActiveNode = true; + } + + // See if there is an active child to this element + let hasActiveChild = false; + for (child of el.children) { + hasActiveChild = walk(child, depth) || hasActiveChild; + } + + // Process the collapse state if this is an UL + if (el.tagName === "UL") { + if (tocOpenDepth === -1 && depth > 1) { + el.classList.add("collapse"); + } else if ( + depth <= tocOpenDepth || + hasActiveChild || + prevSiblingIsActiveLink(el) + ) { + el.classList.remove("collapse"); + } else { + el.classList.add("collapse"); + } + + // untick depth when we leave a UL + depth = depth - 1; + } + return hasActiveChild || isActiveNode; + }; + + // walk the TOC and expand / collapse any items that should be shown + + if (tocEl) { + walk(tocEl, 0); + updateActiveLink(); + } + + // Throttle the scroll event and walk peridiocally + window.document.addEventListener( + "scroll", + throttle(() => { + if (tocEl) { + updateActiveLink(); + walk(tocEl, 0); + } + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 5) + ); + window.addEventListener( + "resize", + throttle(() => { + if (!isReaderMode()) { + hideOverlappedSidebars(); + } + }, 10) + ); + hideOverlappedSidebars(); + highlightReaderToggle(isReaderMode()); +}); + +// grouped tabsets +window.addEventListener("pageshow", (_event) => { + function getTabSettings() { + const data = localStorage.getItem("quarto-persistent-tabsets-data"); + if (!data) { + localStorage.setItem("quarto-persistent-tabsets-data", "{}"); + return {}; + } + if (data) { + return JSON.parse(data); + } + } + + function setTabSettings(data) { + localStorage.setItem( + "quarto-persistent-tabsets-data", + JSON.stringify(data) + ); + } + + function setTabState(groupName, groupValue) { + const data = getTabSettings(); + data[groupName] = groupValue; + setTabSettings(data); + } + + function toggleTab(tab, active) { + const tabPanelId = tab.getAttribute("aria-controls"); + const tabPanel = document.getElementById(tabPanelId); + if (active) { + tab.classList.add("active"); + tabPanel.classList.add("active"); + } else { + tab.classList.remove("active"); + tabPanel.classList.remove("active"); + } + } + + function toggleAll(selectedGroup, selectorsToSync) { + for (const [thisGroup, tabs] of Object.entries(selectorsToSync)) { + const active = selectedGroup === thisGroup; + for (const tab of tabs) { + toggleTab(tab, active); + } + } + } + + function findSelectorsToSyncByLanguage() { + const result = {}; + const tabs = Array.from( + document.querySelectorAll(`div[data-group] a[id^='tabset-']`) + ); + for (const item of tabs) { + const div = item.parentElement.parentElement.parentElement; + const group = div.getAttribute("data-group"); + if (!result[group]) { + result[group] = {}; + } + const selectorsToSync = result[group]; + const value = item.innerHTML; + if (!selectorsToSync[value]) { + selectorsToSync[value] = []; + } + selectorsToSync[value].push(item); + } + return result; + } + + function setupSelectorSync() { + const selectorsToSync = findSelectorsToSyncByLanguage(); + Object.entries(selectorsToSync).forEach(([group, tabSetsByValue]) => { + Object.entries(tabSetsByValue).forEach(([value, items]) => { + items.forEach((item) => { + item.addEventListener("click", (_event) => { + setTabState(group, value); + toggleAll(value, selectorsToSync[group]); + }); + }); + }); + }); + return selectorsToSync; + } + + const selectorsToSync = setupSelectorSync(); + for (const [group, selectedName] of 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mode 100644 index 0000000..7bcf1b4 --- /dev/null +++ b/1 PA Decline/article sankey.R @@ -0,0 +1,325 @@ +# source("1 PA Decline/data_format.R") + +# NEW QUARTILES + +ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv")) + +df_raw <- ds |> + dplyr::filter(!pase_score_missings_0, !pase_score_missings_4) |> + dplyr::transmute( + pase_0_cut = as.numeric(stRoke::quantile_cut(x = pase_score_sum_0, + groups = 4, + group.names = paste0(1:4))), + pase_6_cut = as.numeric(stRoke::quantile_cut(x = pase_score_sum_4, + y = pase_score_sum_0, + groups = 4, + inc.outs = TRUE, + group.names = paste0(1:4))), + change = dplyr::case_when( + pase_0_cut %in% 2:4 & pase_6_cut == 1 ~ "drop", + pase_6_cut %in% 2:4 & pase_0_cut == 1 ~ "hop", + pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh", + pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll" + ) + , + change_any = factor(dplyr::case_when( + pase_6_cut > pase_0_cut ~ "hop", + pase_6_cut < pase_0_cut ~ "drop", + pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh", + pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll" + ) + ) + ) + + +# Visuals - sankey +# https://stackoverflow.com/questions/50395027/beautifying-sankey-alluvial-visualization-using-r + + +## Painting + +sankey_ready <- function(data,change.var="change"){ +df <- data |> + dplyr::count(dplyr::across(dplyr::all_of(c("pase_0_cut", "pase_6_cut",change.var)))) |> + dplyr::mutate(dplyr::across(dplyr::starts_with("pase_"),\(.x) factor(.x))) |> + setNames(c("pase_0_cut", "pase_6_cut","change","n")) + +lbs0 <- + c( + paste0("1st \n(n=", sum(df$n[df$pase_0_cut == "1"]), ")"), + paste0("2nd \n(n=", sum(df$n[df$pase_0_cut == "2"]), ")"), + paste0("3rd \n(n=", sum(df$n[df$pase_0_cut == "3"]), ")"), + paste0("4th \n(n=", sum(df$n[df$pase_0_cut == "4"]), ")") + ) + + +lbs6 <- + c( + paste0("1st \n(n=", sum(df$n[df$pase_6_cut == "1"]), ")"), + paste0("2nd \n(n=", sum(df$n[df$pase_6_cut == "2"]), ")"), + paste0("3rd \n(n=", sum(df$n[df$pase_6_cut == "3"]), ")"), + paste0("4th \n(n=", sum(df$n[df$pase_6_cut == "4"]), ")") + ) + + +levels(df$pase_0_cut) <- lbs0[1:length(levels(df$pase_0_cut))] +levels(df$pase_6_cut) <- lbs6[1:length(levels(df$pase_6_cut))] + +df$pase_0_cut <- factor(df$pase_0_cut, levels = rev(levels(df$pase_0_cut))) +df$pase_6_cut <- factor(df$pase_6_cut, levels = rev(levels(df$pase_6_cut))) + +df$change <- factor(df$change, levels = c("hh","hop", "drop", "ll")) + +if (change.var=="change"){ + df |> dplyr::mutate(first_grp=ifelse(substr(pase_0_cut,1,1)==1,"low","higher")) +} else if (change.var=="change_any"){ + df |> dplyr::mutate(first_grp=dplyr::case_when( + substr(pase_0_cut,1,1)==1 ~ "low", + substr(pase_0_cut,1,1) %in% 2:3 ~ "mid", + substr(pase_0_cut,1,1)==4 ~ "high")) +} +} + +# hops <- "#66c1a3" # grey +# # drops <- "#990033" # Midtrød +# drops <- "#CE0045" # Lighter Midtrød +# nos <- "grey80" # Light grey +# +# # border <- "#00596B" +# # box <- "#008099" +# +# border <- "#EA571D" +# box <- "#1E4B66" +# +# higher <- "yellow" +# low <- "purple" + +library(ggalluvial) + +library(ggplot2) + +# stRoke::color_plot(viridisLite::turbo (4)) + +plot_sankey <- function(data, + # palette=viridisLite::turbo(4), + hops = "#66c1a3", + drops = "#CE0045", + hh = "#fcdc9c", + ll = "#fcdc9c", + border = "#EA571D", + box = "#1E4B66", + higher = "#2986cc", + mid = "#b4a7d6", + low = "#590075", + alpha = 0.8, + a1=pase_0_cut, + a2=pase_6_cut, + a1.grp=first_grp, + text.size = 4 + ){ + + if (length(unique(data[[ncol(data)]]))>2) { + fills <- c(higher,low,mid) + } else { + fills <- c(higher,low) + } + + cls <- c(hh, hops, drops, ll) + # stratum.grp <- c(df[["first_grp"]],df[["last_grp"]]) + + # cls <- palette + # browser() + ggplot(data, aes(y = n, axis1 = {{a1}}, axis2 = {{a2}})) + + geom_alluvium( + aes(fill = change, color = change), + width = 1 / 16, + alpha = alpha, + knot.pos = 0.4, + curve_type ="sigmoid" + ) + + geom_stratum(aes(fill={{a1.grp}}), + # geom_stratum(aes(fill=stratum_grp), + size = 2, + width = 1 / 3.4, + # fill = box, + color = border + ) + + geom_text(stat = "stratum", + aes(label = after_stat(stratum)), + colour = "white", + size = text.size, + lineheight = 1) + + scale_x_continuous( + breaks = 1:2, + labels = c("Pre-stroke\nPASE quartile", "Six months\nPASE quartile") + ) + + scale_fill_manual(values = c(cls,fills),na.value = box) + + scale_color_manual(values = cls) + + ggtitle("PA level changes from \npre-stroke to post-stroke") +} + +## Changes to left colum coloring is needed. + +c("change","change_any") |> purrr::map(\(.x){ + df_raw |> + sankey_ready(change.var = .x) +}) |> + purrr::map(\(.x){ + .x |> plot_sankey(text.size=4.5) + }) |> + patchwork::wrap_plots() + + + +p_delta <- df_raw |> + sankey_ready() |> + plot_sankey(text.size=4.5) + + +# plotly::ggplotly(p_delta) + +# png( +# filename = "sankey_change_ARTICLEA.png", +# units = "mm", +# width = 500, +# height = 600, +# pointsize = 60, +# res = 300 +# ) +ggplot2::ggsave(filename = "1 PA Decline/sankey_change_ARTICLEA_ejn.png", + p_delta + + theme_void() + + theme( + legend.position = "none", + # panel.grid.major = element_blank(), + # panel.grid.minor = element_blank(), + # axis.text.y = element_blank(), + # axis.title.y = element_blank(), + axis.text.x = element_text(), + # text = element_text(size = 5), + plot.title = element_blank(), + # panel.background = element_rect(fill = "white"), + plot.background = element_rect(fill="white"), + panel.border = element_blank() + ), + units = "mm", + width = 84, + height = 70, + # pointsize = 30, + dpi = 600) +# +# +ggplot2::ggsave(filename = "1 PA Decline/sankey_change_ARTICLEA.png", + p_delta + + theme_void() + + theme( + legend.position = "none", + # panel.grid.major = element_blank(), + # panel.grid.minor = element_blank(), + # axis.text.y = element_blank(), + # axis.title.y = element_blank(), + axis.text.x = element_text(), + text = element_text(size = 20), + plot.title = element_blank(), + # panel.background = element_rect(fill = "white"), + plot.background = element_rect(fill="white"), + panel.border = element_blank() + ), + units = "mm", + width = 200, + height = 220, + # pointsize = 30, + dpi = 600) + +ggplot2::ggsave(filename = "1 PA Decline/sankey_change_ARTICLEA.pdf", + p_delta + + theme_void() + + theme( + legend.position = "none", + # panel.grid.major = element_blank(), + # panel.grid.minor = element_blank(), + # axis.text.y = element_blank(), + # axis.title.y = element_blank(), + axis.text.x = element_text(), + text = element_text(size = 20), + plot.title = element_blank(), + # panel.background = element_rect(fill = "white"), + plot.background = element_rect(fill="white"), + panel.border = element_blank() + ), + units = "mm", + width = 200, + height = 220, + # pointsize = 30, + dpi = 1200) + +# png( +# filename = "sankey_change_PhDDay.png", +# units = "mm", +# width = 100, +# height = 200, +# pointsize = 15, +# res = 300 +# ) +# p_delta + +# theme_minimal() + +# theme( +# legend.position = "none", +# panel.grid.major = element_blank(), +# panel.grid.minor = element_blank(), +# axis.text.y = element_blank(), +# axis.title.y = element_blank(), +# axis.text.x = element_text(size = 14, face = "bold"), +# plot.title = element_text(hjust = 0.5, vjust = 1, size = 30, face = "bold") +# ) +# dev.off() + + +# png( +# filename = "sankey_change_PhDDay_min.png", +# units = "mm", +# width = 500, +# height = 500, +# pointsize = 15, +# res = 300 +# ) +# p_delta + +# theme_minimal() + +# theme( +# legend.position = "none", +# panel.grid.major = element_blank(), +# panel.grid.minor = element_blank(), +# axis.text.y = element_blank(), +# axis.title.y = element_blank(), +# axis.text.x = element_blank(), +# plot.title = element_blank(), +# panel.background = element_rect(fill = "transparent"), +# plot.background = element_rect(fill = "transparent", color = NA) +# ) +# dev.off() + + + +# png( +# filename = "sankey_change_ESOC23.png", +# units = "mm", +# width = 500, +# height = 500, +# pointsize = 60, +# res = 300 +# ) +# p_delta + +# theme_minimal() + +# theme( +# legend.position = "none", +# panel.grid.major = element_blank(), +# panel.grid.minor = element_blank(), +# axis.text.y = element_blank(), +# axis.title.y = element_blank(), +# axis.text.x = element_blank(), +# plot.title = element_blank(), +# panel.background = element_rect(fill = "transparent"), +# plot.background = element_rect(fill = "transparent", color = NA) +# ) +# dev.off() + diff --git a/1 PA Decline/calibration plot.R b/1 PA Decline/calibration plot.R new file mode 100644 index 0000000..8c78cc0 --- /dev/null +++ b/1 PA Decline/calibration plot.R @@ -0,0 +1,24 @@ +# calibration plot + + +ds <- openxlsx2::read_xlsx("1 PA Decline/Fra DDV/calibration_imp.xlsx") + +p <- ds |> split(ds$model) |> + purrr::imap(\(.x,.i){ + .x |> predtools::calibration_plot(obs="y",pred="pred")|> + purrr::pluck("calibration_plot")+ + ggplot2::ggtitle(.i)+ + ggplot2::scale_x_continuous(breaks=seq(0,1,.25),limits=c(0,1))+ + ggplot2::scale_y_continuous(breaks=seq(0,1,.25),limits=c(-.1,1.1)) + }) |> + patchwork::wrap_plots(ncol=2) + + +ggplot2::ggsave(filename = "1 PA Decline/calibration_imp.pdf", + plot=p, + units = "mm", + width = 200, + height = 100, + # pointsize = 30, + dpi = 1200) + \ No newline at end of file diff --git a/1 PA Decline/calibration_imp.pdf b/1 PA Decline/calibration_imp.pdf new file mode 100644 index 0000000..d4338ee Binary files /dev/null and b/1 PA Decline/calibration_imp.pdf differ diff --git a/1 PA Decline/coef plot.R b/1 PA Decline/coef plot.R new file mode 100644 index 0000000..6a96342 --- /dev/null +++ b/1 PA Decline/coef plot.R @@ -0,0 +1,335 @@ +## Examples + +# stRoke::talos |> +# dplyr::mutate(across(tidyselect::starts_with("mrs"), as.numeric)) |> +# stRoke::generic_stroke(group = "rtreat", score = "mrs_6", variables = c("hypertension", "diabetes", "civil")) |> +# purrr::pluck(3) +# +# stRoke::talos |> +# dplyr::mutate(mrs_6_bin = as.numeric(mrs_6 < 1)) |> +# finalfit::or_plot(dependent = "mrs_6_bin", explanatory = c("hypertension", "diabetes", "civil")) + + +# Consider utilising plotting like finalfit::or_plot + + +## Sample data +# df_coefs <- list( +# mrs_6 = stRoke::talos |> +# dplyr::select(tidyselect::all_of(c("mrs_6", "rtreat", "hypertension", "diabetes", "civil"))) |> +# lm(data = _, mrs_6 ~ .), +# mrs_1 = stRoke::talos |> +# dplyr::select(tidyselect::all_of(c("mrs_1", "rtreat", "hypertension", "diabetes", "civil"))) |> +# lm(data = _, mrs_1 ~ .) +# ) |> +# lapply(gtsummary::tbl_regression) |> +# purrr::map(function(.x) { +# .x |> purrr::pluck("table_body") |> +# dplyr::select(tidyselect::all_of(c("variable","estimate"))) |> +# na.omit() +# }) |> purrr::reduce(dplyr::full_join,by="variable") |> +# setNames(c("variable","increase","decrease")) + +get_coefs(path = here::here("1 PA Decline/Fra DDV/240624/pa_change_analyses.docx"),index.table = 2) + + +source(here::here("1 PA Decline/dst import.R")) +df_coefs_raw <- get_coefs(path = here::here("1 PA Decline/Fra DDV/240624/pa_change_analyses.docx"),index.table = 2) |> + dplyr::filter(variable != "(Intercept)") |> + dplyr::select(variable, hop_median, drop_median) |> + setNames(c("variable", "INCREASE", "DECREASE")) + +## Real work +df_coefs <- df_coefs_raw |> + dplyr::mutate(dplyr::across( + tidyselect::all_of(c("INCREASE", "DECREASE")), + function(.x) { + # signif( + as.numeric(.x)#, + # 3 + # ) + } + )) |> + dplyr::mutate(variable = dplyr::if_else(variable == "Alcohol consumption above recommendations", + "High alcohol consumption", variable + ))|> + ## Important step to keep the data ordered for ggplot + (function(.y) { + .y |> dplyr::mutate(variable = factor(variable, levels = rev(.y$variable))) + })() + +## Highest ORs +list(df_coefs[c(1,2)],df_coefs[c(1,3)]) |> + setNames(names(df_coefs)[2:3]) |> + purrr::map(function(.x){ + .x |> + setNames(c("var","val"))|> + dplyr::mutate(sorting=abs(log(val))) |> + dplyr::arrange(1-sorting) |> + dplyr::mutate(dplyr::across(dplyr::where(is.numeric),~signif(.x,2))) |> + head(5) +}) + + +df_long <- df_coefs |> + tidyr::pivot_longer(cols = !tidyselect::matches("variable")) |> + dplyr::mutate(name = factor(name, levels = rev(unique(name)))) + + +cols <- c( + "#CE0045", + "#66c1a3" +) # Lighter Midtrød + + +create_log_tics <- function(data){ + sort(round(unique(c(1/data,data)),2)) +} + +x.tics <- create_log_tics(c(.25, .4, .6, .8, 1)) + +legend.title="" + +levels(df_long$name) <- c("OR for decrease", + "OR for increase") + +p1 <- df_long |> + # dplyr::filter(name=="decrease") |> + ggplot2::ggplot(ggplot2::aes(x = log(value), y = variable, color = name, fill = name)) + + ggplot2::geom_vline(ggplot2::aes(xintercept = 0), linewidth = .5, linetype = "dashed") + + # ggplot2::geom_errorbarh(ggplot2::aes(xmax = boxCIHigh, xmin = boxCILow), size = .5, height = + # .2, color = "gray50") + + ggplot2::geom_point(ggplot2::aes(shape = name), size = 6) + + # ggplot2::coord_trans(x = scales:::exp_trans(10)) + + ggplot2::scale_x_continuous( + breaks = log(x.tics), + labels = x.tics, + limits = log(range(x.tics)) + ) + + ggplot2::scale_color_manual(values = cols) + + ggplot2::scale_fill_manual(values = cols) + + ggplot2::scale_shape_manual(values=c(25,24)) + + ggplot2::theme_bw() + + ggplot2::theme(panel.grid.minor = ggplot2::element_blank(), + # legend.title = ggplot2::element_text(""), + legend.position = "bottom") + + ggplot2::ylab("") + + ggplot2::xlab("Odds ratio (log)") + + ggplot2::labs(shape=legend.title, + color=legend.title, + fill=legend.title) + +# +# png( +# filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA.png"), +# units = "mm", +# width = 300, +# height = 300, +# pointsize = 5, +# res = 300 +# ) +# p1 + +# # ggplot2::theme_minimal() + +# ggplot2::theme( +# # legend.position = "none", +# # panel.grid.major = ggplot2::element_blank(), +# # panel.grid.minor = ggplot2::element_blank(), +# # axis.text.y = ggplot2::element_blank(), +# # axis.title.y = ggplot2::element_blank(), +# # axis.text.x = element_blank(), +# text = ggplot2::element_text(size = 25)#, +# # plot.title = element_text(), +# # panel.background = ggplot2::element_rect(fill = "transparent")#, +# # plot.background = ggplot2::element_rect(fill = "transparent", color = NA) +# ) +# dev.off() + + +x.tics <- create_log_tics(c(.25, .6, 1)) + +ggplot2::ggsave( + filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA_facet.png"), + plot = p1 + + ggplot2::scale_x_continuous( + breaks = log(x.tics), + labels = x.tics, + limits = log(range(x.tics)) + ) + + ggplot2::facet_wrap(facets = ggplot2::vars(name),ncol=2) + + # ggplot2::theme_minimal() + + ggplot2::theme( + legend.position = "none", + # panel.grid.major = ggplot2::element_blank(), + # panel.grid.minor = ggplot2::element_blank(), + # axis.text.y = ggplot2::element_blank(), + # axis.title.y = ggplot2::element_blank(), + # axis.text.x = element_blank(), + text = ggplot2::element_text(size = 16)#, + # plot.title = element_text(), + # panel.background = ggplot2::element_rect(fill = "transparent")#, + # plot.background = ggplot2::element_rect(fill = "transparent", color = NA) + ), + units = "mm", + width = 200, + height = 200, + pointsize = 5, + dpi = 600 +) + +ggplot2::ggsave( + filename = here::here("1 PA Decline/coef_plot_change_ARTICLEA_facet.pdf"), + plot = p1 + + ggplot2::scale_x_continuous( + breaks = log(x.tics), + labels = x.tics, + limits = log(range(x.tics)) + ) + + ggplot2::facet_wrap(facets = ggplot2::vars(name),ncol=2) + + # ggplot2::theme_minimal() + + ggplot2::theme( + legend.position = "none", + # panel.grid.major = ggplot2::element_blank(), + # panel.grid.minor = ggplot2::element_blank(), + # axis.text.y = ggplot2::element_blank(), + # axis.title.y = ggplot2::element_blank(), + # axis.text.x = element_blank(), + text = ggplot2::element_text(size = 16)#, + # plot.title = element_text(), + # panel.background = ggplot2::element_rect(fill = "transparent")#, + # plot.background = ggplot2::element_rect(fill = "transparent", color = NA) + ), + units = "mm", + width = 200, + height = 200, + pointsize = 5, + dpi = 1200 +) + + + +# p1 <- df_long |> +# # dplyr::mutate(value=log10(value) +# # ) |> +# ggplot2::ggplot(ggplot2::aes(x = variable, y = log(value), fill = name)) + +# ggplot2::geom_bar(stat = "identity", position = ggplot2::position_dodge()) + +# ggplot2::coord_trans(y = scales:::exp_trans(10)) + +# ggplot2::scale_y_continuous( +# breaks = log10(c(.2, .4, .5, 1, 1.2, 1.4, 1.6, 2, 2.5)), +# labels = c(.2, .4, .5, 1, 1.2, 1.4, 1.6, 2, 2.5), +# limits = log10(c(0.09, 2.5)) +# ) + +# ggplot2::geom_hline(yintercept = 0) + +# ggplot2::coord_flip() + +# ggplot2::scale_fill_manual(values = cols) + +# # REF: https://stackoverflow.com/a/22517219/21019325 +# ggplot2::guides(fill = ggplot2::guide_legend(reverse = TRUE)) + +# ggplot2::ylab("OR (log))") + +# ggplot2::labs( +# fill = "Model" # , +# # title = "Prediction models: increase and decrease after stroke", +# # subtitle = "Median coeficient after cross validation" +# ) + +# ggplot2::theme_classic(11) + +# ggplot2::theme( +# axis.title.x = ggplot2::element_text(), +# axis.title.y = ggplot2::element_blank(), +# axis.text.y = ggplot2::element_blank(), +# axis.line.y = ggplot2::element_blank(), +# axis.ticks.y = ggplot2::element_blank() # , +# # legend.position = "none" +# ) +# p1 + +# df_plot <- df_long |> +# dplyr::mutate( +# id = seq_len(dplyr::n()), +# dplyr::across(where(is.numeric), \(.i) signif(.i, digits = 2)) +# ) |> +# (function(.x) { +# split(.x, .x[["variable"]]) |> +# purrr::map(function(.y) { +# .y |> dplyr::mutate(id = rev(id)) +# }) |> +# dplyr::bind_rows() +# })() |> +# dplyr::mutate(id = rev(id)) |> +# (function(.z) { +# .z |> dplyr::mutate(var_label = variable |> (function(.x) { +# split(.z, .x) |> +# purrr::map(function(.y) { +# c("", unique(as.character(.y[[1]]))) +# }) |> +# purrr::list_c() +# })()) +# })() |> +# dplyr::mutate(val_label = paste("OR:", value)) + + + +# df_plot <- df_long |> +# dplyr::mutate( +# id = seq_len(dplyr::n()), +# dplyr::across(where(is.numeric), \(.i) signif(.i, digits = 2)) +# ) |> +# (function(.x) { +# split(.x, .x[["variable"]]) |> +# purrr::map(function(.y) { +# .y |> dplyr::mutate(id = rev(id)) +# }) |> +# dplyr::bind_rows() +# })() |> +# dplyr::mutate(id = rev(id)) |> +# (function(.z) { +# .z |> dplyr::mutate(var_label = variable |> (function(.x) { +# split(.z, .x) |> +# purrr::map(function(.y) { +# c("", unique(as.character(.y[[1]]))) +# }) |> +# purrr::list_c() +# })()) +# })() |> +# dplyr::mutate(val_label = paste("OR:", value)) +# +# table_text_size <- 5 +# title_text_size <- 20 +# column_space <- c(0, .6) +# +# t1 <- df_plot |> ggplot2::ggplot(ggplot2::aes(x = var, y = variable)) + +# ggplot2::annotate("text", +# x = column_space[1], y = df_plot$variable, +# label = df_plot[[5]], hjust = 0, size = table_text_size +# ) + +# # ggplot2::annotate("text", +# # x = column_space[2], y = df_plot$id, +# # label = df_plot[[2]], hjust = 0, size = table_text_size +# # ) + +# ggplot2::annotate("text", +# x = column_space[2], y = df_plot$variable, +# label = df_plot[[6]], hjust = 0, size = table_text_size +# ) + +# ggplot2::xlim(0, .8) + +# ggplot2::theme_classic(14) + +# ggplot2::theme( +# axis.title.x = ggplot2::element_text(colour = "white"), +# axis.text.x = ggplot2::element_text(colour = "white"), +# axis.title.y = ggplot2::element_blank(), +# axis.text.y = ggplot2::element_blank(), +# axis.ticks.y = ggplot2::element_blank(), +# line = ggplot2::element_blank() +# ) +# +# patchwork::wrap_plots(t1, +# p1, +# ncol = 2, widths = c(1, 1.5) +# ) + patchwork::plot_annotation(title = "Prediction models: decrease and increase PA") +# +# +# gridExtra::grid.arrange(t1, +# p1, +# ncol = 2, +# widths = c(1, 1.5), +# top = grid::textGrob("Prediction models: decrease and increase PA", +# x = 0.02, y = 0.2, gp = grid::gpar(fontsize = title_text_size), +# just = "left" +# ) +# ) diff --git a/1 PA Decline/coef_plot_change_ARTICLEA.png b/1 PA Decline/coef_plot_change_ARTICLEA.png new file mode 100644 index 0000000..6a1419c Binary files /dev/null and b/1 PA Decline/coef_plot_change_ARTICLEA.png differ diff --git a/1 PA Decline/coef_plot_change_ARTICLEA_facet.pdf b/1 PA Decline/coef_plot_change_ARTICLEA_facet.pdf new file mode 100644 index 0000000..bb2f1a8 Binary files /dev/null and b/1 PA Decline/coef_plot_change_ARTICLEA_facet.pdf differ diff --git a/1 PA Decline/coef_plot_change_ARTICLEA_facet.png b/1 PA Decline/coef_plot_change_ARTICLEA_facet.png new file mode 100644 index 0000000..fe10eb4 Binary files /dev/null and b/1 PA Decline/coef_plot_change_ARTICLEA_facet.png differ diff --git a/1 PA Decline/data_format.R b/1 PA Decline/data_format.R new file mode 100644 index 0000000..e3f500e --- /dev/null +++ b/1 PA Decline/data_format.R @@ -0,0 +1,50 @@ +## Article 1 outcome group definition script +## To be enriched from Statistics Denmark +## +## Based on the ItMLiHSmar2022 course + +library(Hmisc) +library(dplyr) +# library(daDoctoR) +library(tidyselect) + +# Setting final primary output from "pout" +if (pout=="drop"){ + X_tbl <- X_tbl|> + mutate(group=pase_drop_fac) + + # print(quantile(as.numeric(X_tbl$pase_0))) + # print(quantile(as.numeric(X_tbl$pase_6))) + # print(summary(X_tbl$pase_0_cut)) + + X_tbl_f <- X_tbl|> + filter(pase_0_cut!=1)|> + select(-starts_with("pase_")) +} + +if (pout=="hop"){ + X_tbl <- X_tbl|> + mutate(group=pase_hop_fac) + + # print(quantile(as.numeric(X_tbl$pase_0))) + # print(quantile(as.numeric(X_tbl$pase_6))) + # print(summary(X_tbl$pase_0_cut)) + + X_tbl_f <- X_tbl|> + filter(pase_6_cut!=1)|> + select(-starts_with("pase_")) +} + +# Dropping non-complete for analysis +Xy <- X_tbl_f|> + na.omit()|> # Keeping only complete observations + select(-c(tci) # Left out of model as no present in drop-group + )|> + mutate(mrs_0=factor(ifelse(mrs_0==1,1,2))) # Sets binary mRS 0 to include in glmnet, 0 or above + +label(Xy) = as.list(var.labels[match(names(Xy), names(var.labels))]) + +X<-dplyr::select(Xy,-c(group, -starts_with("pase_")) # Exclude primary outcome + ) +y<-Xy$group + diff --git a/1 PA Decline/data_set.R b/1 PA Decline/data_set.R new file mode 100644 index 0000000..7211a59 --- /dev/null +++ b/1 PA Decline/data_set.R @@ -0,0 +1,216 @@ +## Article 1 data set definition +## To be enriched from Statistics Denmark +## +## Based on the ItMLiHSmar2022 course + +require(Hmisc) +require(dplyr) +# library(daDoctoR) +require(tidyverse) +require(patchwork) +require(caret) +require(glmnet) +require(leaps) +require(pROC) +require(gt) +require(gtsummary) +require(glue) +# library(ggdendro) +require(corrplot) +require(stRoke) + +## ==================================================================== +# Step 1: Import +## ==================================================================== + +if ("try-error" %in% class(t <- try(read.csv("/Volumes/Data/exercise/source/background.csv")))) { + export <- + read.csv( + "/Volumes/Data 1/exercise/source/background.csv", + colClasses = "character", + na.strings = c("NA", "", "unknown") + ) +} else if (!"try-error" %in% class(t)) { + export <- + read.csv( + "/Volumes/Data/exercise/source/background.csv", + colClasses = "character", + na.strings = c("NA", "", "unknown") + ) +} + +## ==================================================================== +# Step 2: Selection +## ==================================================================== + + +export<-export[,c("pase_0", + "age", + "sex", + "civil", + "smoke_ever", + "smoker", + "rtreat", + "alc", + "afli", + "hypertension", + "diabetes", + "mrs_0", + "nihss_c", + "thrombolysis", + "pad", + "thrombechtomy", + "ami", + "tci", + "pase_6")] + +## ==================================================================== +# Step 3: Formatting variables +## ==================================================================== + +export$diabetes[is.na(export$diabetes)]<-"no" +export$diabetes[is.na(export$hypertension)]<-"no" +export$thrombolysis[is.na(export$thrombolysis)]<-"no" +export$thrombechtomy[is.na(export$thrombechtomy)]<-"no" +export$pad[is.na(export$pad)]<-"no" +export$ami[is.na(export$ami)]<-"no" +# export$smoker_prev <- ifelse(export$smoker=="3","yes","no") +export$smoker <- ifelse(export$smoker=="1","yes","no") +export$smoker[is.na(export$smoker)] <- "no" +# export$mrs_0[export$mrs_0==3]<-NA + +dta <- export %>% + # as_tibble()%>% + mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received + male_sex= factor(ifelse(sex=="female","no","yes")), + # smoke_ever=factor(ifelse(smoke_ever=="never","no","yes")), + civil=factor(ifelse(civil=="partner","no","yes")), # Sets "yes" for not-cohabiting + rtreat=factor(ifelse(rtreat=="Placebo","no","yes")), # "Yes" receives active treatment + alc=factor(ifelse(alc=="more","yes","no")), # Yes for more than guideline + pase_0=as.numeric(pase_0), + pase_6=as.numeric(pase_6), + across(c("diabetes", + "hypertension", + "smoker", + "afli", + "pad", + "ami", + "tci", + "mrs_0"),as.factor), + across(c("nihss_c", + "age"),as.numeric ) + )%>% + select(-c(sex)) + + +## ==================================================================== +# Step 4: Defining outcome +## ==================================================================== + +## Changed to step 7 +## This is to perform proper quantile split based on actually included. + +## ==================================================================== +# Step 5: Ordering variables +## ==================================================================== + +vars <- c("age", + "male_sex", + "civil", + "pase_0", + "smoker", + "alc", + "afli", + "hypertension", + "diabetes", + "pad", + "ami", + "tci", + "mrs_0", + "nihss_c", + "any_rep", + "rtreat", + "pase_6") + +dta<-dta[vars] + +## ==================================================================== +# Step 6: Labeling +## ==================================================================== + +var.labels = c(age="Age", + male_sex="Male", + civil="Living alone", + pase_0="Pre-stroke PASE score", + pase_6="Six month PASE score", + smoker="Daily or occasinally smoking", + alc="More alcohol than recommendation", + afli="AFIB", + hypertension="Hypertension", + diabetes="Diabetes", + pad="PAD", + ami="Previous MI", + tci="Previous TIA", + mrs_0="Pre-stroke mRS [-1]", + nihss_c="Acute NIHSS score", + thrombolysis="Acute thrombolysis", + thrombechtomy="Acute thrombechtomy", + any_rep="Any reperfusion therapy", + rtreat="Active trial treatment", + pase_drop_fac="PASE first quartile drop F", + pase_hop_fac="PASE first quartile hop F", + pase_0_cut="PASE 0 quartiles", + pase_6_cut="PASE 6 quartiles") + + + +## ==================================================================== +# Step 7: final data export +## ==================================================================== + +data_summary<-summary(dta) + +# Saving "old" factorised variables +sel<-sapply(dta,is.factor) +# Reformatting factors as 1/2 for analysis +dta<-dta |> + mutate(across(where(is.factor), as.numeric))|> # Turning factors into 1(no) or 2(yes) for model. Numbered alphabetically. + mutate(across(matches(colnames(dta)[sel]), as.factor), + across(starts_with("pase_"), as.numeric)) + +# Filtering out non-PASE +X_tbl<-dta |> + filter(!is.na(pase_0),!is.na(pase_6)) + +nrow(X_tbl) + +# Defining possible outcome meassures. Keeping in df for characterisation +X_tbl <- X_tbl|> + mutate(## Relative decline + pase_diff=(pase_0-pase_6), + pase_decl_rel = pase_diff/pase_0*100, + # pase_decl_rel_fac=factor(ifelse(pase_decl_rel>=rel_dif,"yes","no")), + ## Absolute decline + # pase_decl_abs_fac=factor(ifelse(pase_diff>=abs_dif,"yes","no")), + ## Drop + pase_0_cut=quantile_cut(as.numeric(pase_0), + groups=4, + group.names = c(as.character(1:4)), + y=as.numeric(pase_0), + ordered.f = TRUE, + inc.outs = TRUE#, + # detail.lst=FALSE + ), + pase_6_cut=quantile_cut(as.numeric(pase_6), + groups=4, + group.names = c(as.character(1:4)), + y=as.numeric(pase_0), + ordered.f = TRUE, + inc.outs = TRUE#, + # detail.lst=FALSE + ), + pase_drop_fac=factor(ifelse(pase_6_cut==1&pase_0_cut!=1,"yes","no")), + pase_hop_fac=factor(ifelse(pase_6_cut!=1&pase_0_cut==1,"yes","no"))) + +Hmisc::label(X_tbl) = as.list(var.labels[match(names(X_tbl), names(var.labels))]) + diff --git a/1 PA Decline/dst import.R b/1 PA Decline/dst import.R new file mode 100644 index 0000000..f54505c --- /dev/null +++ b/1 PA Decline/dst import.R @@ -0,0 +1,83 @@ +#' Reads docx file and splits each table into list +#' +#' @param path file path +#' @param data.type character vector. Could be "paragraph" or "table cell". +#' +#' @return +#' @export +#' +#' @examples +docx2ds <- function(path = here::here("data-raw/Deltagerliste Skirva 2024.docx"), + data.type = "table cell", verbose = TRUE) { + # Ref: https://www.r-bloggers.com/2020/07/how-to-read-and-create-word-documents-in-r/ + doc <- officer::read_docx(path) + + content <- doc |> officer::docx_summary() + + if (verbose) { + message("Content types in the current document are as follows:") + print(content$content_type |> unique()) + } + + table_cells <- content |> dplyr::filter(content_type %in% data.type) + + # .x <- split(table_cells, table_cells$doc_index)[[4]] + + split(table_cells, table_cells$doc_index) |> purrr::map(function(.x) { + table_data <- .x |> + dplyr::filter(!is_header) |> + dplyr::select(row_id, cell_id, text) + + # split data into individual columns + splits <- split(table_data, table_data$cell_id) + splits <- lapply(splits, function(.y) .y$text) + splits <- splits |> + purrr::keep(function(.y) length(.y)>1) + + # If a footer has been added, it is considered part of the first column, + # and will result in unequal col lengths. + # This solution does not handle merged cells + col_lengths <- lengths(splits) + + if (col_lengths[1] > col_lengths[2]){ + splits[[1]] <- splits[[1]][seq_len(col_lengths[2])] + } + + # combine columns back together in wide format + table_result <- splits |> + dplyr::bind_cols() + + # get table headers + cols <- .x |> dplyr::filter(is_header) + names(table_result) <- cols$text + table_result + }) +} + +get_coefs <- function(path, + index.table = 1) { + data = docx2ds( + path = path + ) + + data |> + purrr::pluck(index.table) |> + setNames(c( + "variable", + lapply(c("drop", "hop"), + paste, + c("median", "mean"), + sep = "_" + ) |> + purrr::list_c() + )) |> + dplyr::select(variable, tidyselect::ends_with("median")) #|> + # dplyr::mutate(dplyr::across(tidyselect::ends_with("median"),~as.numeric)) + # setNames(c("variable","decrease","increase")) +} + +gtsummary2docx <- function(data, path) { + data |> + gtsummary::as_flex_table() |> + flextable::save_as_docx(path = path) +} diff --git a/1 PA Decline/flowchart.R b/1 PA Decline/flowchart.R new file mode 100644 index 0000000..1edd0c9 --- /dev/null +++ b/1 PA Decline/flowchart.R @@ -0,0 +1,48 @@ +create_flowchart <- function(data, export.path = NULL) { + out <- data |> + dplyr::select(pase_0,pase_4) |> + dplyr::mutate(id= dplyr::row_number(), + exclude=is.na(pase_0)|is.na(pase_4), + exclude_reason=factor(dplyr::case_when( + is.na(pase_0) ~ "Pre-stroke PASE missing", + is.na(pase_4) ~"Post-stroke PASE missing" + ),levels=c( "Pre-stroke PASE missing","Post-stroke PASE missing")), + ) |> + consort::consort_plot( + orders = c( + id = "Complete TALOS cohort", + exclude_reason = "Excluded", + id = "Main dataset" + ), + side_box = c("exclude_reason"), + labels = c( + "1" = "Identification", + "2" = "Inclusion", + "3" = "Complete data" + ) + ) + + if (!is.null(export.path)) { + out |> export_consort_dot(path = export.path) + } else { + plot(out) + } +} + +source(here::here("2 Longterm/data.R")) +# The original functions from DST unedited +source(here::here("1 PA Decline/Fra DDV/functions200411.R")) +source(here::here("1 PA Decline/Fra DDV/functions240418.R")) +# Modified functions to overwrite original +source(here::here("R/functions.R")) + +df <- df_ddv |> + dplyr::tibble() |> + dplyr::mutate_all(as.character) |> + ready_clin() |> + data_formatting()|> + get_vars(c("clin","lifestyle","ses", "assess.pred")) |> + dplyr::mutate(exclude=ifelse(is.na(pase_0)|is.na(pase_4),"Excluded","Included"), + age=as.numeric(age)) + +df |> create_flowchart() \ No newline at end of file diff --git a/1 PA Decline/grouped table rows.docx b/1 PA Decline/grouped table rows.docx new file mode 100644 index 0000000..cfbb133 Binary files /dev/null and b/1 PA Decline/grouped table rows.docx differ diff --git a/1 PA Decline/grouped table rows.qmd b/1 PA Decline/grouped table rows.qmd new file mode 100644 index 0000000..33ac41c --- /dev/null +++ b/1 PA Decline/grouped table rows.qmd @@ -0,0 +1,59 @@ +--- +title: "nicer tables" +format: docx +editor: visual +--- + +## Tables + +```{r} +library(gtsummary) +library(dplyr) +packageVersion("gtsummary") + +trial%>% + select(age, stage, grade)%>% + tbl_summary()%>% + modify_table_body( + ~.x %>% + + # add your variable + rbind( + tibble( + variable="Demographics", + var_type=NA, + var_label = "Demographics", + row_type="label", + label="Demographics", + stat_0= NA))%>% # expand the components of the tibble as needed if you have more columns + + # can add another one + rbind( + tibble( + variable="Tumor characteristics", + var_type=NA, + var_label = "Tumor characteristics", + row_type="label", + label="Tumor characteristics", + stat_0= NA))%>% + + # specify the position you want these in + + arrange(factor(variable, levels=c("Demographics", + "age", + "Tumor characteristics", + "stage", + "grade"))))%>% + + # and you can then indent the actual variables + modify_column_indent(columns=label, rows=variable%in%c("age", + "stage", + "grade"))%>% + + # and double indent their levels + modify_column_indent(columns=label, rows= (variable%in%c("stage", + "grade") + & row_type=="level"), + double_indent=T) + +``` diff --git a/1 PA Decline/regular_fun.R b/1 PA Decline/regular_fun.R new file mode 100644 index 0000000..1b457b0 --- /dev/null +++ b/1 PA Decline/regular_fun.R @@ -0,0 +1,117 @@ +## ItMLiHSmar2022 +## regular_fun.R, child script +## Regularisation model building function +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + +regular_fun<-function(X,y,K,lambdas,alpha){ +n<-nrow(X) +set.seed(321) + +# Using caret function to ensure both levels represented in all folds +c<-createFolds(y=y, k = K, list = FALSE, returnTrain = TRUE) + +B<-yhatTestProbKeep<-list() +accTrain<-accTest<-err_train<-err_test<-auc_train<-auc_test<-matrix(nrow = K,ncol = length(lambdas)) + +catinfo<-levels(y) + +cMatTrain<-cMatTest<-table(true=factor(c(0,0),levels=catinfo),pred=factor(c(0,0),levels=catinfo)) + + +## Iterate over partitions +for (idx1 in 1:K){ + + # Status + cat('Processing fold', idx1, 'of', K,'\n') + + # idx1=1 + # Get training- and test sets + I_train = c!=idx1 ## Creating selection vector of TRUE/FALSE + I_test = !I_train + + Xtrain = X[I_train,] + ytrain = y[I_train] + Xtest = X[I_test,] + ytest = y[I_test] + + + ## Model matrices for glmnet + ## Using the complicated approach not to include first level. + # Xmat.train<-model.matrix(~ .-1, data=Xtrain, + # contrasts.arg = lapply(Xtrain[,sapply(Xtrain, is.factor)], + # contrasts, contrasts=T)) + # Xmat.test<-model.matrix(~ .-1, data=Xtest, + # contrasts.arg = lapply(Xtest[,sapply(Xtest, is.factor)], + # contrasts, contrasts=T)) + + # Xmat.train<-model.matrix(~.-1,Xtrain) + # Xmat.test<-model.matrix(~.-1,Xtest) + + # Weights + ytrain_weight<-as.vector(1 - (table(ytrain)[ytrain] / length(ytrain))) + # ytest_weight<-as.vector(1 / (table(ytest)[ytest] / length(ytest))) + + # Fit regularized linear regression model + mod<-glmnet(Xtrain, ytrain, + alpha = alpha, ## Alpha = 1 for lasso + lambda = lambdas, ## Setting lambdas + standardize = TRUE, ## Scales and centers + weights = ytrain_weight, + family = "binomial" + ) + + # Keep coefficients for plot + B[[idx1]] <- as.matrix(coef(mod)) + + # Iterate over regularization strengths to compute training- and test + # errors for individual regularization strengths. + for (idx2 in 1:length(lambdas)){ + # idx2=1 + + # Predict + yhatTrainProb<-predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtrain), + type = "response" + ) + + yhatTestProb<-predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtest), + type = "response" + ) + + # Compute training and test error + yhatTrain = round(yhatTrainProb) + yhatTest = round(yhatTestProb) + + # Make predictions categorical again (instead of 0/1 coding) + yhatTrainCat = factor(round(yhatTrainProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + yhatTestCat = factor(round(yhatTestProb),levels=c("0","1"),labels=catinfo,ordered = TRUE) + + # Evaluate classifier performance + # Accuracy + # accTrain[idx1,idx2] <- sum(yhatTrainCat==ytrain)/length(ytrain) + # accTest [idx1,idx2] <- sum(yhatTestCat==ytest)/length(ytest) + # # + # # Error rate + # err_train[idx1,idx2] = 1 - accTrain[idx1,idx2] + # err_test [idx1,idx2] = 1 - accTest[idx1,idx2] + + # AUROC + suppressMessages( + auc_train[idx1,idx2]<-auc(ytrain, yhatTrainCat)) + suppressMessages( + auc_test [idx1,idx2]<-auc(ytest, yhatTestCat)) + + # Compute confusion matrices + cMatTrain = cMatTrain + table(true=ytrain,pred=yhatTrainCat) + cMatTest = cMatTest + table(true=ytest,pred=yhatTestCat) + } +} +ls<-list(mod=mod,B=B,auc_train=auc_train,auc_test=auc_test,cMatTrain=cMatTrain,cMatTest=cMatTest) +return(ls) +} diff --git a/1 PA Decline/regularisation_steps.R b/1 PA Decline/regularisation_steps.R new file mode 100644 index 0000000..bacd072 --- /dev/null +++ b/1 PA Decline/regularisation_steps.R @@ -0,0 +1,150 @@ +## ItMLiHSmar2022 +## regularisation_steps.R, child script +## Regularised model building and analysation for assignment +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + +## ==================================================================== +## Step 0: data import and wrangling +## ==================================================================== + +setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/") + +# source("data_format.R") +y1<-factor(as.integer(y)-1) ## Outcome is required to be factor of 0 or 1. + + +## ==================================================================== +## Step 1: settings +## ==================================================================== + +## Folds +K=10 +set.seed(3) +c<-caret::createFolds(y=y, + k = K, + list = FALSE, + returnTrain = TRUE) # Foldids for alpha tuning + +## Defining tuning parameters +lambdas=2^seq(-10, 5, 1) +alphas<-seq(0,1,.1) + +## Weights for models +weighted=TRUE +if (weighted == TRUE) { + wght<-as.vector(1 - (table(y)[y] / length(y))) +} else { + wght <- rep(1, nrow(y)) +} + + +## Standardise numeric +## Centered and + + + +## ==================================================================== +## Step 2: all cross validations for each alpha +## ==================================================================== + +library(furrr) +library(purrr) +library(doMC) +registerDoMC(cores=6) + +# Nested CVs with analysis for all lambdas for each alpha +# +set.seed(3) +cvs <- future_map(alphas, function(a){ + cv.glmnet(model.matrix(~.-1,X), + y1, + weights = wght, + lambda=lambdas, + type.measure = "deviance", # This is standard measure and recommended for tuning + foldid = c, # Per recommendation the folds are kept for alpha optimisation + alpha=a, + standardize=TRUE, + family=quasibinomial, + keep=TRUE) # Same as binomial, but not as picky +}) + +## ==================================================================== +# Step 3: optimum lambda for each alpha +## ==================================================================== + + +# For each alpha, lambda is chosen for the lowest meassure (deviance) +each_alpha <- sapply(seq_along(alphas), function(id) { + each_cv <- cvs[[id]] + alpha_val <- alphas[id] + index_lmin <- match(each_cv$lambda.min, + each_cv$lambda) + c(lamb = each_cv$lambda.min, + alph = alpha_val, + cvm = each_cv$cvm[index_lmin]) +}) + +# Best lambda +best_lamb <- min(each_alpha["lamb", ]) + +# Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",] +best_alph <- each_alpha["alph",][each_alpha["cvm",]==min(each_alpha["cvm",] + [each_alpha["lamb",] %in% best_lamb])] + +## https://stackoverflow.com/questions/42007313/plot-an-roc-curve-in-r-with-ggplot2 +p_roc<-roc.glmnet(cvs[[1]]$fit.preval, newy = y)[[match(best_alph,alphas)]]|> # Plots performance from model with best alpha + ggplot(aes(FPR,TPR)) + + geom_step() + + coord_cartesian(xlim=c(0,1), ylim=c(0,1)) + + geom_abline()+ + theme_bw() + +## ==================================================================== +# Step 4: Creating the final model +## ==================================================================== + +source("regular_fun.R") # Custom function +optimised_model<-regular_fun(X,y1,K,lambdas=best_lamb,alpha=best_alph) +# With lambda and alpha specified, the function is just a k-fold cross-validation wrapper, +# but keeps model performance figures from each fold. + +list2env(optimised_model,.GlobalEnv) +# Function outputs a list, which is unwrapped to Env. +# See source script for reference. + +## ==================================================================== +# Step 5: creating table of coefficients for inference +## ==================================================================== + +Bmatrix<-matrix(unlist(B),ncol=10) +Bmedian<-apply(Bmatrix,1,median) +Bmean<-apply(Bmatrix,1,mean) + +reg_coef_tbl<-tibble( + name = c("Intercept",Hmisc::label(X)), + medianX = round(Bmedian,5), + ORmed = round(exp(Bmedian),5), + meanX = round(Bmean,5), + ORmea = round(exp(Bmean),5))%>% + # arrange(desc(abs(medianX)))%>% + gt() + +## ==================================================================== +# Step 6: plotting predictive performance +## ==================================================================== + +reg_cfm<-confusionMatrix(cMatTest) +reg_auc_sum<-summary(auc_test[,1]) + +## ==================================================================== +# Step 7: Packing list to save in loop +## ==================================================================== + +ls[[i]] <- list("RegularisedCoefs"=reg_coef_tbl, + "bestA"=best_alph, + "bestL"=best_lamb, + "ConfusionMatrx"=reg_cfm, + "AUROC"=reg_auc_sum) diff --git a/1 PA Decline/repeated measures.R b/1 PA Decline/repeated measures.R new file mode 100644 index 0000000..22f3df5 --- /dev/null +++ b/1 PA Decline/repeated measures.R @@ -0,0 +1,29 @@ +# ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv")) + + +# Tun first 3 lines of code in 00_master.R + +# Label attributes are removed then pivoted to long +df <- purrr::map(X_tbl,\(.x){ + # browser() + class(.x) <- class(.x)[-1] + .x +}) |> + dplyr::bind_cols() |> + dplyr::mutate(id=dplyr::row_number()) |> + dplyr::select(id,dplyr::everything()) |> + tidyr::pivot_longer(c("pase_0","pase_6"),names_to = "time",values_to = "pase") |> + dplyr::mutate(time = as.numeric(factor(time))-1) + +lme4::lmer(formula = pase~age+male_sex+hypertension+diabetes+nihss_c+(1|id),data = df) |> + gtsummary::tbl_regression() + +summary(df$male_sex) + +df |> + dplyr::mutate(dplyr::across(c("id","hypertension","diabetes","time"),\(.x)factor(.x)), + female=male_sex==1) |> + (\(.x){ + mmrm::mmrm(formula = pase~age+female+hypertension+diabetes+nihss_c+us(time|id),data=.x) + })() |> gtsummary::tbl_regression() + diff --git a/1 PA Decline/sankey individual.R b/1 PA Decline/sankey individual.R new file mode 100644 index 0000000..5b76cb6 --- /dev/null +++ b/1 PA Decline/sankey individual.R @@ -0,0 +1,56 @@ +ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv")) + +df_raw <- ds |> + dplyr::filter(!pase_score_missings_0, !pase_score_missings_4) |> + dplyr::transmute( + id = dplyr::row_number(), + pase_0 = pase_score_sum_0, + pase_4 = pase_score_sum_4, + pase_0_cut = as.numeric(stRoke::quantile_cut( + x = pase_0, + groups = 4, + group.names = paste0(1:4) + )), + pase_6_cut = as.numeric(stRoke::quantile_cut( + x = pase_4, + y = pase_0, + groups = 4, + inc.outs = TRUE, + group.names = paste0(1:4) + )), + pase_diff = pase_4 - pase_0, + pase_diff_rel = pase_diff / pase_0, + pase_0_rank = rank(pase_0, ties.method = "first"), + pase_4_rank = rank(pase_4, ties.method = "first"), + change = dplyr::case_when( + pase_0_cut %in% 2:4 & pase_6_cut == 1 ~ "drop", + pase_6_cut %in% 2:4 & pase_0_cut == 1 ~ "hop", + pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh", + pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll" + ), + change_any = factor(dplyr::case_when( + pase_6_cut > pase_0_cut ~ "hop", + pase_6_cut < pase_0_cut ~ "drop", + pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh", + pase_0_cut %in% 1 & pase_6_cut == 1 ~ "ll" + )), + change_rel = factor(dplyr::case_when( + pase_diff_rel > .5 ~ "hop", + pase_diff_rel < -.5 ~ "drop", + .default = "stat" + )) + ) + +df_raw |> skimr::skim() + +df_long <- df_raw |> + dplyr::select(pase_0_rank, pase_4_rank, change_rel,id) |> + tidyr::pivot_longer(dplyr::starts_with("pase_"), names_to = "time", values_to = "pase") + +df_long <- df_raw |> + dplyr::select(pase_0, pase_4, change_rel,id) |> + tidyr::pivot_longer(dplyr::starts_with("pase_"), names_to = "time", values_to = "pase") + +ggplot(df_long, aes(x = time, y = pase,group=id,colour = change_rel)) + + geom_line()+ + facet_wrap(~change_rel) diff --git a/1 PA Decline/sankey.R b/1 PA Decline/sankey.R new file mode 100644 index 0000000..996932a --- /dev/null +++ b/1 PA Decline/sankey.R @@ -0,0 +1,155 @@ + +# source("1 PA Decline/data_format.R") + +# NEW QUARTILES + +df <- X_tbl |> select(pase_0_cut,pase_6_cut) + +df$change <- factor(ifelse( + df$pase_0_cut %in% 2:4 & df$pase_6_cut == 1, + "drop", + ifelse( + df$pase_6_cut %in% 2:4 & df$pase_0_cut == 1, + "hop", + "no" + ))) + + +# Visuals - sankey +# https://stackoverflow.com/questions/50395027/beautifying-sankey-alluvial-visualization-using-r + + +## Painting + +df <- df |> count(pase_0_cut,pase_6_cut,change) + + +lbs0 <- + c( + paste0("1st \n(n=", sum(df$n[df$pase_0_cut == "1"]), ")"), + paste0("2nd \n(n=", sum(df$n[df$pase_0_cut == "2"]), ")"), + paste0("3rd \n(n=", sum(df$n[df$pase_0_cut == "3"]), ")"), + paste0("4th \n(n=", sum(df$n[df$pase_0_cut == "4"]), ")") + ) + + +lbs6 <- + c( + paste0("1st \n(n=", sum(df$n[df$pase_6_cut == "1"]), ")"), + paste0("2nd \n(n=", sum(df$n[df$pase_6_cut == "2"]), ")"), + paste0("3rd \n(n=", sum(df$n[df$pase_6_cut == "3"]), ")"), + paste0("4th \n(n=", sum(df$n[df$pase_6_cut == "4"]), ")") + ) + + +levels(df$pase_0_cut)<-lbs0[1:length(levels(df$pase_0_cut))] +levels(df$pase_6_cut)<-lbs6[1:length(levels(df$pase_6_cut))] + +df$pase_0_cut <- factor(df$pase_0_cut, levels=rev(levels(df$pase_0_cut))) +df$pase_6_cut <- factor(df$pase_6_cut, levels=rev(levels(df$pase_6_cut))) + +df$change <- factor(df$change, levels=c("no", "drop", "hop")) + + +hops <- "#66c1a3" # grey +# drops <- "#990033" # Midtrød +drops <- "#CE0045" #Lighter Midtrød +nos <- "grey90" # Light grey + +# border <- "#00596B" +# box <- "#008099" + +border <- "#EA571D" +box <- "#1E4B66" + + +cls <- c(nos, drops, hops) + +alpha <- 0.7 + +library(ggalluvial) + +p_delta <- ggplot(df,aes(y = n, axis1 = pase_0_cut, axis2 = pase_6_cut)) + + geom_alluvium( + aes(fill = change, color = change), + width = 1 / 16, + alpha = alpha, + knot.pos = 0.4 + ) + + geom_stratum(aes(size=10),width = 1 / 4, + fill = box, + color = border) + + geom_text(stat = "stratum", aes(label = after_stat(stratum)), colour = "white", size = 20) + + scale_x_continuous(breaks = 1:2, + labels = c("Pre-stroke\nquartile", "Six months\nquartile")) + + scale_fill_manual(values = cls) + + scale_color_manual(values = cls) + + ggtitle("Change in PA\nafter stroke") + + +# plotly::ggplotly(p_delta) + +png( + filename = "sankey_change_PhDDay.png", + units = "mm", + width = 100, + height = 200, + pointsize = 15, + res = 300 +); p_delta + + theme_minimal() + + theme( + legend.position = "none", + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.y = element_blank(), + axis.title.y = element_blank(), + axis.text.x = element_text(size = 14, face = "bold"), + plot.title = element_text(hjust = 0.5, vjust = 1, size = 30, face = "bold") + ); dev.off() + + +png( + filename = "sankey_change_PhDDay_min.png", + units = "mm", + width = 500, + height = 500, + pointsize = 15, + res = 300 +); p_delta+ + theme_minimal() + + theme( + legend.position = "none", + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.y = element_blank(), + axis.title.y = element_blank(), + axis.text.x = element_blank(), + plot.title = element_blank(), + panel.background = element_rect(fill='transparent'), + plot.background = element_rect(fill='transparent', color=NA) + ); dev.off() + + + +png( + filename = "sankey_change_ESOC23.png", + units = "mm", + width = 500, + height = 500, + pointsize = 60, + res = 300 +); p_delta + + theme_minimal() + + theme( + legend.position = "none", + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.y = element_blank(), + axis.title.y = element_blank(), + axis.text.x = element_blank(), + plot.title = element_blank(), + panel.background = element_rect(fill='transparent'), + plot.background = element_rect(fill='transparent', color=NA) + ); dev.off() + diff --git a/1 PA Decline/sankey_change_ARTICLEA.pdf b/1 PA Decline/sankey_change_ARTICLEA.pdf new file mode 100644 index 0000000..cef4536 Binary files /dev/null and b/1 PA Decline/sankey_change_ARTICLEA.pdf differ diff --git a/1 PA Decline/sankey_change_ARTICLEA.png b/1 PA Decline/sankey_change_ARTICLEA.png new file mode 100644 index 0000000..ab59c74 Binary files /dev/null and b/1 PA Decline/sankey_change_ARTICLEA.png differ diff --git a/1 PA Decline/sankey_change_ARTICLEA_ejn.png b/1 PA Decline/sankey_change_ARTICLEA_ejn.png new file mode 100644 index 0000000..2a86cd5 Binary files /dev/null and b/1 PA Decline/sankey_change_ARTICLEA_ejn.png differ diff --git a/1 PA Decline/sankey_change_ESOC23.png b/1 PA Decline/sankey_change_ESOC23.png new file mode 100644 index 0000000..a253ed9 Binary files /dev/null and b/1 PA Decline/sankey_change_ESOC23.png differ diff --git a/1 PA Decline/sankey_change_PhDDay.png b/1 PA Decline/sankey_change_PhDDay.png new file mode 100644 index 0000000..8af7c7c Binary files /dev/null and b/1 PA Decline/sankey_change_PhDDay.png differ diff --git a/1 PA Decline/sankey_change_PhDDay_min.png b/1 PA Decline/sankey_change_PhDDay_min.png new file mode 100644 index 0000000..777debe Binary files /dev/null and b/1 PA Decline/sankey_change_PhDDay_min.png differ diff --git a/1 PA Decline/standardise.R b/1 PA Decline/standardise.R new file mode 100644 index 0000000..2521e6d --- /dev/null +++ b/1 PA Decline/standardise.R @@ -0,0 +1,41 @@ +## ItMLiHSmar2022 +## standardise.R, child script +## Data standardisation, returns list +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk + +standardise<-function(train,test,type){ + # From: + # https://datascience.stackexchange.com/questions/13971/standardization-normalization-test-data-in-r + + sel<-sapply(Xtrain,is.numeric) # Deciding which to stadardise (only numeric) + cnm<-colnames(Xtrain) # Saving column names for ordering + + # Subsetting + + ## Data to treat + train.tr<-train[,sel] + test.tr<-test[,sel] + + ## Data to save + train.sv<-train[,!sel] + test.sv<-test[,!sel] + + # Calculate mean and SD of train data + trainMean <- sapply(train.tr,mean) + trainSd <- sapply(train.tr,sd) + + if (type=="c"){ + ## centered + norm.trainData<-sweep(train.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise + norm.testData<-sweep(test.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise + } + + if (type=="cs"){ + ## centered AND scaled (Z-score standardisation) + norm.trainData<-sweep(sweep(train.tr, 2L, trainMean), 2, trainSd, "/") + norm.testData<-sweep(sweep(test.tr, 2L, trainMean), 2, trainSd, "/") + } + return(list(XtrainSt=cbind(norm.trainData,train.sv)[,cnm], # Reordering columns to original + XtestSt=cbind(norm.testData,test.sv)[,cnm])) +} + diff --git a/1 PA Decline/summaries.qmd b/1 PA Decline/summaries.qmd new file mode 100644 index 0000000..55fcfcd --- /dev/null +++ b/1 PA Decline/summaries.qmd @@ -0,0 +1,109 @@ +--- +title: "Article A: Final renders" +format: docx +editor: visual +--- + +```{r} +# ds <- readr::read_csv(here::here("/Volumes/Data/REDCap/DDV/talos_ddv.csv")) +source(here::here("2 Longterm/data.R")) +# The original functions from DST unedited +source(here::here("1 PA Decline/Fra DDV/functions200411.R")) +source(here::here("1 PA Decline/Fra DDV/functions240418.R")) +# Modified functions to overwrite original +source(here::here("R/functions.R")) +``` + +Files and resources used for article ready data: + +- "2 Longterm/data.R" + +- "R/functions240319.R" + +```{r} +# ds |> finalfit::missing_plot() +``` + +On handling missings: https://finalfit.org/articles/missing.html + +```{r} +# unique(df_long$variable) +``` + +```{r} +pred_data <- df_ddv |> + dplyr::tibble() |> + dplyr::mutate_all(as.character) |> + ready_clin() |> + data_formatting() |> + prediction_ready() |> + dplyr::mutate( + dplyr::across( + c( + tidyselect::starts_with("pase_"), + "age" + ), + as.numeric + ) + ) + +pred_data |> labelling_data() |> readr::write_rds("labelled_test.rds") + +skimr::skim(pred_data) + +wilcox.test(pred_data$pase_0, pred_data$pase_4, paired = TRUE) +``` + +```{r} +pred_data |> + true_pred_sum_plot() |> + gtsummary::as_gt() |> + # add_var_groups_gt()|> + gt::gtsave(filename = here::here("1 PA Decline/table1.docx")) +system2("open",here::here("'1 PA Decline/table1.docx'")) +# gtsummary2docx(path=here::here("1 PA Decline/table1.docx")) +``` + +```{r} +pred_data |> + dplyr::mutate(reg_female = dplyr::if_else(reg_female, "Female", "Male")) |> + pase_cutter(drop.pase = FALSE) |> + dplyr::select(pase_change, dplyr::everything()) |> + summary_tblone(by = "reg_female", missing = "no") |> + gtsummary::modify_column_hide("stat_0") |> + gtsummary::add_p() |> + gtsummary::bold_p() |> + micRo::mask_micro_summary() |> + gtsummary::as_gt() |> + gt::gtsave(filename = here::here("1 PA Decline/table_bysex.docx")) +``` + +```{r} +skimr::skim(pred_data) +``` + +## Excluded patients + +```{r} +df_ddv |> + dplyr::tibble() |> + dplyr::mutate_all(as.character) |> + ready_clin() |> + data_formatting()|> + get_vars(c("clin","lifestyle","ses", "assess.pred")) |> + dplyr::mutate(exclude=ifelse(is.na(pase_0)|is.na(pase_4),"Excluded","Included"), + age=as.numeric(age), + pase_0=as.numeric(pase_0), + pase_4=as.numeric(pase_4) + )|> + # dplyr::select(-pase_0,-pase_4) |> + #dplyr::select(exclude,soc_status_nowork, fam_indk_hl, edu_level_hl)|> + gtsummary::tbl_summary(by=exclude, missing = "ifany") |> + gtsummary::add_p() |> + fix_labels() |> + gtsummary::as_gt() |> + gt::gtsave("1 PA Decline/summary_by_missing.docx") +#|> + # mask_micro_summary(micro.n = 5) + +``` diff --git a/1 PA Decline/summary_by_missing.docx b/1 PA Decline/summary_by_missing.docx new file mode 100644 index 0000000..3963638 Binary files /dev/null and b/1 PA Decline/summary_by_missing.docx differ diff --git a/1 PA Decline/table mods.R b/1 PA Decline/table mods.R new file mode 100644 index 0000000..31907ca --- /dev/null +++ b/1 PA Decline/table mods.R @@ -0,0 +1,41 @@ +source(here::here("1 PA Decline/dst import.R")) +ls_tbl <- docx2ds( + path = "/Users/au301842/Library/CloudStorage/OneDrive-Personal/Research/PhD/1 Change in PA/Manuscript/Manuscript_Change in PA_v2_10_IJS.docx" +) + +ls <- ls_tbl |> purrr::pluck(3) |> + (\(.x){ + .x[-c(1:3,nrow(.x)),] + })() + +ls |> setNames(1:5) |> + dplyr::mutate(dplyr::across(2:5,as.numeric)) |> + gt::gt() |> gt::fmt_number(n_sigfig=3) |> gt::gtsave("modtable3.docx") + + + +ls |> setNames(1:5) |> + dplyr::mutate(dplyr::across(2:5, \(.x){ + as.character(sprintf('%#.3g', as.numeric(.x))) + })) |> kableExtra::kable() + + +## Splitting by leveled variables (expanded view with all levels) +n <- 0 +for (i in seq_len(nrow(ls))){ + if (ls[[2]][i]!=""){ + n[i]<-n[length(n)]+1 + } else { + n[i] <- n[length(n)] + } +} + +split(ls,n) |> lapply(function(.x){ + + if (" TRUE" %in% .x[[1]]){ + cbind(.x[1,1:2],.x[3,3:5],.x[1,6],.x[3,7:9]) + } else { + .x + } +}) |> dplyr::bind_rows() |> + gt::gt() \ No 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\clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 }}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1337 +\intbl {\f0 {\f0\fs20     N}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2674 +\intbl {\f0 {\f0\fs20 }}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4011 +\intbl {\f0 {\f0\fs20 522}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5348 +\intbl {\f0 {\f0\fs20 131}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 130}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 130}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 131}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1337 +\intbl {\f0 {\f0\fs20     Median (IQR)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2674 +\intbl {\f0 {\f0\fs20 }}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4011 +\intbl {\f0 {\f0\fs20 68 (59, 76)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5348 +\intbl {\f0 {\f0\fs20 75 (67, 80)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 71 (65, 77)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 67 (56, 75)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 60 (52, 68)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1337 +\intbl {\f0 {\f0\fs20     Range}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2674 +\intbl {\f0 {\f0\fs20 }}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4011 +\intbl {\f0 {\f0\fs20 19, 99}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5348 +\intbl {\f0 {\f0\fs20 19, 99}}\cell + +\pard\plain\uc0\qc\clvertalc 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\clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5348 +\intbl {\f0 {\f0\fs20 73 (11)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 70 (11)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 65 (13)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 60 (11)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1337 +\intbl {\f0 {\f0\fs20 Male}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2674 +\intbl {\f0 {\f0\fs20 522}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4011 +\intbl {\f0 {\f0\fs20 342 (66%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5348 +\intbl {\f0 {\f0\fs20 67 (51%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 82 (63%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 86 (66%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 107 (82%)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1337 +\intbl {\f0 {\f0\fs20 Living alone}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2674 +\intbl {\f0 {\f0\fs20 515}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4011 +\intbl {\f0 {\f0\fs20 170 (33%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5348 +\intbl {\f0 {\f0\fs20 63 (48%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 41 (32%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 36 (28%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 30 (24%)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1337 +\intbl {\f0 {\f0\fs20 Daily or occasinally smoking}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2674 +\intbl {\f0 {\f0\fs20 522}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4011 +\intbl {\f0 {\f0\fs20 164 (31%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx5348 +\intbl {\f0 {\f0\fs20 45 (34%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 40 (31%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 44 (34%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 35 (27%)}}\cell + +\row + +\trowd\trrh0 + 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\clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 76 (58%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 60 (47%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 48 (38%)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx1337 +\intbl {\f0 {\f0\fs20 Diabetes}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx2674 +\intbl {\f0 {\f0\fs20 522}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx4011 +\intbl {\f0 {\f0\fs20 57 (11%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 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\cellx5348 +\intbl {\f0 {\f0\fs20 63 (48%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx6685 +\intbl {\f0 {\f0\fs20 64 (49%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx8022 +\intbl {\f0 {\f0\fs20 61 (47%)}}\cell + +\pard\plain\uc0\qc\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 65 (50%)}}\cell + +\row + +\trowd\trrh0 + +\pard\plain\uc0\ql\clvertalc \clpadfl3\clpadl25 \clpadft3\clpadt85 \clpadfb3\clpadb25 \clpadfr3\clpadr85 \cellx9359 +\intbl {\f0 {\f0\fs20 {\super \i 1}n (%)}}\cell + +\row + +} diff --git a/1 PA Decline/table1.docx b/1 PA Decline/table1.docx new file mode 100644 index 0000000..cc7ba99 Binary files /dev/null and b/1 PA Decline/table1.docx differ diff --git a/1 PA Decline/table2.RTF b/1 PA Decline/table2.RTF new file mode 100644 index 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Decline/table_bysex.docx b/1 PA Decline/table_bysex.docx new file mode 100644 index 0000000..0c4a477 Binary files /dev/null and b/1 PA Decline/table_bysex.docx differ diff --git a/2 Longterm/.DS_Store b/2 Longterm/.DS_Store new file mode 100644 index 0000000..0285727 Binary files /dev/null and b/2 Longterm/.DS_Store differ diff --git a/2 Longterm/260315/_targets.R b/2 Longterm/260315/_targets.R new file mode 100755 index 0000000..b336018 --- /dev/null +++ b/2 Longterm/260315/_targets.R @@ -0,0 +1,285 @@ +# Created by use_targets(). +# Follow the comments below to fill in this target script. +# Then follow the manual to check and run the pipeline: +# https://books.ropensci.org/targets/walkthrough.html#inspect-the-pipeline + +# Load packages required to define the pipeline: +library(targets) +library(tarchetypes) # Load other packages as needed. + +# Set target options: +tar_option_set( + seed = 142, + packages = c("tibble"), # packages that your targets need to run + format = "qs" # , # Optionally set the default storage format. qs is fast. + # + # For distributed computing in tar_make(), supply a {crew} controller + # as discussed at https://books.ropensci.org/targets/crew.html. + # Choose a controller that suits your needs. For example, the following + # sets a controller with 2 workers which will run as local R processes: + # + # controller = crew::crew_controller_local(workers = 2) + # + # Alternatively, if you want workers to run on a high-performance computing + # cluster, select a controller from the {crew.cluster} package. The following + # example is a controller for Sun Grid Engine (SGE). + # + # controller = crew.cluster::crew_controller_sge( + # workers = 50, + # # Many clusters install R as an environment module, and you can load it + # # with the script_lines argument. To select a specific verison of R, + # # you may need to include a version string, e.g. "module load R/4.3.0". + # # Check with your system administrator if you are unsure. + # script_lines = "module load R" + # ) + # + # Set other options as needed. +) + +# tar_make_clustermq() is an older (pre-{crew}) way to do distributed computing +# in {targets}, and its configuration for your machine is below. +options(clustermq.scheduler = "multiprocess") + +# tar_make_future() is an older (pre-{crew}) way to do distributed computing +# in {targets}, and its configuration for your machine is below. +future::plan(future.callr::callr) + +# Run the R scripts in the R/ folder with your custom functions: +tar_source() +source(here::here("R/glmnet-reg.R")) # Source other scripts as needed. + +# Replace the target list below with your own: +list( + tar_target( + name = pop_df, + command = get_clinical() + ), + tar_target( + name = reg_list, + command = get_reg_ls() + ), + tar_target( + name = df_deaths, + command = get_deaths(reg_list) + ), + tar_target( + name = df_events, + command = get_events(reg_list) + ), + tar_target( + name = df_events_deaths, + command = merge_events(list(events = df_events, deaths = df_deaths, clinical = pop_df)) + ), + tar_target( + name = df_treated, + command = get_treated(reg_list) + ), + tar_target( + name = df_dst, + command = get_dst(reg_list, pop_df) + ), + tar_target( + name = list_filtered, + command = list(all_events = df_events_deaths, clinical = pop_df, dst = df_dst) + ), + tar_target( + name = df_all_data, + command = collectall(list_filtered) + ), + tar_target( + name = df_all_data_formatted, + command = data_formatting(df_all_data) + ), + tar_target( + name = ls_all_events, + command = all_events(list(events = df_events, deaths = df_deaths, clinical = pop_df)) + ), + tar_target( + name = df_event_data, + command = events_ready(df_all_data_formatted) + ), + tar_target( + name = df_talos_data, + command = talos_ready(df_all_data_formatted) + ), + tar_target( + name = df_talos_data_imp, + command = talos_imp(df_talos_data) + ), + tar_target( + name = tbl_events_summary, + command = events_tblone(df_event_data) + ), + tar_target( + name = tbl_events_cox_regression, + command = show_table_regression(df_event_data, use.mice = FALSE) + ), + tar_target( + name = tbl_events_cox_regression_uv, + command = uv_cox_table(df_event_data) + ), + tar_target( + name = df_event_data_small, + command = events_ready_small(df_all_data_formatted) + ), + tar_target( + name = tbl_events_summary_small, + command = events_tblone(df_event_data_small) + ), + tar_target( + name = tbl_events_cox_regression_small, + command = show_table_regression(df_event_data_small, use.mice = FALSE) + ), + tar_target( + name = df_events_mids, + command = events_dataset(df_event_data, impute = TRUE) + ), + tar_target( + name = df_events_complete, + command = complete_preds_data(df_event_data) + ), + tar_target( + name = tbl_events_mids_cox_regression, + command = show_table_regression(df_event_data, use.mice = TRUE) + ), + tar_target( + name = plot_events_survival_smooth, + command = df_event_data |> events_dataset(impute = FALSE) |> cox_regression(include_formula=TRUE) |> plot_survival_smooth() + )#, + # tar_target( + # name = plot_events_survival_smooth_mids, + # command = df_events_mids |> cox_regression() |> plot_survival_smooth() + # ), + # tar_target( + # name = df_pred_data, + # command = prediction_ready(df_all_data_formatted) + # ), + # tar_target( + # name = tbl_pred_summary, + # command = preds_tblone(df_pred_data) + # ), + # tar_target( + # name = tbl_pred_summary_true, + # command = df_pred_data |> + # dplyr::select(pase_0, pase_4, soc_status_nowork, fam_indk_hl, edu_level_hl) |> + # true_pred_sum_plot() + # ), + # tar_target( + # name = tbl_pred_summary_exp, + # command = df_pred_data |> + # dplyr::select(pase_0, pase_4, soc_status_nowork, fam_indk_hl, edu_level_hl) |> + # preds_tblone() + # ), + # tar_target( + # name = tbl_pred_summary_exp_sex, + # command = df_pred_data |> + # dplyr::select(reg_female, soc_status_nowork, fam_indk_hl, edu_level_hl) |> + # summary_tblone() + # ), + # tar_target( + # name = tbl_pred_summary_exp_pase, + # command = df_pred_data |> sum_pase_tables() + # ), + # tar_target( + # name = tbl_pred_summary_exp_missing_edu, + # command = df_pred_data |> + # dplyr::select(age,reg_female, reg_trombolyse, reg_trombektomi, reg_hyperten, reg_diabetes, nihss_0, pase_0, pase_4, soc_status_nowork, fam_indk_hl, edu_level_hl) |> + # who_is_missing(var="edu_level_hl") + # ), + # tar_target( + # name = tbl_pred_summary_exp_missing_bmi, + # command = df_pred_data |> + # dplyr::select(age,reg_female, reg_trombolyse, reg_trombektomi, reg_hyperten, reg_diabetes, nihss_0, pase_0, pase_4, reg_bmi, soc_status_nowork, fam_indk_hl, edu_level_hl) |> + # who_is_missing(var="reg_bmi") + # ), + # tar_target( + # name = ls_pred_models, + # command = pred_models(df_pred_data,auto.l = TRUE,weighted = FALSE) + # ), + # tar_target( + # name = ls_pred_models_anyupdown, + # command = pred_models(df_pred_data,auto.l = TRUE,weighted = FALSE,split.type="anyupdown") + # ), + # tar_target( + # name = ls_pred_models_relupdown_20, + # command = pred_models(df_pred_data,auto.l = TRUE,weighted = FALSE,split.type="relupdown",rel.bin=20) + # ), + # tar_target( + # name = ls_pred_models_relupdown_50, + # command = pred_models(df_pred_data,auto.l = TRUE,weighted = FALSE,split.type="relupdown",rel.bin=50) + # ), + # tar_target( + # name = df_pred_mids, + # command = fun_impute(data = df_pred_data, outcome.vars = c("pase_0", "pase_4")) + # ), + # tar_target( + # name = ls_pred_mids_reg, + # command = mids_regularisation(df_pred_mids) + # ), + # tar_target( + # name = ls_pred_mids_reg_anyupdown, + # command = mids_regularisation(df_pred_mids,split.type="anyupdown") + # ), + # tar_target( + # name = ls_pred_mids_reg_relupdown_20, + # command = mids_regularisation(df_pred_mids,split.type="relupdown",rel.bin=20) + # ), + # tar_target( + # name = ls_pred_mids_reg_relupdown_50, + # command = mids_regularisation(df_pred_mids,split.type="relupdown",rel.bin=50) + # ), + # tar_target( + # name = ls_pred_summary, + # command = multi_summary(ls_pred_models) + # ), + # tar_target( + # name = ls_pred_mids_summary, + # command = multi_summary(ls_pred_mids_reg) + # ), + # tar_target( + # name = tbl_preds_log_reg, + # command = pred_log_reg(df_pred_data) + # ), + # tar_target( + # name = tbl_preds_lin_reg, + # command = pred_lin_reg(df_pred_data) + # ), + # tar_target( + # name = tbl_preds_lin_imp_reg, + # command = pred_lin_reg(df_pred_mids) + # ), + # tar_target( + # name = list_pred_clusters, + # command = get_clusters(df_events_complete,rm.out = TRUE) + # ), + # tar_target( + # name = list_pred_clusters_seq, + # command = get_clusters(df_events_complete,n.cl=4) + # )#, + # tar_target( + # name = list_df_multi_grouping, + # command = multi_grouping_df_list(df_all_data_formatted,args.list=df_mega_list()) + # ), + # tar_target( + # name = list_multi_group_results, + # command = multi_results_list(list_df_multi_grouping) + # ), + # tar_target( + # name = list_multi_group_results_direct, + # command = df_all_data_formatted |> multi_grouping_df_list(args.list=df_mega_list()) |> multi_cox_performance_test() + # ) + + + #, + # tar_quarto( + # name = report, + # path = "index.qmd" + # ), + # tar_target( + # name = pa_change_export_files, + # command = quarto::quarto_render(here::here("doc/pa_change.qmd"), output_format = "docx") + # ) +) + +## TODO +## - modify pipeline to only perform imputation once and have the rest use this single object. diff --git a/2 Longterm/260315/alternative_grouping.R b/2 Longterm/260315/alternative_grouping.R new file mode 100755 index 0000000..c89a971 --- /dev/null +++ b/2 Longterm/260315/alternative_grouping.R @@ -0,0 +1,268 @@ +source("R/functions.R") + +### FUNCTIONALISE --> DONE + +df <- targets::tar_read(df_all_data_formatted) |> + group_format(binning.fun = bin_anyupdown) |> + dplyr::mutate(pase_change = factor(pase_change, levels = c("high", "up", "low", "down"))) + + +df |> gtsummary::tbl_summary(by = pase_change) + +cox <- df |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") + + +df |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard() + +df |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression() |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit() + ggsurvfit::add_confidence_interval() + + ggsurvfit::scale_ggsurvfit() + + ggsurvfit::add_risktable() + + +### + + +data <- targets::tar_read(df_all_data_formatted) + + +df <- targets::tar_read(df_all_data_formatted) |> + group_format(binning.fun = bin_relupdown, rel.bin = 50) + + +df |> gtsummary::tbl_summary(by = pase_change) + +df |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard() + +df |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression() |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit() + ggsurvfit::add_confidence_interval() + + ggsurvfit::scale_ggsurvfit() + + ggsurvfit::add_risktable() + + + +targets::tar_read(list_df_multi_grouping) |> purrr::map(\(.x) summary(.x[["pase_change"]])) + + + + + +## BMI out + +### Univariable models + + + +ls_df <- targets::tar_read(df_all_data_formatted) |> + multi_grouping_df_list(args.list=df_mega_list(strategies=c( + # "bin_original", + # "bin_anyupdown", + "bin_relupdown", + "bin_absupdown", + "bin_quantile", + # "bin_clusterlcm", + "bin_percentage")),remove="pase_0") + + +performance_overview_uni <- ls_df |> multi_cox_performance_test(all.vars = FALSE, use.strata = FALSE, outcome.var = "pase_change") + +# performance_overview_uni + +# Comparing the best and the original + + +cox_models_uni <- ls_df|> + purrr::map(\(.x){ + .x |> + cox_regression(all.vars = FALSE, use.strata = FALSE, outcome.var = "pase_change") + }) + +ls_df[c(pick_non_duplicated(performance_overview_uni,"Name","AIC_wt",1:3),"lowest_percentage_25")]|> + purrr::map(\(.x){ + .x |> dplyr::select(pase_change) |> + gtsummary::tbl_summary() + }) |> tbl_merged_named() + + +lapply(best_models_uni, performance::model_performance) |> + (\(.x){ + dplyr::bind_cols(model=names(.x), dplyr::bind_rows(.x)) + })() + + +models_ls_uni <- best_models_uni |> lapply(\(.x){ + .x |> gtsummary::tbl_regression(exponentiate=TRUE) |> gtsummary::bold_p() +}) + + +models_ls_uni |> tbl_merged_named() + + +## Multivariable models + +performance_overview_multi <- ls_df |> multi_cox_performance_test(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") + +# performance_overview_multi + +# Comparing the best and the original +# best_models_multi <- head(performance_overview_multi,10) + +cox_models_multi <- ls_df |> + purrr::map(\(.x){ + .x |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") + }) + +ls_df[c(pick_non_duplicated(performance_overview_multi,"Name","AIC_wt",1:3),"lowest_percentage_25")]|> + purrr::map(\(.x){ + .x |> dplyr::select(pase_change) |> + gtsummary::tbl_summary() + }) |> tbl_merged_named() + +lapply(best_models_multi, performance::model_performance) |> + (\(.x){ + dplyr::bind_cols(model=names(.x), dplyr::bind_rows(.x)) + })() + + +models_ls_multi <- best_models_multi |> lapply(\(.x){ + .x |> gtsummary::tbl_regression(exponentiate=TRUE) |> gtsummary::bold_p() +}) + + +models_ls_multi |> tbl_merged_named() + + + +## Handle for mids objects +# targets::tar_read(df_events_mids) +# targets::tar_read(df_event_data) + +ls_df_mids <- targets::tar_read(df_events_mids) |> multi_grouping_df_list(args.list=df_mega_list(strategies=c( + # "bin_original", + # "bin_anyupdown", + "bin_relupdown", + "bin_absupdown", + "bin_quantile", + # "bin_clusterlcm", + "bin_percentage")),remove="pase_0") + +cox_models_mids <- ls_df_mids|> + purrr::map(\(.x){ + .x |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") + }) + +performance_overview_mids <- lapply(cox_models_mids,mids_model_aic)|> + (\(.x){ + dplyr::bind_cols(Name=names(.x), AIC=Reduce(c,.x)) + })() |> dplyr::arrange(AIC) |> + dplyr::mutate(rank=rank(AIC,ties.method = "min")) + +# performance_overview_mids|> head(20) + +# Comparing the best and the original + +best_models_mids <- head(performance_overview_mids,10) + +# best_models_mids <- cox_models_mids[c(pick_non_duplicated(performance_overview_mids,"Name","AIC",1:3),"quantile_change_6_2_ANY","lowest_percentage_25")] + +models_ls_mids <- best_models_mids |> lapply(\(.x){ + .x |> gtsummary::tbl_regression(exponentiate=TRUE) |> gtsummary::bold_p() |> gtsummary::add_glance_source_note() +}) + +models_ls_mids |> tbl_merged_named() + +## Difference between mids-results +# targets::tar_read(df_event_data) + +## NOTE on investigation +# In the original grouping, PASE-cutting was performed based only on patients without prior events. +# In the new multi-grouping strategies evaluation, PASE grouping is based on all TALOS-patients. +# We already did export some data, so we should probably stick with this. On the other hand, +# this is probably a minor thing, and we should perform analyses based on all patients like intended. +# MAYBE +# + +## + + + + +best_models <- list("uni"=performance_overview_uni, + "multi"=performance_overview_multi, + "mids"=performance_overview_mids) |> + lapply(\(.x){ + .x |> dplyr::select(Name,rank,AIC) + }) |> dplyr::bind_rows() + +models_overall_rank <- split(best_models,best_models$Name) |> + purrr::imap(\(.x,.i){ + tibble::tibble(name=.i,rank_sum=sum(.x$rank), median_AIC=median(.x$AIC)) + })|> dplyr::bind_rows() |> + dplyr::arrange(rank_sum) + +models_overall_rank[models_overall_rank$name %in% c("quantile_change_10_1","quantile_change_8_2","quantile_change_8_2_ANY"),] + +all_cox_models <- list( + "Univariable"=cox_models_uni, + "Multivariable"=cox_models_multi, + "Imputed multivariable"=cox_models_mids +) + +names_best <- models_overall_rank$name[c(1:6)] + +best_sum_tables <- names_best|> + lapply(\(.x){ + ls_df[[.x]] |> + dplyr::select(pase_change) |> gtsummary::tbl_summary() + }) |> + setNames(glue::glue("{rank(models_overall_rank$rank_sum,ties.method = 'min')[c(1:6)]}_{names_best}")) + +best_cox_tables <- names_best |> + lapply(\(.x){ + list("Group counts"=ls_df[[.x]] |> + dplyr::select(pase_change) |> gtsummary::tbl_summary() |> fix_labels(), + all_cox_models |> + lapply(\(.y){ + .y[[.x]] |> tbl_regression_standard() |> gtsummary::modify_table_styling(columns=tidyselect::starts_with("p.value"),hide = TRUE) |> + gtsummary::remove_row_type(variables=-pase_change,type="all") + })) |> purrr::list_flatten() |> + tbl_merged_named() + }) |> + setNames(glue::glue("{rank(models_overall_rank$rank_sum,ties.method = 'min')[c(1:6)]}_{names_best}")) + +## Counts also for multivariable analyses could be added as well + +best_stack <- best_cox_tables |> tbl_stack_named() + +best_stack |> gtsummary::as_gt() |> + gt::gtsave(filename = here::here("out/sens_cox.docx")) + +## Checking on a specific model +ls_df_fun[[24]] |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression() |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit() + ggsurvfit::add_confidence_interval() + + ggsurvfit::scale_ggsurvfit() + + ggsurvfit::add_risktable() + +ls_df_fun[[24]] |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard() diff --git a/2 Longterm/260315/combined_script.R b/2 Longterm/260315/combined_script.R new file mode 100755 index 0000000..3a17fe9 --- /dev/null +++ b/2 Longterm/260315/combined_script.R @@ -0,0 +1,740 @@ +# Code from: cont_pase_sens.qmd +targets::tar_config_set(store = here::here("_targets")) +source(here::here("R/functions.R")) +source(here::here("R/glmnet-reg.R")) +library(targets) +library(tidyverse) +tbl_cont_0 <- targets::tar_read("df_all_data_formatted")|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> + # pase_cutter(drop.nas = TRUE) |> + events_ready(v.groups=c("clin","lifestyle.events","ses", "assess.events","quartiles")) |> + dplyr::select(dplyr::all_of(c("pase_0", "age", "reg_female", "nihss_0", "reg_trombolyse", +"reg_trombektomi", "rtreat_placebo", "reg_alone", "reg_smoker", +"reg_more_alc", "reg_hyperten", "reg_diabetes", "reg_atriefli", +"reg_ami", "soc_status_nowork", "fam_indk_hl", "edu_level_hl", +"who_4", "mdi_4", "mfi_gen_4", "mrs_4_above1", "time", "status" +))) |> + (\(data){ + # c("pase_0","pase_4") |> + # purrr::map(\(exp){ + list("Univariable"=cox_regression(data=data,all.vars = FALSE, use.strata = FALSE, outcome.var = "pase_0"), + "Multivariable"=cox_regression(data=data,all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_0")) |> + purrr::map(\(.x){ + .x |> gtsummary::tbl_regression(exponentiate=TRUE) |> gtsummary::add_nevent()|> + fix_labels() + }) |> + tbl_merged_named() + # }) + })() |> + gtsummary::modify_table_body( ~ filter(.x, variable == "pase_0")) +tbl_cont_4 <- targets::tar_read("df_all_data_formatted")|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> + # pase_cutter(drop.nas = TRUE) |> + events_ready(v.groups=c("clin","lifestyle.events","ses", "assess.events","quartiles")) |> + dplyr::select(dplyr::all_of(c("pase_4", "age", "reg_female", "nihss_0", "reg_trombolyse", +"reg_trombektomi", "rtreat_placebo", "reg_alone", "reg_smoker", +"reg_more_alc", "reg_hyperten", "reg_diabetes", "reg_atriefli", +"reg_ami", "soc_status_nowork", "fam_indk_hl", "edu_level_hl", +"who_4", "mdi_4", "mfi_gen_4", "mrs_4_above1", "time", "status" +))) |> + (\(data){ + # c("pase_0","pase_4") |> + # purrr::map(\(exp){ + list("Univariable"=cox_regression(data=data,all.vars = FALSE, use.strata = FALSE, outcome.var = "pase_4"), + "Multivariable"=cox_regression(data=data,all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_4")) |> + purrr::map(\(.x){ + .x |> gtsummary::tbl_regression(exponentiate=TRUE) |> gtsummary::add_nevent()|> + fix_labels() + }) |> + tbl_merged_named() + # }) + })() |> + gtsummary::modify_table_body( ~ filter(.x, variable == "pase_4")) +list("PASE 0"=tbl_cont_0, + "PASE 6"=tbl_cont_4) |> tbl_stack_named() + + + +# Code from: events_type_sens.qmd +targets::tar_config_set(store = here::here("_targets")) +source(here::here("R/functions.R")) +source(here::here("R/glmnet-reg.R")) +library(targets) +library(tidyverse) +df <- targets::tar_read(df_all_data_formatted) |> + dplyr::mutate( + status.all=dplyr::if_else( + startsWith(as.character(event),"death"),"death","cve",missing = NA_character_)|> factor() + )|> + get_vars(vars.groups = c("clin","lifestyle.events","ses", "ssri")) |> + dplyr::rename(status=status.all, + time=time.all) +df$status |> summary() +tbl <- df |> + pase_cutter(drop.nas = TRUE, drop.pase = TRUE) |> + (\(.x) { + list( + "death" = .x |> dplyr::mutate(status = dplyr::if_else(status == "death", 1, 0, 0)), + "cve" = .x |> dplyr::mutate(status = dplyr::if_else(status == "cve", 1, 0, 0)) + ) + })() |> lapply(\(.x) { + ls <- list( + "Univariate" = .x |> dplyr::select(-rtreat) |> + standard_multi_cox_table(all.vars = FALSE) |> gtsummary::add_nevent(), + "Multivariate" = .x |> dplyr::select(-rtreat) |> + standard_multi_cox_table(all.vars = TRUE) |> gtsummary::add_nevent() + ) |> + purrr::map(gtsummary::bold_p) |> + + tbl_merged_named() + ls |> + gtsummary::modify_table_body( ~ filter(.x, variable == "pase_change")) + }) |> + gtsummary::tbl_stack(group_header = c("death","cve"))# |> + # gtsummary::as_gt() |> + # gt::gtsave(here::here("out/trial_strat_sens.docx")) + +tbl + +names(tbl) + + + +# Code from: mrs_sensitivity.qmd +source(here::here("R/functions.R")) +ls_mrs0 <- targets::tar_read(df_all_data_formatted) |> + dplyr::filter(mrs_0==0)|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> + events_ready() |> + (\(.x){ + list( + std=.x, + imp=.x |> events_dataset(impute = TRUE) + ) + })() |> + purrr::map(\(.x){ + .x |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + }) + +# targets::tar_read(df_all_data_formatted) |> +# dplyr::filter(mrs_0==0) |> +# dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> events_ready() |> +# pase_cutter(drop.pase = TRUE,drop.nas = TRUE) |> +# cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> +# tbl_regression_standard() + +ls <- list( + "Univariable"= ls_mrs0$std |> + dplyr::select(pase_change,dplyr::everything()) |> + splitdf4uvcox(include=c("time","status")) |> + purrr::map(\(.x){ + .x |> tbl_regression_standard() + }) |> + gtsummary::tbl_stack(), + "Multivariable" = ls_mrs0$std |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard() |> gtsummary::add_glance_source_note(), + "Multivariable Imputed"= ls_mrs0$imp |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard() +) |> purrr::map(\(.x){ + .x|> + gtsummary::modify_table_styling(column = p.value, + hide=TRUE) +}) |> tbl_merged_named() + +ls +# |> gtsummary::as_gt() |> +# gt::gtsave(filename = here::here(glue::glue("out/sens_subset_mrs0_0.docx"))) + +# ls_mrs0$std |> +# cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> performance::check_model() +targets::tar_read(df_all_data_formatted)|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> + dplyr::filter(mrs_0==0) |> + dplyr::select(pase_0,pase_4) |> + summary() +# ls_mrs0$std |> gtsummary::tbl_summary(by = pase_change) |> fix_labels() +ls_mrs0_change <- targets::tar_read(df_all_data_formatted) |> + get_vars(vars.groups =c("clin","lifestyle.events","ses", "assess.events.pre")) |> + dplyr::filter(event.include) |> + dplyr::select(-tidyselect::all_of("event.include")) |> + (\(.x){ + list( + std=.x, + imp=.x |> + dplyr::select(-tidyselect::any_of("reg_bmi"))|> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = FALSE) + ) + })() |> + purrr::map(\(.x){ + .x |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + }) + +# targets::tar_read(df_all_data_formatted) |> +# dplyr::filter(mrs_0==0) |> +# dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> events_ready() |> +# pase_cutter(drop.pase = TRUE,drop.nas = TRUE) |> +# cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> +# tbl_regression_standard() + +ls_change <- list( + "Univariable"= ls_mrs0_change$std |> + dplyr::select(pase_change,dplyr::everything()) |> + splitdf4uvcox(include=c("time","status")) |> + purrr::map(\(.x){ + .x |> tbl_regression_standard() + }) |> + gtsummary::tbl_stack(), + "Multivariable" = ls_mrs0_change$std |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard() |> gtsummary::add_glance_source_note(), + "Multivariable Imputed"= ls_mrs0_change$imp |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard() +) |> purrr::map(\(.x){ + .x|> + gtsummary::modify_table_styling(column = p.value, + hide=TRUE) +}) |> tbl_merged_named() + +ls_change +#| eval: false +coll <- list( + mrs0_0=ls_mrs0$std|> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change"), + pase_change_mrs0 = ls_mrs0_change$std|> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change"), + pase_change=df |> + get_vars(vars.groups =c("clin","lifestyle.events","ses", "assess.events")) |> + dplyr::filter(event.include) |> + dplyr::select(-tidyselect::all_of("event.include")) |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE)|> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change"), + prestroke_pase=df |> + pase_cutter(drop.nas = FALSE) |> + get_vars(vars.groups = c("clin", "lifestyle.events", "ses", "assess.pred", "quartiles"), vars.vec = c("inc_time", + "time", + "status")) |> + dplyr::mutate(time = time + inc_time / 365) |> + dplyr::select(-dplyr::any_of(c("pase_0", "pase_4", "pase_change", "inc_time", "event.include", "pase_4_quartile")))|> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_0_quartile")) |> + purrr::map(performance::check_collinearity) + +coll |> purrr::imap(\(.x,.i){ + .x |> dplyr::as_tibble() |> gt::gt() |> gt::tab_header(.i)|> + gt::gtsave(filename = here::here(glue::glue("out/coll_{.i}.docx"))) +}) + + + +# Code from: pa_event_plots.qmd +targets::tar_config_set(store = here::here("_targets")) +source(here::here("R/functions.R")) +source(here::here("R/glmnet-reg.R")) +library(targets) +library(tidyverse) +# targets::tar_read(plot_events_survival_smooth) +p1 <- targets::tar_read(df_event_data) |> + events_dataset(impute = FALSE) |> + dplyr::mutate(pase_change = factor(pase_change, + levels = c("Persistently high", "Decrease", "Increase", "Persistently low")), + pase_change = dplyr::recode(pase_change, + "Persistently high"="Consistently above lowest", + "Decrease"="Decrease to lowest", + "Increase"="Increase from lowest", + "Persistently low"="Consistently lowest") + ) |> + cox_regression() |> + plot_survival_smooth(line.w = 1) + + ggplot2::labs( + fill = "PA level change group", + color = "PA level change group", + linetype = "PA level change group" + ) + + ggplot2::scale_x_continuous(limits = c(0, 9.5), breaks = c(0, 3, 6, 9)) + + +ggplot2::ggsave(here::here("out/smooth_surv.png"), + p1, + dpi = 600, + units = "cm", + height = 8, + width = 15) +p1 +surv.data <- targets::tar_read(df_event_data) |> + events_dataset(impute = FALSE) |> + cox_regression(all.vars = FALSE) |> # Minimal model to just give risk table + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit(times = c(0, 3, 6, 9)) + + +risk_event <- c("n.risk", "cum.event") |> + purrr::map2(c("Numbers at risk", "Events"), \(.x, .y){ + surv.data |> + tidyr::pivot_wider(id_cols = strata, names_from = time, values_from = {{ .x }}) |> + mask_micro_table(col.sel = -strata) |> # Masking columns + tidyr::pivot_longer(cols = -strata) |> + setNames(c("strata", "time", .x)) |> + dplyr::mutate(dplyr::across(time, ~ as.numeric(.x))) |> + dplyr::mutate(strata = factor(strata, levels = rev(c("Persistently high", "Decrease", "Increase", "Persistently low")))) |> + ggplot2::ggplot(ggplot2::aes(x = time, y = strata, label = get(.x))) + + ggplot2::geom_text() + + ggplot2::labs(y = NULL, title = .y) + + ggplot2::theme_minimal() + + ggsurvfit::theme_risktable_default() + }) + +p2 <- ggsurvfit::ggsurvfit_align_plots(list(p1, risk_event[1]) |> purrr::list_flatten()) |> + patchwork::wrap_plots(ncol = 1, heights = c(2, 1), guides = "collect") + +ggplot2::ggsave(here::here("out/smooth_surv_tables.png"), + p2, + dpi = 600, + units = "cm", + height = 9, + width = 15) +p2 +#| include: false +targets::tar_read(df_event_data) |> + events_dataset(impute = FALSE) |> + dplyr::mutate(pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Increase", "Persistently low"))) |> + cox_regression(all.vars = FALSE) |> # Minimal model to just give risk table + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit() + + ggplot2::scale_y_continuous( + limits = c(0, 1.02), + breaks = seq(0, 1, .25), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = c(0, 4, 8.5), expand = c(0.02, 0)) + + ggsurvfit::add_risktable() +#| include: false +targets::tar_read(df_event_data_small) |> + events_dataset(impute = FALSE) |> + dplyr::mutate(pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Increase", "Persistently low"))) |> + cox_regression() |> + plot_survival_smooth() + + ggplot2::labs( + fill = "PA change group", + color = "PA change group", + linetype = "PA change group" + ) +#| include: false +targets::tar_read(df_event_data) |> + events_dataset(impute = FALSE) |> + dplyr::mutate(pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Increase", "Persistently low"))) |> + cox_regression() |> + plot_survival_smooth() + + ggplot2::labs( + fill = "PA change group", + color = "PA change group", + linetype = "PA change group" + ) + + + +# Code from: pa_events_analyses.qmd +targets::tar_config_set(store = here::here("_targets")) +source(here::here("R/functions.R")) +source(here::here("R/glmnet-reg.R")) +library(targets) +library(tidyverse) +#| include: false +list("Univariate"=targets::tar_read(tbl_events_cox_regression_uv), +"Multivariate"=targets::tar_read(tbl_events_cox_regression), +"Imputed Multivariate"=targets::tar_read(tbl_events_mids_cox_regression)) |> + # purrr::map(gtsummary::modify_table_styling,column=p.value,hide=TRUE) |> + purrr::map(gtsummary::bold_p) |> + tbl_merged_named() +#| include: true +list("Univariate"=targets::tar_read(tbl_events_cox_regression_uv), +"Multivariate"=targets::tar_read(tbl_events_cox_regression), +"Imputed Multivariate"=targets::tar_read(tbl_events_mids_cox_regression)) |> + purrr::map(gtsummary::modify_table_styling,column=p.value,hide=TRUE) |> + tbl_merged_named() +#| echo: true +targets::tar_read(df_event_data) |> + # dplyr::select(-reg_bmi) |> + na.omit() |> + nrow() +#| include: false +targets::tar_read(tbl_events_cox_regression_small) + +targets::tar_read(df_event_data_small) +#| include: false +targets::tar_read(tbl_events_mids_cox_regression) +targets::tar_read("df_all_data_formatted") |> + events_ready() |> + dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> + (\(data){ + c("pase_0","pase_4") |> + purrr::map(\(exp){ + with(data,survival::coxph(as.formula(glue::glue("survival::Surv(time, status)~{exp}")))) |> + gtsummary::tbl_regression(exponentiate=TRUE)|> + fix_labels() + }) + })() |> + gtsummary::tbl_stack() +targets::tar_read("df_all_data_formatted") |> + pase_cutter(drop.nas = TRUE) |> + events_ready(v.groups=c("clin","lifestyle.events","ses", "assess.events","quartiles")) |> + dplyr::select(-dplyr::any_of(c("pase_0","pase_4","pase_change"))) |> + (\(data){ + c("pase_0_quartile","pase_4_quartile") |> + purrr::map(\(exp){ + list("Univariable"=cox_regression(data=data,all.vars = FALSE, use.strata = FALSE, outcome.var = exp), + "Multivariable"=cox_regression(data=data,all.vars = TRUE, use.strata = FALSE, outcome.var = exp)) |> + purrr::map(\(.x){ + .x |> gtsummary::tbl_regression(exponentiate=TRUE)|> + fix_labels() + }) |> + tbl_merged_named() + }) + })() |> + gtsummary::tbl_stack() +targets::tar_read("df_all_data_formatted") |> + pase_cutter(drop.nas = TRUE) |> + get_vars(vars.groups = c("clin","lifestyle.events","ses", "assess.events","quartiles"),vars.vec = c("inc_time")) |> + dplyr::mutate(time=time+inc_time/365) |> + dplyr::select(-dplyr::any_of(c("pase_0","pase_4","pase_change","inc_time","event.include","pase_4_quartile"))) |> + (\(data){ + list( + with(data,survival::coxph(as.formula(glue::glue("survival::Surv(time, status)~{exp}")))) |> + gtsummary::tbl_regression(exponentiate=TRUE)|> + fix_labels() |> gtsummary::add_glance_source_note() + ) + })() |> + gtsummary::tbl_stack() + + + +# Code from: pa_events_summaries.qmd +targets::tar_config_set(store = here::here("_targets")) +source(here::here("R/functions.R")) +source(here::here("R/glmnet-reg.R")) +library(targets) +library(tidyverse) +#| include: false +targets::tar_read(tbl_events_summary) +#| include: true +targets::tar_read(tbl_events_summary) |> mask_micro_summary(micro.n = 5) +#| include: false +ls <- targets::tar_read(df_event_data)|> + pase_cutter(drop.pase = TRUE) |> + dplyr::select(-tidyselect::all_of(c("status", "time"))) |> + dplyr::filter(!is.na(pase_change)) |> + labelling_data() |> + gtsummary::tbl_summary( + missing = "ifany", + by = pase_change, + # value = list(where(is.logical) ~ TRUE), + missing_text = "Missing" + ) |> + gtsummary::add_overall() |> + gtsummary::add_n() + +ls |> add_missing_stats() |> mask_micro_summary(micro.n = 5) +targets::tar_read(df_event_data)|> + pase_cutter(drop.pase = TRUE) |> + dplyr::select(pase_change, time) |> + dplyr::filter(!is.na(pase_change)) |> + labelling_data() |> + gtsummary::tbl_summary( + missing = "ifany", + by = pase_change, + type = gtsummary::all_continuous() ~ "continuous2", + statistic = list(gtsummary::all_continuous() ~ c("{median} ({p25}, {p75})","{mean} ({sd})")), + # value = list(where(is.logical) ~ TRUE), + + missing_text = "Missing" + ) |> + gtsummary::add_overall() +#| include: true +targets::tar_read(df_all_data_formatted) |> + (\(.x)summary(.x$pase_0))() + +# targets::tar_read(df_all_data_formatted) |> +# dplyr::filter(!is.na(pase_0),!is.na(pase_4))|> +# (\(.x)summary(.x$pase_0))() +#| include: false +targets::tar_read(df_all_data_formatted) |> + pase_cutter(drop.pase = TRUE) |> + dplyr::select(mrs_4, pase_change) |> + dplyr::mutate(pase_change = forcats::fct_relevel(pase_change, c("Persistent high", "Increase", "Decrease", "Persistent low"))) |> + na.omit() |> + (\(.x){ + table(mrs = .x$mrs_4, pase = .x$pase_change) + })() |> + rankinPlot::grottaBar(groupName = "pase", scoreName = "mrs") +targets::tar_read(df_event_data)|> + pase_cutter(drop.pase = FALSE) |> + dplyr::filter(!is.na(pase_change))|> + dplyr::select(pase_0,pase_4) |> + labelling_data() |> + gtsummary::tbl_summary() +targets::tar_read(df_all_data_formatted)|> + pase_cutter(drop.pase = FALSE) |> + dplyr::filter(!is.na(pase_change))|> + dplyr::count(pase_0_quartile,pase_4_quartile) |> + write_csv(here::here("out/event_sankey_data.csv")) +ds <- targets::tar_read(df_all_data_formatted) |> + get_vars(vars.groups = c("clin", "lifestyle", "ses", "assess.events", "extra")) |> + dplyr::select(-time, -status, -soc_status) |> + pase_cutter(drop.pase = TRUE) |> + labelling_data() + +ls <- list( + pase_out = ds |> + dplyr::mutate(event.filter = factor( + dplyr::case_when(is.na(pase_change) ~ "pase_incomplete", + !event.include ~ "early_event", + .default = "included" + ), + levels = c("pase_incomplete", "early_event", "included") + )), + event_out = ds |> + dplyr::mutate(event.filter = factor( + dplyr::case_when(!event.include ~ "early_event", + is.na(pase_change) ~ "pase_incomplete", + .default = "included" + ), + levels = c("early_event", "pase_incomplete", "included") + )) +) |> + purrr::map(dplyr::select, -event.include, -pase_change, -event) + +ls_tbl <- ls |> + purrr::map(\(.x){ + .x |> + dplyr::filter(event.filter != "pase_incomplete") |> + dplyr::mutate(event.filter = factor(event.filter)) + }) |> + (\(.x) list(.x, purrr::pluck(ls, 1) |> dplyr::mutate(event.filter = event.filter == "included")))() |> + purrr::list_flatten() +#| include: false +ls_tbl |> + purrr::map(\(.x){ + .x |> + gtsummary::tbl_summary( + by = event.filter, + missing = "ifany" + ) |> + gtsummary::add_p() + }) |> + (\(.x)gtsummary::tbl_merge(.x, c(names(.x)[1:2], "all_out")))() +targets::tar_read(df_all_data_formatted) |> + get_vars(vars.groups = c("clin", "lifestyle", "ses", "assess.events", "extra", "assess.pred")) |> + dplyr::select(-time, + # -status, + -soc_status) |> + pase_cutter(drop.pase = FALSE) |> + labelling_data() |> + dplyr::mutate(event.filter = factor( + dplyr::case_when( + !event.include | is.na(pase_change) ~ "excluded", + .default = "included" + ) + )) |> + dplyr::select(-who_4, -mdi_4, -mrs_4_above1, -mfi_gen_4) |> + gtsummary::tbl_summary( + by = event.filter, + missing = "ifany" + ) |> + gtsummary::add_p() +ds |> + dplyr::mutate(pase_incomplete=is.na(pase_change), + excluded=pase_incomplete | !event.include, + early_event=!event.include) |> + dplyr::select(early_event,pase_incomplete,excluded) |> + gtsummary::tbl_summary(by=excluded) +#| include: true +ls_tbl |> + purrr::pluck(3) |> + dplyr::select(-who_4, -mdi_4, -mrs_4_above1, -mfi_gen_4) |> + (\(.x){ + .x |> + gtsummary::tbl_summary( + by = event.filter, + missing = "no" + ) |> + gtsummary::add_p() + })() |> mask_micro_summary(micro.n = 5) +#| include: true +targets::tar_read(df_event_data)|> + pase_cutter(drop.pase = FALSE)|> + dplyr::filter(!is.na(pase_change)) |> + dplyr::select(-time, -status, -pase_0_quartile, -pase_4_quartile, -pase_change) |> + labelling_data() |> + dplyr::mutate(reg_female=ifelse(reg_female,"Female","Male")) |> + gtsummary::tbl_summary( + missing = "ifany", + by = reg_female, + value = list(where(is.logical) ~ TRUE) + )|> gtsummary::add_p() |> + mask_micro_summary() +#| include: false +events <- targets::tar_read(ls_all_events) |> + dplyr::bind_rows() |> + dplyr::left_join(targets::tar_read(df_all_data_formatted) |> + dplyr::select(c("event.include", "rdate", "enddate", "PNR")), by = c("CPR" = "PNR")) |> + dplyr::mutate( + date.event = as.Date(date.event), + event.trial = !date.event > enddate, + event.itt = !date.event > (lubridate::dmonths(6) + rdate) + ) |> + dplyr::mutate(event.type = dplyr::if_else(grepl("^death", event.type), "death", event.type)) + +ls <- list( + # Events during inclusion and during first 6 months after inclusion (Intention to treat) + events |> + dplyr::select(event.type, event.trial, event.itt) |> + tidyr::pivot_longer(cols = c("event.trial", "event.itt")) |> + dplyr::filter(value) |> + dplyr::select(-value) |> + gtsummary::tbl_summary(by = name), + + # All registred events + events |> + dplyr::select(event.type) |> + gtsummary::tbl_summary(), + + # All events in the selected group + events |> + dplyr::select(event.type, event.include) |> + # tidyr::pivot_longer(cols = c("event.trial","event.itt")) |> + dplyr::filter(event.include) |> + dplyr::select(-event.include) |> + gtsummary::tbl_summary(), + + # All considered events (first event) + targets::tar_read(df_events_deaths)|> + dplyr::mutate(event.type = dplyr::if_else(grepl("^death", event.type), "death", event.type)) |> + dplyr::select(event.type) |> + gtsummary::tbl_summary(), + + # All included events (first event) + targets::tar_read(df_all_data_formatted)|> + pase_cutter(drop.pase = TRUE) |> + dplyr::filter(!is.na(pase_change))|> + dplyr::filter(event.include) |> + # dplyr::select(-event.include)|> + dplyr::mutate(event = dplyr::if_else(grepl("^death", event), "death", event)) |> + dplyr::select(event) |> + dplyr::filter(!is.na(event)) |> + gtsummary::tbl_summary() +) |> + setNames(c("During trial", "All events", "Selected events", "Considered", "Included")) + +ls[4] |> + purrr::map(\(.x) .x |> + mask_micro_summary(micro.n = 5)) |> + tbl_merged_named() +targets::tar_read(df_all_data_formatted)|> + pase_cutter(drop.pase = TRUE) |> + dplyr::filter(!is.na(pase_change))|> + dplyr::filter(event.include) |> + # dplyr::select(-event.include)|> + dplyr::mutate(event = dplyr::case_when(grepl("^death", event)~"Mors", + grepl("^DI6", event)~ "DI61-4", + .default = event)) |> + dplyr::select(event) |> + dplyr::filter(!is.na(event)) |> + gtsummary::tbl_summary() |> mask_micro_summary(micro.n = 5) +targets::tar_read(df_event_data) |> + pase_cutter(drop.pase = TRUE) |> + dplyr::filter(!is.na(pase_change)) |> + events_table(by="pase_change") + + + +# Code from: sex_events.qmd +targets::tar_config_set(store = here::here("_targets")) +source(here::here("R/functions.R")) +source(here::here("R/glmnet-reg.R")) +library(targets) +library(tidyverse) + +df <- targets::tar_read(df_all_data_formatted) |> + get_vars(vars.groups = c("clin","lifestyle.events","ses", "ssri")) |> + dplyr::rename(status=status.all, + time=time.all) +df |> + pase_cutter(drop.nas = TRUE,drop.pase = TRUE) |> + dplyr::mutate(reg_female=factor(ifelse(reg_female,"Female","Male"))) |> + (\(.x) { + split(.x, .x$reg_female) + })() |> lapply(\(.x) { + ls <- list("Univariate"=.x |> dplyr::select(-reg_female,-rtreat) |> + standard_multi_cox_table(all.vars = FALSE) |> gtsummary::add_nevent(), +"Multivariate"=.x |> dplyr::select(-reg_female,-rtreat) |> + standard_multi_cox_table(all.vars = TRUE) |> gtsummary::add_nevent())|> + purrr::map(gtsummary::bold_p) |> + tbl_merged_named() + ls |> + gtsummary::modify_table_body(~filter(.x, variable == "pase_change")) + }) |> + (\(.x) { + gtsummary::tbl_stack(.x,group_header=names(.x)) + })() + + + + + + +# Code from: ssri_events.qmd +targets::tar_config_set(store = here::here("_targets")) +source(here::here("R/functions.R")) +source(here::here("R/glmnet-reg.R")) +library(targets) +library(tidyverse) + +df <- targets::tar_read(df_all_data_formatted) |> + get_vars(vars.groups = c("clin","lifestyle.events","ses", "ssri")) |> + dplyr::rename(status=status.all, + time=time.all) +df |> + dplyr::select(#-event.include, + -rtreat_placebo)|> + labelling_data() |> + gtsummary::tbl_summary(by=rtreat) |> + gtsummary::add_overall() #|> + # mask_micro_summary() +df |> + dplyr::select( + -rtreat_placebo, + -pase_4#, + # -reg_bmi + ) |> + cox_regression(outcome.var = "rtreat",use.strata = FALSE) |> + gtsummary::tbl_regression(exponentiate = TRUE, + add_estimate_to_reference_rows = TRUE, + show_single_row = where(is.logical)) |> + gtsummary::bold_p() |> + fix_labels() +df |> + dplyr::select(-rtreat_placebo) |> + cox_regression(outcome.var = "rtreat",all.vars = FALSE, use.strata = TRUE,include_formula = TRUE) |> + plot_survival_smooth() +df |> + pase_cutter(drop.nas = TRUE,drop.pase = TRUE) |> + (\(.x) { + split(.x, .x$rtreat) + })() |> lapply(\(.x) { + ls <- list("Univariate"=.x |> dplyr::select(-rtreat_placebo, -rtreat) |> + standard_multi_cox_table(all.vars = FALSE), +"Multivariate"=.x |> dplyr::select(-rtreat_placebo, -rtreat) |> + standard_multi_cox_table(all.vars = TRUE))|> + purrr::map(gtsummary::bold_p) |> + tbl_merged_named() + ls |> + gtsummary::modify_table_body(~filter(.x, variable == "pase_change")) + }) |> tbl_stack_named() + # gtsummary::tbl_stack(group_header=levels(factor(df$rtreat))) + + + diff --git a/2 Longterm/260315/cont_pase_sens.docx b/2 Longterm/260315/cont_pase_sens.docx new file mode 100755 index 0000000..5bb4616 Binary files /dev/null and b/2 Longterm/260315/cont_pase_sens.docx differ diff --git a/2 Longterm/260315/events_type_sens.docx b/2 Longterm/260315/events_type_sens.docx new file mode 100755 index 0000000..9b60a40 Binary files /dev/null and b/2 Longterm/260315/events_type_sens.docx differ diff --git a/2 Longterm/260315/functions.R b/2 Longterm/260315/functions.R new file mode 100755 index 0000000..6affb00 --- /dev/null +++ b/2 Longterm/260315/functions.R @@ -0,0 +1,4131 @@ +# pop <- haven::read_sas(here::here("E:/rawdata/709203/Population/pop_talos.sas7bdat")) + +# sst <- list.files(here::here("E:/rawdata/709203/Eksterne data"), pattern = "*.sas7bdat", full.names = TRUE) |> +# purrr::map(haven::read_sas) + +source(here::here("R/glmnet-reg.R")) + +#' Read all sas files in folder to list +#' +#' @param path folder path +#' +#' @return list +sas2list <- function(path) { + ls <- list.files(here::here(path), pattern = "*.sas7bdat", full.names = TRUE) |> + purrr::map(haven::read_sas) + names(ls) <- list.files(here::here(path), pattern = "*.sas7bdat") |> + gsub(".sas7bdat", "", x = _) |> + toupper() + ls +} + + +#' Flatten multilevel list +#' +#' @param paths character vector of folder paths +#' +#' @return flattened list +flatmultiread <- function(paths) { + paths |> + purrr::map(sas2list) |> + purrr::list_flatten() +} + +# ls <- targets::tar_read(reg_data) + +# Vectors are kept for compatibility. Calling functions can be done from within other functions. So much easier! + +date_cutter <- function() as.Date("2023-01-01") +date.cut <- date_cutter() # The earliest date will define the overall date cut + +censor_cutter <- function() 12 +censor.cut <- censor_cutter() # years of maximum follow up, due to small numbers + +vasc.diags <- c("I21", "I61", "I63", "I64","G45", "K28") + +#' Extract deaths from Dødsårsagsregiseret +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(reg_data) |> get_deaths() +get_deaths <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + + + # vasc.death.tilg <- mapply(ls$DAR_T_DODSAARSAG_2[, "C_DODTILGRUNDL_ACME"], + # FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + # i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + # i_3 %in% diags.vasc # Rowvise matching + # } + # ) + + vasc.death.tilg <- ls$DAR_T_DODSAARSAG_2[["C_DODTILGRUNDL_ACME"]] |> substr(1, 3) %in% diags.vasc + + + vasc.death.any <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DODTILGRUNDL_ACME", "C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + # + vasc.death.other <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + + diag.either <- xor(vasc.death.other, vasc.death.tilg) + diag.both <- vasc.death.other & vasc.death.tilg + + vasc.death.diags <- + apply( + mapply( + ls$DAR_T_DODSAARSAG_2[, c( + "C_DODTILGRUNDL_ACME", + "C_DOD_1A", + "C_DOD_1B", + "C_DOD_1C", + "C_DOD_1D" + )], + FUN = function(i) { + # mapply inside apply call to handle rowvise matching in in matrix + substr(i, 1, 3) # Substr only the 3 first characters to match by group + } + ), + 1, + paste, + collapse = "," + ) + + deaths.vasc <- + ls$DAR_T_DODSAARSAG_2 |> + dplyr::select(K_CPR, D_STATDATO) |> + dplyr::filter(vasc.death.tilg) + + df.death.all <- ls$CPR3_T_PERSON |> + dplyr::filter(C_STATUS == 90) |> # People migrating are filtered (n ~ 1) + dplyr::select(c( + "V_PNR", + "D_STATUS_HEN_START" + )) |> + dplyr::left_join(deaths.vasc, by = c("V_PNR" = "K_CPR")) |> + dplyr::mutate(vasc_death = !is.na(D_STATDATO)) |> + dplyr::transmute( + PNR = V_PNR, + death_date = D_STATUS_HEN_START, + vasc_death = vasc_death + ) + + df.death.all |> dplyr::filter(death_date < max.date) +} + + +#' Title +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' ls <- targets::tar_read(reg_list) +get_events <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + ident.vars <- toupper(c("_recnum", "_cpr")) + + df.vasc.events.lpr <- ls$LPR_T_DIAG |> + dplyr::mutate(dia.f = substr(C_DIAG, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # C_DIAGTYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars), + "C_DIAG", + "C_DIAGTYPE" + ) |> + dplyr::left_join( + ls$LPR_T_ADM |> dplyr::select( + tidyselect::ends_with(ident.vars), + "D_INDDTO", + "C_INDM", + "D_UDDTO", + "C_UDM", + "C_SGH", + "C_AFD", + "C_ADIAG" + ), + by = c("V_RECNUM" = "K_RECNUM") + ) + + ## LPR-F - LPR 3 + + ident.vars.lpr3 <- toupper(c("cpr", "_kontakt", "DW_EK_FORLOEB")) + + df.vasc.events.lpr3 <- ls$LPR_F_DIAGNOSER |> + dplyr::mutate(dia.f = substr(DIAGNOSEKODE, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # DIAGNOSETYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars.lpr3), + "DIAGNOSEKODE", + "DIAGNOSETYPE" + ) |> + dplyr::left_join( + ls$LPR_F_KONTAKTER |> dplyr::select( + ends_with(ident.vars.lpr3), + "DATO_START", + "DATO_SLUT", + "PRIORITET" + ), + by = c("DW_EK_KONTAKT") + ) |> + dplyr::mutate(PRIORITET = as.character((PRIORITET == "ATA1") + 1)) # If ATA1 then 1, if not (ATA3) then 2, cowboy coding + + df.vasc.events <- dplyr::full_join(df.vasc.events.lpr, df.vasc.events.lpr3, by = c( + "V_CPR" = "CPR", + "C_DIAG" = "DIAGNOSEKODE", + "C_DIAGTYPE" = "DIAGNOSETYPE", + "D_INDDTO" = "DATO_START", + "D_UDDTO" = "DATO_SLUT", + "C_INDM" = "PRIORITET" + )) |> + dplyr::mutate(date.event = D_INDDTO) + + df.vasc.events |> dplyr::filter(date.event < max.date) +} + + +# deaths <- targets::tar_read(df_deaths) +# events <- targets::tar_read(df_events) +# clinical <- targets::tar_read(pop_df) + + +#' Filter only truly considered events +#' +#' @param data +#' +#' @return tibble +define_events <- function(data) { + data |> dplyr::filter( + C_DIAGTYPE == "A", # Primary diagnosis + C_INDM == "1", # Acutely admitted + difftime(date.event, rdate, units = "days") > 5 # More than five (5) days after randomisation/primary stroke + ) +} + +#' The big merger and filter of events +#' +#' @param ls list of events, deaths and clinical +#' +#' @return tibble +#' +#' @examples +#' ls <- list(events = targets::tar_read(df_events), deaths = targets::tar_read(df_deaths), clinical = targets::tar_read(pop_df)) +#' ls |> all_events() +all_events <- function(ls) { + # ls <- list(events = targets::tar_read(df_events), deaths = targets::tar_read(df_deaths), clinical = targets::tar_read(pop_df)) + df.events <- dplyr::full_join( + purrr::pluck(ls, "events"), + purrr::pluck(ls, "deaths") |> + dplyr::mutate( + # These are just added to ease later filtering + C_DIAGTYPE = "A", + C_INDM = "1" + ), # Ads diagtype=A, C_INDM=1 for easier sorting later + by = c( + "V_CPR" = "PNR", + "date.event" = "death_date", + "C_DIAGTYPE", + "C_INDM" + ) + ) |> + dplyr::full_join(dplyr::select(purrr::pluck(ls, "clinical"), c("PNR", "rdate", "enddate")), + by = c("V_CPR" = "PNR") + ) |> + dplyr::arrange(date.event) |> # Sort by event date + dplyr::mutate(event.type = dplyr::if_else( + is.na(C_DIAG), + dplyr::if_else(vasc_death, "death.vasc", "death.other"), + substr(C_DIAG, 1, 4) + )) + + df.events |> + dplyr::group_split(V_CPR) |> # Splits by CPR + purrr::map(define_events) |> # Custom function to specify criteria for events + purrr::discard(\(x) nrow(x) == 0) |> # Discard empty elements + purrr::map(dplyr::transmute, # Saving only relevant variables + CPR = V_CPR, + date.event, + event.type) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' ls <- list(events = targets::tar_read(df_events), deaths = targets::tar_read(df_deaths), clinical = targets::tar_read(pop_df)) +#' ls |> merge_events() +merge_events <- function(data){ + data|> + all_events() |> + purrr::modify(\(x) x[1, ]) |> # Select first event + purrr::list_rbind() +} + + +#' Count number of prescriptions of given ATC group for each CPR +#' +#' @param atc.code atc group +#' @param data dataset from LMS +#' +#' @return tibble +count_treat <- function(atc.code, data) { + data |> + dplyr::filter(grepl(atc.code, ATC)) |> + dplyr::count(CPR) |> + dplyr::filter(n > 1) +} + + +#' Get LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_lms <- function(ls, max.date, atc.tbl) { + ls$LMS_EPIKUR |> + dplyr::filter(grepl(atc.tbl[1], ATC)) |> + dplyr::left_join(ls$LMS_LAEGEMIDDELOPLYSNINGER) |> + dplyr::filter(as.Date(ACTDATE) < max.date) +} + +#' Get count of treated patients from LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_treated <- function(ls, + max.date = date.cut, + atc.tbl = c( + atc.antidep = "N06A", + atc.ssri = "N06AB" + )) { + df <- atc.tbl |> + purrr::map(count_treat, get_lms(ls, max.date, atc.tbl)) |> + purrr::reduce(dplyr::full_join, by = "CPR") + colnames(df) <- c("CPR", paste0("n.", names(atc.tbl))) + df +} + +#' BMI calc, drops +#' +#' @param w weight in kg +#' @param h height in cm +#' @param data data set +#' +#' @return tibble +bmi_calc <- function(data, drop = TRUE) { + # After inspection, both h+w are missing if any is missing + out <- data |> dplyr::mutate(reg_bmi = suppressWarnings(as.numeric(dplyr::if_else(reg_vaegt == "NA", reg_vaegt_anslaaet, reg_vaegt)) / ((as.numeric(reg_hojde) / 100)^2))) + + if (drop) { + out <- out |> + dplyr::select(-dplyr::all_of(c("reg_vaegt", "reg_vaegt_anslaaet", "reg_hojde"))) + } + out +} + +is_equal <- function(data, test) { + data == test +} + +#' Load clinical population data +#' +#' @return tibble +#' @examples +#' get_clinical() |> colnames() +#' +get_clinical <- function() { + sas2list("E:/rawdata/709203/Population")[[2]] |> + correct_na() |> + dplyr::mutate( + reg_smoker = dplyr::case_match( + reg_rygning, "1" ~ TRUE, + c("2", "3", "4") ~ FALSE, + "9" ~ NA + ), + # Living alone defined as not together with somebody + reg_alone = dplyr::case_match( + reg_civil, "1" ~ FALSE, + c("2", "3") ~ TRUE, + "9" ~ NA + ), + reg_more_alc = dplyr::case_match( + reg_alkohol, "1" ~ FALSE, + "2" ~ TRUE, + "9" ~ NA + ), + reg_female = sex == "Kvinde", + dplyr::across( + .cols = c( + "reg_hyperten", + "reg_diabetes", + "reg_atriefli", + "reg_perifer_arteriel", + "reg_tidl_tci", + "reg_ami" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + "2" ~ FALSE, + "9" ~ NA + ) + ), + dplyr::across( + .cols = c( + "reg_trombolyse", + "reg_trombektomi" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + c("3","4") ~ FALSE, + "9" ~ NA + ) + ), + reg_any_perf = reg_trombolyse | reg_trombektomi + ) |> + bmi_calc() +} + +# get_clinical() |> pragmatic_imputation() |> skimr::skim() + +# get_clinical <- function() { +# sas2list("E:/rawdata/709203/Population")[[2]] |> +# dplyr::mutate( +# reg_smoker = reg_rygning == 1, +# reg_cohabiting = reg_civil == 1, +# reg_more_alc = reg_alkohol == 2, +# reg_female = sex == "Kvinde", +# dplyr::across(.cols = c("reg_hyperten", "reg_diabetes", "reg_atriefli", "reg_perifer_arteriel", "reg_tidl_tci", "reg_ami", "reg_trombolyse", "reg_trombektomi"), ~ .x == 1), +# reg_any_perf = reg_trombolyse | reg_trombektomi +# ) |> +# bmi_calc() +# } + +pragmatic_imputation <- function(data, vec=c("reg_trombolyse", "reg_trombektomi","reg_any_perf")) { + # Assumes, if not TRUE, then FALSE (gets rid of NAs) + data |> dplyr::mutate(dplyr::across(.cols = tidyselect::any_of(vec), ~dplyr::if_else(.x,TRUE,FALSE,missing = FALSE))) +} + +#' Load all registry tables to list +#' +#' @return list +get_reg_ls <- function() { + flatmultiread(c("E:/rawdata/709203/Eksterne data", "E:/rawdata/709203/Grunddata")) +} + + +#' Definition of relevant variables from DST tables +#' +#' @return +define_dst_vars <- function() { + list( + bef = c("PNR", "FAMILIE_ID"), + faik = c("FAMILIE_ID", "FAMAEKVIVADISP_13", "FAMSOCIOGRUP_13"), + ras = c("PNR", "SOC_STATUS_KODE"), + uddf = c("PNR", "HFAUDD") + ) +} + +#' Simple wrapper of dplyr::select +#' +#' @param data +#' @param vars +#' +#' @return +select_vars <- function(data, vars) { + data |> dplyr::select({{ vars }}) +} + +#' Subset DST tables to only include relvant variables. +#' +#' @param ls List of all registry tables +#' +#' @return +get_dst_tables <- function(ls) { + dst_vars <- define_dst_vars() + dst_tbl <- toupper(names(dst_vars)) + ls.all <- purrr::map(seq_along(dst_vars), function(i) { + ls.reg <- ls[grepl(paste0("^", dst_tbl[i]), names(ls))] |> purrr::map(select_vars, vars = dst_vars[[i]]) + names(ls.reg) <- paste0("y", stringr::str_extract(names(ls.reg), "[0-9]{4}")) + ls.reg + }) + names(ls.all) <- dst_tbl + ls.all +} + +#' Wrapper to generate string matching pattern for stringr::str_detect() +#' +#' @param data +#' +#' @return +match_str <- function(data) { + paste0("[", paste0(data, collapse = ","), "]") +} + +#' Wrapper to generate string matching pattern for grepl() +#' +#' @param data character vector +#' +#' @return +match_str_grepl <- function(data) { + paste0("(", paste0(data, collapse = "|"), ")") +} + +# get_dst_tables(ls) + +#' Generate sequence of previous N length +#' +#' @param data numeric vector of length 1 +#' +#' @return +#' +#' @examples +#' last5y(10) +#' last5y(c(10, 6, 3)) +lastNy <- function(data, n = 5) { + paste0("y", seq((data - n), data) - 1) +} + +#' Filter PNR (cpr) across list elements +#' +#' @param data list of tibbles to pass through +#' @param index index number (PNR/CPR) +#' +#' @return tibble +filterCPRacross <- function(data, index) { + data |> + purrr::map(function(i) { + i[i$PNR == index, ] + }) |> + purrr::list_rbind() +} + +#' Summarise data from last 5 years prior to inclusion +#' +#' @param data list with +#' @param v.median variables to get median +#' @param v.latest variables to get latest +#' @param v.mean variables to get mean +#' +#' @return tibble +previousNyears <- function(data, data.clin, n.years = 5, v.median = NULL, v.latest = c("FAMSOCIOGRUP_13", "SOC_STATUS_KODE"), v.mean = c("FAMAEKVIVADISP_13")) { + df.cpryear <- data.clin |> dplyr::transmute( + CPR = PNR, + year = as.numeric(format(as.Date(rdate), "%Y")) + ) + + seqs <- purrr::map(df.cpryear$year, lastNy, n = n.years) + + seq_along(seqs) |> + purrr::map(function(i) { + df <- data[c(seqs[[i]])] |> + filterCPRacross(index = df.cpryear$CPR[i]) |> + dplyr::group_by(PNR) |> + dplyr::summarise( + dplyr::across(tidyselect::any_of(v.latest), \(x) tail(x, n = 1), .names = "{.col}.latest"), + dplyr::across(tidyselect::any_of(v.mean), \(x) mean(x, na.rm = TRUE), .names = "{.col}.{n.years}.mean"), + dplyr::across(tidyselect::any_of(v.median), \(x) median(x, na.rm = TRUE), .names = "{.col}.{n.years}.median") + ) + }) |> + purrr::list_rbind() +} + + +#' Extract relevant and summarised data from BAF and FAIK +#' +#' @param data list of dst data tables +#' +#' @return tibble +#' +#' @examples +#' get_reg_ls() |> +#' get_dst_tables() |> +#' get_beffaikras() +get_beffaikras <- function(data, clin.data = get_clinical()) { + data <- data[stringr::str_detect(match_str(c("BEF", "FAIK", "RAS")), names(data))] |> purrr::list_flatten() + + years <- stringr::str_extract(names(data), "y[0-9]{4}") + + years[duplicated(years)] |> + purrr::map(grep, years) |> + purrr::map(function(i) { + data[c(i)] |> purrr::reduce(dplyr::full_join) + }) |> + purrr::set_names(years[duplicated(years)]) |> + previousNyears(data.clin = clin.data) + + ## BEF + ## # Befolkningsoversigt. Data skal bruges for at kunne udtrække husstandsindkomst. + ## FAIK + ## # Familieindkomst. Familie id skal flættes med ID fra BEF for hvert år for at tage hensyn til evt skifte i status. + ## FAMAEKVIVADISP_13 er relevante variabel for ækvivaleret indkomst + ## Der findes også familiesocioøkonomisk status. Gør som Sine. Be done with it! +} + +#' Title +#' +#' @return +#' @export +#' +#' @examples +#' data <- read_edu_level() +#' data |> dplyr::count(ISCED) +read_edu_level <- function() { + haven::read_dta("E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l4_k.dta") |> + dplyr::transmute( + HFAUDD = start, + ISCED = AUDD_LEVEL_L1L4_K + ) +} + +haven::read_dta( + "E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l3_k.dta") |> + dplyr::count(AUDD_LEVEL_L1L3_K) + +# ls <- get_reg_ls() |> get_dst_tables() + +get_uddf <- function(ls) { + ls |> + purrr::pluck("UDDF") |> + purrr::pluck(1) |> + dplyr::mutate(HFAUDD = as.character(HFAUDD)) |> + dplyr::left_join(read_edu_level()) |> + dplyr::group_by(PNR) |> + dplyr::summarise(ISCED = max(ISCED), .groups = "keep") |> + dplyr::mutate( + ISCED = as.numeric(ISCED), + ISCED_lvl = dplyr::case_match(ISCED, 0:2 ~ "low", + 3:4 ~ "medium", + 5:9 ~ "high", + .default = NA + ), + ISCED_bin = dplyr::case_match(ISCED, 0:3 ~ "low", + 4:9 ~ "high", + .default = NA + ) + ) +} + + +#' Collects all relevant variables from DST tables +#' +#' @param ls list of all registry tables +#' @param df.clin clinical data set +#' +#' @return tibble +#' @examples +#' get_reg_ls() |> get_dst(df.clin = get_clinical()) +get_dst <- function(ls, df.clin) { + ls_dst <- get_dst_tables(ls) + ls_dst |> + ## BEFxFAIKxRAS + get_beffaikras(clin.data = df.clin) |> + dplyr::full_join( + ## UDDF + get_uddf(ls_dst) + ) +} + +#' Eases pipe renaming of columns +#' +#' @param data tibble, list or other object, for which names() makes sense. see ?setNames +#' @param prefix prefix to add +#' @param exclude names not to modify +#' @param new.names character vector of all new names +#' +#' @return object of same class as data +#' +#' @examples +#' set_colnames(data = mtcars, prefix = "WOW", exclude = "mpg") +set_colnames <- function(data, new.names = NULL, prefix = NULL, exclude = c("PNR", "CPR"), prefix.sep = "_") { + if (is.null(new.names)) { + nms <- names(data) + } else { + nms <- new.names + } + + if (is.null(prefix)) { + nms.mod <- nms + } else { + nms.mod <- paste(prefix, nms, sep = prefix.sep) + } + + setNames( + object = data, + nm = dplyr::if_else(stringr::str_detect(match_str(exclude), nms), + nms, + nms.mod + ) + ) +} + + +#' Store of variable names for data sub-setting +#' +#' @return list +#' @examples +#' define_variables() +define_variables <- function() { + list( + talos = c( + "age", + "reg_female", + "nihss_0", + "reg_trombolyse", + "reg_trombektomi", + # "rtreat", + "pase_0", + "reg_alone", + "reg_bmi", + # "reg_hojde", + # "reg_vaegt_alt", + "reg_smoker", + "reg_more_alc", + "reg_hyperten", + "reg_diabetes", + "reg_tidl_tci", + "reg_atriefli", + "reg_ami", + "reg_perifer_arteriel" + ), + clin = c( + "age", + "reg_female", + "nihss_0", + "reg_trombolyse", + "reg_trombektomi", + # "rtreat", + "rtreat_placebo"), + lifestyle=c( + "pase_0", + "pase_4", + "reg_alone", + "reg_bmi", + # "reg_hojde", + # "reg_vaegt_alt", + "reg_smoker", + "reg_more_alc", + "reg_hyperten", + "reg_diabetes", + "reg_tidl_tci", + "reg_atriefli", + "reg_ami", + "reg_perifer_arteriel"), + lifestyle.events=c( + "pase_0", + "pase_4", + "reg_alone", + # "reg_bmi", + # "reg_hojde", + # "reg_vaegt_alt", + "reg_smoker", + "reg_more_alc", + "reg_hyperten", + "reg_diabetes", + # "reg_tidl_tci", + "reg_atriefli", + # "reg_perifer_arteriel", + "reg_ami"), + lifestyle.bmi=c( + "reg_bmi" + ), + ses=c( + # "soc_status", + # "soc_status_work", + "soc_status_nowork", + # "fam_indk", + "fam_indk_hl", + # "fam_indk_high", + # "fam_indk_low", + # "edu_level", + # "edu_high", + # "edu_low", + "edu_level_hl" + ), + assess.events = c( + "who_4", + "mdi_4", + "mfi_gen_4", + "mrs_4_above1", + "time", + "status", + "event.include" + ), + assess.events.pre = c( + "who_4", + "mdi_4", + "mfi_gen_4", + "mrs_0_above0", + "time", + "status", + "event.include" + ), + assess.pred = c( + "who_0", + "mrs_0_above0" + ), + extra = c( + "soc_status", + "pase_0", + "pase_4" , + "event" + ), + cpr = "PNR", + ssri=c("rtreat", + "time.all", + "status.all", + "rdate", + "enddate"), + quartiles = c( + "pase_0_quartile", + "pase_4_quartile" + ) + ) +} + +#' Get var names in vector from group names. Possibility to keep all vars for as log as possible. Can be supplied to `gtsummary` functions +#' +#' @param groups vector of group names. See names(define_variables()) for options +#' +#' @return +#' @export +#' +#' @examples +get_var_vec <- function(v.groups){ + define_variables()[{{ v.groups }}] |> purrr::list_c() +} + +#' SUbsets dataset based on variable group names as defined +#' +#' @param vector character vector of category names +#' +#' @return character vector +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> get_vars(c("universal", "events")) +get_vars <- function(data, vars.groups, vars.vec=NULL) { + data |> dplyr::select(tidyselect::any_of(c("pase_0","pase_4",".imp",".id",get_var_vec(vars.groups),vars.vec))) +} + + + +#' Collect all relevant data for the events analysis data set +#' +#' @param ls ls of tibbles +#' +#' @return tibble +#' +#' @examples +#' ls <- targets::tar_read(list_filtered) +#' +collectall <- function(ls) { + purrr::pluck(ls, "clinical") |> + dplyr::left_join(purrr::pluck(ls, "all_events") |> set_colnames(prefix = "event"), by = c("PNR" = "CPR")) |> + dplyr::left_join(purrr::pluck(ls, "dst") |> set_colnames(prefix = "dst"), by = "PNR") +} + + +## Formatting for analysis + +#' Function to cut and group PASE data +#' +#' @param data data set including pase_0 and _4 +#' +#' @return tibble +pase_cutter <- function(data, pase.rev = TRUE, drop.pase = FALSE, drop.nas=FALSE) { + data.classes <- class(data) + if ("mids" %in% data.classes) { + data <- data |> mice::complete(action = "long", include = TRUE) + } + + data <- data |> + dplyr::mutate(dplyr::across(.cols = c("pase_0", "pase_4"), \(i) { + cut(x = i, breaks = quantile(pase_0, na.rm = TRUE), labels = 1:4, include.lowest = TRUE) + }, .names = "{.col}_quartile")) |> + dplyr::mutate(pase_change = factor(dplyr::case_when( + pase_0_quartile == 1 & pase_4_quartile == 1 ~ "Persistently low", + pase_0_quartile %in% 2:4 & + pase_4_quartile %in% 2:4 ~ "Persistently high", + pase_0_quartile == 1 & + pase_4_quartile %in% 2:4 ~ "Increase", + pase_0_quartile %in% 2:4 & + pase_4_quartile == 1 ~ "Decrease" + ), ordered = FALSE), + pase_change=factor(pase_change,levels=c("Persistently low", "Increase", "Decrease", "Persistently high"))) + + + if (drop.pase) { + data <- data |> dplyr::select(-tidyselect::all_of(c("pase_0_quartile", "pase_4_quartile", "pase_0", "pase_4"))) + } + + + if (pase.rev) { + data <- data |> dplyr::mutate( + pase_change = factor(pase_change, levels = c("Persistently high", "Decrease", "Increase", "Persistently low")) + ) + } + + if (drop.nas) { + data <- data |> + dplyr::filter(!is.na(pase_change)) + } + + + if ("mids" %in% data.classes) { + data |> mice::as.mids() + } else { + data + } +} + +# as.Date(data$event_date.event) +define_status_time <- function(data) { + data |> dplyr::mutate(dplyr::across(c("rdate", "enddate", "event_date.event"), ~ as.Date(.x)), + time = difftime(dplyr::if_else(is.na(event_date.event), date_cutter(), event_date.event), enddate) |> lubridate::time_length("years"), + time.all = difftime(dplyr::if_else(is.na(event_date.event), date_cutter(), event_date.event), rdate) |> lubridate::time_length("years"), + status = as.integer(!is.na(event_event.type)), + time = dplyr::if_else(time > censor_cutter(), censor_cutter(), time), + time.all = dplyr::if_else(time.all > censor_cutter(), censor_cutter(), time.all), + status = dplyr::if_else(time > censor_cutter(), FALSE, status), + status.all = dplyr::if_else(time.all > censor_cutter(), FALSE, status), + # status= dplyr::if_else(status,1,0), + event.include = time > 0 + ) +} + +# as.integer(c(TRUE,FALSE)) + +#' Grouping soc status +#' +#' @param data tibble +#' +#' @return tibble +group_soc_status <- function(data) { + data |> + dplyr::mutate( + soc_status = factor(dplyr::case_when( + soc_status < 200 ~ "work", + soc_status == 200 ~ "off", + # only ~4 in the data set off work + soc_status >= 200 ~ "outside" + )), + soc_status_work = soc_status == "work", + soc_status_nowork = !soc_status_work + ) +} + +#' Correction of character NA +#' +#' @param data tibble +#' @param char.missing character vector of entries to consider as NA +#' +#' @return tibble +correct_na <- function(data, char.missing = "NA") { + data |> dplyr::mutate(dplyr::across(dplyr::where(is.character), ~ dplyr::na_if(.x, char.missing))) +} + +#' Formatting the complete data set +#' +#' @param data the merged raw data set +#' +#' @return tibble +#' @examples +#' ds <- targets::tar_read(df_all_data) |> +#' data_formatting() |> +#' subset_df("mdi") +#' ds |> skimr::skim() +#' ds |> View() +data_formatting <- function(data) { + to_logical <- grep(match_str_grepl(c("missings", "incompletes")), names(data)) + + suppressWarnings( + data |> + correct_na() |> + dplyr::mutate(dplyr::across(all_of(to_logical), ~ .x == "TRUE")) |> + dplyr::mutate( + # This uses the work-corrected score + # pase_0 = dplyr::if_else(pase_score_missings_w_0 | is.na(talos_pase10_0), NA, pase_score_sum_w_0), + # pase_4 = dplyr::if_else(pase_score_missings_w_4 | is.na(talos_pase10_4), NA, pase_score_sum_w_4), + # Below is the plain PASE scor used according to the manual used with TALOS + pase_0 = dplyr::if_else(pase_score_missings_0, NA,pase_score_sum_0), + pase_4 = dplyr::if_else(pase_score_missings_4, NA,pase_score_sum_4), + who_0 = as.numeric(talos_who07_0), + who_4 = as.numeric(talos_who07_4), + mrs_0 = factor(substr(talos_mrs01_0, 1, 1), ordered = FALSE), + mrs_0_above0 = (as.numeric(mrs_0) - 1) > 0, + mrs_4 = factor(substr(talos_mrs01_4, 1, 1), ordered = FALSE), + mrs_4_above1 = (as.numeric(mrs_4) - 1) > 1, + mdi_4 = as.numeric(talos_mdi12_4), + mfi_gen_4 = as.numeric(talos_mfi_gen_4), + nihss_0 = as.numeric(talos_nihss16_0), + soc_status = dst_SOC_STATUS_KODE.latest, + fam_indk = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 3), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "medium", "high"), + include.lowest = TRUE + ), + fam_indk_bin = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 2), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "high"), + include.lowest = TRUE + ), + fam_indk_hl=forcats::fct_rev(fam_indk), + fam_indk_high = dplyr::if_else(fam_indk_bin=="high",TRUE,FALSE), + fam_indk_low = dplyr::if_else(fam_indk_bin=="low",TRUE,FALSE), + edu_level = factor(dst_ISCED_lvl, ordered = FALSE, levels = c("low", "medium", "high")), + edu_high = dplyr::if_else(dst_ISCED_bin=="high",TRUE,FALSE), + edu_low = dplyr::if_else(dst_ISCED_lvl=="low",TRUE,FALSE), + edu_level_hl=forcats::fct_rev(edu_level), + rtreat_placebo=rtreat=="Placebo", + event = factor(event_event.type) + ) |> + group_soc_status() |> + define_status_time() |> + pragmatic_imputation(vec = c("reg_trombolyse", "reg_trombektomi","reg_any_perf")) + ) +} + + +#' Title +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> +#' events_ready() |> +#' View() +events_ready <- function(data,v.groups=c("clin","lifestyle.events","ses", "assess.events"),vars.vec=NULL) { + out <- data |> + get_vars(vars.groups = v.groups,vars.vec=vars.vec) + + + if ("event.include" %in% names(out)){ + out <- out |> + dplyr::filter(event.include) |> + # dplyr::filter(!is.na(pase_0),!is.na(pase_4))|> + dplyr::select(-tidyselect::all_of("event.include"))# |> + # labelling_data() + } + + return(out) +} + + +#' Title +#' +#' @param data +#' +#' @return +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' data |> events_ready() |> +#' View() +talos_ready <- function(data,v.groups=c("talos","ssri")) { + data |> + get_vars(vars.groups = v.groups) |> + dplyr::mutate(inc_time=lubridate::time_length(difftime(enddate,rdate),"years"), + rtreat=factor(rtreat,labels=c("Active","Placebo"))) |> + dplyr::select(-rdate,-enddate) |> + dplyr::rename(status="status.all", + time="time.all") |> + labelling_data() +} + + +#' A good-enough approximation of the defined analysis-population in the TALOS protocol +#' +#' In practice this includes patients in the study for more than ~35 days +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +talos_analysis_pop_filter <- function(data){ + data |> + (\(.x){ + .x |> split(.x$rtreat) + })() |> + purrr::map2(c(268,284),\(.x,.y){ + .x |> + dplyr::slice_max(inc_time,n = .y,with_ties = FALSE) + }) |> + dplyr::bind_rows() +} + + +talos_analysis_pop_filter_imp <- function(data){ + data |> + mice::complete(action = "long", include = TRUE) |> + (\(.x){ + split(.x,.x$.imp) + })() |> + purrr::map(talos_analysis_pop_filter) |> + dplyr::bind_rows() |> + mice::as.mids() +} + +events_table <- function(data,by){ + list( + "Overall"=data, + split(data,data[by]) + ) |> + purrr::list_flatten() |> + purrr::imap(\(.x,.i){ + .x |> + dplyr::summarise( + group=.i, + py = sum(time), + events = sum(status), + events_pr_100 = 100 * events / py + ) + }) |> + dplyr::bind_rows() |> + setNames(c("group","Patient Years","Events","Events pr 100 patient years")) |> + tidyr::pivot_longer(-group) |> + tidyr::pivot_wider(names_from = group,values_from = value)|> + gt::gt() |> + gt::fmt_number(columns = -1, decimals = 1) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +talos_imp <- function(data){ + fun_impute(data=data,outcome.vars = c("time","status"), + ignore = c("inc_time","rtreat"), + pragmatic.reg = FALSE, + pase.mod = FALSE) +} + +#' Title +#' +#' @param data +#' @param v.groups +#' +#' @return +#' @export +#' +#' @examples +events_ready_small <- function(data,v.groups=c("clin","lifestyle.events","ses", "assess.events")) { + data |> + get_vars(vars.groups = v.groups) |> + dplyr::filter(event.include) |> + dplyr::filter(!is.na(pase_0),!is.na(pase_4))|> + dplyr::select(-tidyselect::all_of("event.include"))# |> + # labelling_data() +} + +#' Title +#' +#' @param date +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' +#' data |> +#' prediction_ready() |> +#' View() +prediction_ready <- function(data, + var.grps=c("clin","lifestyle","ses", "assess.pred")) { + data |> + get_vars(var.grps)|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4))#|> + # labelling_data() +} + +## Data inspection and exploration +## +## +#' Title +#' +#' @param data +#' @param subdf +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data) |> +#' subset_df() |> +#' View() +subset_df <- function(data, subdf = "pase") { + data[grepl(paste0("^(", paste("PNR", subdf, paste0("talos_", subdf), sep = "|"), ")"), names(data))] +} + +#' Imputation as a function, includes "pragmatic imputation" +#' +#' @param data +#' @param outcome.vars +#' @param ignore +#' @param pragmatic.reg +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_ready() +#' data |> labelling_data() |> fun_impute() +fun_impute <- function(data, outcome.vars = c("status", "time"), ignore = NULL,pragmatic.reg=TRUE,pase.mod=FALSE) { + # data |> mice::md.pattern() + if (pragmatic.reg){ + data <- data |> pragmatic_imputation() + } + + ## Excluding entries with missing outcome measures + data <- data |> + dplyr::filter(!dplyr::if_any(tidyselect::all_of(c(outcome.vars)), ~ is.na(.x))) + + init <- data |> + mice::mice(maxit = 0) + + meth <- init$method + meth[ignore] <- "" + + pred <- init$predictorMatrix + pred[, c(outcome.vars)] <- 0 + + # data_out <- data |> mice::futuremice( + # pred = pred, + # method = meth, + # print = FALSE, + # parallelseed = 8123, + # use.logical = FALSE, + # maxit = 20, + # m = 10 + # ) + + data_out <- data |> mice::mice( + pred = pred, + method = meth, + print = FALSE, + seed = 8123, + maxit = 20, + m = 10 + ) + + if (pase.mod){ + data_out <- data_out |> pase_cutter_mids() + } + + data_out + + # lattice::densityplot(imp_data) + # Regarding EVENTS + # + # On inspection/eye-balling densityplots looks reasonable with the current settings + # +} + +#' Function to cut PASE in mids object +#' +#' @param data mids object +#' +#' @return mids object +#' @export +#' +pase_cutter_mids <- function(data){ +data |> + mice::complete(action = "long", include = TRUE) |> + pase_cutter(drop.pase = FALSE, drop.nas = TRUE)|> + mice::as.mids() + } + + +#' Completes events data set, option to impute +#' +#' @param data +#' @param impute +#' +#' @return mids or tibble +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset(impute=FALSE) +#' targets::tar_read(df_all_data_formatted) |> +events_dataset <- function(data, impute = TRUE, uv=FALSE) { + # data <- data |> events_ready() + if (impute) { + data |> events_ready()|> + dplyr::select(-tidyselect::any_of("reg_bmi"))|> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = FALSE) + } else if (uv){ + data |> events_ready()|> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + } else if ("mids" %in% class(data)){ + data + } else { + data |> events_ready()|> + dplyr::select(-tidyselect::any_of("reg_bmi")) |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + } +} + +#' Title +#' +#' @param data +#' @param all.vars +#' @param outcome.var +#' @param use.strata +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_ready() |> subset_df("pase") +#' data <- targets::tar_read(df_event_data) |> events_dataset(impute = FALSE) + +cox_regression <- function(data, all.vars = TRUE,outcome.var="pase_change",use.strata=TRUE, include_formula=FALSE) { + if ("mids" %in% class(data)) { + nms <- names(data$data) + } else { + nms <- names(data) + data <- data |> + labelling_data() + # BMI meassure is excluded from non-imputed dataset + # data <- data |> dplyr::select(-tidyselect::all_of(c("reg_bmi"))) + } + + vars <- nms[!nms %in% c("time", "status", outcome.var)] + + form.prefix <- "survival::Surv(time, status) ~" + + if (use.strata) { + reg.form <- glue::glue("{form.prefix} strata({outcome.var})") + } else { + reg.form <- glue::glue("{form.prefix} {outcome.var}") + } + + if (all.vars) reg.form <- paste0(reg.form, " + ", paste(vars, collapse = " + ")) + + require(survival) + out <- with(data, survival::coxph( + as.formula(reg.form) + )) + + + if (isTRUE(include_formula)){ + out$call$formula <- as.formula(reg.form) + } + + out +} + +#' Creates UV cox models for all variables in data set (but time and status) +#' +#' @param data +#' @param include +#' +#' @return +#' @export +#' +#' @examples +splitdf4uvcox <- function(data,include=c("pase_change","time","status")){ + names(data)[!names(data)%in%include] |> + purrr::map(\(.x){ + data |> dplyr::select(tidyselect::all_of(c(.x,include))) |> + cox_regression(outcome.var = .x,use.strata=FALSE,all.vars = FALSE) + + }) +} + +#' Creates and prints UV Cox analyses +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_event_data) |> uv_cox_table() +uv_cox_table <- function(data){ + data |> + events_dataset(impute = FALSE,uv = TRUE) |> + dplyr::select(pase_change,dplyr::everything()) |> + splitdf4uvcox(include=c("time","status")) |> + purrr::map(\(.x){ + .x |> tbl_regression_standard() + }) |> + gtsummary::tbl_stack() + } + +tbl_merged_named <- function(data){ + data |> gtsummary::tbl_merge(tab_spanner = names(data)) +} + +tbl_stack_named <- function(data){ + data |> gtsummary::tbl_stack(group_header = names(data)) +} + +tbl_regression_standard <- function(data){ + # browser() + data |> gtsummary::tbl_regression(exponentiate = TRUE, + add_estimate_to_reference_rows = TRUE, + show_single_row = where(is.logical), + statistics=list(gtsummary::all_continuous()~"[{conf.low};{conf.high}]", + gtsummary::all_categorical()~"[{conf.low}%;{conf.high}%]") + ) |> + fix_labels() +} + +#' Wrapper to print summary table with extended info +#' +#' @param data formatted and subset data set +#' @param by.var stratify by +#' +#' @return +#' @examples +#' targets::tar_read(df_pred_data)|>print_table_summary(by="reg_female") +print_table_summary <- function(data, by.var = "pase_change") { + data |> + labelling_data() |> + # pase_cutter(drop.pase = TRUE) |> + gtsummary::tbl_summary( + missing = "ifany", + by = tidyselect::all_of(by.var), + value = list(where(is.logical) ~ TRUE) + ) |> + gtsummary::add_overall() +} + +print_table_summary_explorer <- function(data, by.var = "pase_change") { + data |> + labelling_data() |> + # pase_cutter(drop.pase = TRUE) |> + gtsummary::tbl_summary( + missing = "ifany", + by = tidyselect::all_of(by.var), + value = list(where(is.logical) ~ TRUE), + type = list(gtsummary::all_continuous() ~ "continuous2"), + statistic = list(gtsummary::all_continuous() ~ c( + # # "{N_nonmiss} ({p_nonmiss}%)", + "{median} ({p25}, {p75})", + # # "{min}, {max}", + "{mean} ({sd})"#, + # # "{N_miss} ({p_miss}%)" + )#, + # gtsummary::all_categorical() ~ c( + # "{N_obs} ({p_nonmiss}%)"#, + # # "{N_miss} ({p_miss})" + # ) + ) + ) |> + gtsummary::add_overall() |> + gtsummary::add_n() #|> + # gtsummary::add_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' targets::tar_read(df_all_data_formatted) |> events_tblone() +events_tblone <- function(data) { + #browser() + data |> + # events_ready() |> + pase_cutter(drop.pase = TRUE) |> + dplyr::select(-tidyselect::all_of(c("status", "time"))) |> + dplyr::filter(!is.na(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data)|> +#' dplyr::transmute(stRoke::quantile_cut(pase_0,4,group.names = 1:4),soc_status_work,fam_indk,edu_level) |> +#' summary_tblone() +summary_tblone <- function(data,by=names(data)[1]) { + # data <- targets::tar_read(df_pred_data) + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + # pase_cutter(drop.pase = TRUE) |> + # dplyr::filter(!is.na(pase_change)) |> + # dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary(by.var = by) +} + +# +#' Summaries of DST data for PASE quartiles at 0 and 4 +#' +#' @param data +#' @param vars +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> sum_pase_tables() +sum_pase_tables <- function(data,vars=c("pase_0","pase_4")){ + vars |> lapply(function(.x){ + dplyr::tibble(stRoke::quantile_cut(data[[.x]],y=data[["pase_0"]],4,group.names = 1:4), + dplyr::select(data,soc_status_nowork,fam_indk_hl,edu_level_hl)) |> + summary_tblone() + }) +} + + +#' Creating a truthful stratified table for predictions +#' +#' @param data data frame +#' +#' @return list +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> true_pred_sum_plot() +true_pred_sum_plot <- function(data){ + true_sum <- data |> + pase_cutter(drop.pase = TRUE) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) + + list(#true_sum, + true_sum |> (function(.x){ + split(.x,.x$pase_change %in% c("Persistently low","Increase")) + })() |> + purrr::map(function(.y){ + .y |> + dplyr::mutate(pase_change=factor(pase_change)) + })) |> + purrr::list_flatten() |> + purrr::map(summary_tblone,by="pase_change") |> + purrr::map(mask_micro_summary) |> + gtsummary::tbl_merge() +} + +#' Get quick summary of missing vs non-missing for each given variable +#' +#' @param data data set +#' @param var variable to summarise over +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> dplyr::select(soc_status_work,fam_indk,edu_level) |> who_is_missing() +#' targets::tar_read(df_pred_data) |> who_is_missing(var="reg_bmi") +who_is_missing <- function(data, var = "edu_level") { + data |> + dplyr::mutate(log = factor(c("non-missing","missing")[is.na(data[[var]])+1])) |> + dplyr::select(log, tidyselect::everything(),-tidyselect::all_of(var)) |> + summary_tblone() #|> gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data) |> preds_tblone() +preds_tblone <- function(data) { + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + pase_cutter(drop.pase = TRUE) |> + dplyr::filter(!is.na(pase_change)) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' gt <- targets::tar_read(ls_pred_summary)[[1]] +#' gt |> add_var_groups_gt() +#' +#' # For this to work, the function would need to handle labels and levels +#' gt <- targets::tar_read(tbl_pred_summary)|> gtsummary::as_gt() +#' gt |> add_var_groups_gt() +add_var_groups_gt <- function(gt){ + # gt <- sex_ls |> purrr::pluck(2) |> gtsummary::as_gt() + # gt <- fix_labels(gt) + cls <- class(gt) + + b.cols <- names(gt$`_data`) + + if (b.cols[[1]]!="variable"){ + # Flag to indicate if format is native gt or not. Simple assumption + # class(gt) gt is not enough + labels <- gt$`_data`[[1]] + group.var <- names(gt$`_data`[[1]]) + } else { + labels <- gt$`_data`[["label"]][gt$`_data`[["row_type"]]=="label"] + group.var <- gt$`_data`[["variable"]] + } + + + + groups <- matrix(ncol=length(labels)) |> + data.frame() |> + setNames(ifelse(labels=="","unknown_var",labels)) |> + tibble::as_tibble() |> groups_in_ds(labels = TRUE) + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + labels.all <- group.labels |> purrr::imap(function(.x,.y){ + c(.x,groups[[.y]][["label"]]) + }) |> purrr::list_c() + + for (i in rev(seq_along(group.labels))){ + gt <- gt |> gt::tab_row_group(label=gt::md(glue::glue("*{group.labels[[i]]}*")), + rows=which(group.var %in% groups[[names(group.labels)[[i]]]][["var"]])) + + } + + class(gt) <- cls + gt +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(tbl_preds_lin_imp_reg) +#' data |> fix_labels() +fix_labels <- function(data){ + cls <- class(data) + data[[1]][["variable"]][data[[1]][["row_type"]]=="label"] |> + subset_named_labels(var_labels()) |> + unname() -> data[[1]][["label"]][data[[1]][["row_type"]]=="label"] + class(data) <- cls + data +} + + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' tbl <- targets::tar_read(ls_pred_summary)[[1]] +add_var_groups_pre_calc <- function(data,b.cols){ + + groups <- data |> groups_in_ds() + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + t0 <- data.frame(matrix(ncol=length(b.cols))) |> + setNames(b.cols) |> + tibble::tibble() + list(ext = group.labels |> purrr::imap(function(.x,.y){ + t0 |> dplyr::mutate( + variable=.y, + val_label=.x, + row_type="group", + label=.x + ) + }) |> dplyr::bind_rows(), + lvls = group.labels |> purrr::imap(function(.x,.y){ + c(.y,groups[[.y]][["var"]]) + }) |> purrr::list_c() + ) +} + +#' Adds variable grouping and formatting to gtsummary tables +#' +#' @param tbl +#' +#' @return +#' @export +#' +#' @examples +#' +#' tbl <- targets::tar_read(tbl_pred_summary) +#' targets::tar_read(tbl_pred_summary) |> add_var_groups() +#' +add_var_groups <- function(tbl, + pre_ls=add_var_groups_pre_calc(tbl$inputs$data, + names(tbl$table_body))){ + + + tbl |> gtsummary::modify_table_body( + ~.x |> dplyr::bind_rows(pre_ls[["ext"]]) |> + dplyr::arrange(factor(variable,levels=pre_ls[["lvls"]])) + ) |> + gtsummary::modify_table_styling(columns=label, + rows= row_type%in%"level",text_format = "indent2") |> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"label",text_format = "indent")|> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"group",text_format = c("italic")) +} + +#' Functionalised character vector of all labels +#' +#' @return +#' @export +#' +#' @examples +var_labels <- function(){ + c( + age = "Age", + reg_female = "Female sex", + reg_bmi = "Body mass index", + reg_smoker = "Current smoker", + reg_alone = "Living alone", + reg_more_alc = "High alcohol consumption", + reg_hyperten = "Hypertension", + reg_diabetes = "Diabetes", + reg_atriefli = "Atrial fibrillation", + reg_perifer_arteriel = "Peripheral arterial disease", + reg_tidl_tci = "Previous TIA", + reg_ami = "Previous MI", + reg_trombolyse = "Treated with IVT", + reg_trombektomi = "Treated with EVT", + # reg_any_perf, + rtreat = "Trial allocation", + rtreat_placebo = "Placebo trial treatment", + pase_0 = "Pre-stroke PASE score", + pase_4 = "6 months post-stroke PASE score", + pase_0_quartile = "Pre-stroke PASE score quartile", + pase_4_quartile = "6 months post-stroke PASE score quartile", + # pase_change, + nihss_0 = "Admission NIHSS", + # soc_status, + soc_status_work = "Employed", + soc_status_nowork = "Not employed", + fam_indk = "Family income group", + fam_indk_hl = "Lower family income", + fam_indk_high = "Higher family income", + fam_indk_low = "Lower family income", + edu_level = "Educational level group", + edu_level_hl = "Lower educational level", + edu_high = "Higher educational level", + edu_low = "Low educational level", + who_4 = "WHO-5 score 6 months post-stroke", + mdi_4 = "MDI score 6 months post-stroke", + mrs_4_above1 = "mRS > 1 at 6 months post-stroke", + mfi_gen_4 = "General fatigue (MFI domain) 6 months post-stroke", + time = "Time", + status = "Status", + event.include = "Include event", + who_0 = "Pre-stroke WHO-5 score", + mrs_0_above0 = "Pre-stroke mRS > 0", + pase_change = "PA change group" + ) +} + + +group_labels <- function(data){ + c("clin" = "Clinical data", + "lifestyle" = "Lifestyle and chronic diseases", + "ses" = "Socio-economic factors", + "assess.events" = "Assessments", + "assess.pred" = "Assessments", + "extra" = "extras") +} + +rev_naming <- function(x){ + setNames(names(x),x) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +groups_in_ds <- function(data, labels=FALSE){ + groups <- define_variables() |> purrr::imap(function(.x,.y){ + tibble::tibble(group=.y,var=.x) + }) |> + dplyr::bind_rows() + + if (labels){ + matching <- subset_named_labels(names(data), + rev_naming(var_labels())) + }else { + matching <- names(data) + } + groups[match(matching,groups[["var"]]),] |> + (\(.x){ + .x |> dplyr::mutate(group=factor(group,levels=unique(.x[["group"]]))) + })() |> + cbind( + tibble::tibble( + label=labelling_data(data) |> labelled::var_label() |> purrr::list_c() + ) + )|> + (\(.x){ + split(.x,.x[["group"]]) + })() +} + + +#' Subset labels +#' +#' @param data +#' @param labels.raw +#' +#' @return character vector +#' @export +#' +subset_named_labels <- function(data,labels.raw){ + labels.raw[match(data,names(labels.raw))] +} + +#' Assign labels to data.frame or tibble +#' +#' @param data +#' @param labels +#' +#' @return +#' @export +#' +#' @examples +assign_labels <- function(data,labels){ + # data |> labelled::set_variable_labels(labels) + + labelled::var_label(data) <- labels + + data +} + +#' Flexible labelling using labelled for nicer tables +#' +#' @param data data set +#' +#' @return +#' @export labelled data.frame/tibble +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' data <- data |> dplyr::mutate(test="test") +#' data |> labelling_data() |> labelled::var_label() +labelling_data <- function(data,label.list=var_labels()){ + + labs <- subset_named_labels(names(data),label.list) + labs[is.na(labs)] <- names(data)[is.na(labs)] + + data |> assign_labels(labels = labs) +} + + + +#' Print regression table +#' +#' @param data cox regression ready data set +#' +#' @return gtsummary tbl_regression list object +#' @examples +#' data <- targets::tar_read(df_event_data) +#' targets::tar_read(df_events_mids) |> show_table_regression() +#' targets::tar_read(df_event_data) |> show_table_regression(use.mice=FALSE) +show_table_regression <- function(data, use.mice=FALSE, by.var="pase_change") { + # browser() + imp <- data |> + events_dataset(impute = use.mice) + + if ("mids" %in% class(imp)){ + imp <- imp |> pase_cutter_mids() |> + mice::complete(action = "long", include = TRUE) |> + dplyr::select(-tidyselect::any_of(c("pase_0","pase_4","pase_0_quartile","pase_4_quartile"))) |> + mice::as.mids() + } else { + imp <- imp |> + dplyr::select(-tidyselect::any_of(c("pase_0","pase_4","pase_0_quartile","pase_4_quartile"))) + } + imp |> standard_multi_cox_table(by.var="pase_change") +} + +standard_multi_cox_table <- function(data, by.var="pase_change",all.vars = TRUE){ + # browser() + data |> + cox_regression(all.vars = all.vars, use.strata = FALSE,outcome.var = by.var) |> + tbl_regression_standard() +} + + +#' Splitting df to list by PA trajectory +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_ls_split <- function(data, excluded.vars = "reg_bmi"){ + data |> + pase_cutter(drop.pase = FALSE) |> + dplyr::select(-tidyselect::all_of(c("pase_4","pase_0_quartile","pase_4_quartile"))) |> + dplyr::group_split(pase_split = pase_change %in% c("Increase", "Persistently low")) |> + setNames(c("drop", "hop")) |> + purrr::map2(.y = c("Decrease", "Increase"), .f = \(x, y){ + x |> + dplyr::mutate(pase_bin = pase_change == y) |> + dplyr::select(-tidyselect::all_of(c(excluded.vars, c("pase_change", "pase_split")))) |> + na.omit() + }) +} + +bin_original <- function(data,...){ + ## This will just follow the original pase_cutter binning + data +} + +#' Help developing new binning functions without using "browser()" +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> bin_ready_boiler() +bin_ready_boiler <- function(data){ + data |> + events_ready()|> + pase_cutter(drop.pase = FALSE) +} + +#' Handles overall quantile change with dynamic definition of lowest and any change +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +bin_quantile <- function(data,quantiles=5,lowest=1,any=FALSE,...){ + + highest_seq <- seq_len(quantiles)[-seq_len(lowest)] + + ## This will just follow the original pase_cutter binning + df <- data|> + dplyr::mutate(dplyr::across(.cols = c("pase_0", "pase_4"), \(i) { + as.numeric(stRoke::quantile_cut(x=i,groups=quantiles,y=pase_0,na.rm = TRUE,group.names=seq_len(quantiles))) + }, .names = "{.col}_quantile")) + + if (any){ + out <- df |> + dplyr::mutate(pase_change = factor(dplyr::case_when( + pase_0_quantile < pase_4_quantile ~ "up", + pase_0_quantile > pase_4_quantile ~ "down", + pase_0_quantile %in% seq_len(lowest) & pase_4_quantile %in% seq_len(lowest) ~ "low", + pase_0_quantile %in% highest_seq & + pase_4_quantile %in% highest_seq ~ "high" + ), ordered = FALSE)) + } else { + out <- df |> + dplyr::mutate(pase_change = factor(dplyr::case_when( + pase_0_quantile %in% seq_len(lowest) & + pase_4_quantile %in% highest_seq ~ "up", + pase_0_quantile %in% highest_seq & + pase_4_quantile %in% seq_len(lowest) ~ "down", + pase_0_quantile %in% seq_len(lowest) & pase_4_quantile %in% seq_len(lowest) ~ "low", + pase_0_quantile %in% highest_seq & + pase_4_quantile %in% highest_seq ~ "high" + ), ordered = FALSE)) + } + + + out |> dplyr::mutate(pase_change=factor(pase_change,levels=c("high", "down", "up", "low"))) +} + +bin_percentage<-function(data,percentage,...){ + data |> + dplyr::mutate(pase_0_cut = as.numeric(cut(pase_0,quantile(pase_0,probs = c(0,percentage/100,1),na.rm = TRUE),include.lowest = TRUE,labels = 1:2)), + pase_4_cut = as.numeric(cut(pase_4,quantile(pase_0,probs = c(0,percentage/100,1),na.rm = TRUE),include.lowest = TRUE,labels = 1:2)), + pase_change = dplyr::case_when( + pase_0_cut > pase_4_cut ~ "down", + pase_0_cut < pase_4_cut ~ "up", + pase_0_cut %in% 1 ~ "low", + pase_0_cut %in% 2 ~ "high" + ), + pase_change = factor(pase_change, levels= c("high", "down", "up", "low")) + ) +} + +bin_anyupdown<-function(data,low.q=1,high.q=2:4,...){ +data |> + dplyr::mutate(pase_0_quartile=as.numeric(pase_0_quartile), + pase_4_quartile=as.numeric(pase_4_quartile), + pase_change = dplyr::case_when( + pase_0_quartile > pase_4_quartile ~ "down", + pase_0_quartile < pase_4_quartile ~ "up", + pase_0_quartile %in% low.q ~ "low", + pase_0_quartile %in% high.q ~ "high" + ), + pase_change = factor(pase_change, levels= c("high", "down", "up", "low")) + ) +} + +bin_absupdown<-function(data,abs.bin,low.q=1,high.q=2:4,...){ + data |> + dplyr::mutate(pase_dif=(pase_4-pase_0), + pase_change = dplyr::case_when( + pase_dif > (abs.bin) ~ "up", + pase_dif < -(abs.bin) | pase_0 == 0 ~ "down", + pase_0_quartile %in% low.q ~ "low", + pase_0_quartile %in% high.q ~ "high" + ), + pase_change = factor(pase_change, levels= c("high", "down", "up", "low"))) +} + +bin_relupdown<-function(data,rel.bin,low.q=1,high.q=2:4,...){ + data |> + dplyr::mutate(pase_rel_dif=(pase_4-pase_0)/pase_0, + pase_change = dplyr::case_when( + pase_rel_dif > (rel.bin/100) ~ "up", + pase_rel_dif < -(rel.bin/100) ~ "down", + pase_0_quartile %in% low.q ~ "low", + pase_0_quartile %in% high.q ~ "high" + ), + pase_change = factor(pase_change, levels= c("high", "down", "up", "low"))) +} + +#' Title +#' +#' @param data +#' @param ... +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> group_format(binning.fun=bin_clusterlcm) +bin_clusterlcm <- function(data,...){ + data|> + #events_ready()|> + #pase_cutter(drop.pase = FALSE)|> + #dplyr::filter(!is.na(pase_0),!is.na(pase_4)) |> + dplyr::select(-tidyselect::any_of(c("pase_0_quartile","pase_4_quartile","pase_split","pase_dif","pase_rel_dif")))|> + lcm_cluster(n.clusters = 4) |> + final_membership() |> + dplyr::mutate(pase_change=clust) |> + dplyr::select(-clust) +} + +#' Multi grouping +#' +#' @param data +#' @param binning.fun +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' targets::tar_read(df_all_data_formatted) |> group_format(binning.fun = bin_relupdown,rel.bin=20) +#' targets::tar_read(df_all_data_formatted) |> group_format(binning.fun = bin_original,var.excl=NULL) +#' targets::tar_read(df_all_data_formatted) |> group_format(binning.fun = bin_clusterlcm,var.excl=c("pase_0_quartile","pase_4_quartile","pase_split","pase_dif","pase_rel_dif")) +group_format <- function(data,var.keep=NULL,binning.fun,var.excl=c("pase_0","pase_4","pase_0_quartile","pase_4_quartile","pase_split","pase_dif","pase_rel_dif"),...){ + out_all <- data |> + events_ready()|> + pase_cutter(drop.pase = FALSE) |> + #bin_relupdown(rel.bin=20) + binning.fun(...)|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4)) + + exl.ndx <- !names(out_all) %in% var.excl + if (all(exl.ndx)){ + out <- out_all + } else { + out <- out_all[exl.ndx] + } + + return(out) +} + +ls_pase_pred_clean<-function(data,excluded.vars){ +data|> + purrr::map(\(.x){ + .x |> + dplyr::select(-tidyselect::any_of(c(excluded.vars, + c("pase_4","pase_0_quartile","pase_4_quartile","pase_change", "pase_split","pase_rel_dif")))) |> + na.omit() + }) +} + + +pred_ls_split_alt <- function(data, excluded.vars = "reg_bmi",type="relupdown",rel.bin){ + #browser() + df<-data |> + pase_cutter(drop.pase = FALSE) + + if (type=="anyupdown"){ + ls_binned<-df |> + bin_anyupdown()|> + (\(.x){ + list(.x|>dplyr::mutate(pase_bin=pase_change=="down")|>dplyr::filter(!pase_0_quartile==1), + .x|>dplyr::mutate(pase_bin=pase_change=="up")|>dplyr::filter(!pase_0_quartile==4)) + })() + + names<-c("Any quartile down","Any quartile up") + } else if (type=="relupdown"){ + ls_binned<-df |> + bin_relupdown(rel.bin=rel.bin)|> + (\(.x){ + list(.x|>dplyr::mutate(pase_bin=pase_change=="down"), + .x|>dplyr::mutate(pase_bin=pase_change=="up")) + })() + + names<-c(paste0("More than ",rel.bin,"% down"),paste0("More than ",rel.bin,"% up")) + } + + + ls_binned |> + setNames(names)|> + ls_pase_pred_clean(excluded.vars = excluded.vars) +} + + +#' Run regularisation steps for split data set +#' +#' @param data selected data set +#' +#' @return list +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' targets::tar_read(df_pred_data) |> pred_models(auto.l = TRUE,weighted = FALSE,rel.bin=20,split.type="relupdown") +pred_models <- function(data, split.type="pase_bin", excludes = "reg_bmi",rel.bin=50,...) { + if (split.type=="pase_bin"){ + ls_split <- data |> + pred_ls_split(excluded.vars = excludes) + } else { + ls_split <- data |> + pred_ls_split_alt(excluded.vars = excludes,type=split.type,rel.bin=rel.bin) + } + + ls <- ls_split|> + purrr::map(\(.x) regularisation_steps(.x,...)) + + class(ls) <- c("regular_list", class(ls)) + ls +} + +cross_mean_median_exp_table <- function(data) { + nms <- paste0("v", seq_len(ncol(data))) + + cross_calcs <- data |> + as.data.frame() |> + setNames(nms) |> + dplyr::rowwise() |> + dplyr::transmute( + median = median(dplyr::c_across(tidyselect::all_of(nms))), + medianOR = exp(median), + mean = mean(dplyr::c_across(tidyselect::all_of(nms))), + meanOR = exp(mean) + ) + + dplyr::tibble(names = rownames(data), cross_calcs) |> + dplyr::select(-tidyselect::all_of(c("mean","median"))) +} + + +gather_coefs_step1 <- function(data) { + data |> + list3levelpluck(lvl1 = "model", lvl2 = "B") |> + purrr::map(purrr::reduce, cbind) +} + +gather_coefs <- function(data) { + # imputed.list <- "mids_regular_list" %in% class(data) + + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(gather_coefs_step1) |> + purrr::map(purrr::reduce, cbind) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> gather_coefs_step1() + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map(cross_mean_median_exp_table) |> + purrr::reduce(dplyr::full_join, by = "names", suffix = paste0("_", names(data))) +} + + +#' Merge and print model coefficients. Pools datafrom mids analyses. +#' +#' @param data list +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(ls_pred_models) +#' targets::tar_read(ls_pred_models) |> print_pred_coefs() +#' targets::tar_read(ls_pred_mids_reg) |> print_pred_coefs() |> add_var_groups_gt() +print_pred_coefs <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + # nms <- names(data) + + if ("mids_regular_list" %in% class(data)) { + type.table <- "Pooled regularised models" + } else if ("regular_list" %in% class(data)) { + type.table <- "Single regularised model" + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + merged_tbl <- data |> + gather_coefs() |> + ## Leaving out the intercept + (function(.x) .x[-1,])() + + sel_mean_med <- colnames(merged_tbl)[!grepl(pattern = "OR",colnames(merged_tbl))][-1] + sel_or <- colnames(merged_tbl)[grepl(pattern = "OR",colnames(merged_tbl))] + + news <- subset_named_labels(merged_tbl$names,var_labels()) + + merged_tbl <- merged_tbl |> dplyr::mutate(names=dplyr::if_else(is.na(news),names,news)) + + gt_merged_tbl <- merged_tbl|> + gt::gt() |> + gt::fmt_number(decimals = 5) + + merged_tbl_log <- merged_tbl |> dplyr::mutate(dplyr::across(tidyselect::all_of(sel_or), ~.x!=1), + dplyr::across(tidyselect::all_of(sel_mean_med), ~.x!=0)) + + for (j in colnames(merged_tbl)[-1]) { + + i <- merged_tbl_log[[j]] + + gt_merged_tbl <- gt_merged_tbl |> gt::tab_style(style = list( + gt::cell_text(weight="bold") + ), + locations = gt::cells_body( + columns=j, + rows = i + ) + )} + + for (i in names(data)) { + gt_merged_tbl <- gt_merged_tbl |> + gt::tab_spanner(label = i, columns = tidyselect::ends_with(i)) + } + + gt_merged_tbl |> gt::tab_spanner( + label = type.table, + columns = -1 + ) +} + +#' Calculates confusionMatrix from contingency tables. Pools if object class is . +#' +#' @param data +#' +#' @return list +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_table_cfm() +#' targets::tar_read(ls_pred_models) |> multi_table_cfm() +multi_table_cfm <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + if ("mids_regular_list" %in% class(data)) { + data <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + # purrr::map(\(y) y |> purrr::pluck("model") |> purrr::pluck("cMatTest"))|> + list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") |> + # All tables are add together + purrr::reduce(\(i, j) i + j) + }) + } else if ("regular_list" %in% class(data)) { + data <- data |> list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data |> + purrr::map(caret::confusionMatrix) +} + +#' Collect and summarise auc meassures. Pools if "mids_regular_list" object +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_auc_summary() +#' targets::tar_read(ls_pred_models) |> multi_auc_summary() +multi_auc_summary <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + # All tables are add together + purrr::reduce(c) + }) + } else if ("regular_list" %in% class(data)) { + data_step <- data |> + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + purrr::map(c) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step |> + purrr::map(summary) +} + +#' Map and 2 level recursive purrr::pluck to ease regular_list subsetting +#' +#' @param data +#' @param lvl1 +#' @param lvl2 +#' +#' @return +#' @export +#' +#' @examples +list3levelpluck <- function(data, lvl1 = "model", lvl2 = "cMatTest") { + data |> purrr::map(\(y) y |> + purrr::pluck(lvl1) |> + purrr::pluck(lvl2)) +} + +#' Plot performance curve from glmnet regularisation +#' +#' @param data list of cvs.glmnet objects +#' +#' @return ggplot list object +#' @export +#' +#' @examples +plot_roc_curve <- function(data, title.text) { + ggplot2::ggplot() + + purrr::map(data, function(i) { + ggplot2::geom_step(data = i, ggplot2::aes(x = FPR, y = TPR)) + }) + + ggplot2::coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) + + ggplot2::geom_abline() + + ggplot2::theme_bw() + + ggplot2::ggtitle(title.text) +} + +roc_gather_step <- function(x) { + with(x, glmnet::roc.glmnet(cvs[[1]]$fit.preval, newy = y1)[match(bestL, lambdas)]) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_roc_plot() +#' targets::tar_read(ls_pred_models) |> multi_roc_plot() +multi_roc_plot <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, roc_gather_step) |> + purrr::map(purrr::list_flatten) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> purrr::map(roc_gather_step) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map2(.y = names(data), plot_roc_curve) |> + patchwork::wrap_plots() +} + +#' Title +#' +#' @param tuning.param +#' @param data +#' +#' @return +#' @export +#' +#' @examples +multi_tuning_gather <- function(tuning.param = "bestA", data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, \(x) x |> purrr::pluck(tuning.param)) |> + purrr::map(purrr::reduce, c) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::pluck, tuning.param) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> purrr::map(summary) +} + + +#' Tidied tuning summary call +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> tuning_summary() +#' targets::tar_read(ls_pred_models) |> tuning_summary() +tuning_summary <- function(data) { + c(ALPHA = "bestA", LAMBDA = "bestL") |> purrr::map(\(x) x |> multi_tuning_gather(data = data)) +} + +#' Apply regularisation steps to MIDS object, output arranged by grouping +#' +#' @param data mids object from mice package +#' +#' @return list +#' +#' @examples +#' targets::tar_read(df_pred_mids) |> mids_regularisation() +#' ls <- targets::tar_read(df_pred_mids) |> mids_regularisation(weighted = FALSE,rel.bin=20,split.type="relupdown") +mids_regularisation <- function(data,...) { + ls <- data |> + mice::complete(action = "long") |> + dplyr::group_split(.imp) |> + purrr::modify(\(x){ + x |> dplyr::select(-tidyselect::all_of(c(".imp", ".id"))) + }) |> + purrr::map(\(.x)pred_models(.x,auto.l=TRUE,excludes = NULL,...)) + + nms <- ls |> + purrr::map(names) |> + unique() |> + purrr::reduce(c) + + # As a consequence of the above code each "set" of analyses are together. + # Here the same group analyses are subset and grouped + ls_n <- purrr::map(nms, function(i) { + ls |> purrr::map(purrr::pluck, i) + }) |> + setNames(nms) + + # Special class is applied to ease future handling + class(ls_n) <- c("mids_regular_list", class(ls_n)) + ls_n +} + +#' A collection of all the summary functions to be applied to list of +#' pred_models() output +#' +#' @param data list of data +#' +#' @return list +#' @export +#' +multi_summary <- function(data){ + list( "coefTable" = print_pred_coefs(data) |> + gt::fmt_number(n_sigfig = 3) |> + fix_labels() #|> add_var_groups_gt() + , + "confusionMatrices" = multi_table_cfm(data), + "summaryAUC" = multi_auc_summary(data), + "rocPlots" = multi_roc_plot(data), + "tuningSummaries" = tuning_summary(data)) +} + +# funs <-list( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +# multi_summary <- plyr::each( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +#' Subset multiple elements from list +#' +#' @param data list +#' @param indices numeric or character vector +#' +#' @return list +#' @examples +#' targets::tar_read(ls_pred_summary)$confusionMatrices |> purrr::map(list_subset) +list_subset <- function(data,indices=c("overall","byClass")){ + data[indices] +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_summary) |> print_model_resutls() +print_model_resutls <- function(data){ + par(mfrow=c(1,2)) + + list(data$coefTable, + invisible(data$confusionMatrices |> purrr::map(\(x) x |> purrr::pluck("table"))), + data$confusionMatrices |> purrr::map(list_subset), + data$summaryAUC, + data$tuningSummaries + ) +} + + +#' Classic logistic regression on prediction covariables +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' targets::tar_read(df_pred_data) |> pred_log_reg() +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_log_reg <- function(data){ + data |> pred_ls_split() |> + purrr::map(\(x) { + gtsummary::tbl_regression(glm(pase_bin~.,family = binomial,data = x), + exponentiate= TRUE)#|> + # gtsummary::bold_p() + } + ) |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() } + +#' Small wrapper to format CI with square brackets +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +square_ci <- function(data){ + gsub("(-?\\d*\\.?\\d*)(, )(-?\\d*\\.?\\d*)", + "\\[\\1; \\3\\]",data)} + +#' Apply CI formatting across gtsummary table including merged tables +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +fix_ci <- function(data){ + data |> gtsummary::modify_table_body(~ .x |> + dplyr::mutate(dplyr::across(dplyr::starts_with("ci"), + function(.y){square_ci(.y)}))) +} + +#' Classic linear regression on 6 months PASE score. Uni and multi. +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' data <- targets::tar_read(df_pred_data) +#' data <- targets::tar_read(df_pred_mids) +#' targets::tar_read(df_pred_data) |> pred_lin_reg() +#' targets::tar_read(df_pred_mids) |> pred_lin_reg() +pred_lin_reg <- function(data){ + + # list("tbl_regression-str:ref_row_text"="Reference") |> + # gtsummary::set_gtsummary_theme() + + if ("mids" %in% class(data)){ + cols <- names(data$data) + + } else { + cols <- names(data) + data <- data |> + labelling_data() + } + + vars <- cols[cols!="pase_4"] + + formula_pase <- paste("pase_4",paste(vars,collapse = "+"),sep="~" ) + + # multi <- with(data=data,lm(pase_4~.)) |> + # gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE)|> + # gtsummary::bold_p() |> gtsummary::add_n() + + if (!"mids" %in% class(data)){ + ls <- list("Univariable"=data |> + gtsummary::tbl_uvregression(method=lm, show_single_row = dplyr::where(is.logical), + y=pase_4, + add_estimate_to_reference_rows = TRUE,pvalue_fun = NULL)#|> gtsummary::bold_p() + , + "Multivariable (no BMI)"=lm(pase_4~.,data=dplyr::select(data,-reg_bmi)) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n() + , + "Multivariable (ALL)"= lm(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n() + ) + + } else { + ls <- list("Multivariable (ALL)"= suppressWarnings(mice::lm.mids(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n())) + } + + ls |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() |> + fix_ci() +} + + +#' Simple standard plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_event_data) |> events_dataset(impute = FALSE)|> cox_regression() |> plot_survival() +plot_survival <- function(data){ + data |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit(linetype_aes = TRUE, size = 0.8) + + ggsurvfit::add_confidence_interval() + + ggsurvfit::add_risktable( + risktable_stats = c("n.risk", "cum.event"), + stats_label = list(cum.event = "Cumulative Observed Events", + n.risk = "Number at Risk"), + theme = + list( + ggsurvfit::theme_risktable_default(axis.text.y.size = 11, + plot.title.size = 11), + ggplot2::theme(plot.title = ggplot2::element_text(face = "bold")) + ) + ) + + ggplot2::scale_y_continuous( + limits = c(0, 1), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0)) +} + + +#' Smooth tidy survfit object +#' +#' @param data survfit object +#' +#' @return tibble +#' @export +#' +#' @examples +#' ls <- targets::tar_read(df_event_data) |> events_dataset(impute = FALSE)|> cox_regression() +#' data <- ls |> ggsurvfit::survfit2(robust=TRUE) |> +#' ggsurvfit::tidy_survfit(type="survival") |> +#' dplyr::group_split(strata) |> purrr::pluck(1) +#' +#' ls |> ggsurvfit::survfit2(robust=TRUE) |> +#' ggsurvfit::tidy_survfit(type="survival") |> +#' dplyr::group_split(strata) |> +#' purrr::map(smooth_col) +smooth_col <- function(data, force_mono=TRUE){ + smoothed <- lapply(c("estimate","conf.high","conf.low"),function(i){ + # stats::predict(cobs::cobs(x = data$time, + # y = data[i], + # constraint = "decrease", + # nknots=4, + # pointwise = rbind(c(0,min(data$time),1)), + # degree = 2,)) |> + # tibble::as_tibble() |> dplyr::select(fit) |> + stats::predict(mgcv::gam(data=data,formula = as.formula(glue::glue("{i}~s(time,bs='cs')")))) |> + tibble::as_tibble()|> + setNames(glue::glue("{i}_smooth")) + }) |> purrr::list_cbind() + + if (force_mono){ + ## Forcing starting point to be 1 + smoothed[1,1] <- 1 + + smoothed[1] <- force_decrease(smoothed[1]) ## Only monotonize the esitimate + } + + dplyr::tibble(data, + smoothed) + +} + +#' Forces the direction +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +force_decrease <- function(data){ + data |> purrr::imap(function(.x,.n){ + s <- c() + for (i in seq_along(.x)){ + if (i == 1) { + s[1] <- .x[1] + } else { + if (.x[i]>s[i-1]){ + s[i] <- s[i-1] + } else { + s[i] <- .x[i] + } + } + } + s + }) |> + dplyr::bind_cols() +} + + +#' Prepare cox regression for smooth survival plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() |> smooth_cox_data() +smooth_cox_data <- function(data){ + data |> + ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(smooth_col) |> + purrr::list_rbind() +} + +#' Plot smooth survival plot +#' +#' @param data df from cox regression +#' +#' @return ggplot list object +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_event_data) |> events_dataset(impute = FALSE) |> cox_regression(include_formula=TRUE) +#' data <- targets::tar_read(df_events_mids) |> cox_regression() +#' data |> plot_survival_smooth() +plot_survival_smooth <- function(data,line.w=1.2){ + if ("mira" %in% class(data)) stop("Only plots non-imputed survival data") + + n.level <- length(data$xlevels[[1]]) + + # data |> + # ggsurvfit::survfit2() |> + # ggsurvfit::tidy_survfit() + + if (data |> ggsurvfit::survfit2() |> purrr::pluck("n") |> length() ==1 ){ + ds <- data |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() + p <- ds |> + ggplot2::ggplot(ggplot2::aes(x=time, y=estimate))+ + ggplot2::geom_smooth(se=TRUE, method="loess", formula = "y~x", linewidth=line.w, color="grey10") + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high) + + } else { + ds <- data |> + smooth_cox_data() + p <- ds |> + ggplot2::ggplot()+ + ggplot2::geom_line(ggplot2::aes(x=time, y=estimate_smooth, color=strata, linetype=strata), linewidth=line.w)+ + ggplot2::geom_ribbon(ggplot2::aes(x=time, ymin=conf.low_smooth,ymax=conf.high_smooth, fill=strata), alpha=.2) + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high_smooth) + + } + + if (n.level==4){ + colors <- viridisLite::turbo(n=n.level,direction = 1)[c(1,3,2,4)] + } else { + colors <- viridisLite::turbo(n=n.level,direction = 1) + } + + p+ + ggplot2::scale_y_continuous(limits = c(0,1.02), + breaks = seq(0,1,.25), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0))+ + ggplot2::scale_fill_manual(values=colors)+ + ggplot2::scale_color_manual(values=colors)+ + ggplot2::theme_minimal()+ + ggplot2::theme(axis.title.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + # axis.text = ggplot2::element_blank(), + # legend.position = "none", + panel.grid.minor.y = ggplot2::element_blank(), + panel.grid.major.y = ggplot2::element_line(color="grey45",linewidth = line.w/2)) + +} + +# viridisLite::turbo(n=4,direction = 1)[c(1,3,2,4)] + +cluster_rank <- function(data){ + data |> cox_regression(outcome.var = "clust",use.strata = TRUE) |> ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(\(x){ + min(x[["estimate"]]) + }) |> purrr::list_c() |> rank() |> rev() +} + +cox_relevel <- function(data){ + data |> dplyr::mutate(clust=factor(clust,levels=cluster_rank(data))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +complete_preds_data <- function(data){ + data |> + # events_ready() |> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = FALSE) |> + mice::complete() |> + dplyr::filter((!is.na(pase_0)&!is.na(pase_4))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam <- targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam |>print_table_summary(by.var = "kam_grp") +#' data_kam |> cox_regression(outcome.var="kam_grp")|> plot_survival_smooth() +#' data_kam |> cox_regression(outcome.var="kam_grp",use.strata = FALSE)|> gtsummary::tbl_regression(exponentiate = TRUE, add_estimate_to_reference_rows = TRUE) |> gtsummary::bold_p() +#' targets::tar_read(df_events_complete) |> kamila_cluster(n.clusters=3) +kamila_cluster <- function(data, n.clusters=3,include.out=FALSE){ + # An index number could be added to later join pack. Of input a complete data set from imputation and pooling?? + data_orig <- data + + + if (!include.out){ + data <- data |> + dplyr::select(-tidyselect::one_of(c("time","status"))) + } + + + catInd <- data |> lapply(\(x) is.character(x)|is.logical(x)) |> purrr::list_c() + conInd <- data |> lapply(\(x) is.numeric(x)|is.integer(x)) |> purrr::list_c() + + catVars <- data[,catInd] + catVars <- catVars |> lapply(factor) |> dplyr::bind_cols() |> as.data.frame() + conVars <- data[,conInd] |> scale()|> as.data.frame() + + if (is.null(n.clusters)){ + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = 2:7, numInit = 10, + calcNumClust = "ps" + ) + }else { + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = n.clusters, numInit = 10) + } + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kamila_cluster",class(ls)) + + ls + +} + + +#' VarSelLCM wrapper +#' +#' @param data complete dataset with no missings +#' @param n.clusters number of clusters (if length 1, n is fixed, in n>1 given clusters are tested) +#' @param include.out flag to include outcome variables or not +#' @param memb.out output data frame with final membership or not (then outputs standard model output) +#' +#' @return list with VarSelLCM output and original dataset with cluster appended +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> lcm_cluster() +lcm_cluster <- function(data, n.clusters=3, include.out=FALSE, var.sel=FALSE,out.vars=c("time", "status")){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(-tidyselect::any_of(out.vars)) + } + + + set.seed(5432) + + out <- data |> + dplyr::mutate(dplyr::across(where(is.logical)|where(is.character),~factor(.x))) |> + as.data.frame() |> + VarSelLCM::VarSelCluster( + gvals=n.clusters, + crit.varsel="BIC", + vbleSelec = var.sel, + nbcores = round(parallel::detectCores()*.8) + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("lcm_cluster",class(ls)) + + ls + +} + + + +#' Kmeans clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> kmeans_cluster() +#' data |> kmeans_cluster(n.clusters=3) +kmeans_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + out <- data |> + dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + dplyr::across(where(is.factor),~as.numeric(.x))) |> + stats::kmeans( + centers=n.clusters + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kmeans_cluster",class(ls)) + + ls + +} + +#' dbscan clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> dbscan_cluster() +#' data |> dbscan_cluster(n.clusters=3) +dbscan_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + data <- data |> na.omit() |> dplyr::mutate(rtreat=rtreat!="Placebo", + dplyr::across(dplyr::everything(), as.numeric)) + + + ## This plot indicates that eps should be set around 60, but at this value everything is one cluster. + dbscan::kNNdistplot(data,k = 5) + + ## Performing hierachical clustering, it is clear, that the algorithm is not able to seperate clusters. + hds <- dbscan::hdbscan(data,minPts = 5) + + plot(hds,show_flat = TRUE) + + ## Clustering with set eps value and minPts + ds <- dbscan::dbscan(data,eps = 25,minPts = 2) + + ds[["cluster"]] + + ## dbscan is not an interesting approach, apparently + + # + # + # + # + # out <- data |> + # dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + # dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + # dplyr::across(where(is.factor),~as.numeric(.x))) |> + # stats::kmeans( + # centers=n.clusters + # ) + # + # ls <- list("out"=out,"data_orig"=data_orig) + # + # class(ls) <- c("kmeans_cluster",class(ls)) + # + # ls + +} + +#' Title +#' +#' @param ls +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' ls <- data |> lcm_cluster() +#' ls |> final_membership() +final_membership <- function(ls){ + cls <- class(ls) + if ("kamila_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$finalMemb), + ls$data_orig) + + } else if ("lcm_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out@partitions@zMAP), + ls$data_orig) + + } else if ("kmeans_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$cluster), + ls$data_orig) + + } else stop("Class not recognised") + +} + + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> get_clusters() +#' data |> get_clusters(n.cl=2:7) +get_clusters <- function(data,n.cl=4,rm.out=TRUE){ + set.seed(1123) + + if (length(n.cl)>1){ + list( + # "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } else { + list( + "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } + +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(list_pred_clusters) +#' data |> final_clusters() +final_clusters <- function(data,new.levels=NULL){ + out <- data |> lapply(final_membership) |> lapply(labelling_data) + + if (is.null(new.levels)){ + out + } else { + out |> + purrr::map2(relevels,function(x,y){ + # x$clust <- factor(factor(x$clust,levels=y),labels=1:4) + x$clust <- factor(x$clust,levels=y) + x #|> + # dplyr::filter(clust %in% range(as.numeric(clust))) |> + # dplyr::mutate(clust=factor(clust)) + }) + } + + } + + + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_summary_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> + # print_table_summary(by.var = by) + gtsummary::tbl_summary(by=by) #|> + # gtsummary::add_p() |> gtsummary::bold_p() + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Easy cox regression tbl for uniform results +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +cox2tbl <- function(data,by="clust",all.vars=FALSE){ +data|> cox_regression(outcome.var = by,use.strata = FALSE,all.vars = all.vars) |> + gtsummary::tbl_regression(exponentiate =TRUE, + statistics=list(gtsummary::all_continuous()~"[{conf.low};{conf.high}]", + gtsummary::all_categorical()~"[{conf.low}%;{conf.high}%]")) |> + gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_cox_reg_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> cox2tbl(by=by) + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +wrapped_surv_plot <- function(data,by="clust"){ +data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = by,use.strata = TRUE,all.vars = FALSE) |> + plot_survival_smooth()+ggplot2::labs(color="Cluster",fill="Cluster",linetype="Cluster") + } + ) |> (\(x){ + x |> patchwork::wrap_plots(ncol=1) + patchwork::plot_annotation(tag_levels = list(names(x))) + })() +} + + +# Ranking by most events +relevel_by_rank <- function(data){ + ## Assigning clusters to each dataset +data <- targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = NULL) + +## Calculating cox regressions and ranking by the final point on the survival plot +relevels <- data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = "clust",use.strata = TRUE,all.vars = FALSE) |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() |> + (\(x){ + split(x,x[["strata"]]) |> + purrr::map(function(.y){ + .y[["estimate"]][nrow(.y)] + }) + })() |> purrr::reduce(c) |> rank() + } + ) + +#3 Assigning the new, ranked levels +targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = relevels) +} + +## TODO +## Verify definitions +## Do remaining documentation of functions +## +## +## How does elastic net work with imputed dataset? +## Functionalise to allow for imputed and non-imputed (both analyses) - in both cases with and without BMI - include department of inclusion to investigate reason of missing BMI data +## +## tidymodels does not allow pmm in mice. Thy're out! + +## + +missing_stats <- function(index,data,glue.mask= "{N_miss} ({round(p_miss*100,dec)}%)",dec){ + index.var <- unique(data$table_body$variable)[index] + + table_body <- data$table_body |> + dplyr::filter(variable == index.var) + + if ("missing" %in% table_body$row_type){ + f1 <- grep("stat_\\d+",names(table_body)) + + ## This approach + masks <- data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::filter(!duplicated(col_name)) |> + dplyr::arrange(col_name) |> + dplyr::mutate(mask=glue::glue(glue.mask))|> + dplyr::pull(mask) + + table_body[table_body$row_type=="missing",f1] <- masks |> + as.matrix() |> + t() |> + tibble::as_tibble(.name_repair = "unique_quiet") + + } + table_body +} + +missing_stats_steps <- function(body,ls,glue.mask,dec){ + seq_along(unique(body$variable)) |> + purrr::map(function(.x) { + missing_stats(index=.x,data=ls,glue.mask = glue.mask,dec=dec) + }) |> + dplyr::bind_rows() +} + +add_missing_stats <- function(data,glue.mask= "{N_miss} ({round(p_miss*100,dec)}%)",dec=1){ + data |> + gtsummary::modify_table_body( + ~ .x |> missing_stats_steps(ls=data,glue.mask = glue.mask,dec=dec) + ) +} + +variable_masks <- function(index, data, cut.off,glue.mask= "<{n} (<{p}%)",dec=dec) { + index.var <- unique(data$table_body$variable)[index] + + table_body <- data$table_body |> + dplyr::filter(variable == index.var) + + + ## Filtering bu two different approaches + if (table_body$var_type[1] %in% c("dichotomous", "categorical")) { + if (any(grepl("^stat_[1-9]|[1-9]\\d",names(table_body)))){ + masked <- micro_n_masks( + ## Handling nominal/binary + + body = table_body |> + dplyr::filter(row_type!="missing"), + n.all = data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(n), + N.all=data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(N), + cut.off=cut.off, + glue.mask = glue.mask + ) + } else { + masked <- table_body |> dplyr::filter(row_type!="missing") + } + + + if ("stat_0" %in% names(masked)){ + body <- masked + tb <- body |> dplyr::filter(row_type!="missing",!is.na(stat_0)) + f1 <- grep("^stat_0", names(tb)) + + meta.index <- data$meta_data$df_stats |> + purrr::pluck(index) + + if ("by" %in% names(meta.index)) { + meta.index <- meta.index |> dplyr::filter(is.na(by)) + } + + masked <- masking(body=body, + tb=tb, + f1=f1, + ns = meta.index |> + dplyr::select(n)|> + dplyr::slice(seq_len(length(f1) * nrow(tb))) |> + unlist(use.names = FALSE) |> + matrix(ncol = length(f1), byrow = TRUE) |> + tibble::as_tibble(.name_repair = "unique_quiet"), + Ns= meta.index |> + dplyr::select(N_obs) |> + dplyr::slice(1)|> + unlist(use.names = FALSE), + cut.off=cut.off, + glue.mask=glue.mask, + dec=dec) + } + + out <- rbind( + masked, + table_body |> dplyr::filter(row_type=="missing")) + + } else { + out <- table_body + } + + if ("missing" %in% out$row_type) { + ## Handling missings n is N_miss, and N is N_obs + + missings <- micro_n_masks( + body = out |> + dplyr::filter(row_type == "missing"), + n.all = data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(N_miss), + N.all=data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(N_obs), + cut.off=cut.off, + glue.mask=glue.mask + ) + + if ("stat_0" %in% names(missings)) { + ## Handling overall column + body <- missings + tb <- body |> dplyr::filter(row_type=="missing") + f1 <- grep("^stat_0", names(tb)) + + masking(body=body, + tb=tb, + f1=f1, + ns = data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::filter(is.na(by)) |> + dplyr::select(N_miss) |> + dplyr::slice(1) |> + unlist(use.names = FALSE) |> + matrix(ncol = length(f1), byrow = TRUE) |> + tibble::as_tibble(.name_repair = "unique_quiet"), + Ns=data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::filter(is.na(by)) |> + dplyr::select(N_obs) |> + dplyr::slice(1) |> + unlist(use.names = FALSE), + cut.off=cut.off*2, + glue.mask=glue.mask, + dec=dec) + + } + + out <- rbind( + out |> + dplyr::filter(row_type != "missing"), + missings) + + + + + + } + + out +} + +micro_n_masks <- function(body, n.all, N.all, cut.off, glue.mask,dec=1) { + if (nrow(body) > 1) { + # First row removed in case of categorical + # Possibly change to include in filter, to have whole df, or just rbind in the end + tb <- body |> dplyr::filter(row_type!="label") + } else { # last option is "dichotomous" + tb <- body + } + + ## Supports any number of stat columns (overkill!) + f1 <- grep("^stat_[1-9]|[1-9]\\d", names(tb)) + + masking(body=body, + tb=tb, + f1 = f1, + ns=n.all |> + dplyr::slice(seq_len(length(f1) * nrow(tb))) |> + unlist(use.names = FALSE) |> + matrix(ncol = length(f1), byrow = TRUE) |> + tibble::as_tibble(.name_repair = "unique_quiet"), + Ns=N.all |> + dplyr::slice(seq_len(length(f1))) |> + unlist(use.names = FALSE), + cut.off=cut.off, + glue.mask=glue.mask, + dec=dec + ) + +} + + +#' Title +#' +#' @param body full table body +#' @param tb filtered table body +#' @param f1 Subsets indexes of relevant columns +#' @param ns Relevant ns arranged in matrix +#' @param Ns All Ns +#' @param cut.off +#' @param glue.mask +#' @param dec +#' +#' @return +#' @export +masking <- function(body, + tb=NULL, + f1, + ns, + Ns, + cut.off=cut.off, + glue.mask=glue.mask, + dec=dec + ){ + + if (is.null(tb)) tb <- body + + ## Logical matrix of relevant ns to mask + f2 <- ns |> + purrr::map(\(.x) .x %in% 1:(cut.off - 1)) |> + dplyr::bind_cols() |> + as.matrix() + + ## Number of cells with zero observations in each matrix row + n0 <- apply(ns == 0, 1, sum) + + ## Number of cells with ns to mask in each matrix row + ns.low <- apply(f2, 1, sum) + + + if (all(ns.low==0)){ + out <- body + } else { + + ls.rows <- seq_len(nrow(f2)) |> + purrr::map(\(.i){ + + ds <- tb[.i, ] + # Creating masked matrix to record maskings + masked <- matrix(FALSE, ncol = ncol(f2)) + + # Only modify in case of low, handle one col matrices + if (apply(f2, 1, any)[.i]) { + n <- cut.off + N <- Ns[f2[.i,]] + p <- round(100 * n / N, dec) + + ds[f1[f2[.i,]]] <- glue::glue(glue.mask) |> + as.matrix() |> + t() |> + tibble::as_tibble(.name_repair = "unique_quiet") + masked[f2[.i,]] <- TRUE + + ## loop to add masks until satisfied + while (sum(masked)==1 & # The case of only one low + nrow(masked)>1 | # But ignored in case of ncol==1 + sum(ns[.i,][masked]) <= cut.off & + (sum(masked) + n0[.i]) < ncol(masked)) { + # in the case that sum of smalls is below cutoff, another field is added. + + ranked <- apply(ns[.i,], 1, rank, ties.method = "first") |> t() + + ranked.i <- ranked == sum(masked) + n0[.i] + 1 + n <- ns[.i,][ranked.i] |> plyr::round_any(accuracy = cut.off, f = ceiling) + N <- Ns[ranked.i] + p <- round(100 * n / N, dec) + + ds[f1[ranked.i]] <- glue::glue(glue.mask) + masked[ranked.i] <- TRUE + }} + + list( + ds = ds, + masked = masked + ) + }) + + # The case for dichotomous + if (nrow(tb) == 1) { + out <- ls.rows |> + purrr::map(purrr::pluck, "ds") |> + dplyr::bind_rows() + + # Handling categorical data + } else if (nrow(tb) > 1) { + masks <- ls.rows |> + purrr::map(purrr::pluck, "masked") |> + purrr::reduce(rbind) + + out <- ls.rows |> + purrr::map(purrr::pluck, "ds") |> + dplyr::bind_rows() + + ## Indices by row + col.i <- seq_len(nrow(masks)) |> purrr::map(\(.j){ + which(masks[.j,]) + }) + + col.i.vec <- purrr::list_c(col.i) |> unique() + + if (purrr::compact(col.i) |> length() == 1){ + # As this is only the case with overall column + # This should be reworked + + # This was the approach, to just select the first + # which(purrr::map_lgl(col.i,is_empty))[1] + + # This will select the cell with the second lowest number + col.i[[which(rank(ns,ties.method = "first")==2)]] <- c("") + } + + out <- col.i |> + purrr::map(\(.y){ + # length(.y) + if (length(.y) > 0) { + cols <- col.i.vec[!col.i.vec %in% .y] + if (length(cols)==0){ + cols <- "" + } else { + cols + } + } else { + .y + } + }) |> + purrr::imap(\(.y, .i){ + ds <- out[.i, ] + if (length(.y) > 0 & all(.y!="")) { + n <- ns[.i, .y] |> + purrr::map_dfr(plyr::round_any,accuracy = cut.off, f = ceiling) + N <- Ns[.y] + p <- round(100 * n / N, dec) + if (n==0) glue.mask <- "{n}" + + ds[f1[.y]] <- glue::glue(glue.mask)|> + as.matrix() |> + t() |> + tibble::as_tibble(.name_repair = "unique_quiet") + ds + } else { + ds + } + }) |> + dplyr::bind_rows() + + out <- rbind( + body |> + dplyr::filter(row_type=="label"), + out + ) + } + } + out + +} + + +summary_masks <- function(body,ls,cut.off=5,dec=dec){ + seq_along(unique(body$variable)) |> + purrr::map(function(.x) { + variable_masks(index=.x,data=ls,cut.off=cut.off,dec=dec) + }) |> + dplyr::bind_rows() +} + +mask_micro_summary <- function(data,micro.n=5){ + data |> + gtsummary::modify_table_body( + ~ .x |> summary_masks(ls=data,cut.off = micro.n,dec=1) + ) +} + +#' Title +#' +#' @param mask +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' minimal_mask(mask=tibble::as_tibble(matrix(c(FALSE,FALSE,TRUE,TRUE),nrow=1),.name_repair="unique_quiet"), +#' data=tibble::as_tibble(matrix(c(20,12,8,4),nrow=1),.name_repair="unique_quiet"),micro.n=5) +minimal_mask <- function(mask,data,micro.n){ + ## Function assumes monotonous data + ## Use with column_diffs() for risk tables + + ## FUnction works in practice, but example doesn't?? + + out <- mask + + for (i in seq_len(nrow(mask))){ + if (i>1){ + for (j in seq_len(ncol(mask))){ + if(dplyr::pull(mask[i,j])){ + n <- max(which(which(!out[i,]) +#' events_dataset(impute = FALSE) |> +#' cox_regression(all.vars = FALSE) |> +#' ggsurvfit::survfit2() |> +#' ggsurvfit::tidy_survfit(times = c(0, 2, 4, 6, 8.5))|> +#' tidyr::pivot_wider(id_cols = strata, names_from = time, values_from = cum.event) +#' data |> mask_micro_table(col.sel=-strata) +#' data.sel <- data |> dplyr::select(-strata) +mask_micro_table <- function(data, micro.n = 5, down = TRUE, col.sel) { + data.sel <- data |> + dplyr::select({{ col.sel }}) + + ## List of the two selection matrices + masked <- list( + data.sel, # The actual data + data.sel |> + column_diffs(include.first = TRUE) # Row differences (incl first row) + ) |> + purrr::map(\(.y){ # For each element in the list, do colwise test + .y |> + purrr::map_dfr(\(.x) .x %in% 1:(micro.n-1)) + }) |> + purrr::imap(\(.y,.i){ # apply minimal masking to last list object + if (.i==2){ + .y |> minimal_mask(data = data.sel,micro.n=micro.n) + }else { + .y + } + }) |> + purrr::reduce(`|`) |> # Combine matrices + tibble::as_tibble() |> # To tibble + purrr::map2(data.sel, \(.x, .y){ # Apply masking based on combined selection + ifelse(.x, rounded_interval(.y, round = micro.n, down = down), .y) + }) |> + dplyr::bind_cols() + + ## Bind masked data to original columns + dplyr::bind_cols( + data |> + dplyr::select(-{{ col.sel }}), + masked|> + # Converts new to character for uniform data + dplyr::mutate(dplyr::across(dplyr::everything(), ~ as.character(.x))) + ) |> + dplyr::select(colnames(data)) # Order columns as original input data +} + +rounded_interval <- function(data, round = 5, down = TRUE) { + # Handle "ties" + sub <- ifelse(down, -1, 1) + data <- ifelse(data %% round == 0 & data != 0, data + 1, data) + + c(floor, ceiling) |> + purrr::map(\(.x) { + plyr::round_any(x = data, accuracy = round, f = .x) + }) |> + dplyr::bind_cols(.name_repair = "unique_quiet") |> + setNames(c("l", "h")) |> + dplyr::transmute(mask = glue::glue("{l}-{h}")) |> + dplyr::pull(mask) +} + +column_diffs <- function(data, prefix.pattern = NULL, include.first = TRUE, suffix.out = "_diff") { + if (!is.null(prefix.pattern)) { + data <- data |> + dplyr::select(tidyselect::starts_with(prefix.pattern)) + } + + index <- seq_along(data)[-1] + + diff <- index |> + purrr::map(\(.y){ + abs(data[.y - 1] - data[.y]) + }) |> + dplyr::bind_cols() |> + (\(.x) setNames(.x, paste0(names(.x), suffix.out)))() + + if (include.first) { + out <- dplyr::bind_cols(data[1], diff) + } else { + out <- diff + } + out +} + + +collect_calibration<-function(data){ + data|> + purrr::map(\(.y) { + .y |> + purrr::pluck("model") |> + purrr::pluck("TestProb") |> + dplyr::bind_rows() + }) +} + +collect_calibration_mids<-function(data){ + data|> + purrr::map(\(.z) { + .z |> + purrr::map(\(.y) { + .y |> + purrr::pluck("model") |> + purrr::pluck("TestProb") |> + dplyr::bind_rows() + }) |> + dplyr::bind_rows() + }) +} + +plot_calibration<-function(data,name) { + predtools::calibration_plot( + data = as.data.frame( + dplyr::select(data, y, pred) |> + dplyr::mutate(y = as.numeric(y) - + 1) + ), + obs = "y", + pred = "pred"#, + # x_lim = c(0, 1), + # y_lim = c(-.1, 1.1) + ) |> + purrr::pluck("calibration_plot") + + ggplot2::labs(title = name)+ + ggplot2::scale_x_continuous(breaks=seq(0,1,.25),limits=c(0, 1))+ + ggplot2::scale_y_continuous(breaks=seq(0,1,.25),limits = c(-.1, 1.1)) +} + +print_calibration<-function(data,file){ + ggplot2::ggsave(filename =file,plot = data,device = "png",dpi = 600,width = 84,height = 150,units = "mm") +} + +map_summary_results <- function(data){ + data |> lapply(\(.x){ + .x |> + multi_summary() |> + print_model_resutls() + }) +} + + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' list("Raw dec/inc quartiles" = targets::tar_read(ls_pred_models)) |> map_summary_results() |> minimal_performance_table() +minimal_performance_table <- function(data){ + data |> + purrr::imap(\(.x, .i){ + df_perf <- lapply(.x[[3]], \(.y){ + Reduce(c, .y) + }) |> dplyr::bind_rows() + + df_ns <- .x[[2]] |> + purrr::map(\(.y) { + tibble::tibble(N=sum(.y), + n=sum(.y[2,])) + }) |> + dplyr::bind_rows() + + + df_auc <- .x[[4]] |> + purrr::map(\(.y) .y[["Median"]]) |> + purrr::reduce(c) + + ## Here should follow a block to add median alpha and lambda to the table overview + + df_tune <- purrr::map(.x[[5]], \(.y){ + .y |> + purrr::map(\(.z){ + .z[["Median"]] + }) |> + purrr::reduce(c) + }) |> dplyr::bind_cols() + + tibble::tibble( + group = .i, + model = names(.x[[3]]), + df_ns, + df_perf, + "Median AUC" = df_auc, + setNames(df_tune, paste("Median", names(df_tune))) + ) + }) |> + dplyr::bind_rows() |> + dplyr::select(group, model, N,n,Sensitivity, Specificity, `Pos Pred Value`, `Neg Pred Value`, tidyselect::starts_with("Median")) +} + +data_prep_mmrm <- function(data){ + data |> + dplyr::mutate(id = factor(dplyr::row_number())) |> + dplyr::select(id, dplyr::everything(), -reg_bmi) |> + tidyr::pivot_longer( + cols = dplyr::starts_with("pase_"), + values_to = "pase", names_to = "time" + ) |> + dplyr::mutate(time = factor(time), ) |> + dplyr::mutate(dplyr::across(dplyr::where(is.logical), \(.x) as.numeric(.x))) +} + +split_sex <- function(data,var="reg_female"){ + data |> + (\(.x){ + split(dplyr::select(.x,-tidyselect::all_of(var)),.x[[var]]) |> + setNames(c("male","female")) + })() +} + +simple_multi_mmrm <- function(data,vars.out=c("pase", "time", "id")){ + vars <- names(data)[!names(data) %in% c("pase", "time", "id")] + + ## mmrm doesn't work too well with gtsummary as variable sorting is lost + mmrm::mmrm(as.formula(paste0("pase~", paste(vars, collapse = "+"), "+us(time|id)")), data = data) +} + +simple_uni_mmrm <- function(data,vars.out=c("pase", "time", "id")){ + vars <- names(data)[!names(data) %in% c("pase", "time", "id")] + + ## mmrm doesn't work too well with gtsummary as variable sorting is lost + vars |> purrr::map(\(.x){ + mmrm::mmrm(as.formula(paste0("pase~", .x, "+us(time|id)")), data = data) + }) + +} + +mmrm_summary <- function(data){ + gtsummary::tbl_regression(data, + add_header_row = TRUE, + show_single_row = tidyselect::where(is.logical), + tidy_fun = broom.helpers::tidy_parameters + ) |> + fix_labels() |> + gtsummary::modify_table_styling(column = p.value, + hide=TRUE) +} + + +df_mega_list <- function( + strategies=c( + "bin_original", + "bin_anyupdown", + "bin_relupdown", + "bin_absupdown", + "bin_quantile", + "bin_clusterlcm", + "bin_percentage")){ + purrr::map(strategies, \(.z){ + if (.z == "bin_original") { + f <- eval(str2expression(.z)) + list(c(list(binning.fun = f), list(var.excl = c("pase_4", "pase_0_quartile", "pase_4_quartile", "pase_split", "pase_dif", "pase_rel_dif")), list(name = "original_change"))) + } else if (.z == "bin_anyupdown") { + f <- eval(str2expression(.z)) + purrr::map(c("_q1", "_half"), \(.y){ + if (.y == "_q1") { + out <- list(binning.fun = f, low.q = 1, high.q = 2:4) + } else if (.y == "_half") { + out <- list(binning.fun = f, low.q = 1:2, high.q = 3:4) + } + c(out, list(var.excl = c("pase_4", "pase_0_quartile", "pase_4_quartile", "pase_split", "pase_dif", "pase_rel_dif")), list(name = glue::glue("any_change{.y}"))) + }) # |> purrr::list_flatten() + } else if (.z == "bin_relupdown") { + f <- eval(str2expression(.z)) + purrr::map(c(20, 50, 75), \(.x){ + out <- purrr::map(c("_q1", "_half"), \(.y){ + if (.y == "_q1") { + out <- list(binning.fun = f, rel.bin = .x, low.q = 1, high.q = 2:4) + } else if (.y == "_half") { + out <- list(binning.fun = f, rel.bin = .x, low.q = 1:2, high.q = 3:4) + } + c(out, list(var.excl = c("pase_4", "pase_0_quartile", "pase_4_quartile", "pase_split", "pase_dif", "pase_rel_dif")), list(name = glue::glue("rel_change_{.x}{.y}"))) + }) + }) |> purrr::list_flatten() + } else if (.z == "bin_absupdown") { + f <- eval(str2expression(.z)) + purrr::map(seq(40, 100, 20), \(.x){ + out <- purrr::map(c("_q1", "_half"), \(.y){ + if (.y == "_q1") { + out <- list(binning.fun = f, abs.bin = .x, low.q = 1, high.q = 2:4) + } else if (.y == "_half") { + out <- list(binning.fun = f, abs.bin = .x, low.q = 1:2, high.q = 3:4) + } + c(out, list(var.excl = c("pase_4", "pase_0_quartile", "pase_4_quartile", "pase_split", "pase_dif", "pase_rel_dif")), list(name = glue::glue("abs_change_{.x}{.y}"))) + }) + }) |> purrr::list_flatten() + } else if (.z == "bin_percentage") { + f <- eval(str2expression(.z)) + purrr::map(c(5, 15), \(.x){ + out <- list(binning.fun = f, percentage = .x) + c(out, list(var.excl = c("pase_4", "pase_0_quartile", "pase_4_quartile", "pase_split", "pase_dif", "pase_rel_dif","pase_0_cut","pase_4_cut")), list(name = glue::glue("lowest_percentage_{.x}"))) + }) + } else if (.z == "bin_quantile") { + f <- eval(str2expression(.z)) + purrr::map(seq(6, 10, 2), \(.x){ + purrr::map(seq_len(floor(.x/2)), \(.y){ + purrr::map(c(FALSE,TRUE),\(.any){ + out <- list(binning.fun = f, quantiles = .x, lowest = .y, any = .any) + + c(out, + list(var.excl = c("pase_4", "pase_0_quartile", "pase_4_quartile", "pase_split", "pase_dif", "pase_rel_dif" + , + "pase_0_quantile", "pase_4_quantile" + )), + list(name = glue::glue("quantile_change_{.x}_{.y}{ifelse(.any,'_ANY','')}")) + ) + + }) + + })|> purrr::list_flatten() + }) |> purrr::list_flatten() + } else if (.z == "bin_clusterlcm") { + f <- eval(str2expression(.z)) + list(c( + list(binning.fun = f), + list(var.excl=c("pase_0_quartile","pase_4_quartile","pase_split","pase_dif","pase_rel_dif")), #Returns all false, which crashes the function + list(name = "cluster_lcm") + )) + } + }) |> purrr::list_flatten() +} + + +multi_grouping_wrapper <- function(name, ...) { + .f <- function(...) { + data |> + group_format(...) + } + + list(.f(...)) |> setNames(name) +} + +# ls <- df_mega_list() + +# ls <- c(list(binning.fun = bin_quantile, quantiles = 5, lowest = 2, any=FALSE), list(var.excl = c("pase_4", "pase_0_quartile", "pase_4_quartile", "pase_split", "pase_dif", "pase_rel_dif")), list(name = glue::glue("quantile_change_5_2"))) +# # , "pase_0_quantile", "pase_4_quantile" +# multi_grouping_df_list(data=targets::tar_read(df_all_data_formatted),list(ls)) |> purrr::pluck(1) |> View() + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> multi_grouping_df_list(args.list=df_mega_list()) +multi_grouping_df_list <- function(data,args.list,remove=NULL){ + purrr::map(args.list, \(.x){ + if ("mids" %in% class(data)){ + out <- data |> + mice::complete(action = "long", include = TRUE) |> + (\(.y){ + do.call(multi_grouping_wrapper, c(list(data = .y), .x)) + })() |> + purrr::map(\(.z){ + .z |> + dplyr::select(-tidyselect::any_of(remove)) |> + mice::as.mids() + }) + } else { + out <- do.call(multi_grouping_wrapper, c(list(data = data), .x))|> + purrr::map(\(.z){ + .z |> + dplyr::select(-tidyselect::any_of(remove)) + }) + } + out + }) |> purrr::list_flatten() + } + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(list_df_multi_grouping)[24] |> multi_results_list() +multi_results_list <- function(data){ + data |> + purrr::map(\(.x){ + list(summary=.x |> gtsummary::tbl_summary(by = pase_change), + cox = .x |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression(all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_change") |> + tbl_regression_standard(), + plot=.x |> + # dplyr::select(-pase_0,-pase_4,-pase_rel_dif) |> + cox_regression() |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit() + ggsurvfit::add_confidence_interval() + + ggsurvfit::scale_ggsurvfit() + + ggsurvfit::add_risktable() + ) + })} + +multi_cox_performance_test <- function(data,...){ + data |> + purrr::imap(\(.x,.i){ + out <- .x |> + cox_regression(...) |> + performance::model_performance() + tibble::tibble(Name=.i,out) + })|> + dplyr::bind_rows() |> + dplyr::mutate(rank=rank(AIC,ties.method = "min")) + # performance::compare_performance(rank = TRUE) +} + + +mids_model_aic <- function(data){ + sapply(data[["analyses"]],AIC) |> + median() +} + +pick_non_duplicated <- function(data,name,aic,i){ + data[[name]][!duplicated(data[[aic]])][i] +} diff --git a/2 Longterm/260315/glmnet-reg.R b/2 Longterm/260315/glmnet-reg.R new file mode 100755 index 0000000..72b6eec --- /dev/null +++ b/2 Longterm/260315/glmnet-reg.R @@ -0,0 +1,340 @@ +## ItMLiHSmar2022 +## regular_fun.R, child script +## Regularisation model building function +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + +regular_fun <- function(X, y, K, lambdas, alpha) { + n <- nrow(X) + set.seed(321) + + # Using caret function to ensure both levels represented in all folds + c <- caret::createFolds(y = y, k = K, list = FALSE, returnTrain = TRUE) + + B <- yhatTestProbKeep <- list() + accTrain <- accTest <- err_train <- err_test <- auc_train <- auc_test <- matrix(nrow = K, ncol = length(lambdas)) + + TrainProb <- TestProb <- list() + + catinfo <- levels(y) + + cMatTrain <- cMatTest <- table(true = factor(c(0, 0), levels = catinfo), pred = factor(c(0, 0), levels = catinfo)) + + + ## Iterate over partitions + for (idx1 in 1:K) { + # Status + cat("Processing fold", idx1, "of", K, "\n") + + # idx1=1 + # Get training- and test sets + I_train <- c != idx1 ## Creating selection vector of TRUE/FALSE + I_test <- !I_train + + Xtrain <- X[I_train, ] + ytrain <- y[I_train] + Xtest <- X[I_test, ] + ytest <- y[I_test] + + + ## Model matrices for glmnet + ## Using the complicated approach not to include first level. + # Xmat.train<-model.matrix(~ .-1, data=Xtrain, + # contrasts.arg = lapply(Xtrain[,sapply(Xtrain, is.factor)], + # contrasts, contrasts=T)) + # Xmat.test<-model.matrix(~ .-1, data=Xtest, + # contrasts.arg = lapply(Xtest[,sapply(Xtest, is.factor)], + # contrasts, contrasts=T)) + + # Xmat.train<-model.matrix(~.-1,Xtrain) + # Xmat.test<-model.matrix(~.-1,Xtest) + + # Weights + ytrain_weight <- as.vector(1 - (table(ytrain)[ytrain] / length(ytrain))) + # ytest_weight<-as.vector(1 / (table(ytest)[ytest] / length(ytest))) + + # Fit regularized linear regression model + mod <- glmnet::glmnet(Xtrain, ytrain, + alpha = alpha, ## Alpha = 1 for lasso + lambda = lambdas, ## Setting lambdas + standardize = TRUE, ## Scales and centers + weights = ytrain_weight, + family = "binomial" + ) + + # Keep coefficients for plot + B[[idx1]] <- as.matrix(coef(mod)) + + # TrainProb[[idx1]] <- list() + TestProb[[idx1]] <- list() + + # Iterate over regularization strengths to compute training- and test + # errors for individual regularization strengths. + for (idx2 in 1:length(lambdas)) { + # idx2=1 + + # Predict + yhatTrainProb <- predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtrain), + type = "response" + ) + + yhatTestProb <- predict(mod, + s = lambdas[idx2], + newx = data.matrix(Xtest), + type = "response" + ) + + # Compute training and test error + yhatTrain <- round(yhatTrainProb) + yhatTest <- round(yhatTestProb) + + TestProb[[idx1]][[idx2]] <- dplyr::bind_cols(data.matrix(Xtest),y=ytest,pred=yhatTestProb,.name_repair = "unique_quiet") + + # Make predictions categorical again (instead of 0/1 coding) + yhatTrainCat <- factor(round(yhatTrainProb), levels = c("0", "1"), labels = catinfo, ordered = TRUE) + yhatTestCat <- factor(round(yhatTestProb), levels = c("0", "1"), labels = catinfo, ordered = TRUE) + + # Evaluate classifier performance + # Accuracy + # accTrain[idx1,idx2] <- sum(yhatTrainCat==ytrain)/length(ytrain) + # accTest [idx1,idx2] <- sum(yhatTestCat==ytest)/length(ytest) + # # + # # Error rate + # err_train[idx1,idx2] = 1 - accTrain[idx1,idx2] + # err_test [idx1,idx2] = 1 - accTest[idx1,idx2] + + # AUROC + suppressMessages( + auc_train[idx1, idx2] <- pROC::auc(ytrain, yhatTrainCat) + ) + suppressMessages( + auc_test[idx1, idx2] <- pROC::auc(ytest, yhatTestCat) + ) + + # Compute confusion matrices + cMatTrain <- cMatTrain + table(true = ytrain, pred = yhatTrainCat) + cMatTest <- cMatTest + table(true = ytest, pred = yhatTestCat) + } + } + list(mod = mod, B = B, auc_train = auc_train, auc_test = auc_test, cMatTrain = cMatTrain, cMatTest = cMatTest, + TrainProb=TrainProb, + TestProb=TestProb) +} + + +## ItMLiHSmar2022 +## regularisation_steps.R, child script +## Regularised model building and analysation for assignment +## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk +## +## Now modified to use in publication +## + + + +#' Title +#' +#' @param data +#' @param outcome.var +#' @param weighted +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) |> +#' pred_ls_split(excluded.vars = "reg_bmi") +#' +#' data <- data[[1]] +#' mod <- data |> regularisation_steps(auto.l=TRUE) +regularisation_steps <- function(data, outcome.var = "pase_bin", weighted = FALSE, auto.l = FALSE) { + n <- nrow(data) + + y <- data |> dplyr::select({{ outcome.var }}) + X <- data |> dplyr::select(-{{ outcome.var }}) + + ## ==================================================================== + ## Step 0: data import and wrangling + ## ==================================================================== + + # setwd("/Users/au301842/PhysicalActivityandStrokeOutcome/1 PA Decline/") + + # source("data_format.R") + y1 <- factor(as.integer(y[[1]])) ## Outcome is required to be factor of 0 or 1. + # summary(y1) + + ## ==================================================================== + ## Step 1: settings + ## ==================================================================== + + ## Folds + K <- 10 + set.seed(3) + c <- caret::createFolds( + y = y1, + k = K, + list = FALSE, + returnTrain = TRUE + ) # Fold IDs for tuning + + ## Defining tuning parameters + if (auto.l){ + lambdas <- NULL + } else { + lambdas <- 2^seq(-10, 20, 1) + } + + alphas <- seq(0, 1, .1) + + ## Weights for models + if (weighted) { + wght <- as.vector(1 - (table(y1)[y1] / length(y1))) + } else { + wght <- rep(1, length(y1)) + } + + + ## Standardise numeric + ## Centered and + + + + ## ==================================================================== + ## Step 2: all cross validations for each alpha + ## ==================================================================== + + # library(furrr) + # library(purrr) + # library(doMC) + # registerDoMC(cores=6) + + future::plan(strategy = "multisession", workers = 2) + + # Nested CVs with analysis for all lambdas for each alpha + # + set.seed(3) + cvs <- furrr::future_map(alphas, .options = furrr::furrr_options(seed = 3), function(a) { + glmnet::cv.glmnet(model.matrix(~ . - 1, X), + y1, + weights = wght, + lambda = lambdas, + type.measure = "deviance", # This is standard measure and recommended for tuning + foldid = c, # Per recommendation the folds are kept for alpha optimisation + alpha = a, + standardize = TRUE, + family = quasibinomial, # Same as binomial, but not as picky + keep = TRUE + ) + }) + + ## ==================================================================== + # Step 3: optimum lambda for each alpha + ## ==================================================================== + + + # For each alpha, lambda is chosen for the lowest meassure (deviance) + each_alpha <- sapply(seq_along(alphas), function(id) { + each_cv <- cvs[[id]] + alpha_val <- alphas[id] + index_lmin <- match( + each_cv$lambda.min, + each_cv$lambda + ) + c( + lamb = each_cv$lambda.min, + alph = alpha_val, + cvm = each_cv$cvm[index_lmin] + ) + }) + + if (auto.l){ + # Best (min) lambda + best_lamb <- min(each_alpha["lamb", ]) + + # Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",] + best_alph <- each_alpha["alph", ][each_alpha["cvm", ] == min(each_alpha["cvm", ] + [each_alpha["lamb", ] %in% best_lamb])] + + # BEst lamb is set to NULL to allow glm.net to use the optimal method, which is bult in. + # best_lamb <- NULL + + } else { + # Best (min) lambda + best_lamb <- min(each_alpha["lamb", ]) + + # Alpha is chosen for best lambda with lowest model deviance, each_alpha["cvm",] + best_alph <- each_alpha["alph", ][each_alpha["cvm", ] == min(each_alpha["cvm", ] + [each_alpha["lamb", ] %in% best_lamb])] + } + + + ## https://stackoverflow.com/questions/42007313/plot-an-roc-curve-in-r-with-ggplot2 + # df_roc <- glmnet::roc.glmnet(cvs[[match(best_alph, alphas)]]$fit.preval, newy = y1)[match(best_lamb, lambdas)]# |> # Plots performance from model with best alpha + # + # df_roc |> plot_roc_curve() + + ## ==================================================================== + # Step 4: Creating the final model + ## ==================================================================== + + # source(here::here("R/regular_fun.R")) # Custom function + optimised_model <- regular_fun(X = X, y = y1, K = K, lambdas = best_lamb, alpha = best_alph) + # With lambda and alpha specified, the function is just a k-fold cross-validation wrapper, + # but keeps model performance figures from each fold. + + # list2env(optimised_model, .GlobalEnv) + # Function outputs a list, which is unwrapped to Env. + # See source script for reference. + + ## ==================================================================== + # Step 5: creating table of coefficients for inference + ## ==================================================================== + + # reg_coef_tbl <- optimised_model$B |> purrr::reduce(cbind) + + # Bmatrix <- optimised_model$B |> purrr::reduce(cbind) + # Bmedian <- apply(Bmatrix, 1, median) + # Bmean <- apply(Bmatrix, 1, mean) + # + # reg_coef_tbl <- dplyr::tibble( + # name = rownames(Bmatrix), + # medianX = round(Bmedian, 5), + # ORmed = round(exp(Bmedian), 5), + # meanX = round(Bmean, 5), + # ORmea = round(exp(Bmean), 5) + # ) # |> + # arrange(desc(abs(medianX)))%>% + # gt::gt() + + ## ==================================================================== + # Step 6: plotting predictive performance + ## ==================================================================== + + # reg_cfm <- caret::confusionMatrix(optimised_model$cMatTest) + # reg_cfm <- optimised_model$cMatTest + # reg_auc_sum <- optimised_model$auc_test[, 1] + + ## ==================================================================== + # Step 7: Packing list to save in loop + ## ==================================================================== + + list( + "IncludedN" = n, + "model" = optimised_model, + "alphas" = alphas, + "bestA" = best_alph, + "lambdas" = lambdas, + "bestL" = best_lamb, + # "TestTable" = reg_cfm, + # "AUROC" = reg_auc_sum, + # "ROC curve" = df_roc, + "y1" = y1, + "X" = X, + "data" = data, + "cvs" = cvs + ) +} diff --git a/2 Longterm/260315/last_sensitivity.R b/2 Longterm/260315/last_sensitivity.R new file mode 100755 index 0000000..3ebd40d --- /dev/null +++ b/2 Longterm/260315/last_sensitivity.R @@ -0,0 +1,172 @@ +targets::tar_read("df_all_data_formatted") |> + events_ready() |> + dplyr::filter(!is.na(pase_0), !is.na(pase_4)) |> + (\(data){ + c("pase_0", "pase_4") |> + purrr::map(\(exp){ + list( + "Univariable" = cox_regression(data = data, all.vars = FALSE, use.strata = FALSE, outcome.var = exp), + "Multivariable" = cox_regression(data = data, all.vars = TRUE, use.strata = FALSE, outcome.var = exp) + ) |> + purrr::map(\(.x){ + .x |> + gtsummary::tbl_regression(exponentiate = TRUE) |> + fix_labels() + }) |> + tbl_merged_named() + }) + })() |> + gtsummary::tbl_stack() + + +targets::tar_read("df_all_data_formatted") |> + pase_cutter(drop.nas = TRUE) |> + events_ready(v.groups = c("clin", "lifestyle.events", "ses", "assess.events", "quartiles")) |> + dplyr::select(-dplyr::any_of(c("pase_0", "pase_4", "pase_change"))) |> + (\(data){ + c("pase_0_quartile", "pase_4_quartile") |> + purrr::map(\(exp){ + list( + "Univariable" = cox_regression(data = data, all.vars = FALSE, use.strata = FALSE, outcome.var = exp), + "Multivariable" = cox_regression(data = data, all.vars = TRUE, use.strata = FALSE, outcome.var = exp) + ) |> + purrr::map(\(.x){ + .x |> + gtsummary::tbl_regression(exponentiate = TRUE) |> + fix_labels() + }) |> + tbl_merged_named() + }) + })() |> + gtsummary::tbl_stack() + + +targets::tar_read("df_all_data_formatted") |> + pase_cutter(drop.nas = TRUE) |> + get_vars(vars.groups = c("clin", "lifestyle.events", "ses", "assess.events", "quartiles"), vars.vec = c("inc_time")) |> + dplyr::mutate(time = time + inc_time / 365) |> + dplyr::select(-dplyr::any_of(c("pase_0", "pase_4", "pase_change", "inc_time", "event.include", "pase_4_quartile"))) |> + (\(data){ + list( + "Univariable" = cox_regression(data = data, all.vars = FALSE, use.strata = FALSE, outcome.var = "pase_0_quartile"), + "Multivariable" = cox_regression(data = data, all.vars = TRUE, use.strata = FALSE, outcome.var = "pase_0_quartile") + ) |> + purrr::map(\(.x){ + .x |> + gtsummary::tbl_regression(exponentiate = TRUE) |> + fix_labels() + }) |> + tbl_merged_named() + })() + +ls_sens <- list( + pase_0_all_quartile = list( + data = targets::tar_read("df_all_data_formatted") |> + pase_cutter(drop.nas = FALSE) |> + get_vars(vars.groups = c("clin", "lifestyle.events", "ses", "assess.pred", "quartiles"), vars.vec = c("inc_time", + "time", + "status")) |> + dplyr::mutate(time = time + inc_time / 365) |> + dplyr::select(-dplyr::any_of(c("pase_0", "pase_4", "pase_change", "inc_time", "event.include", "pase_4_quartile"))), + main.exp = "pase_0_quartile" + ), + # pase_0_all_contin = list( + # data = targets::tar_read("df_all_data_formatted") |> + # get_vars(vars.groups = c("clin", "lifestyle.events", "ses", "assess.pred", "quartiles"), vars.vec = c("inc_time", + # "time", + # "status")) |> + # dplyr::mutate(time = time + inc_time / 365) |> + # dplyr::select(-dplyr::any_of(c("pase_4", "pase_change", "inc_time", "event.include", "pase_0_quartile", "pase_4_quartile"))), + # main.exp = "pase_0" + # ), + pase_0_excl_quartile = list( + data = targets::tar_read("df_all_data_formatted") |> + pase_cutter(drop.nas = TRUE,drop.pase = FALSE) |> + events_ready(v.groups = c("clin", "lifestyle.events", "ses", "assess.pred", "quartiles"), vars.vec = c("inc_time", + "time", + "status")) |> + dplyr::select(-dplyr::any_of(c("pase_0","pase_4", "pase_change", "inc_time", "event.include", "pase_4_quartile"))), + main.exp = "pase_0_quartile" + ), + # pase_0_excl_contin = list( + # data = targets::tar_read("df_all_data_formatted") |> + # events_ready(v.groups = c("clin", "lifestyle.events", "ses", "assess.pred", "quartiles"), vars.vec = c("inc_time", + # "time", + # "status")) |> + # dplyr::filter(!is.na(pase_0), !is.na(pase_4)) |> + # dplyr::select(-pase_4), + # main.exp = "pase_0" + # ), + pase_4_quartile = list( + data = targets::tar_read("df_all_data_formatted") |> + dplyr::mutate(pase_4_quartile=cut(x = pase_4, breaks = quantile(pase_4, na.rm = TRUE), labels = 1:4, include.lowest = TRUE)) |> + get_vars(vars.groups = c("clin", "lifestyle.events", "ses", "assess.events", "quartiles"))|> + dplyr::filter(!is.na(pase_4_quartile),event.include) |> + dplyr::select(-dplyr::any_of(c("pase_0","pase_4", "pase_change", "inc_time", "event.include", "pase_0_quartile"))), + main.exp = "pase_4_quartile" + ), + # pase_4_contin = list( + # data = targets::tar_read("df_all_data_formatted") |> + # events_ready() |> + # dplyr::filter(!is.na(pase_0), !is.na(pase_4)) |> + # dplyr::select(-pase_0), + # main.exp = "pase_4" + # ), + pase_4_change_late_cut = list( + data = targets::tar_read("df_all_data_formatted") |> + events_ready() |> + dplyr::filter(!is.na(pase_0), !is.na(pase_4)) |> + pase_cutter(drop.pase = TRUE), + main.exp = "pase_change" + # ), + # pase_4_change_earliest_cut = list( + # data = targets::tar_read("df_all_data_formatted") |> + # pase_cutter(drop.pase = TRUE)|> + # events_ready(vars.vec = c("pase_change")) |> + # dplyr::filter(!is.na(pase_change)) , + # main.exp = "pase_change" + ) +) + + + +f <- function(data, main.exp, ...) { + set.seed(3023) + imp <- fun_impute(data = data, ignore = main.exp) + + list( + "Univariable" = cox_regression(data = data, all.vars = FALSE, use.strata = FALSE, outcome.var = main.exp), + "Multivariable" = cox_regression(data = data, all.vars = TRUE, use.strata = FALSE, outcome.var = main.exp), + "Multivariable Imputed" = cox_regression(data = imp, all.vars = TRUE, use.strata = FALSE, outcome.var = main.exp) + ) +} + + +ls_out <- purrr::map(ls_sens, \(.x){ + do.call(f, .x) +}) + +ls_stack <- ls_out |> + purrr::imap(\(.x, .i){ + list( + "Group counts" = ls_sens[[.i]][["data"]] |> + dplyr::select(ls_sens[[.i]][["main.exp"]]) |> + gtsummary::tbl_summary(statistic = list(gtsummary::all_continuous() ~ "{N_nonmiss} ({p_nonmiss}%)", gtsummary::all_categorical() ~ "{n} ({p}%)")) |> + fix_labels(), + .x |> + lapply(\(.y){ + .y |> + tbl_regression_standard() |> + # gtsummary::modify_table_styling(columns = tidyselect::starts_with("p.value"), hide = TRUE) |> + gtsummary::remove_row_type(variables = -dplyr::any_of(ls_sens[[.i]][["main.exp"]]), type = "all") |> + gtsummary::bold_p() + }) + ) |> + purrr::list_flatten() |> + tbl_merged_named() + }) |> + tbl_stack_named() + +ls_stack <- ls_stack|> gtsummary::as_gt() |> gt::tab_style(style = gt::cell_text(weight="bold"),locations = gt::cells_row_groups(dplyr::everything())) + +ls_stack |> gt::gtsave(filename = here::here("out/pase_extra_cox2.docx")) diff --git a/2 Longterm/260315/sex_events.docx b/2 Longterm/260315/sex_events.docx new file mode 100755 index 0000000..7a15e78 Binary files /dev/null and b/2 Longterm/260315/sex_events.docx differ diff --git a/2 Longterm/260315/ssri_events.docx b/2 Longterm/260315/ssri_events.docx new file mode 100755 index 0000000..68690c0 Binary files /dev/null and b/2 Longterm/260315/ssri_events.docx differ diff --git a/2 Longterm/DDV 240607/functions.R b/2 Longterm/DDV 240607/functions.R new file mode 100755 index 0000000..00caaf7 --- /dev/null +++ b/2 Longterm/DDV 240607/functions.R @@ -0,0 +1,3389 @@ +# pop <- haven::read_sas(here::here("E:/rawdata/709203/Population/pop_talos.sas7bdat")) + +# sst <- list.files(here::here("E:/rawdata/709203/Eksterne data"), pattern = "*.sas7bdat", full.names = TRUE) |> +# purrr::map(haven::read_sas) + + + +#' Read all sas files in folder to list +#' +#' @param path folder path +#' +#' @return list +sas2list <- function(path) { + ls <- list.files(here::here(path), pattern = "*.sas7bdat", full.names = TRUE) |> + purrr::map(haven::read_sas) + names(ls) <- list.files(here::here(path), pattern = "*.sas7bdat") |> + gsub(".sas7bdat", "", x = _) |> + toupper() + ls +} + + +#' Flatten multilevel list +#' +#' @param paths character vector of folder paths +#' +#' @return flattened list +flatmultiread <- function(paths) { + paths |> + purrr::map(sas2list) |> + purrr::list_flatten() +} + +# ls <- targets::tar_read(reg_data) + +# Vectors are kept for compatibility. Calling functions can be done from within other functions. So much easier! + +date_cutter <- function() as.Date("2023-01-01") +date.cut <- date_cutter() # The earliest date will define the overall date cut + +censor_cutter <- function() 8.5 +censor.cut <- censor_cutter() # years of maximum follow up, due to small numbers + +vasc.diags <- c("I21", "I61", "I63", "I64", "G45", "K28") + +#' Extract deaths from Dødsårsagsregiseret +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(reg_data) |> get_deaths() +get_deaths <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + + + # vasc.death.tilg <- mapply(ls$DAR_T_DODSAARSAG_2[, "C_DODTILGRUNDL_ACME"], + # FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + # i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + # i_3 %in% diags.vasc # Rowvise matching + # } + # ) + + vasc.death.tilg <- ls$DAR_T_DODSAARSAG_2[["C_DODTILGRUNDL_ACME"]] |> substr(1, 3) %in% diags.vasc + + + vasc.death.any <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DODTILGRUNDL_ACME", "C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + # + vasc.death.other <- apply(mapply(ls$DAR_T_DODSAARSAG_2[, c("C_DOD_1A", "C_DOD_1B", "C_DOD_1C", "C_DOD_1D")], + FUN = function(i) { # mapply inside apply call to handle rowvise matching in in matrix + i_3 <- substr(i, 1, 3) # Substr only the 3 first characters to match by group + i_3 %in% diags.vasc # Rowvise matching + } + ), 1, any) # Simplify to TRUE if any + + diag.either <- xor(vasc.death.other, vasc.death.tilg) + diag.both <- vasc.death.other & vasc.death.tilg + + vasc.death.diags <- + apply( + mapply( + ls$DAR_T_DODSAARSAG_2[, c( + "C_DODTILGRUNDL_ACME", + "C_DOD_1A", + "C_DOD_1B", + "C_DOD_1C", + "C_DOD_1D" + )], + FUN = function(i) { + # mapply inside apply call to handle rowvise matching in in matrix + substr(i, 1, 3) # Substr only the 3 first characters to match by group + } + ), + 1, + paste, + collapse = "," + ) + + deaths.vasc <- + ls$DAR_T_DODSAARSAG_2 |> + dplyr::select(K_CPR, D_STATDATO) |> + dplyr::filter(vasc.death.tilg) + + df.death.all <- ls$CPR3_T_PERSON |> + dplyr::filter(C_STATUS == 90) |> # People migrating are filtered (n ~ 1) + dplyr::select(c( + "V_PNR", + "D_STATUS_HEN_START" + )) |> + dplyr::left_join(deaths.vasc, by = c("V_PNR" = "K_CPR")) |> + dplyr::mutate(vasc_death = !is.na(D_STATDATO)) |> + dplyr::transmute( + PNR = V_PNR, + death_date = D_STATUS_HEN_START, + vasc_death = vasc_death + ) + + df.death.all |> dplyr::filter(death_date < max.date) +} + + +#' Title +#' +#' @param ls +#' @param max.date +#' @param diags.vasc +#' +#' @return +#' @export +#' +#' @examples +#' ls <- targets::tar_read(reg_list) +get_events <- function(ls, max.date = date.cut, diags.vasc = vasc.diags) { + ident.vars <- toupper(c("_recnum", "_cpr")) + + df.vasc.events.lpr <- ls$LPR_T_DIAG |> + dplyr::mutate(dia.f = substr(C_DIAG, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # C_DIAGTYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars), + "C_DIAG", + "C_DIAGTYPE" + ) |> + dplyr::left_join( + ls$LPR_T_ADM |> dplyr::select( + tidyselect::ends_with(ident.vars), + "D_INDDTO", + "C_INDM", + "D_UDDTO", + "C_UDM", + "C_SGH", + "C_AFD", + "C_ADIAG" + ), + by = c("V_RECNUM" = "K_RECNUM") + ) + + ## LPR-F - LPR 3 + + ident.vars.lpr3 <- toupper(c("cpr", "_kontakt", "DW_EK_FORLOEB")) + + df.vasc.events.lpr3 <- ls$LPR_F_DIAGNOSER |> + dplyr::mutate(dia.f = substr(DIAGNOSEKODE, 2, 4)) |> # Subsets only 2:4 chars, to get main group + dplyr::filter( + dia.f %in% diags.vasc + # & # Filters to only include pre-defined diagnoses + # DIAGNOSETYPE=="A" + ) |> # Filters to only include if main diagnosis + dplyr::select( + ends_with(ident.vars.lpr3), + "DIAGNOSEKODE", + "DIAGNOSETYPE" + ) |> + dplyr::left_join( + ls$LPR_F_KONTAKTER |> dplyr::select( + ends_with(ident.vars.lpr3), + "DATO_START", + "DATO_SLUT", + "PRIORITET" + ), + by = c("DW_EK_KONTAKT") + ) |> + dplyr::mutate(PRIORITET = as.character((PRIORITET == "ATA1") + 1)) # If ATA1 then 1, if not (ATA3) then 2, cowboy coding + + df.vasc.events <- dplyr::full_join(df.vasc.events.lpr, df.vasc.events.lpr3, by = c( + "V_CPR" = "CPR", + "C_DIAG" = "DIAGNOSEKODE", + "C_DIAGTYPE" = "DIAGNOSETYPE", + "D_INDDTO" = "DATO_START", + "D_UDDTO" = "DATO_SLUT", + "C_INDM" = "PRIORITET" + )) |> + dplyr::mutate(date.event = D_INDDTO) + + df.vasc.events |> dplyr::filter(date.event < max.date) +} + + +# deaths <- targets::tar_read(df_deaths) +# events <- targets::tar_read(df_events) +# clinical <- targets::tar_read(pop_df) + + +#' Filter only truly considered events +#' +#' @param data +#' +#' @return tibble +define_events <- function(data) { + data |> dplyr::filter( + C_DIAGTYPE == "A", # Primary diagnosis + C_INDM == "1", # Acutely admitted + difftime(date.event, rdate, units = "days") > 5 # More than five (5) days after randomisation/primary stroke + ) +} + +#' The big merger and filter of events +#' +#' @param ls list of events, deaths and clinical +#' +#' @return tibble +#' +#' @examples +#' ls <- list(events = targets::tar_read(df_events), deaths = targets::tar_read(df_deaths), clinical = targets::tar_read(pop_df)) +#' ls |> all_events() +all_events <- function(ls) { + # ls <- list(events = targets::tar_read(df_events), deaths = targets::tar_read(df_deaths), clinical = targets::tar_read(pop_df)) + df.events <- dplyr::full_join( + purrr::pluck(ls, "events"), + purrr::pluck(ls, "deaths") |> + dplyr::mutate( + # These are just added to ease later filtering + C_DIAGTYPE = "A", + C_INDM = "1" + ), # Ads diagtype=A, C_INDM=1 for easier sorting later + by = c( + "V_CPR" = "PNR", + "date.event" = "death_date", + "C_DIAGTYPE", + "C_INDM" + ) + ) |> + dplyr::full_join(dplyr::select(purrr::pluck(ls, "clinical"), c("PNR", "rdate", "enddate")), + by = c("V_CPR" = "PNR") + ) |> + dplyr::arrange(date.event) |> # Sort by event date + dplyr::mutate(event.type = dplyr::if_else( + is.na(C_DIAG), + dplyr::if_else(vasc_death, "death.vasc", "death.other"), + substr(C_DIAG, 1, 4) + )) + + df.events |> + dplyr::group_split(V_CPR) |> # Splits by CPR + purrr::map(define_events) |> # Custom function to specify criteria for events + purrr::discard(\(x) nrow(x) == 0) |> # Discard empty elements + purrr::map(dplyr::transmute, # Saving only relevant variables + CPR = V_CPR, + date.event, + event.type) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' ls <- list(events = targets::tar_read(df_events), deaths = targets::tar_read(df_deaths), clinical = targets::tar_read(pop_df)) +#' ls |> merge_events() +merge_events <- function(data){ + data|> + all_events() |> + purrr::modify(\(x) x[1, ]) |> # Select first event + purrr::list_rbind() +} + + +#' Count number of prescriptions of given ATC group for each CPR +#' +#' @param atc.code atc group +#' @param data dataset from LMS +#' +#' @return tibble +count_treat <- function(atc.code, data) { + data |> + dplyr::filter(grepl(atc.code, ATC)) |> + dplyr::count(CPR) |> + dplyr::filter(n > 1) +} + + +#' Get LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_lms <- function(ls, max.date, atc.tbl) { + ls$LMS_EPIKUR |> + dplyr::filter(grepl(atc.tbl[1], ATC)) |> + dplyr::left_join(ls$LMS_LAEGEMIDDELOPLYSNINGER) |> + dplyr::filter(as.Date(ACTDATE) < max.date) +} + +#' Get count of treated patients from LMS data +#' +#' @param ls list of datasets +#' @param max.date filter date for max inclusion +#' @param atc.tbl tibble of atc codes +#' +#' @return tibble +get_treated <- function(ls, + max.date = date.cut, + atc.tbl = c( + atc.antidep = "N06A", + atc.ssri = "N06AB" + )) { + df <- atc.tbl |> + purrr::map(count_treat, get_lms(ls, max.date, atc.tbl)) |> + purrr::reduce(dplyr::full_join, by = "CPR") + colnames(df) <- c("CPR", paste0("n.", names(atc.tbl))) + df +} + +#' BMI calc, drops +#' +#' @param w weight in kg +#' @param h height in cm +#' @param data data set +#' +#' @return tibble +bmi_calc <- function(data, drop = TRUE) { + # After inspection, both h+w are missing if any is missing + out <- data |> dplyr::mutate(reg_bmi = suppressWarnings(as.numeric(dplyr::if_else(reg_vaegt == "NA", reg_vaegt_anslaaet, reg_vaegt)) / ((as.numeric(reg_hojde) / 100)^2))) + + if (drop) { + out <- out |> + dplyr::select(-dplyr::all_of(c("reg_vaegt", "reg_vaegt_anslaaet", "reg_hojde"))) + } + out +} + +is_equal <- function(data, test) { + data == test +} + +#' Load clinical population data +#' +#' @return tibble +#' @examples +#' get_clinical() |> colnames() +#' +get_clinical <- function() { + sas2list("E:/rawdata/709203/Population")[[2]] |> + correct_na() |> + dplyr::mutate( + reg_smoker = dplyr::case_match( + reg_rygning, "1" ~ TRUE, + c("2", "3", "4") ~ FALSE, + "9" ~ NA + ), + # Living alone defined as not together with somebody + reg_alone = dplyr::case_match( + reg_civil, "1" ~ FALSE, + c("2", "3") ~ TRUE, + "9" ~ NA + ), + reg_more_alc = dplyr::case_match( + reg_alkohol, "1" ~ FALSE, + "2" ~ TRUE, + "9" ~ NA + ), + reg_female = sex == "Kvinde", + dplyr::across( + .cols = c( + "reg_hyperten", + "reg_diabetes", + "reg_atriefli", + "reg_perifer_arteriel", + "reg_tidl_tci", + "reg_ami" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + "2" ~ FALSE, + "9" ~ NA + ) + ), + dplyr::across( + .cols = c( + "reg_trombolyse", + "reg_trombektomi" + ), + ~ dplyr::case_match( + .x, "1" ~ TRUE, + c("3","4") ~ FALSE, + "9" ~ NA + ) + ), + reg_any_perf = reg_trombolyse | reg_trombektomi + ) |> + bmi_calc() +} + +# get_clinical() |> pragmatic_imputation() |> skimr::skim() + +# get_clinical <- function() { +# sas2list("E:/rawdata/709203/Population")[[2]] |> +# dplyr::mutate( +# reg_smoker = reg_rygning == 1, +# reg_cohabiting = reg_civil == 1, +# reg_more_alc = reg_alkohol == 2, +# reg_female = sex == "Kvinde", +# dplyr::across(.cols = c("reg_hyperten", "reg_diabetes", "reg_atriefli", "reg_perifer_arteriel", "reg_tidl_tci", "reg_ami", "reg_trombolyse", "reg_trombektomi"), ~ .x == 1), +# reg_any_perf = reg_trombolyse | reg_trombektomi +# ) |> +# bmi_calc() +# } + +pragmatic_imputation <- function(data, vec=c("reg_smoker","reg_cohabiting","reg_more_alc","reg_hyperten", "reg_diabetes", "reg_atriefli", "reg_perifer_arteriel", "reg_tidl_tci", "reg_ami", "reg_trombolyse", "reg_trombektomi","reg_any_perf")) { + # Assumes, if not TRUE, then FALSE (gets rid of NAs) + data |> dplyr::mutate(dplyr::across(.cols = tidyselect::any_of(vec), ~dplyr::if_else(.x,TRUE,FALSE,missing = FALSE))) +} + +#' Load all registry tables to list +#' +#' @return list +get_reg_ls <- function() { + flatmultiread(c("E:/rawdata/709203/Eksterne data", "E:/rawdata/709203/Grunddata")) +} + + +#' Definition of relevant variables from DST tables +#' +#' @return +define_dst_vars <- function() { + list( + bef = c("PNR", "FAMILIE_ID"), + faik = c("FAMILIE_ID", "FAMAEKVIVADISP_13", "FAMSOCIOGRUP_13"), + ras = c("PNR", "SOC_STATUS_KODE"), + uddf = c("PNR", "HFAUDD") + ) +} + +#' Simple wrapper of dplyr::select +#' +#' @param data +#' @param vars +#' +#' @return +select_vars <- function(data, vars) { + data |> dplyr::select({{ vars }}) +} + +#' Subset DST tables to only include relvant variables. +#' +#' @param ls List of all registry tables +#' +#' @return +get_dst_tables <- function(ls) { + dst_vars <- define_dst_vars() + dst_tbl <- toupper(names(dst_vars)) + ls.all <- purrr::map(seq_along(dst_vars), function(i) { + ls.reg <- ls[grepl(paste0("^", dst_tbl[i]), names(ls))] |> purrr::map(select_vars, vars = dst_vars[[i]]) + names(ls.reg) <- paste0("y", stringr::str_extract(names(ls.reg), "[0-9]{4}")) + ls.reg + }) + names(ls.all) <- dst_tbl + ls.all +} + +#' Wrapper to generate string matching pattern for stringr::str_detect() +#' +#' @param data +#' +#' @return +match_str <- function(data) { + paste0("[", paste0(data, collapse = ","), "]") +} + +#' Wrapper to generate string matching pattern for grepl() +#' +#' @param data character vector +#' +#' @return +match_str_grepl <- function(data) { + paste0("(", paste0(data, collapse = "|"), ")") +} + +# get_dst_tables(ls) + +#' Generate sequence of previous N length +#' +#' @param data numeric vector of length 1 +#' +#' @return +#' +#' @examples +#' last5y(10) +#' last5y(c(10, 6, 3)) +lastNy <- function(data, n = 5) { + paste0("y", seq((data - n), data) - 1) +} + +#' Filter PNR (cpr) across list elements +#' +#' @param data list of tibbles to pass through +#' @param index index number (PNR/CPR) +#' +#' @return tibble +filterCPRacross <- function(data, index) { + data |> + purrr::map(function(i) { + i[i$PNR == index, ] + }) |> + purrr::list_rbind() +} + +#' Summarise data from last 5 years prior to inclusion +#' +#' @param data list with +#' @param v.median variables to get median +#' @param v.latest variables to get latest +#' @param v.mean variables to get mean +#' +#' @return tibble +previousNyears <- function(data, data.clin, n.years = 5, v.median = NULL, v.latest = c("FAMSOCIOGRUP_13", "SOC_STATUS_KODE"), v.mean = c("FAMAEKVIVADISP_13")) { + df.cpryear <- data.clin |> dplyr::transmute( + CPR = PNR, + year = as.numeric(format(as.Date(rdate), "%Y")) + ) + + seqs <- purrr::map(df.cpryear$year, lastNy, n = n.years) + + seq_along(seqs) |> + purrr::map(function(i) { + df <- data[c(seqs[[i]])] |> + filterCPRacross(index = df.cpryear$CPR[i]) |> + dplyr::group_by(PNR) |> + dplyr::summarise( + dplyr::across(tidyselect::any_of(v.latest), \(x) tail(x, n = 1), .names = "{.col}.latest"), + dplyr::across(tidyselect::any_of(v.mean), \(x) mean(x, na.rm = TRUE), .names = "{.col}.{n.years}.mean"), + dplyr::across(tidyselect::any_of(v.median), \(x) median(x, na.rm = TRUE), .names = "{.col}.{n.years}.median") + ) + }) |> + purrr::list_rbind() +} + + +#' Extract relevant and summarised data from BAF and FAIK +#' +#' @param data list of dst data tables +#' +#' @return tibble +#' +#' @examples +#' get_reg_ls() |> +#' get_dst_tables() |> +#' get_beffaikras() +get_beffaikras <- function(data, clin.data = get_clinical()) { + data <- data[stringr::str_detect(match_str(c("BEF", "FAIK", "RAS")), names(data))] |> purrr::list_flatten() + + years <- stringr::str_extract(names(data), "y[0-9]{4}") + + years[duplicated(years)] |> + purrr::map(grep, years) |> + purrr::map(function(i) { + data[c(i)] |> purrr::reduce(dplyr::full_join) + }) |> + purrr::set_names(years[duplicated(years)]) |> + previousNyears(data.clin = clin.data) + + ## BEF + ## # Befolkningsoversigt. Data skal bruges for at kunne udtrække husstandsindkomst. + ## FAIK + ## # Familieindkomst. Familie id skal flættes med ID fra BEF for hvert år for at tage hensyn til evt skifte i status. + ## FAMAEKVIVADISP_13 er relevante variabel for ækvivaleret indkomst + ## Der findes også familiesocioøkonomisk status. Gør som Sine. Be done with it! +} + +#' Title +#' +#' @return +#' @export +#' +#' @examples +#' data <- read_edu_level() +#' data |> dplyr::count(ISCED) +read_edu_level <- function() { + haven::read_dta("E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l4_k.dta") |> + dplyr::transmute( + HFAUDD = start, + ISCED = AUDD_LEVEL_L1L4_K + ) +} + +haven::read_dta( + "E:/Formater/SAS formater i Danmarks Statistik/STATA_datasaet/Disced/c_audd_level_l1l3_k.dta") |> + dplyr::count(AUDD_LEVEL_L1L3_K) + +# ls <- get_reg_ls() |> get_dst_tables() + +get_uddf <- function(ls) { + ls |> + purrr::pluck("UDDF") |> + purrr::pluck(1) |> + dplyr::mutate(HFAUDD = as.character(HFAUDD)) |> + dplyr::left_join(read_edu_level()) |> + dplyr::group_by(PNR) |> + dplyr::summarise(ISCED = max(ISCED), .groups = "keep") |> + dplyr::mutate( + ISCED = as.numeric(ISCED), + ISCED_lvl = dplyr::case_match(ISCED, 0:2 ~ "low", + 3:4 ~ "medium", + 5:9 ~ "high", + .default = NA + ), + ISCED_bin = dplyr::case_match(ISCED, 0:3 ~ "low", + 4:9 ~ "high", + .default = NA + ) + ) +} + + +#' Collects all relevant variables from DST tables +#' +#' @param ls list of all registry tables +#' @param df.clin clinical data set +#' +#' @return tibble +#' @examples +#' get_reg_ls() |> get_dst(df.clin = get_clinical()) +get_dst <- function(ls, df.clin) { + ls_dst <- get_dst_tables(ls) + ls_dst |> + ## BEFxFAIKxRAS + get_beffaikras(clin.data = df.clin) |> + dplyr::full_join( + ## UDDF + get_uddf(ls_dst) + ) +} + +#' Eases pipe renaming of columns +#' +#' @param data tibble, list or other object, for which names() makes sense. see ?setNames +#' @param prefix prefix to add +#' @param exclude names not to modify +#' @param new.names character vector of all new names +#' +#' @return object of same class as data +#' +#' @examples +#' set_colnames(data = mtcars, prefix = "WOW", exclude = "mpg") +set_colnames <- function(data, new.names = NULL, prefix = NULL, exclude = c("PNR", "CPR"), prefix.sep = "_") { + if (is.null(new.names)) { + nms <- names(data) + } else { + nms <- new.names + } + + if (is.null(prefix)) { + nms.mod <- nms + } else { + nms.mod <- paste(prefix, nms, sep = prefix.sep) + } + + setNames( + object = data, + nm = dplyr::if_else(stringr::str_detect(match_str(exclude), nms), + nms, + nms.mod + ) + ) +} + + +#' Store of variable names for data sub-setting +#' +#' @return list +#' @examples +#' define_variables() +define_variables <- function() { + list( + clin = c( + "age", + "reg_female", + "nihss_0", + "reg_trombolyse", + "reg_trombektomi", + # "rtreat", + "rtreat_placebo"), + lifestyle=c( + "pase_0", + "pase_4", + "reg_alone", + "reg_bmi", + # "reg_hojde", + # "reg_vaegt_alt", + "reg_smoker", + "reg_more_alc", + "reg_hyperten", + "reg_diabetes", + "reg_tidl_tci", + "reg_atriefli", + "reg_ami", + "reg_perifer_arteriel"), + lifestyle.events=c( + "pase_0", + "pase_4", + "reg_alone", + "reg_bmi", + # "reg_hojde", + # "reg_vaegt_alt", + "reg_smoker", + "reg_more_alc", + "reg_hyperten", + "reg_diabetes", + # "reg_tidl_tci", + "reg_atriefli", + # "reg_perifer_arteriel", + "reg_ami"), + ses=c( + # "soc_status", + # "soc_status_work", + "soc_status_nowork", + # "fam_indk", + "fam_indk_hl", + # "fam_indk_high", + # "fam_indk_low", + # "edu_level", + # "edu_high", + # "edu_low", + "edu_level_hl" + ), + assess.events = c( + "who_4", + "mdi_4", + "mfi_gen_4", + "mrs_4_above1", + "time", + "status", + "event.include" + ), + assess.pred = c( + "who_0", + "mrs_0_above0" + ), + extra = c( + "soc_status", + "pase_0", + "pase_4" , + "event" + ), + cpr = "PNR" + ) +} + +#' Get var names in vector from group names. Possibility to keep all vars for as log as possible. Can be supplied to `gtsummary` functions +#' +#' @param groups vector of group names. See names(define_variables()) for options +#' +#' @return +#' @export +#' +#' @examples +get_var_vec <- function(v.groups){ + define_variables()[{{ v.groups }}] |> purrr::list_c() +} + +#' SUbsets dataset based on variable group names as defined +#' +#' @param vector character vector of category names +#' +#' @return character vector +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> get_vars(c("universal", "events")) +get_vars <- function(data, vars.groups) { + data |> dplyr::select(tidyselect::all_of(get_var_vec(vars.groups))) +} + + + +#' Collect all relevant data for the events analysis data set +#' +#' @param ls ls of tibbles +#' +#' @return tibble +#' +#' @examples +#' ls <- targets::tar_read(list_filtered) +#' +collectall <- function(ls) { + purrr::pluck(ls, "clinical") |> + dplyr::left_join(purrr::pluck(ls, "all_events") |> set_colnames(prefix = "event"), by = c("PNR" = "CPR")) |> + dplyr::left_join(purrr::pluck(ls, "dst") |> set_colnames(prefix = "dst"), by = "PNR") +} + + +## Formatting for analysis + +#' Function to cut and group PASE data +#' +#' @param data data set including pase_0 and _4 +#' +#' @return tibble +pase_cutter <- function(data, pase.rev = TRUE, drop.pase = FALSE, drop.nas=FALSE) { + data.classes <- class(data) + if ("mids" %in% data.classes) { + data <- data |> mice::complete(action = "long", include = TRUE) + } + + data <- data |> + dplyr::mutate(dplyr::across(.cols = c("pase_0", "pase_4"), \(i) { + cut(x = i, breaks = quantile(pase_0, na.rm = TRUE), labels = 1:4, include.lowest = TRUE) + }, .names = "{.col}_quartile")) |> + dplyr::mutate(pase_change = factor(dplyr::case_when( + pase_0_quartile == 1 & pase_4_quartile == 1 ~ "Persistent low", + pase_0_quartile %in% 2:4 & + pase_4_quartile %in% 2:4 ~ "Persistent high", + pase_0_quartile == 1 & + pase_4_quartile %in% 2:4 ~ "Increase", + pase_0_quartile %in% 2:4 & + pase_4_quartile == 1 ~ "Decrease" + ), ordered = FALSE), + pase_change=factor(pase_change,levels=c("Persistent low", "Increase", "Decrease", "Persistent high"))) + + + if (drop.pase) { + data <- data |> dplyr::select(-tidyselect::all_of(c("pase_0_quartile", "pase_4_quartile", "pase_0", "pase_4"))) + } + + + if (pase.rev) { + data <- data |> dplyr::mutate( + pase_change = factor(pase_change, levels = c("Persistent high", "Decrease", "Increase", "Persistent low")) + ) + } + + if (drop.nas) { + data <- data |> + dplyr::filter(!is.na(pase_change)) + } + + + if ("mids" %in% data.classes) { + data |> mice::as.mids() + } else { + data + } +} + +# as.Date(data$event_date.event) +define_status_time <- function(data) { + data |> dplyr::mutate(dplyr::across(c("rdate", "enddate", "event_date.event"), ~ as.Date(.x)), + time = difftime(dplyr::if_else(is.na(event_date.event), date_cutter(), event_date.event), enddate) |> lubridate::time_length("years"), + status = as.integer(!is.na(event_event.type)), + time = dplyr::if_else(time > censor_cutter(), censor_cutter(), time), + status = dplyr::if_else(time > censor_cutter(), FALSE, status), + # status= dplyr::if_else(status,1,0), + event.include = time > 0 + ) +} + +# as.integer(c(TRUE,FALSE)) + +#' Grouping soc status +#' +#' @param data tibble +#' +#' @return tibble +group_soc_status <- function(data) { + data |> + dplyr::mutate( + soc_status = factor(dplyr::case_when( + soc_status < 200 ~ "work", + soc_status == 200 ~ "off", + # only ~4 in the data set off work + soc_status >= 200 ~ "outside" + )), + soc_status_work = soc_status == "work", + soc_status_nowork = !soc_status_work + ) +} + +#' Correction of character NA +#' +#' @param data tibble +#' @param char.missing character vector of entries to consider as NA +#' +#' @return tibble +correct_na <- function(data, char.missing = "NA") { + data |> dplyr::mutate(dplyr::across(dplyr::where(is.character), ~ dplyr::na_if(.x, char.missing))) +} + +#' Formatting the complete data set +#' +#' @param data the merged raw data set +#' +#' @return tibble +#' @examples +#' ds <- targets::tar_read(df_all_data) |> +#' data_formatting() |> +#' subset_df("mdi") +#' ds |> skimr::skim() +#' ds |> View() +data_formatting <- function(data) { + to_logical <- grep(match_str_grepl(c("missings", "incompletes")), names(data)) + + suppressWarnings( + data |> + correct_na() |> + dplyr::mutate(dplyr::across(all_of(to_logical), ~ .x == "TRUE")) |> + dplyr::mutate( + # This uses the work-corrected score + # pase_0 = dplyr::if_else(pase_score_missings_w_0 | is.na(talos_pase10_0), NA, pase_score_sum_w_0), + # pase_4 = dplyr::if_else(pase_score_missings_w_4 | is.na(talos_pase10_4), NA, pase_score_sum_w_4), + # Below is the plain PASE scor used according to the manual used with TALOS + pase_0 = dplyr::if_else(pase_score_missings_0, NA,pase_score_sum_0), + pase_4 = dplyr::if_else(pase_score_missings_4, NA,pase_score_sum_4), + who_0 = as.numeric(talos_who07_0), + who_4 = as.numeric(talos_who07_4), + mrs_0 = factor(substr(talos_mrs01_0, 1, 1), ordered = FALSE), + mrs_0_above0 = (as.numeric(mrs_0) - 1) > 0, + mrs_4 = factor(substr(talos_mrs01_4, 1, 1), ordered = FALSE), + mrs_4_above1 = (as.numeric(mrs_4) - 1) > 1, + mdi_4 = as.numeric(talos_mdi12_4), + mfi_gen_4 = as.numeric(talos_mfi_gen_4), + nihss_0 = as.numeric(talos_nihss16_0), + soc_status = dst_SOC_STATUS_KODE.latest, + fam_indk = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 3), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "medium", "high"), + include.lowest = TRUE + ), + fam_indk_bin = cut(dst_FAMAEKVIVADISP_13.5.mean, + breaks = quantile(dst_FAMAEKVIVADISP_13.5.mean, probs = seq(0, 1, 1 / 2), na.rm = TRUE), + ordered_results = FALSE, + labels = c("low", "high"), + include.lowest = TRUE + ), + fam_indk_hl=forcats::fct_rev(fam_indk), + fam_indk_high = dplyr::if_else(fam_indk_bin=="high",TRUE,FALSE), + fam_indk_low = dplyr::if_else(fam_indk_bin=="low",TRUE,FALSE), + edu_level = factor(dst_ISCED_lvl, ordered = FALSE, levels = c("low", "medium", "high")), + edu_high = dplyr::if_else(dst_ISCED_bin=="high",TRUE,FALSE), + edu_low = dplyr::if_else(dst_ISCED_lvl=="low",TRUE,FALSE), + edu_level_hl=forcats::fct_rev(edu_level), + rtreat_placebo=rtreat=="Placebo", + event = factor(event_event.type) + ) |> + group_soc_status() |> + define_status_time() + ) +} + + +#' Title +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> +#' events_ready() |> +#' View() +events_ready <- function(data,v.groups=c("clin","lifestyle.events","ses", "assess.events")) { + data |> + get_vars(vars.groups = v.groups) |> + dplyr::filter(event.include) |> + # dplyr::filter(!is.na(pase_0),!is.na(pase_4))|> + dplyr::select(-tidyselect::all_of("event.include"))# |> + # labelling_data() +} + +#' Title +#' +#' @param data +#' @param v.groups +#' +#' @return +#' @export +#' +#' @examples +events_ready_small <- function(data,v.groups=c("clin","lifestyle.events","ses", "assess.events")) { + data |> + get_vars(vars.groups = v.groups) |> + dplyr::filter(event.include) |> + dplyr::filter(!is.na(pase_0),!is.na(pase_4))|> + dplyr::select(-tidyselect::all_of("event.include"))# |> + # labelling_data() +} + +#' Title +#' +#' @param date +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' +#' data |> +#' prediction_ready() |> +#' View() +prediction_ready <- function(data) { + data |> + get_vars(c("clin","lifestyle","ses", "assess.pred"))|> + dplyr::filter(!is.na(pase_0),!is.na(pase_4))#|> + # labelling_data() +} + +## Data inspection and exploration +## +## +#' Title +#' +#' @param data +#' @param subdf +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data) |> +#' subset_df() |> +#' View() +subset_df <- function(data, subdf = "pase") { + data[grepl(paste0("^(", paste("PNR", subdf, paste0("talos_", subdf), sep = "|"), ")"), names(data))] +} + +#' Imputation as a function, includes "pragmatic imputation" +#' +#' @param data +#' @param outcome.vars +#' @param ignore +#' @param pragmatic.reg +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) |> events_ready() +#' data |> labelling_data() |> fun_impute() +fun_impute <- function(data, outcome.vars = c("status", "time"), ignore = NULL,pragmatic.reg=TRUE,pase.mod=FALSE) { + # data |> mice::md.pattern() + if (pragmatic.reg){ + data <- data |> pragmatic_imputation() + } + + ## Excluding entries with missing outcome measures + data <- data |> + dplyr::filter(!dplyr::if_any(tidyselect::all_of(c(outcome.vars)), ~ is.na(.x))) + + init <- data |> + mice::mice(maxit = 0) + + meth <- init$method + meth[ignore] <- "" + + pred <- init$predictorMatrix + pred[, c(outcome.vars)] <- 0 + + # data_out <- data |> mice::futuremice( + # pred = pred, + # method = meth, + # print = FALSE, + # parallelseed = 8123, + # use.logical = FALSE, + # maxit = 20, + # m = 10 + # ) + + data_out <- data |> mice::mice( + pred = pred, + method = meth, + print = FALSE, + seed = 8123, + maxit = 20, + m = 10 + ) + + if (pase.mod){ + data_out <- data_out |> pase_cutter_mids() + } + + data_out + + # lattice::densityplot(imp_data) + # Regarding EVENTS + # + # On inspection/eye-balling densityplots looks reasonable with the current settings + # +} + +#' Function to cut PASE in mids object +#' +#' @param data mids object +#' +#' @return mids object +#' @export +#' +pase_cutter_mids <- function(data){ +data |> + mice::complete(action = "long", include = TRUE) |> + pase_cutter(drop.pase = TRUE, drop.nas = TRUE)|> + mice::as.mids() + } + + +#' Completes events data set, option to impute +#' +#' @param data +#' @param impute +#' +#' @return mids or tibble +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset() |> +#' targets::tar_read(df_all_data_formatted) |> +events_dataset <- function(data, impute = TRUE, uv=FALSE) { + # data <- data |> events_ready() + if (impute) { + data |> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = TRUE) + } else if (uv){ + data |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + } else { + data |> + dplyr::select(-tidyselect::all_of("reg_bmi")) |> + pase_cutter(drop.pase = TRUE,drop.nas = TRUE) + } +} + +#' Title +#' +#' @param data +#' @param all.vars +#' @param outcome.var +#' @param use.strata +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_ready() |> subset_df("pase") +#' data <- targets::tar_read(df_event_data) |> events_dataset(impute = FALSE) + +cox_regression <- function(data, all.vars = TRUE,outcome.var="pase_change",use.strata=TRUE) { + if ("mids" %in% class(data)) { + nms <- names(data$data) + } else { + nms <- names(data) + data <- data |> + labelling_data() + # BMI meassure is excluded from non-imputed dataset + # data <- data |> dplyr::select(-tidyselect::all_of(c("reg_bmi"))) + } + + vars <- nms[!nms %in% c("time", "status", outcome.var)] + + form.prefix <- "survival::Surv(time, status) ~" + + if (use.strata) { + reg.form <- glue::glue("{form.prefix} strata({outcome.var})") + } else { + reg.form <- glue::glue("{form.prefix} {outcome.var}") + } + + if (all.vars) reg.form <- paste0(reg.form, " + ", paste(vars, collapse = " + ")) + + require(survival) + out <- with(data, survival::coxph( + as.formula(reg.form) + )) + + out$call$formula <- as.formula(reg.form) + + out +} + +#' Creates UV cox models for all variables in data set (but time and status) +#' +#' @param data +#' @param include +#' +#' @return +#' @export +#' +#' @examples +splitdf4uvcox <- function(data,include=c("pase_change","time","status")){ + names(data)[!names(data)%in%include] |> + purrr::map(\(.x){ + data |> dplyr::select(tidyselect::all_of(c(.x,include))) |> + cox_regression(outcome.var = .x,use.strata=FALSE,all.vars = FALSE) + + }) +} + +#' Creates and prints UV Cox analyses +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_event_data) |> uv_cox_table() +uv_cox_table <- function(data){ + data |> + events_dataset(impute = FALSE,uv = TRUE) |> + dplyr::select(pase_change,dplyr::everything()) |> + splitdf4uvcox(include=c("time","status")) |> + purrr::map(\(.x){ + .x |> tbl_regression_standard() + }) |> + gtsummary::tbl_stack() + } + +tbl_merged_named <- function(data){ + data |> gtsummary::tbl_merge(tab_spanner = names(data)) +} + +tbl_regression_standard <- function(data){ + data |> gtsummary::tbl_regression(exponentiate = TRUE, + add_estimate_to_reference_rows = TRUE, + show_single_row = where(is.logical), + statistics=list(gtsummary::all_continuous()~"[{conf.low};{conf.high}]", + gtsummary::all_categorical()~"[{conf.low}%;{conf.high}%]") + ) |> + fix_labels() +} + +#' Wrapper to print summary table with extended info +#' +#' @param data formatted and subset data set +#' @param by.var stratify by +#' +#' @return +#' @examples +#' targets::tar_read(df_pred_data)|>print_table_summary(by="reg_female") +print_table_summary <- function(data, by.var = "pase_change") { + data |> + labelling_data() |> + # pase_cutter(drop.pase = TRUE) |> + gtsummary::tbl_summary( + missing = "no", + by = tidyselect::all_of(by.var), + value = list(where(is.logical) ~ TRUE) + ) |> + gtsummary::add_overall() |> + gtsummary::add_n() +} + +print_table_summary_explorer <- function(data, by.var = "pase_change") { + data |> + labelling_data() |> + # pase_cutter(drop.pase = TRUE) |> + gtsummary::tbl_summary( + missing = "no", + by = tidyselect::all_of(by.var), + value = list(where(is.logical) ~ TRUE), + type = list(gtsummary::all_continuous() ~ "continuous2"), + statistic = list(gtsummary::all_continuous() ~ c( + # "{N_nonmiss} ({p_nonmiss}%)", + "{median} ({p25}, {p75})", + # "{min}, {max}", + "{mean} ({sd})"#, + # "{N_miss} ({p_miss}%)" + )#, + # gtsummary::all_categorical() ~ c( + # "{N_obs} ({p_nonmiss}%)"#, + # # "{N_miss} ({p_miss})" + # ) + ) + ) |> + gtsummary::add_overall() |> + gtsummary::add_n() #|> + # gtsummary::add_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' targets::tar_read(df_all_data_formatted) |> events_tblone() +events_tblone <- function(data) { + data |> + # events_ready() |> + pase_cutter(drop.pase = TRUE) |> + dplyr::select(-tidyselect::all_of(c("status", "time"))) |> + dplyr::filter(!is.na(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data)|> +#' dplyr::transmute(stRoke::quantile_cut(pase_0,4,group.names = 1:4),soc_status_work,fam_indk,edu_level) |> +#' summary_tblone() +summary_tblone <- function(data,by=names(data)[1]) { + # data <- targets::tar_read(df_pred_data) + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + # pase_cutter(drop.pase = TRUE) |> + # dplyr::filter(!is.na(pase_change)) |> + # dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary(by.var = by) +} + +# +#' Summaries of DST data for PASE quartiles at 0 and 4 +#' +#' @param data +#' @param vars +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> sum_pase_tables() +sum_pase_tables <- function(data,vars=c("pase_0","pase_4")){ + vars |> lapply(function(.x){ + dplyr::tibble(stRoke::quantile_cut(data[[.x]],y=data[["pase_0"]],4,group.names = 1:4), + dplyr::select(data,soc_status_nowork,fam_indk_hl,edu_level_hl)) |> + summary_tblone() + }) +} + + +#' Creating a truthful stratified table for predictions +#' +#' @param data data frame +#' +#' @return list +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> true_pred_sum_plot() +true_pred_sum_plot <- function(data){ + true_sum <- data |> + pase_cutter(drop.pase = TRUE) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) + + list(true_sum,true_sum |> (function(.x){ + split(.x,.x$pase_change %in% c("Persistent low","Increase")) + })() |> purrr::map(function(.y){.y |> dplyr::mutate(pase_change=factor(pase_change))})) |> + purrr::list_flatten() |> purrr::map(summary_tblone,by="pase_change") |> + gtsummary::tbl_merge() +} + +#' Get quick summary of missing vs non-missing for each given variable +#' +#' @param data data set +#' @param var variable to summarise over +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> dplyr::select(soc_status_work,fam_indk,edu_level) |> who_is_missing() +#' targets::tar_read(df_pred_data) |> who_is_missing(var="reg_bmi") +who_is_missing <- function(data, var = "edu_level") { + data |> + dplyr::mutate(log = factor(c("non-missing","missing")[is.na(data[[var]])+1])) |> + dplyr::select(log, tidyselect::everything(),-tidyselect::all_of(var)) |> + summary_tblone() #|> gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +#' targets::tar_read(df_all_data_formatted) |> View() +#' targets::tar_read(df_pred_data) |> preds_tblone() +preds_tblone <- function(data) { + data |> + labelling_data() |> + # prediction_ready() |> + # dplyr::select(-reg_bmi) |> + pase_cutter(drop.pase = TRUE) |> + dplyr::filter(!is.na(pase_change)) |> + dplyr::mutate(pase_change=forcats::fct_rev(pase_change)) |> + print_table_summary() +} + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' gt <- targets::tar_read(ls_pred_summary)[[1]] +#' gt |> add_var_groups_gt() +#' +#' # For this to work, the function would need to handle labels and levels +#' gt <- targets::tar_read(tbl_pred_summary)|> gtsummary::as_gt() +#' gt |> add_var_groups_gt() +add_var_groups_gt <- function(gt){ + # gt <- sex_ls |> purrr::pluck(2) |> gtsummary::as_gt() + # gt <- fix_labels(gt) + cls <- class(gt) + + b.cols <- names(gt$`_data`) + + if (b.cols[[1]]!="variable"){ + # Flag to indicate if format is native gt or not. Simple assumption + # class(gt) gt is not enough + labels <- gt$`_data`[[1]] + group.var <- names(gt$`_data`[[1]]) + } else { + labels <- gt$`_data`[["label"]][gt$`_data`[["row_type"]]=="label"] + group.var <- gt$`_data`[["variable"]] + } + + + + groups <- matrix(ncol=length(labels)) |> + data.frame() |> + setNames(ifelse(labels=="","unknown_var",labels)) |> + tibble::as_tibble() |> groups_in_ds(labels = TRUE) + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + labels.all <- group.labels |> purrr::imap(function(.x,.y){ + c(.x,groups[[.y]][["label"]]) + }) |> purrr::list_c() + + for (i in rev(seq_along(group.labels))){ + gt <- gt |> gt::tab_row_group(label=gt::md(glue::glue("*{group.labels[[i]]}*")), + rows=which(group.var %in% groups[[names(group.labels)[[i]]]][["var"]])) + + } + + class(gt) <- cls + gt +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(tbl_preds_lin_imp_reg) +#' data |> fix_labels() +fix_labels <- function(data){ + cls <- class(data) + data[[1]][["variable"]][data[[1]][["row_type"]]=="label"] |> + subset_named_labels(var_labels()) |> + unname() -> data[[1]][["label"]][data[[1]][["row_type"]]=="label"] + class(data) <- cls + data +} + + +#' Title +#' +#' @param data +#' @param b.cols +#' +#' @return +#' @export +#' +#' @examples +#' tbl <- targets::tar_read(ls_pred_summary)[[1]] +add_var_groups_pre_calc <- function(data,b.cols){ + + groups <- data |> groups_in_ds() + + group.labels <- names(groups) |> subset_named_labels(labels.raw = group_labels()) + + t0 <- data.frame(matrix(ncol=length(b.cols))) |> + setNames(b.cols) |> + tibble::tibble() + list(ext = group.labels |> purrr::imap(function(.x,.y){ + t0 |> dplyr::mutate( + variable=.y, + val_label=.x, + row_type="group", + label=.x + ) + }) |> dplyr::bind_rows(), + lvls = group.labels |> purrr::imap(function(.x,.y){ + c(.y,groups[[.y]][["var"]]) + }) |> purrr::list_c() + ) +} + +#' Adds variable grouping and formatting to gtsummary tables +#' +#' @param tbl +#' +#' @return +#' @export +#' +#' @examples +#' +#' tbl <- targets::tar_read(tbl_pred_summary) +#' targets::tar_read(tbl_pred_summary) |> add_var_groups() +#' +add_var_groups <- function(tbl, + pre_ls=add_var_groups_pre_calc(tbl$inputs$data, + names(tbl$table_body))){ + + + tbl |> gtsummary::modify_table_body( + ~.x |> dplyr::bind_rows(pre_ls[["ext"]]) |> + dplyr::arrange(factor(variable,levels=pre_ls[["lvls"]])) + ) |> + gtsummary::modify_table_styling(columns=label, + rows= row_type%in%"level",text_format = "indent2") |> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"label",text_format = "indent")|> + gtsummary::modify_table_styling(columns=label,rows= row_type%in%"group",text_format = c("italic")) +} + +#' Functionalised character vector of all labels +#' +#' @return +#' @export +#' +#' @examples +var_labels <- function(){ + c( + age = "Age", + reg_female = "Female sex", + reg_bmi = "Body mass index", + reg_smoker = "Current smoker", + reg_alone = "Living alone", + reg_more_alc = "High alcohol consumption", + reg_hyperten = "Hypertension", + reg_diabetes = "Diabetes", + reg_atriefli = "Atrial fibrillation", + reg_perifer_arteriel = "Peripheral arterial disease", + reg_tidl_tci = "Previous TIA", + reg_ami = "Previous MI", + reg_trombolyse = "Treated with IVT", + reg_trombektomi = "Treated with EVT", + # reg_any_perf, + # rtreat = "Study group allocation", + rtreat_placebo = "Placebo trial treatment", + pase_0 = "Pre-stroke PASE score", + pase_4 = "6 months post-stroke PASE score", + # pase_change, + nihss_0 = "Admission NIHSS", + # soc_status, + soc_status_work = "Employed", + soc_status_nowork = "Not employed", + fam_indk = "Family income group", + fam_indk_hl = "Lower family income", + fam_indk_high = "Higher family income", + fam_indk_low = "Lower family income", + edu_level = "Educational level group", + edu_level_hl = "Lower educational level", + edu_high = "Higher educational level", + edu_low = "Low educational level", + who_4 = "WHO-5 score 6 months post-stroke", + mdi_4 = "MDI score 6 months post-stroke", + mrs_4_above1 = "mRS > 1 at 6 months post-stroke", + mfi_gen_4 = "General fatigue (MFI domain) 6 months post-stroke", + time = "Time", + status = "Status", + event.include = "Include event", + who_0 = "Pre-stroke WHO-5 score", + mrs_0_above0 = "Pre-stroke mRS > 0", + pase_change = "PA change group" + ) +} + + +group_labels <- function(data){ + c("clin" = "Clinical data", + "lifestyle" = "Lifestyle and chronic diseases", + "ses" = "Socio-economic factors", + "assess.events" = "Assessments", + "assess.pred" = "Assessments", + "extra" = "extras") +} + +rev_naming <- function(x){ + setNames(names(x),x) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +groups_in_ds <- function(data, labels=FALSE){ + groups <- define_variables() |> purrr::imap(function(.x,.y){ + tibble::tibble(group=.y,var=.x) + }) |> + dplyr::bind_rows() + + if (labels){ + matching <- subset_named_labels(names(data), + rev_naming(var_labels())) + }else { + matching <- names(data) + } + groups[match(matching,groups[["var"]]),] |> + (\(.x){ + .x |> dplyr::mutate(group=factor(group,levels=unique(.x[["group"]]))) + })() |> + cbind( + tibble::tibble( + label=labelling_data(data) |> labelled::var_label() |> purrr::list_c() + ) + )|> + (\(.x){ + split(.x,.x[["group"]]) + })() +} + + +#' Subset labels +#' +#' @param data +#' @param labels.raw +#' +#' @return character vector +#' @export +#' +subset_named_labels <- function(data,labels.raw){ + labels.raw[match(data,names(labels.raw))] +} + +#' Assign labels to data.frame or tibble +#' +#' @param data +#' @param labels +#' +#' @return +#' @export +#' +#' @examples +assign_labels <- function(data,labels){ + # data |> labelled::set_variable_labels(labels) + + labelled::var_label(data) <- labels + + data +} + +#' Flexible labelling using labelled for nicer tables +#' +#' @param data data set +#' +#' @return +#' @export labelled data.frame/tibble +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' data <- data |> dplyr::mutate(test="test") +#' data |> labelling_data() |> labelled::var_label() +labelling_data <- function(data,label.list=var_labels()){ + + labs <- subset_named_labels(names(data),label.list) + labs[is.na(labs)] <- names(data)[is.na(labs)] + + data |> assign_labels(labels = labs) +} + + + +#' Print regression table +#' +#' @param data cox regression ready data set +#' +#' @return gtsummary tbl_regression list object +#' @examples +#' data <- targets::tar_read(df_event_data) +#' targets::tar_read(df_event_data) |> show_table_regression(use.mice=FALSE) +show_table_regression <- function(data, use.mice=FALSE, by.var="pase_change") { + data |> + events_dataset(impute = use.mice) |> + cox_regression(all.vars = TRUE, use.strata = FALSE,outcome.var = by.var) |> + tbl_regression_standard() + # gtsummary::add_n() #|> + # gtsummary::bold_p() +} + + + + +#' Splitting df to list by PA trajectory +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_ls_split <- function(data, excluded.vars = "reg_bmi"){ + data |> + pase_cutter(drop.pase = FALSE) |> + dplyr::select(-tidyselect::all_of(c("pase_4","pase_0_quartile","pase_4_quartile"))) |> + dplyr::group_split(pase_split = pase_change %in% c("Increase", "Persistent low")) |> + setNames(c("drop", "hop")) |> + purrr::map2(.y = c("Decrease", "Increase"), .f = \(x, y){ + x |> + dplyr::mutate(pase_bin = pase_change == y) |> + dplyr::select(-tidyselect::all_of(c(excluded.vars, c("pase_change", "pase_split")))) |> + na.omit() + }) +} + +#' Run regularisation steps for split data set +#' +#' @param data selected data set +#' +#' @return list +#' +#' @examples +#' data <- targets::tar_read(df_pred_data) +#' targets::tar_read(df_pred_data) |> pred_models() +pred_models <- function(data, excludes = "reg_bmi") { + ls <- data |> + pred_ls_split(excluded.vars = excludes) |> + purrr::map(regularisation_steps) + + class(ls) <- c("regular_list", class(ls)) + ls +} + +cross_mean_median_exp_table <- function(data) { + nms <- paste0("v", seq_len(ncol(data))) + + cross_calcs <- data |> + as.data.frame() |> + setNames(nms) |> + dplyr::rowwise() |> + dplyr::transmute( + median = median(dplyr::c_across(tidyselect::all_of(nms))), + medianOR = exp(median), + mean = mean(dplyr::c_across(tidyselect::all_of(nms))), + meanOR = exp(mean) + ) + + dplyr::tibble(names = rownames(data), cross_calcs) |> + dplyr::select(-tidyselect::all_of(c("mean","median"))) +} + + +gather_coefs_step1 <- function(data) { + data |> + list3levelpluck(lvl1 = "model", lvl2 = "B") |> + purrr::map(purrr::reduce, cbind) +} + +gather_coefs <- function(data) { + # imputed.list <- "mids_regular_list" %in% class(data) + + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(gather_coefs_step1) |> + purrr::map(purrr::reduce, cbind) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> gather_coefs_step1() + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map(cross_mean_median_exp_table) |> + purrr::reduce(dplyr::full_join, by = "names", suffix = paste0("_", names(data))) +} + + +#' Merge and print model coefficients. Pools datafrom mids analyses. +#' +#' @param data list +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(ls_pred_models) +#' targets::tar_read(ls_pred_models) |> print_pred_coefs() +#' targets::tar_read(ls_pred_mids_reg) |> print_pred_coefs() |> add_var_groups_gt() +print_pred_coefs <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + # nms <- names(data) + + if ("mids_regular_list" %in% class(data)) { + type.table <- "Pooled regularised models" + } else if ("regular_list" %in% class(data)) { + type.table <- "Single regularised model" + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + merged_tbl <- data |> + gather_coefs() |> + ## Leaving out the intercept + (function(.x) .x[-1,])() + + sel_mean_med <- colnames(merged_tbl)[!grepl(pattern = "OR",colnames(merged_tbl))][-1] + sel_or <- colnames(merged_tbl)[grepl(pattern = "OR",colnames(merged_tbl))] + + news <- subset_named_labels(merged_tbl$names,var_labels()) + + merged_tbl <- merged_tbl |> dplyr::mutate(names=dplyr::if_else(is.na(news),names,news)) + + gt_merged_tbl <- merged_tbl|> + gt::gt() |> + gt::fmt_number(decimals = 5) + + merged_tbl_log <- merged_tbl |> dplyr::mutate(dplyr::across(tidyselect::all_of(sel_or), ~.x!=1), + dplyr::across(tidyselect::all_of(sel_mean_med), ~.x!=0)) + + for (j in colnames(merged_tbl)[-1]) { + + i <- merged_tbl_log[[j]] + + gt_merged_tbl <- gt_merged_tbl |> gt::tab_style(style = list( + gt::cell_text(weight="bold") + ), + locations = gt::cells_body( + columns=j, + rows = i + ) + )} + + for (i in names(data)) { + gt_merged_tbl <- gt_merged_tbl |> + gt::tab_spanner(label = i, columns = tidyselect::ends_with(i)) + } + + gt_merged_tbl |> gt::tab_spanner( + label = type.table, + columns = -1 + ) +} + +#' Calculates confusionMatrix from contingency tables. Pools if object class is . +#' +#' @param data +#' +#' @return list +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_table_cfm() +#' targets::tar_read(ls_pred_models) |> multi_table_cfm() +multi_table_cfm <- function(data) { + # data <- targets::tar_read(ls_pred_mids_reg) + if ("mids_regular_list" %in% class(data)) { + data <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + # purrr::map(\(y) y |> purrr::pluck("model") |> purrr::pluck("cMatTest"))|> + list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") |> + # All tables are add together + purrr::reduce(\(i, j) i + j) + }) + } else if ("regular_list" %in% class(data)) { + data <- data |> list3levelpluck(lvl1 = "model", lvl2 = "cMatTest") + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data |> + purrr::map(caret::confusionMatrix) +} + +#' Collect and summarise auc meassures. Pools if "mids_regular_list" object +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_auc_summary() +#' targets::tar_read(ls_pred_models) |> multi_auc_summary() +multi_auc_summary <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step <- data |> purrr::map(\(x){ + x |> + # Test tables are plucked + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + # All tables are add together + purrr::reduce(c) + }) + } else if ("regular_list" %in% class(data)) { + data_step <- data |> + list3levelpluck(lvl1 = "model", lvl2 = "auc_test") |> + purrr::map(c) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step |> + purrr::map(summary) +} + +#' Map and 2 level recursive purrr::pluck to ease regular_list subsetting +#' +#' @param data +#' @param lvl1 +#' @param lvl2 +#' +#' @return +#' @export +#' +#' @examples +list3levelpluck <- function(data, lvl1 = "model", lvl2 = "cMatTest") { + data |> purrr::map(\(y) y |> + purrr::pluck(lvl1) |> + purrr::pluck(lvl2)) +} + +#' Plot performance curve from glmnet regularisation +#' +#' @param data list of cvs.glmnet objects +#' +#' @return ggplot list object +#' @export +#' +#' @examples +plot_roc_curve <- function(data, title.text) { + ggplot2::ggplot() + + purrr::map(data, function(i) { + ggplot2::geom_step(data = i, ggplot2::aes(x = FPR, y = TPR)) + }) + + ggplot2::coord_cartesian(xlim = c(0, 1), ylim = c(0, 1)) + + ggplot2::geom_abline() + + ggplot2::theme_bw() + + ggplot2::ggtitle(title.text) +} + +roc_gather_step <- function(x) { + with(x, glmnet::roc.glmnet(cvs[[1]]$fit.preval, newy = y1)[match(bestL, lambdas)]) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> multi_roc_plot() +#' targets::tar_read(ls_pred_models) |> multi_roc_plot() +multi_roc_plot <- function(data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, roc_gather_step) |> + purrr::map(purrr::list_flatten) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> purrr::map(roc_gather_step) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> + purrr::map2(.y = names(data), plot_roc_curve) |> + patchwork::wrap_plots() +} + +#' Title +#' +#' @param tuning.param +#' @param data +#' +#' @return +#' @export +#' +#' @examples +multi_tuning_gather <- function(tuning.param = "bestA", data) { + if ("mids_regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::map, \(x) x |> purrr::pluck(tuning.param)) |> + purrr::map(purrr::reduce, c) + } else if ("regular_list" %in% class(data)) { + data_step1 <- data |> + purrr::map(purrr::pluck, tuning.param) + } else { + stop("The supplied list has to be class 'mids_regular_list' or 'regular_list'") + } + + data_step1 |> purrr::map(summary) +} + + +#' Tidied tuning summary call +#' +#' @param data +#' +#' @return +#' +#' @examples +#' targets::tar_read(ls_pred_mids_reg) |> tuning_summary() +#' targets::tar_read(ls_pred_models) |> tuning_summary() +tuning_summary <- function(data) { + c(ALPHA = "bestA", LAMBDA = "bestL") |> purrr::map(\(x) x |> multi_tuning_gather(data = data)) +} + +#' Apply regularisation steps to MIDS object, output arranged by grouping +#' +#' @param data mids object from mice package +#' +#' @return list +#' +#' @examples +#' targets::tar_read(df_pred_mids) |> mids_regularisation() +mids_regularisation <- function(data) { + ls <- data |> + mice::complete(action = "long") |> + dplyr::group_split(.imp) |> + purrr::modify(\(x){ + x |> dplyr::select(-tidyselect::all_of(c(".imp", ".id"))) + }) |> + purrr::map(pred_models) + + nms <- ls |> + purrr::map(names) |> + unique() |> + purrr::reduce(c) + + # As a consequence of the above code each "set" of analyses are together. + # Here the same group analyses are subset and grouped + ls_n <- purrr::map(nms, function(i) { + ls |> purrr::map(purrr::pluck, i) + }) |> + setNames(nms) + + # Special class is applied to ease future handling + class(ls_n) <- c("mids_regular_list", class(ls_n)) + ls_n +} + +#' A collection of all the summary functions to be applied to list of +#' pred_models() output +#' +#' @param data list of data +#' +#' @return list +#' @export +#' +multi_summary <- function(data){ + list( "coefTable" = print_pred_coefs(data) |> + gt::fmt_number(n_sigfig = 3) |> + fix_labels() #|> add_var_groups_gt() + , + "confusionMatrices" = multi_table_cfm(data), + "summaryAUC" = multi_auc_summary(data), + "rocPlots" = multi_roc_plot(data), + "tuningSummaries" = tuning_summary(data)) +} + +# funs <-list( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +# multi_summary <- plyr::each( +# "coefTable" = print_pred_coefs, +# "confusionMatrices" = multi_table_cfm, +# "summaryAUC" = multi_auc_summary, +# "rocPlots" = multi_roc_plot, +# "tuningSummaries" = tuning_summary +# ) + +#' Subset multiple elements from list +#' +#' @param data list +#' @param indices numeric or character vector +#' +#' @return list +#' @examples +#' targets::tar_read(ls_pred_summary)$confusionMatrices |> purrr::map(list_subset) +list_subset <- function(data,indices=c("overall","byClass")){ + data[indices] +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(ls_pred_summary) |> print_model_resutls() +print_model_resutls <- function(data){ + par(mfrow=c(1,2)) + + list(data$coefTable, + invisible(data$confusionMatrices |> purrr::map(\(x) x |> purrr::pluck("table"))), + data$confusionMatrices |> purrr::map(list_subset), + data$summaryAUC, + data$tuningSummaries + ) +} + + +#' Classic logistic regression on prediction covariates +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' targets::tar_read(df_pred_data) |> pred_log_reg() +#' targets::tar_read(df_pred_data) |> pred_ls_split() +pred_log_reg <- function(data){ + data |> pred_ls_split() |> + purrr::map(\(x) { + gtsummary::tbl_regression(glm(pase_bin~.,family = binomial,data = x), + exponentiate= TRUE)#|> + # gtsummary::bold_p() + } + ) |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() } + +#' Small wrapper to format CI with square brackets +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +square_ci <- function(data){ + gsub("(-?\\d*\\.?\\d*)(, )(-?\\d*\\.?\\d*)", + "\\[\\1; \\3\\]",data)} + +#' Apply CI formatting across gtsummary table including merged tables +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +fix_ci <- function(data){ + data |> gtsummary::modify_table_body(~ .x |> + dplyr::mutate(dplyr::across(dplyr::starts_with("ci"), + function(.y){square_ci(.y)}))) +} + +#' Classic linear regression on 6 months PASE score. Uni and multi. +#' +#' @param data data frame +#' +#' @return +#' @export +#' +#' @examples gtsummary list elemnt +#' data <- targets::tar_read(df_pred_data) +#' data <- targets::tar_read(df_pred_mids) +#' targets::tar_read(df_pred_data) |> pred_lin_reg() +#' targets::tar_read(df_pred_mids) |> pred_lin_reg() +pred_lin_reg <- function(data){ + + # list("tbl_regression-str:ref_row_text"="Reference") |> + # gtsummary::set_gtsummary_theme() + + if ("mids" %in% class(data)){ + cols <- names(data$data) + + } else { + cols <- names(data) + data <- data |> + labelling_data() + } + + vars <- cols[cols!="pase_4"] + + formula_pase <- paste("pase_4",paste(vars,collapse = "+"),sep="~" ) + + # multi <- with(data=data,lm(pase_4~.)) |> + # gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE)|> + # gtsummary::bold_p() |> gtsummary::add_n() + + if (!"mids" %in% class(data)){ + ls <- list("Univariate"=data |> + gtsummary::tbl_uvregression(method=lm, show_single_row = dplyr::where(is.logical), + y=pase_4, + add_estimate_to_reference_rows = TRUE,pvalue_fun = NULL)#|> gtsummary::bold_p() + , + "Multivariate (no BMI)"=lm(pase_4~.,data=dplyr::select(data,-reg_bmi)) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n() + , + "Multivariate (ALL)"= lm(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n() + ) + + } else { + ls <- list("Multivariate (ALL)"= suppressWarnings(mice::lm.mids(pase_4~.,data=data) |> + gtsummary::tbl_regression(add_estimate_to_reference_rows = TRUE, show_single_row = dplyr::where(is.logical))|> + # gtsummary::bold_p() |> + gtsummary::add_n())) + } + + ls |> (\(x){gtsummary::tbl_merge(tbls = x, + tab_spanner = names(x))})() |> + fix_ci() +} + + +#' Simple standard plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_event_data) |> events_dataset(impute = FALSE)|> cox_regression() |> plot_survival() +plot_survival <- function(data){ + data |> + ggsurvfit::survfit2() |> + ggsurvfit::ggsurvfit(linetype_aes = TRUE, size = 0.8) + + ggsurvfit::add_confidence_interval() + + ggsurvfit::add_risktable( + risktable_stats = c("n.risk", "cum.event"), + stats_label = list(cum.event = "Cumulative Observed Events", + n.risk = "Number at Risk"), + theme = + list( + ggsurvfit::theme_risktable_default(axis.text.y.size = 11, + plot.title.size = 11), + ggplot2::theme(plot.title = ggplot2::element_text(face = "bold")) + ) + ) + + ggplot2::scale_y_continuous( + limits = c(0, 1), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0)) +} + + +#' Smooth tidy survfit object +#' +#' @param data survfit object +#' +#' @return tibble +#' @export +#' +#' @examples +#' ls <- targets::tar_read(df_event_data) |> events_dataset(impute = FALSE)|> cox_regression() +#' data <- ls |> ggsurvfit::survfit2(robust=TRUE) |> +#' ggsurvfit::tidy_survfit(type="survival") |> +#' dplyr::group_split(strata) |> purrr::pluck(1) +#' +#' ls |> ggsurvfit::survfit2(robust=TRUE) |> +#' ggsurvfit::tidy_survfit(type="survival") |> +#' dplyr::group_split(strata) |> +#' purrr::map(smooth_col) +smooth_col <- function(data, force_mono=TRUE){ + smoothed <- lapply(c("estimate","conf.high","conf.low"),function(i){ + # stats::predict(cobs::cobs(x = data$time, + # y = data[i], + # constraint = "decrease", + # nknots=4, + # pointwise = rbind(c(0,min(data$time),1)), + # degree = 2,)) |> + # tibble::as_tibble() |> dplyr::select(fit) |> + stats::predict(mgcv::gam(data=data,formula = as.formula(glue::glue("{i}~s(time,bs='cs')")))) |> + tibble::as_tibble()|> + setNames(glue::glue("{i}_smooth")) + }) |> purrr::list_cbind() + + if (force_mono){ + ## Forcing starting point to be 1 + smoothed[1,1] <- 1 + + smoothed[1] <- force_decrease(smoothed[1]) ## Only monotonize the esitimate + } + + dplyr::tibble(data, + smoothed) + +} + +#' Forces the direction +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +force_decrease <- function(data){ + data |> purrr::imap(function(.x,.n){ + s <- c() + for (i in seq_along(.x)){ + if (i == 1) { + s[1] <- .x[1] + } else { + if (.x[i]>s[i-1]){ + s[i] <- s[i-1] + } else { + s[i] <- .x[i] + } + } + } + s + }) |> + dplyr::bind_cols() +} + + +#' Prepare cox regression for smooth survival plot +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> events_dataset(impute = FALSE)|> cox_regression() |> smooth_cox_data() +smooth_cox_data <- function(data){ + data |> + ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(smooth_col) |> + purrr::list_rbind() +} + +#' Plot smooth survival plot +#' +#' @param data df from cox regression +#' +#' @return ggplot list object +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_event_data) |> events_dataset(impute = FALSE) |> cox_regression() +#' data <- targets::tar_read(df_events_mids) |> cox_regression() +#' data |> plot_survival_smooth() +plot_survival_smooth <- function(data){ + if ("mira" %in% class(data)) stop("Only plots non-imputed survival data") + + n.level <- length(data$xlevels[[1]]) + + # data |> + # ggsurvfit::survfit2() |> + # ggsurvfit::tidy_survfit() + + if (data |> ggsurvfit::survfit2() |> purrr::pluck("n") |> length() ==1 ){ + ds <- data |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() + p <- ds |> + ggplot2::ggplot(ggplot2::aes(x=time, y=estimate))+ + ggplot2::geom_smooth(se=TRUE, method="loess", formula = "y~x", linewidth=2, color="grey10") + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high) + + } else { + ds <- data |> + smooth_cox_data() + p <- ds |> + ggplot2::ggplot()+ + ggplot2::geom_line(ggplot2::aes(x=time, y=estimate_smooth, color=strata, linetype=strata), linewidth=2)+ + ggplot2::geom_ribbon(ggplot2::aes(x=time, ymin=conf.low_smooth,ymax=conf.high_smooth, fill=strata), alpha=.2) + # Added auto max for y axis removed again to ensure same y axis + # max_y <- max(ds$conf.high_smooth) + + } + + if (n.level==4){ + colors <- viridisLite::turbo(n=n.level,direction = 1)[c(1,3,2,4)] + } else { + colors <- viridisLite::turbo(n=n.level,direction = 1) + } + + p+ + ggplot2::scale_y_continuous(limits = c(0,1.02), + breaks = seq(0,1,.25), + labels = scales::percent, + expand = c(0.01, 0) + ) + + ggplot2::scale_x_continuous(breaks = 0:9, expand = c(0.02, 0))+ + ggplot2::scale_fill_manual(values=colors)+ + ggplot2::scale_color_manual(values=colors)+ + ggplot2::theme_minimal()+ + ggplot2::theme(axis.title.x = ggplot2::element_blank(), + axis.title.y = ggplot2::element_blank(), + # axis.text = ggplot2::element_blank(), + # legend.position = "none", + panel.grid.minor.y = ggplot2::element_blank(), + panel.grid.major.y = ggplot2::element_line(color="grey45",linewidth = 1)) + +} + +# viridisLite::turbo(n=4,direction = 1)[c(1,3,2,4)] + +cluster_rank <- function(data){ + data |> cox_regression(outcome.var = "clust",use.strata = TRUE) |> ggsurvfit::survfit2(robust=TRUE) |> + ggsurvfit::tidy_survfit(type="survival") |> + dplyr::group_split(strata) |> + purrr::map(\(x){ + min(x[["estimate"]]) + }) |> purrr::list_c() |> rank() |> rev() +} + +cox_relevel <- function(data){ + data |> dplyr::mutate(clust=factor(clust,levels=cluster_rank(data))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_all_data_formatted) +complete_preds_data <- function(data){ + data |> + # events_ready() |> + fun_impute(ignore = c("pase_0","pase_4"),pase.mod = FALSE) |> + mice::complete() |> + dplyr::filter((!is.na(pase_0)&!is.na(pase_4))) +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam <- targets::tar_read(df_all_data_formatted) |> add_kamila_cluster() +#' data_kam |>print_table_summary(by.var = "kam_grp") +#' data_kam |> cox_regression(outcome.var="kam_grp")|> plot_survival_smooth() +#' data_kam |> cox_regression(outcome.var="kam_grp",use.strata = FALSE)|> gtsummary::tbl_regression(exponentiate = TRUE, add_estimate_to_reference_rows = TRUE) |> gtsummary::bold_p() +#' targets::tar_read(df_events_complete) |> kamila_cluster(n.clusters=3) +kamila_cluster <- function(data, n.clusters=3,include.out=FALSE){ + # An index number could be added to later join pack. Of input a complete data set from imputation and pooling?? + data_orig <- data + + + if (!include.out){ + data <- data |> + dplyr::select(-tidyselect::one_of(c("time","status"))) + } + + + catInd <- data |> lapply(\(x) is.character(x)|is.logical(x)) |> purrr::list_c() + conInd <- data |> lapply(\(x) is.numeric(x)|is.integer(x)) |> purrr::list_c() + + catVars <- data[,catInd] + catVars <- catVars |> lapply(factor) |> dplyr::bind_cols() |> as.data.frame() + conVars <- data[,conInd] |> scale()|> as.data.frame() + + if (is.null(n.clusters)){ + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = 2:7, numInit = 10, + calcNumClust = "ps" + ) + }else { + out <- kamila::kamila(conVar = conVars, catFactor = catVars, numClust = n.clusters, numInit = 10) + } + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kamila_cluster",class(ls)) + + ls + +} + + +#' VarSelLCM wrapper +#' +#' @param data complete dataset with no missings +#' @param n.clusters number of clusters (if length 1, n is fixed, in n>1 given clusters are tested) +#' @param include.out flag to include outcome variables or not +#' @param memb.out output data frame with final membership or not (then outputs standard model output) +#' +#' @return list with VarSelLCM output and original dataset with cluster appended +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> lcm_cluster() +lcm_cluster <- function(data, n.clusters=3, include.out=FALSE, var.sel=FALSE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(-c("time", "status")) + } + + + set.seed(5432) + + out <- data |> + dplyr::mutate(dplyr::across(where(is.logical)|where(is.character),~factor(.x))) |> + as.data.frame() |> + VarSelLCM::VarSelCluster( + gvals=n.clusters, + crit.varsel="BIC", + vbleSelec = var.sel, + nbcores = round(parallel::detectCores()*.8) + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("lcm_cluster",class(ls)) + + ls + +} + +#' Kmeans clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> kmeans_cluster() +#' data |> kmeans_cluster(n.clusters=3) +kmeans_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + out <- data |> + dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + dplyr::across(where(is.factor),~as.numeric(.x))) |> + stats::kmeans( + centers=n.clusters + ) + + ls <- list("out"=out,"data_orig"=data_orig) + + class(ls) <- c("kmeans_cluster",class(ls)) + + ls + +} + +#' dbscan clustering +#' +#' @param data +#' @param n.clusters +#' @param include.out +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> dbscan_cluster() +#' data |> dbscan_cluster(n.clusters=3) +dbscan_cluster <- function(data, n.clusters=3, include.out=FALSE, memb.out=TRUE){ + data_orig <- data + + if (!include.out){ + data <- data |> + dplyr::select(!tidyselect::one_of(c("time", "status"))) + } + + data <- data |> na.omit() |> dplyr::mutate(rtreat=rtreat!="Placebo", + dplyr::across(dplyr::everything(), as.numeric)) + + + ## This plot indicates that eps should be set around 60, but at this value everything is one cluster. + dbscan::kNNdistplot(data,k = 5) + + ## Performing hierachical clustering, it is clear, that the algorithm is not able to seperate clusters. + hds <- dbscan::hdbscan(data,minPts = 5) + + plot(hds,show_flat = TRUE) + + ## Clustering with set eps value and minPts + ds <- dbscan::dbscan(data,eps = 25,minPts = 2) + + ds[["cluster"]] + + ## dbscan is not an interesting approach, apparently + + # + # + # + # + # out <- data |> + # dplyr::mutate(dplyr::across(where(is.double),~scale(.x)), + # dplyr::across(where(is.logical)|where(is.character),~factor(.x)), + # dplyr::across(where(is.factor),~as.numeric(.x))) |> + # stats::kmeans( + # centers=n.clusters + # ) + # + # ls <- list("out"=out,"data_orig"=data_orig) + # + # class(ls) <- c("kmeans_cluster",class(ls)) + # + # ls + +} + +#' Title +#' +#' @param ls +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' ls <- data |> lcm_cluster() +#' ls |> final_membership() +final_membership <- function(ls){ + cls <- class(ls) + if ("kamila_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$finalMemb), + ls$data_orig) + + } else if ("lcm_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out@partitions@zMAP), + ls$data_orig) + + } else if ("kmeans_cluster" %in% cls) { + + tibble::tibble(clust=factor(ls$out$cluster), + ls$data_orig) + + } else stop("Class not recognised") + +} + + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(df_events_complete) +#' data |> get_clusters() +#' data |> get_clusters(n.cl=2:7) +get_clusters <- function(data,n.cl=4,rm.out=TRUE){ + set.seed(1123) + + if (length(n.cl)>1){ + list( + # "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } else { + list( + "kmeans"=data |> kmeans_cluster(n.clusters = n.cl,include.out = !rm.out), + "lcm"= data |> lcm_cluster(n.clusters = n.cl,include.out = !rm.out), + "kamila"=data |> kamila_cluster(n.clusters = n.cl,include.out = !rm.out) + ) + } + +} + +#' Title +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' data <- targets::tar_read(list_pred_clusters) +#' data |> final_clusters() +final_clusters <- function(data,new.levels=NULL){ + out <- data |> lapply(final_membership) |> lapply(labelling_data) + + if (is.null(new.levels)){ + out + } else { + out |> + purrr::map2(relevels,function(x,y){ + # x$clust <- factor(factor(x$clust,levels=y),labels=1:4) + x$clust <- factor(x$clust,levels=y) + x #|> + # dplyr::filter(clust %in% range(as.numeric(clust))) |> + # dplyr::mutate(clust=factor(clust)) + }) + } + + } + + + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_summary_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> + # print_table_summary(by.var = by) + gtsummary::tbl_summary(by=by) #|> + # gtsummary::add_p() |> gtsummary::bold_p() + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Easy cox regression tbl for uniform results +#' +#' @param data +#' +#' @return +#' @export +#' +#' @examples +cox2tbl <- function(data,by="clust",all.vars=FALSE){ +data|> cox_regression(outcome.var = by,use.strata = FALSE,all.vars = all.vars) |> + gtsummary::tbl_regression(exponentiate =TRUE, + statistics=list(gtsummary::all_continuous()~"[{conf.low};{conf.high}]", + gtsummary::all_categorical()~"[{conf.low}%;{conf.high}%]")) |> + gtsummary::bold_p() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +merged_cox_reg_tbl <- function(data,by="clust"){ + data |> + purrr::map(function(x){ + x |> cox2tbl(by=by) + } + ) |> (\(x){ + x |> gtsummary::tbl_merge(tab_spanner = names(x)) + })() +} + +#' Title +#' +#' @param data +#' @param by +#' +#' @return +#' @export +#' +#' @examples +wrapped_surv_plot <- function(data,by="clust"){ +data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = by,use.strata = TRUE,all.vars = FALSE) |> + plot_survival_smooth()+ggplot2::labs(color="Cluster",fill="Cluster",linetype="Cluster") + } + ) |> (\(x){ + x |> patchwork::wrap_plots(ncol=1) + patchwork::plot_annotation(tag_levels = list(names(x))) + })() +} + + +# Ranking by most events +relevel_by_rank <- function(data){ + ## Assigning clusters to each dataset +data <- targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = NULL) + +## Calculating cox regressions and ranking by the final point on the survival plot +relevels <- data |> + purrr::map(function(x){ + x |> cox_regression(outcome.var = "clust",use.strata = TRUE,all.vars = FALSE) |> + ggsurvfit::survfit2() |> + ggsurvfit::tidy_survfit() |> + (\(x){ + split(x,x[["strata"]]) |> + purrr::map(function(.y){ + .y[["estimate"]][nrow(.y)] + }) + })() |> purrr::reduce(c) |> rank() + } + ) + +#3 Assigning the new, ranked levels +targets::tar_read(list_pred_clusters) |> + final_clusters(new.levels = relevels) +} + +## TODO +## Verify definitions +## Do remaining documentation of functions +## +## +## How does elastic net work with imputed dataset? +## Functionalise to allow for imputed and non-imputed (both analyses) - in both cases with and without BMI - include department of inclusion to investigate reason of missing BMI data +## +## tidymodels does not allow pmm in mice. Thy're out! + +## + +missing_stats <- function(index,data,glue.mask= "{N_miss} ({round(p_miss*100,dec)}%)",dec){ + index.var <- unique(data$table_body$variable)[index] + + table_body <- data$table_body |> + dplyr::filter(variable == index.var) + + if ("missing" %in% table_body$row_type){ + f1 <- grep("stat_\\d+",names(table_body)) + + ## This approach + masks <- data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::filter(!duplicated(col_name)) |> + dplyr::arrange(col_name) |> + dplyr::mutate(mask=glue::glue(glue.mask))|> + dplyr::pull(mask) + + table_body[table_body$row_type=="missing",f1] <- masks |> + as.matrix() |> + t() |> + tibble::as_tibble(.name_repair = "unique_quiet") + + } + table_body +} + +missing_stats_steps <- function(body,ls,glue.mask,dec){ + seq_along(unique(body$variable)) |> + purrr::map(function(.x) { + missing_stats(index=.x,data=ls,glue.mask = glue.mask,dec=dec) + }) |> + dplyr::bind_rows() +} + +add_missing_stats <- function(data,glue.mask= "{N_miss} ({round(p_miss*100,dec)}%)",dec=1){ + data |> + gtsummary::modify_table_body( + ~ .x |> missing_stats_steps(ls=data,glue.mask = glue.mask,dec=dec) + ) +} + +variable_masks <- function(index, data, cut.off,glue.mask= "<{n} (<{p}%)",dec=dec) { + index.var <- unique(data$table_body$variable)[index] + + table_body <- data$table_body |> + dplyr::filter(variable == index.var) + + + ## Filtering bu two different approaches + if (table_body$var_type[1] %in% c("dichotomous", "categorical")) { + if (any(grepl("^stat_[1-9]|[1-9]\\d",names(table_body)))){ + masked <- micro_n_masks( + ## Handling nominal/binary + + body = table_body |> + dplyr::filter(row_type!="missing"), + n.all = data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(n), + N.all=data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(N), + cut.off=cut.off, + glue.mask = glue.mask + ) + } else { + masked <- table_body |> dplyr::filter(row_type!="missing") + } + + + if ("stat_0" %in% names(masked)){ + body <- masked + tb <- body |> dplyr::filter(row_type!="missing",!is.na(stat_0)) + f1 <- grep("^stat_0", names(tb)) + + meta.index <- data$meta_data$df_stats |> + purrr::pluck(index) + + if ("by" %in% names(meta.index)) { + meta.index <- meta.index |> dplyr::filter(is.na(by)) + } + + masked <- masking(body=body, + tb=tb, + f1=f1, + ns = meta.index |> + dplyr::select(n)|> + dplyr::slice(seq_len(length(f1) * nrow(tb))) |> + unlist(use.names = FALSE) |> + matrix(ncol = length(f1), byrow = TRUE) |> + tibble::as_tibble(.name_repair = "unique_quiet"), + Ns= meta.index |> + dplyr::select(N_obs) |> + dplyr::slice(1)|> + unlist(use.names = FALSE), + cut.off=cut.off, + glue.mask=glue.mask, + dec=dec) + } + + out <- rbind( + masked, + table_body |> dplyr::filter(row_type=="missing")) + + } else { + out <- table_body + } + + if ("missing" %in% out$row_type) { + ## Handling missings n is N_miss, and N is N_obs + + missings <- micro_n_masks( + body = out |> + dplyr::filter(row_type == "missing"), + n.all = data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(N_miss), + N.all=data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::select(N_obs), + cut.off=cut.off, + glue.mask=glue.mask + ) + + if ("stat_0" %in% names(missings)) { + ## Handling overall column + body <- missings + tb <- body |> dplyr::filter(row_type=="missing") + f1 <- grep("^stat_0", names(tb)) + + masking(body=body, + tb=tb, + f1=f1, + ns = data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::filter(is.na(by)) |> + dplyr::select(N_miss) |> + dplyr::slice(1) |> + unlist(use.names = FALSE) |> + matrix(ncol = length(f1), byrow = TRUE) |> + tibble::as_tibble(.name_repair = "unique_quiet"), + Ns=data$meta_data$df_stats |> + purrr::pluck(index) |> + dplyr::filter(is.na(by)) |> + dplyr::select(N_obs) |> + dplyr::slice(1) |> + unlist(use.names = FALSE), + cut.off=cut.off*2, + glue.mask=glue.mask, + dec=dec) + + } + + out <- rbind( + out |> + dplyr::filter(row_type != "missing"), + missings) + + + + + + } + + out +} + +micro_n_masks <- function(body, n.all, N.all, cut.off, glue.mask,dec=1) { + if (nrow(body) > 1) { + # First row removed in case of categorical + # Possibly change to include in filter, to have whole df, or just rbind in the end + tb <- body |> dplyr::filter(row_type!="label") + } else { # last option is "dichotomous" + tb <- body + } + + ## Supports any number of stat columns (overkill!) + f1 <- grep("^stat_[1-9]|[1-9]\\d", names(tb)) + + masking(body=body, + tb=tb, + f1 = f1, + ns=n.all |> + dplyr::slice(seq_len(length(f1) * nrow(tb))) |> + unlist(use.names = FALSE) |> + matrix(ncol = length(f1), byrow = TRUE) |> + tibble::as_tibble(.name_repair = "unique_quiet"), + Ns=N.all |> + dplyr::slice(seq_len(length(f1))) |> + unlist(use.names = FALSE), + cut.off=cut.off, + glue.mask=glue.mask, + dec=dec + ) + +} + + +#' Title +#' +#' @param body full table body +#' @param tb filtered table body +#' @param f1 Subsets indexes of relevant columns +#' @param ns Relevant ns arranged in matrix +#' @param Ns All Ns +#' @param cut.off +#' @param glue.mask +#' @param dec +#' +#' @return +#' @export +masking <- function(body, + tb=NULL, + f1, + ns, + Ns, + cut.off=cut.off, + glue.mask=glue.mask, + dec=dec + ){ + + if (is.null(tb)) tb <- body + + ## Logical matrix of relevant ns to mask + f2 <- ns |> + purrr::map(\(.x) .x %in% 1:(cut.off - 1)) |> + dplyr::bind_cols() |> + as.matrix() + + ## Number of cells with zero observations in each matrix row + n0 <- apply(ns == 0, 1, sum) + + ## Number of cells with ns to mask in each matrix row + ns.low <- apply(f2, 1, sum) + + + if (all(ns.low==0)){ + out <- body + } else { + + ls.rows <- seq_len(nrow(f2)) |> + purrr::map(\(.i){ + + ds <- tb[.i, ] + # Creating masked matrix to record maskings + masked <- matrix(FALSE, ncol = ncol(f2)) + + # Only modify in case of low, handle one col matrices + if (apply(f2, 1, any)[.i]) { + n <- cut.off + N <- Ns[f2[.i,]] + p <- round(100 * n / N, dec) + + ds[f1[f2[.i,]]] <- glue::glue(glue.mask) |> + as.matrix() |> + t() |> + tibble::as_tibble(.name_repair = "unique_quiet") + masked[f2[.i,]] <- TRUE + + ## loop to add masks until satisfied + while (sum(masked)==1 & # The case of only one low + nrow(masked)>1 | # But ignored in case of ncol==1 + sum(ns[.i,][masked]) <= cut.off & + (sum(masked) + n0[.i]) < ncol(masked)) { + # in the case that sum of smalls is below cutoff, another field is added. + + ranked <- apply(ns[.i,], 1, rank, ties.method = "first") |> t() + + ranked.i <- ranked == sum(masked) + n0[.i] + 1 + n <- ns[.i,][ranked.i] |> plyr::round_any(accuracy = cut.off, f = ceiling) + N <- Ns[ranked.i] + p <- round(100 * n / N, dec) + + ds[f1[ranked.i]] <- glue::glue(glue.mask) + masked[ranked.i] <- TRUE + }} + + list( + ds = ds, + masked = masked + ) + }) + + # The case for dichotomous + if (nrow(tb) == 1) { + out <- ls.rows |> + purrr::map(purrr::pluck, "ds") |> + dplyr::bind_rows() + + # Handling categorical data + } else if (nrow(tb) > 1) { + masks <- ls.rows |> + purrr::map(purrr::pluck, "masked") |> + purrr::reduce(rbind) + + out <- ls.rows |> + purrr::map(purrr::pluck, "ds") |> + dplyr::bind_rows() + + ## Indices by row + col.i <- seq_len(nrow(masks)) |> purrr::map(\(.j){ + which(masks[.j,]) + }) + + col.i.vec <- purrr::list_c(col.i) |> unique() + + if (purrr::compact(col.i) |> length() == 1){ + # As this is only the case with overall column + # This should be reworked + + # This was the approach, to just select the first + # which(purrr::map_lgl(col.i,is_empty))[1] + + # This will select the cell with the second lowest number + col.i[[which(rank(ns,ties.method = "first")==2)]] <- c("") + } + + out <- col.i |> + purrr::map(\(.y){ + # length(.y) + if (length(.y) > 0) { + cols <- col.i.vec[!col.i.vec %in% .y] + if (length(cols)==0){ + cols <- "" + } else { + cols + } + } else { + .y + } + }) |> + purrr::imap(\(.y, .i){ + ds <- out[.i, ] + if (length(.y) > 0 & all(.y!="")) { + n <- ns[.i, .y] |> + purrr::map_dfr(plyr::round_any,accuracy = cut.off, f = ceiling) + N <- Ns[.y] + p <- round(100 * n / N, dec) + if (n==0) glue.mask <- "{n}" + + ds[f1[.y]] <- glue::glue(glue.mask)|> + as.matrix() |> + t() |> + tibble::as_tibble(.name_repair = "unique_quiet") + ds + } else { + ds + } + }) |> + dplyr::bind_rows() + + out <- rbind( + body |> + dplyr::filter(row_type=="label"), + out + ) + } + } + out + +} + + +summary_masks <- function(body,ls,cut.off=5,dec=dec){ + seq_along(unique(body$variable)) |> + purrr::map(function(.x) { + variable_masks(index=.x,data=ls,cut.off=cut.off,dec=dec) + }) |> + dplyr::bind_rows() +} + +mask_micro_summary <- function(data,micro.n=5){ + data |> + gtsummary::modify_table_body( + ~ .x |> summary_masks(ls=data,cut.off = micro.n,dec=1) + ) +} + +#' Title +#' +#' @param mask +#' @param data +#' +#' @return +#' @export +#' +#' @examples +#' minimal_mask(mask=tibble::as_tibble(matrix(c(FALSE,FALSE,TRUE,TRUE),nrow=1),.name_repair="unique_quiet"), +#' data=tibble::as_tibble(matrix(c(20,12,8,4),nrow=1),.name_repair="unique_quiet"),micro.n=5) +minimal_mask <- function(mask,data,micro.n){ + ## Function assumes monotonous data + ## Use with column_diffs() for risk tables + + ## FUnction works in practice, but example doesn't?? + + out <- mask + + for (i in seq_len(nrow(mask))){ + if (i>1){ + for (j in seq_len(ncol(mask))){ + if(dplyr::pull(mask[i,j])){ + n <- max(which(which(!out[i,]) +#' events_dataset(impute = FALSE) |> +#' cox_regression(all.vars = FALSE) |> +#' ggsurvfit::survfit2() |> +#' ggsurvfit::tidy_survfit(times = c(0, 2, 4, 6, 8.5))|> +#' tidyr::pivot_wider(id_cols = strata, names_from = time, values_from = cum.event) +#' data |> mask_micro_table(col.sel=-strata) +#' data.sel <- data |> dplyr::select(-strata) +mask_micro_table <- function(data, micro.n = 5, down = TRUE, col.sel) { + data.sel <- data |> + dplyr::select({{ col.sel }}) + + ## List of the two selection matrices + masked <- list( + data.sel, # The actual data + data.sel |> + column_diffs(include.first = TRUE) # Row differences (incl first row) + ) |> + purrr::map(\(.y){ # For each element in the list, do colwise test + .y |> + purrr::map_dfr(\(.x) .x %in% 1:(micro.n-1)) + }) |> + purrr::imap(\(.y,.i){ # apply minimal masking to last list object + if (.i==2){ + .y |> minimal_mask(data = data.sel,micro.n=micro.n) + }else { + .y + } + }) |> + purrr::reduce(`|`) |> # Combine matrices + tibble::as_tibble() |> # To tibble + purrr::map2(data.sel, \(.x, .y){ # Apply masking based on combined selection + ifelse(.x, rounded_interval(.y, round = micro.n, down = down), .y) + }) |> + dplyr::bind_cols() + + ## Bind masked data to original columns + dplyr::bind_cols( + data |> + dplyr::select(-{{ col.sel }}), + masked|> + # Converts new to character for uniform data + dplyr::mutate(dplyr::across(dplyr::everything(), ~ as.character(.x))) + ) |> + dplyr::select(colnames(data)) # Order columns as original input data +} + +rounded_interval <- function(data, round = 5, down = TRUE) { + # Handle "ties" + sub <- ifelse(down, -1, 1) + data <- ifelse(data %% round == 0 & data != 0, data + 1, data) + + c(floor, ceiling) |> + purrr::map(\(.x) { + plyr::round_any(x = data, accuracy = round, f = .x) + }) |> + dplyr::bind_cols(.name_repair = "unique_quiet") |> + setNames(c("l", "h")) |> + dplyr::transmute(mask = glue::glue("{l}-{h}")) |> + dplyr::pull(mask) +} + +column_diffs <- function(data, prefix.pattern = NULL, include.first = TRUE, suffix.out = "_diff") { + if (!is.null(prefix.pattern)) { + data <- data |> + dplyr::select(tidyselect::starts_with(prefix.pattern)) + } + + index <- seq_along(data)[-1] + + diff <- index |> + purrr::map(\(.y){ + abs(data[.y - 1] - data[.y]) + }) |> + dplyr::bind_cols() |> + (\(.x) setNames(.x, paste0(names(.x), suffix.out)))() + + if (include.first) { + out <- dplyr::bind_cols(data[1], diff) + } else { + out <- diff + } + out +} + diff --git a/2 Longterm/DDV 240607/pa_event_plots.docx b/2 Longterm/DDV 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+ +# Hypothesis + +```{r} +labels_all<-list(rtreat~"Trial treatment", + pase_0~"PASE score", + age~"Age", + sex~"Sex", + smoker~"History of smoking", + civil~"Cohabitation", + diabetes~"Known diabetes", + hypertension~"Known hypertension", + afli~"Known Atrialfibrillation", + ami~"Previos myocardial infarction", + tci~"Previos TIA", + pad~"Known peripheral artery disease", + nihss_0~"Acute NIHSS score", + thrombolysis~"Thrombolytic therapy", + thrombechtomy~"Endovascular treatment", + SES~"Socio economic status", + education~"Education", + ad_treat~"Anti depression treatment", + vasc_event~"Vascular event") +``` + +```{r} +vars <- c( + "pase_0", + "age", + "sex", + "civil", + "smoker", + "rtreat", + "alc", + "afli", + "hypertension", + "diabetes", + "mrs_0", + "nihss_c", + "thrombolysis", + "pad", + "thrombechtomy", + "ami", + "tci", + "compliant", + "SES", + "education" +) +``` + +```{r} +library(ggdag) +library(ggplot2) +``` + +```{r} + +dag <- + dagify( + vasc_event ~ pase_0 + age + sex + civil + smoker + rtreat + alc + afli + + hypertension + diabetes + mrs_0 + nihss_0 + thrombolysis + pad + thrombechtomy + + ami + tci + SES + education + ad_treat + svd, + pase_0 ~ sex + civil + alc + pad + SES + education + hypertension + diabetes, + diabetes ~ civil, + svd~hypertension + diabetes + alc, + mrs_0 ~ tci + ami + hypertension + diabetes + svd, + age ~ smoker+SES+education, + civil ~ sex + age + SES + civil, + # smoker~ , + alc ~ sex+SES+education, + # afli~ , + hypertension~ alc, + nihss_0 ~ hypertension + diabetes + pase_0 + age, + thrombolysis ~ mrs_0+nihss_0, + # pad~ , + thrombechtomy ~ mrs_0+nihss_0, + # ami~ , + # tci~ , + # compliant~ , + # SES~ , + # education, + ad_treat~education+SES, + # labels = labels_all, + latent = c("svd"), + exposure = "pase_0", + outcome = "vasc_event" + ) +``` + +```{r} +dag |> ggdag(text = TRUE) + theme_dag(6) + geom_dag_edges_arc() +``` + +```{r} +dag |> ggdag_parents("svd") +``` + +```{r} +dag |> ggdag_paths(text = TRUE, shadow = TRUE) +``` + +```{r} +dag |> ggdag_adjustment_set(text = TRUE, shadow = TRUE,stylized = TRUE) +``` diff --git a/2 Longterm/Project-B---DAG.html b/2 Longterm/Project-B---DAG.html new file mode 100644 index 0000000..f151150 --- /dev/null +++ b/2 Longterm/Project-B---DAG.html @@ -0,0 +1,496 @@ + + + + + + + + + + + + + + + +Project B - DAG + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Hypothesis

+
labels_all<-list(rtreat~"Trial treatment",
+                 pase_0~"PASE score",
+                 age~"Age",
+                 sex~"Sex",
+                 smoker~"History of smoking",
+                 civil~"Cohabitation",
+                 diabetes~"Known diabetes", 
+                 hypertension~"Known hypertension", 
+                 afli~"Known Atrialfibrillation", 
+                 ami~"Previos myocardial infarction", 
+                 tci~"Previos TIA",
+                 pad~"Known peripheral artery disease",
+                 nihss_0~"Acute NIHSS score", 
+                 thrombolysis~"Thrombolytic therapy", 
+                 thrombechtomy~"Endovascular treatment",
+                 SES~"Socio economic status",
+                 education~"Education",
+                 ad_treat~"Anti depression treatment",
+                 vasc_event~"Vascular event")
+
vars <- c(
+  "pase_0",
+  "age",
+  "sex",
+  "civil",
+  "smoker",
+  "rtreat",
+  "alc",
+  "afli",
+  "hypertension",
+  "diabetes",
+  "mrs_0",
+  "nihss_c",
+  "thrombolysis",
+  "pad",
+  "thrombechtomy",
+  "ami",
+  "tci",
+  "compliant",
+  "SES",
+  "education"
+)
+
library(ggdag)
+
## 
+## Attaching package: 'ggdag'
+
## The following object is masked from 'package:stats':
+## 
+##     filter
+
library(ggplot2)
+
dag <-
+  dagify(
+    vasc_event ~ pase_0 + age + sex + civil + smoker + rtreat + alc + afli +
+      hypertension + diabetes + mrs_0 + nihss_0 + thrombolysis + pad + thrombechtomy +
+      ami + tci + SES + education + ad_treat + svd,
+    pase_0 ~ sex + civil + alc + pad + SES + education + hypertension + diabetes,
+    diabetes ~ civil,
+    svd~hypertension + diabetes + alc,
+    mrs_0 ~ tci + ami + hypertension + diabetes + svd,
+    age ~ smoker+SES+education,
+    civil ~ sex + age + SES + civil,
+    # smoker~ ,
+    alc ~ sex+SES+education,
+    # afli~ ,
+    hypertension~ alc,
+    nihss_0 ~ hypertension + diabetes + pase_0 + age,
+    thrombolysis ~ mrs_0+nihss_0,
+    # pad~ ,
+    thrombechtomy ~ mrs_0+nihss_0,
+    # ami~ ,
+    # tci~ ,
+    # compliant~ ,
+    # SES~ ,
+    # education,
+    ad_treat~education+SES,
+    # labels = labels_all,
+    latent = c("svd"),
+    exposure = "pase_0",
+    outcome = "vasc_event"
+  )
+
dag |> ggdag(text = TRUE) + theme_dag(6) + geom_dag_edges_arc()
+

+
dag |> ggdag_parents("svd")
+

+
dag |> ggdag_paths(text = TRUE, shadow = TRUE)
+

+
dag |> ggdag_adjustment_set(text = TRUE, shadow = TRUE,stylized = TRUE)
+

+
+ + + + +
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+NA,"63","male","partner","never","Placebo","guideline","no","no","no","0","2","yes","no","no","no","no",NA,NA,NA,NA,NA,NA,NA,NA,NA diff --git a/2 Longterm/dags.R b/2 Longterm/dags.R new file mode 100644 index 0000000..0ee21c8 --- /dev/null +++ b/2 Longterm/dags.R @@ -0,0 +1,145 @@ +## Everything + +dag <- 'dag { + bb="-4.79,-6.035,4.818,5.504" + SES [pos="-4.463,-2.925"] + ad_treat [pos="2.007,-1.567"] + afli [pos="0.028,-1.851"] + age [pos="-1.242,-0.765"] + alc [pos="-3.716,-0.111"] + ami [pos="-3.137,3.986"] + civil [pos="-4.099,0.703"] + diabetes [pos="-3.838,3.579"] + education [pos="-3.632,-3.678"] + gen_PA [pos="-2.820,-1.247"] + hypertension [pos="-4.034,2.468"] + mrs_0 [pos="-1.746,2.715"] + mrs_1 [pos="1.326,-0.469"] + nihss_0 [pos="0.271,1.296"] + pase_0 [exposure,pos="-0.542,0.173"] + revasc [pos="0.980,2.332"] + rtreat [pos="2.334,2.036"] + sex [pos="-4.538,-0.778"] + smoker [pos="-1.877,-2.160"] + stroke [adjusted,pos="0.766,0.321"] + svd [latent,pos="-2.400,1.493"] + tci [pos="-2.418,4.467"] + vasc_event [outcome,pos="3.754,0.358"] + SES -> ad_treat + SES -> age + SES -> alc + SES -> gen_PA + SES -> stroke + SES -> vasc_event + ad_treat -> vasc_event + afli -> pase_0 + afli -> stroke + afli -> vasc_event + age -> afli + age -> civil + age -> mrs_1 + age -> nihss_0 + age -> vasc_event + alc -> hypertension + alc -> pase_0 + alc -> stroke + alc -> svd + alc -> vasc_event + ami -> afli + ami -> mrs_0 + ami -> stroke + ami -> vasc_event + civil -> pase_0 + civil -> stroke + civil -> vasc_event + diabetes -> mrs_0 + diabetes -> nihss_0 + diabetes -> stroke + diabetes -> svd + diabetes -> vasc_event + education -> SES + education -> ad_treat + education -> age + education -> alc + education -> gen_PA + education -> stroke + education -> vasc_event + gen_PA -> ami + gen_PA -> diabetes + gen_PA -> hypertension + gen_PA -> pase_0 + gen_PA -> smoker + gen_PA -> tci + hypertension -> afli + hypertension -> mrs_0 + hypertension -> nihss_0 + hypertension -> stroke + hypertension -> svd + hypertension -> vasc_event + mrs_0 -> pase_0 + mrs_0 -> revasc + mrs_1 -> ad_treat + mrs_1 -> vasc_event + nihss_0 -> revasc + pase_0 -> ad_treat + pase_0 -> nihss_0 + pase_0 -> stroke + revasc -> mrs_1 + rtreat -> mrs_1 + rtreat -> vasc_event + sex -> ad_treat + sex -> alc + sex -> education + sex -> mrs_1 + sex -> pase_0 + sex -> stroke + sex -> vasc_event + smoker -> age + smoker -> mrs_1 + smoker -> vasc_event + stroke -> mrs_1 + stroke -> nihss_0 + stroke -> rtreat + stroke -> vasc_event + svd -> mrs_0 + svd -> pase_0 + svd -> vasc_event + tci -> mrs_0 + tci -> stroke + tci -> vasc_event +}' + + +## Simplified + +dag <- 'dag { +bb="-4.79,-6.035,4.818,5.504" +"Higher SES" [pos="-1.671,-1.740"] +"U: higher svd score" [latent,pos="-3.091,2.172"] +"active treat" [pos="1.914,3.209"] +"higher PA" [exposure,pos="-0.999,0.555"] +"lower mrs_0" [pos="-2.437,1.259"] +ad_treat [pos="-0.448,-0.358"] +cardio_vasc [pos="-4.202,0.839"] +male [pos="-3.119,-0.926"] +vasc_event [outcome,pos="3.754,0.358"] +"Higher SES" -> "higher PA" +"Higher SES" -> ad_treat +"Higher SES" -> vasc_event +"U: higher svd score" -> "lower mrs_0" +"U: higher svd score" -> vasc_event +"active treat" -> vasc_event +"higher PA" -> ad_treat +"higher PA" -> vasc_event +"lower mrs_0" -> "higher PA" +ad_treat -> vasc_event +cardio_vasc -> "U: higher svd score" +cardio_vasc -> "higher PA" [pos="-2.876,-0.185"] +cardio_vasc -> "lower mrs_0" +cardio_vasc -> vasc_event [pos="-4.762,5.060"] +male -> "higher PA" +male -> vasc_event [pos="-2.540,-5.134"] +}' + +dag |> ggdag::ggdag_adjustment_set(node_size = 14, text_col = "black") + + theme(legend.position = "bottom") diff --git a/2 Longterm/data.R b/2 Longterm/data.R new file mode 100644 index 0000000..5d286a6 --- /dev/null +++ b/2 Longterm/data.R @@ -0,0 +1,260 @@ +## +## Data pull and export for Forskermaskinen +## +## Exports are made in REDCap instead +## +## Data set is made here, not in REDCap, as data here is better organised. +## + +library(haven) +library(dplyr) +library(purrr) + +## All data +# source("2 Longterm/data_import_files.R") +source("/Users/au301842/PAaSO/2 Longterm/data_import_api.R") + +## Helpers +source("/Users/au301842/PAaSO/2 Longterm/funs.R") + +# Streamlining missings for easier handling + +# NA values +## Empty fields ("") are not included as NA, to ease later corrections. +nas <- c("9.", "9. NA", "Not available", "Not relevant (filter/fold)") + +# Replaces all NAs in factors, and rejoins data frame +# Factors keeps old levels; doesn't matter when exported as .csv and reimported... +ls_nas <- lapply(seq_along(ls_sel), function(i) { + ds <- ls_sel[[i]] |> select(starts_with("talos_") + # & where(is.factor) + ) |> + data.frame() + + ds_n <- lapply(seq_len(ncol(ds)),function(j){ + as.character(if_else(ds[,j] %in% nas, NA, ds[,j])) + }) |> bind_cols() + + colnames(ds_n) <- colnames(ds) + + ds_sub <- ls_sel[[i]] |> select(-colnames(ds_n)) + + ds_fin <- tibble(ds_sub,ds_n) + + ds_fin |> select(colnames(ls_sel[[i]])) |> select(-grep("[0-9]x$",colnames(ls_sel[[i]]))) +}) + +names(ls_nas) <- names(ls_sel) + + + +## MFI domain scores + +# MFI variables to reverse +# -> Create MFI function +mfi_rev <- tolower(c("TALOS_MFI02","TALOS_MFI05","TALOS_MFI09","TALOS_MFI10","TALOS_MFI13","TALOS_MFI14","TALOS_MFI16","TALOS_MFI17","TALOS_MFI18","TALOS_MFI19")) + +match(mfi_rev,colnames(ls_nas$mfi|> select(matches(paste0("talos_mfi",stRoke::add_padding(1:20)))))) + +domain_scores <- ls_nas$mfi |> select(matches(paste0("talos_mfi",stRoke::add_padding(1:20)))) |> mfi_domains(var = mfi_rev) + +colnames(domain_scores) <- paste0("talos_mfi_",colnames(domain_scores)) + +ls_nas$mfi <- tibble(ls_nas$mfi,domain_scores) + +## SDMT score correction + +# Correction table +# +# Holds manually determined 90 second times, additionally, "2015-02-18" is used +# as cut date for change from 60 seconds to 90 seconds +# +# + +if (FALSE){ +sdmt_time<-openxlsx::read.xlsx("/Volumes/Data/source/tid.sdmt.xlsx") |> + tidyr::pivot_longer(cols = c(tid.1md, tid.6md)) |> mutate(deltager=as.character(deltager)) +sdmt_time$name <- as.double(as.character(factor(sdmt_time$name,labels = c("2","4")))) + +# Setting cut date +sdmt_cut<-as.Date("2015-02-18") + +# Joining datasets to have date of visit +sdmt_corr <- left_join(ls_nas$sdmt,sdmt_time |> mutate(name=as.character(name)), by = c("rnumb"="deltager","instance"="name")) + +# Modyfying old correction table to be complete +sdmt_corr$value <- if_else(sdmt_corr$talos_sdmt00 select(ends_with(pase_index)) |> + pase_calc(adjust_work = FALSE) + +ensure_prefix <- function(vec,prefix){ + ifelse(!grepl(paste0("^",prefix),vec),paste0(prefix,vec),vec) +} + +colnames(pase_scores) <- colnames(pase_scores)# |> ensure_prefix(prefix="pase_") + +last_cols <- function(ds, n){ + ds[,(ncol(ds)-n+1):ncol(ds)]} + +pase_scores_work <- ls_nas$pase |> select(ends_with(pase_index)) |> + pase_calc(adjust_work = TRUE) |> last_cols(4) %>% + rename_with(~ paste0(., "_w")) + +colnames(pase_scores_work) <- colnames(pase_scores_work) #|> ensure_prefix(prefix="pase_") + +ls_nas$pase <- tibble(ls_nas$pase,pase_scores,pase_scores_work) + +## Registration correction + +# Mutate all ends_with "00" as.Date in original data import file +# source("2 Longterm/funs.R") + +ls_corr <- lapply(ls_nas,time_reg_correction, ref=subjects) + +run_eval=FALSE +if (run_eval){ +library(compareDF) + +ls_comp <- lapply(seq_along(ls_nas),function(i){ + compare_df(ls_corr[[i]],ls_nas[[i]],group_col = c("rnumb","instance"),stop_on_error = FALSE) +}) + +ls_comp[[1]] |> + create_output_table() +} + +ls_wide <- ls_corr |> longlist2wide() + +df_wide <- ls_wide |> purrr::reduce(full_join,by="rnumb") + +## The final assembly + +df_ddv <- subjects |> + mutate(rnumb=as.character(rnumb)) |> + left_join(df_wide %>% + ## Keep only first "SITE" occurance + ## Using magrittr pipe for easy placeholder use + select(-grep("_site_",colnames(.))[-1]) + ) + +# write.csv(df_ddv,"/Volumes/Data/REDCap/DDV/talos_ddv.csv",row.names = FALSE) + + +## Generate data description +attr_files <- list.files("/Users/au301842/PAaSO/REDCap/attr",pattern = ".csv$",full.names = TRUE) +attr_files_short <- list.files("/Users/au301842/PAaSO/REDCap/attr",pattern = ".csv$",full.names = FALSE) + +nms <- do.call(c,lapply(strsplit(attr_files_short,"_"),"[[",2)) |> gsub("['.']csv","",x=_) |> tolower() + +attr_lst <- lapply(attr_files,read.csv) +names(attr_lst) <- nms + +attr_df <- lapply(seq_along(attr_lst),function(i){ + + data.frame(name = tolower(ifelse( + !grepl("^TALOS|cpr|record_id", attr_lst[[i]][, 1]), + paste0(nms[i], "_", attr_lst[[i]][, 1]), + attr_lst[[i]][, 1] + )), + attr = attr_lst[[i]][, 2], + instr = paste(toupper(nms[i]),"instrument")) + +}) |> bind_rows() + +attr_df$attr[grepl("sys_date$",attr_df$name)] <- "System date" +attr_df$attr[grepl("sys_site$",attr_df$name)] <- "Trial site" +attr_uni <- attr_df[!duplicated(attr_df$name),] + +# data.frame(variabel=colnames(df_ddv), +# visit=stRoke::str_extract(colnames(df_ddv),"[0124]$"), +# attr_df[match(gsub("_[0124]$","",colnames(df_ddv)),attr_df$name),-1]) |> +# write.csv("REDCap/ddv_variabelbeskrivelse_raw.csv",row.names = FALSE) +# +# read.csv("REDCap/ddv_variabelbeskrivelse.csv") |> +# filter_at(1,all_vars(.%in%colnames(df_ddv))) |> +# write.csv("REDCap/ddv_variabelbeskrivelse_mod.csv",row.names = FALSE) + + +is.na(ds$talos_pase01_0) |> summary() + + + +## Cumulated data + +# dta<-read.csv("/Volumes/Data/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown")) +# +# export<-dta[,c("pase_0", +# "age", +# "sex", +# "civil", +# "smoker", +# "rtreat", +# "alc", +# "afli", +# "hypertension", +# "diabetes", +# "mrs_0", +# "nihss_c", +# "thrombolysis", +# "pad", +# "thrombechtomy", +# "ami", +# "tci", +# "rdate", +# "cpr", +# "rnumb", +# "height", +# "weight", +# "weight_est", +# "inc_time", +# "compliant", +# "mrs_1", +# "mrs_6", +# "pase_6", +# "visit_1", +# "visit_6")] + +# export$diabetes[is.na(export$diabetes)]<-"no" +# export$hypertension[is.na(export$hypertension)]<-"no" +# export$thrombolysis[is.na(export$thrombolysis)]<-"no" +# export$thrombechtomy[is.na(export$thrombechtomy)]<-"no" +# export$pad[is.na(export$pad)]<-"no" +# export$ami[is.na(export$ami)]<-"no" +# export$inc_time[export$inc_time<0]<-0 +# export$compliant<-as.numeric(factor(export$compliant)) +# export$rdate<-as.Date(export$rdate) +# export$mrs_0[export$mrs_0==3]<-NA + +# export <- export|> +# mutate(any_rep=factor(ifelse(thrombolysis=="yes"|thrombechtomy=="yes","yes","no")), # If not noted, no therapy was received +# weight=ifelse(is.na(weight),weight_est,weight))|> +# select(-c(weight_est)) +# +# dput(names(export)) + +# write.csv(export|>select(c(rnumb,rtreat)),"/Volumes/Data/SDS upload/study_treatment.csv",row.names = FALSE) +# write.csv(export,"/Volumes/Data/SDS upload/data_all.csv",row.names = FALSE) +# write.csv(export|>select(-c(rtreat)),"/Volumes/Data/SDS upload/background.csv",row.names = FALSE) + +# max(export$visit_6[!is.na(export$visit_6)]) + + diff --git a/2 Longterm/data_import_api.R b/2 Longterm/data_import_api.R new file mode 100644 index 0000000..d73528c --- /dev/null +++ b/2 Longterm/data_import_api.R @@ -0,0 +1,110 @@ + +token=keyring::key_get("TALOS_REDCAP_API") + +## Primary data set of recorded data + +inst <- REDCapR::redcap_instruments(redcap_uri = "https://redcap.au.dk/api/",token = token)$data + +tools <- tolower(c( + "PASE", + "MFI", + "mdi", + "mmse", + "mrs", + "SDMT", + "who", + "ham", + "bi", + "grad", + "nihss" +)) + +vec_starts_with <- function(d,v){ + filt <- paste0("^(", paste(v, collapse="|"), ")") + + d[grep(filt,d)] +} + +vec_ends_with <- function(d,v){ + filt <- paste0("(", paste(v, collapse="|"), ")$") + # sub(paste0(".*?_(",paste(v,collapse = "|"),")$"),"\\1",colnames(d)) + d[grep(filt,d)] +} + +# vec_starts_with(inst$instrument_name,tools) + +data <- REDCapR::redcap_read(redcap_uri = "https://redcap.au.dk/api/",token = token, + forms = vec_starts_with(inst$instrument_name,tools), + fields = "record_id")$data + +ds <- data |> select(!ends_with("complete")) + +talos2long <- function(ds,exclude=c("talos","record"),instance_vals=c("0","1","2","4"),exclude_col_ends=c("user","lock")){ + nms <- unique(do.call(c,lapply(strsplit(colnames(ds),"_"),"[[",1))) + nms <- nms[!nms %in% exclude] + + ls <- lapply(seq_along(nms),function(i){ + cols <- c("record_id",vec_starts_with(colnames(ds),c(paste0("talos_",nms[i]),nms[i]))) + + d <- select(ds,all_of(cols)) + + ends <- unique(do.call(c,lapply(strsplit(colnames(d),"_"),function(i){ + i[length(i)] + }))) + + ins <- colnames(d) %in% vec_ends_with(colnames(d),paste0("_",instance_vals)) + + ls_ins <- split.default(d[-1],factor(stRoke::str_extract(colnames(d[ins]),paste0("[",paste(instance_vals,collapse=""),"]$")))) + + dat <- lapply(seq_along(ls_ins),function(i){ + dr <- cbind(d[1],instance=names(ls_ins)[i],ls_ins[[i]]) + colnames(dr) <- unique(gsub(pattern = paste0("_[",paste(instance_vals,collapse=""),"]$"),"",colnames(dr))) + dr |> select(record_id,contains("_sys_"),instance,everything()) + }) |> bind_rows() |> rename(rnumb=record_id) |> mutate(rnumb=as.character(rnumb)) + + ## Col name ending on 00 is date col of measure in all tools + dat[,vec_ends_with(colnames(dat),"00")] <- as.Date(dat[,vec_ends_with(colnames(dat),"00")]) + + dat |> select(!ends_with(exclude_col_ends)) + }) + + names(ls) <- nms + ls + +} + +ls_sel <- data |> select(!ends_with("complete"))|> talos2long() + +## Data set of baseline and DAP data + +subjects_raw <- + REDCapR::redcap_read( + redcap_uri = "https://redcap.au.dk/api/", + token = token, + forms = vec_starts_with(inst$instrument_name, c("end", "inkl", "reg", "basis")), + fields = "record_id" + )$data + +subjects <- subjects_raw |> + select( + record_id, + talos_end00, + talos_end01, + rdate, + rtreat, + cpr, + basis_kon, + starts_with("reg_") + ) |> select(!ends_with("complete")) |> + rename(rnumb = record_id, + enddate = talos_end00, + eos_early = talos_end01, + sex=basis_kon) |> + mutate( + enddate = as.Date(enddate), + rdate = as.Date(rdate), + inc_time = as.numeric(difftime(enddate, rdate, units = "days")), + age = stRoke::age_calc(as.Date(stRoke::cpr_dob(cpr,"%Y-%m-%d")),enddate=rdate) + ) + +# colnames(subjects) <- gsub("^reg_","",colnames(subjects)) diff --git a/2 Longterm/data_import_files.R b/2 Longterm/data_import_files.R new file mode 100644 index 0000000..4c8ff26 --- /dev/null +++ b/2 Longterm/data_import_files.R @@ -0,0 +1,49 @@ +# Specifying needed data collection files +data_source <- c( + "PASE_rev_v13.dta", + "MFI_rev_v13.dta", + "mdi_rev_v13.dta", + "mmse_rev_v13.dta", + "mrs_rev_v13.dta", + "SDMT_rev_v13.dta", + "who_rev_v13.dta" +) + +## Cumulated data +dta<-read.csv("/Volumes/Data/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown")) + +# Getting full filenames +file_nms <- list.files("/Volumes/Data/STATA13",full.names = TRUE)[match(data_source,list.files("/Volumes/Data/STATA13"))] + +# Loading datafiles + +dta<-read.csv("/Volumes/Data/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown")) + +ls <- lapply(file_nms,function(i){ + d <- read_dta(i) + colnames(d) <- tolower(gsub("instance","INSTANCE",colnames(d))) #in the sdmt dataset, instance column is lower case + d +}) + +# Selecting desired variables +ls_sel <- lapply(seq_along(ls), function(i) { + ls[[i]] |> select(cpr, + SYS_SITE, + INSTANCE, + starts_with("TALOS_")) |> as_factor() |> + full_join(select(dta,cpr, rnumb)) |> select(rnumb,everything()) +}) + +# Naming lists according to file names +names(ls_sel) <- tolower(unlist(lapply(data_source,function(x){strsplit(x,"_")[[1]][1]}))) + +## Screening list and EOS data +subjects <- read_dta("/Volumes/Data/STATA13/inkl_rev_v13.dta") |> + select(c("cpr", "rnumb", "rdate", "rtreat")) |> + filter(rnumb != 999) |> + left_join(read_dta("/Volumes/Data/STATA13/end_rev_v13.dta") |> + select(c("cpr", "TALOS_end00", "TALOS_end01")) + ) |> + rename(enddate = TALOS_end00, + eos_early = TALOS_end01) |> + as_factor() diff --git a/2 Longterm/flowchart.R b/2 Longterm/flowchart.R new file mode 100644 index 0000000..2f41c81 --- /dev/null +++ b/2 Longterm/flowchart.R @@ -0,0 +1,33 @@ +data <- list(a=1000, b=800, c=600, d=400) + + +DiagrammeR::grViz(" +digraph graph2 { + +graph [layout = dot] + +# node definitions with substituted label text +node [shape = rectangle, width = 4, fillcolor = Biege] +a [label = '@@1'] +b [label = '@@2'] +c [label = '@@3'] +node [shape = rectangle, width = 2, fillcolor = Biege] +d [label = '@@4'] +e [label = '@@5'] + +a -> b -> c [dir=s] +a -> d [dir=e] +b -> e [dir=e] +# c -> f + +{rank=same; a -> b -> c [dir=s]} + +} + +[1]: paste0('All patients in TALOS (n = ', data$a, ')') +[2]: paste0('Patients with PASE (n = ', data$b, ')') +[3]: paste0('Patients with first event after follow-up (n = ', data$c, ')') +[4]: paste0('Excluded due to missing PASE (n = ', data$d, ')') +[5]: paste0('Excluded due to early event (n = ', data$d, ')') +# [6]: paste0('Excluded due to missing PASE (n = ', data$d, ')') +") diff --git a/2 Longterm/funs.R b/2 Longterm/funs.R new file mode 100644 index 0000000..4fa2b5a --- /dev/null +++ b/2 Longterm/funs.R @@ -0,0 +1,248 @@ + +## Utils + +multi_rev <- function(ds, var){ + ndx <- match(var,colnames(ds)) + for (i in seq_along(ndx)){ + j <- ndx[i] + ds[[j]]<-as.character(factor(ds[[j]],labels = c(rev(levels(factor(ds[[j]])))))) + } + ds +} + +reg2score <- function(ds){ + # Removes all padding from registred scores + l <- c() + for (i in seq_len(ncol(ds))){ + l[[i]] <- gsub("[^a-zA-Z0-9]","",ds[[i]]) + } + + d <- do.call(cbind,l) |> data.frame() + + colnames(d) <- colnames(ds) + d |> tibble() +} + + + +## MFI modding + +mfi_domains <- function(ds, reverse=TRUE, var){ + + # Subscore indexes + indexes <- list( + data.frame(grp="gen", ndx=c(1, 5, 12, 16)), + data.frame(grp="phy", ndx=c(2, 8, 14, 20)), + data.frame(grp="act", ndx=c(3, 6, 10, 17)), + data.frame(grp="mot", ndx=c(4, 9, 15, 18)), + data.frame(grp="men", ndx=c(7, 11, 13, 19)) + ) |> bind_rows() |> arrange(ndx) + + # Assumes reverse scores are not correctly reversed + if (reverse){ds <- ds |> multi_rev(var)} + + # Removes padding and converts to numeric + d <- ds |> reg2score() |> + mutate_if(is.character, as.numeric) + + split.default(d, factor(indexes$grp)) |> + lapply(function(x){ + apply(x, MARGIN = 1, sum) + }) |> bind_cols() + +} + +## Registration correction + +# 1. If the inc_time is 38 days or less MDI 6 scores are moved to MDI 1 and visit 6 is defined as visit 1. +# 2. If both visit 1 and 6 dates are NA, use enddate as visit 1 date. This is the case if patients were excluded early. +# 3. If visit 6 is recorded later than enddate, use enddate instead. MDI 6 score is dropped. +# 4. If visit delay is 7 days or less, and inclusion time is more than 38, MDI 1 is moved to MDI 6 and dropped. If MDI 1 and 6 are different both are kept. Enddate is moved to visit 6 date. +# 5. Defining the visit 6 date as same as enddate if visit delay is <7. + +# ds <- ls_nas$mdi +# ref <- subjects + + +time_reg_correction <- function(ds, ref) { + + # Splitting by rnumb, to treat each subj + ls <- split(ds, ds$rnumb) + + # ref: https://r-coder.com/progress-bar-r/ + # Setting up simple progress bar + pb <- txtProgressBar(min = 0, max = length(ls), style = 3) + + + # Getting the rnumbs for subsetting in order of the list + nms <- names(ls) + # Applying correction to each element in list + ls_c <- lapply(seq_along(ls), function(i) { + cat(paste(i,"af",length(ls))) + # Updates the current state + setTxtProgressBar(pb, i) + + # Current rnumb + nm <- nms[i] + # print(i) + df <- ls[[i]] + # Only run if both instance 2 and 4 are present + if (all(c(2, 4) %in% select(df, matches("instance", ignore.case = TRUE))[[1]])) { + + # Define common variables + inc_time <- as.numeric(ref$inc_time[ref$rnumb == nm]) + end_date <- as.Date(ref$enddate[ref$rnumb == nm]) + rdate <- as.Date(ref$rdate[ref$rnumb == nm]) + inst <- grep("instance", tolower(colnames(df))) + + # Define relevant data columns to substitute/modify + cols <- (inst + 1):ncol(df) + + # Define date column index + date_col_index <- + match(colnames(select(df, ends_with("00"))), colnames(df)) + + # Step 1 + + ## Correction if inc_time is performed + if (inc_time <= 38 & all(is.na(df[df[inst] == 2, cols]))) { + # Substitutions to transfer instance 4 meassures and leave as NA + df[df[inst] == 2, cols] <- df[df[inst] == 4, cols] + df[df[inst] == 4, -c(inst,date_col_index)] <- NA + } + + # Step 2 + + if (all(is.na(df[date_col_index]))) { + # If both visit dates are missing, instance 2 date is substituted with enddate + df[date_col_index][df[inst] == 2] <- + as.character(end_date) + } + + # Step HELPA + ## Extra help step to impute enddate as visit 4 if instance 4 is present but no date. + if (is.na(df[date_col_index][df[inst] == 4])){ + df[date_col_index][df[inst] == 4] <- as.character(end_date) + } + + ## Last steps are only performed if date values are available for both instance 2 and 4 + if (!any(is.na(df[date_col_index]))){ + + # Step 3 + end_delay <- as.numeric(difftime(df[date_col_index][df[inst] == 4], + end_date, + units = "days")) + + # If enddate is before last visit, the last visit data is dropped. + if (end_delay > 2) { + df[df[inst] == 4,-c(inst,date_col_index)] <- NA + } + + # Step 4 + visit_delay <- + as.numeric(difftime(df[date_col_index][df[inst] == 4], df[date_col_index][df[inst] == + 2], units = "days")) + + if (purrr::is_empty(visit_delay)){ + visit_delay <- NA + } + + + ## Test if data has manually been inserted at visit 2, though should should be visit 4 time wise + second2fourth <- + if_else((visit_delay <= 7 | + all(is.na(df[df[inst] == 2, cols]))) & + inc_time > 38, + TRUE, + FALSE, + missing = FALSE) + + ## Move data from 2. visit to 4. + if (second2fourth) { + # df[date_col_index][df[inst] == 2] + + ## If all missing at last visit, 1. visit is copied + if (all(is.na(df[df[inst] == 4, cols]))) { + df[df[inst] == 4, cols] <- df[df[inst] == 2, cols] + } + + ## If entries at visit 2 and 4 are identical, vist 2 is deleted + if (identical(df[df[inst] == 4, cols], df[df[inst] == 2, cols])) { + df[df[inst] == 2, -c(inst,date_col_index)] <- NA + } + + ## If data entries are NA at visit 2, the date is also deleted + if (all(is.na(df[df[inst] == 2, cols]))) { + df[,date_col_index][df[inst] == 2] <- NA + } + + ## The registered enddate is copied to the visit 4 date + df[,date_col_index][df[inst] == 4] <- + as.character(end_date) + } + + # Step 5 + ## Ensure enddate is visit 4 if visit delay is <7 + if (visit_delay < 7) { + df[,date_col_index][df[inst] == 4] <- as.character(end_date) + } + } + } + df + }) + + ls_c |> bind_rows() + +} + +longlist2wide <- + function(list, + id.name = "rnumb", + instance = "instance", + inst.glue = "{.value}_{instance}") { + + # ref: https://r-coder.com/progress-bar-r/ + # Setting up simple progress bar + pb <- txtProgressBar(min = 0, max = length(list), style = 3) + + l <- lapply(seq_along(list), function(i) { + + # Updates the current state + setTxtProgressBar(pb, i) + + lst <- list[[i]] |> data.frame() + + rep_inst <- + length(levels(factor(lst[, colnames(lst) == instance]))) > 1 + + + k <- lapply(split(lst, f = lst[[id.name]]), function(j) { + cname <- colnames(j) + vals <- + cname[!cname %in% c(id.name, + instance)] + s <- tidyr::pivot_wider( + j, + names_from = instance, + values_from = all_of(vals), + names_glue = inst.glue + ) + s[!colnames(s) %in% instance] + }) + + k |> dplyr::bind_rows() + + + }) + + } + +na_recode <- function(ds,vars_na,new_na="no"){ + for (i in seq_along(vars_na)){ + ds[i][is.na(ds[i])] <- new_na + } + ds +} + + + diff --git a/2 Longterm/generalised odds ratio.R b/2 Longterm/generalised odds ratio.R new file mode 100644 index 0000000..5a0b0e8 --- /dev/null +++ b/2 Longterm/generalised odds ratio.R @@ -0,0 +1,9 @@ +## Generaloised odds ratio +## +## Tournament based approach +## + + +library(genodds) + + diff --git a/2 Longterm/grotta_bars.R b/2 Longterm/grotta_bars.R new file mode 100644 index 0000000..df97ecc --- /dev/null +++ b/2 Longterm/grotta_bars.R @@ -0,0 +1,26 @@ +library(rankinPlot) + +?rankinPlot::grottaBar + +dta<-read.csv("/Volumes/Data/exercise/source/background.csv",colClasses = "character", na.strings = c("NA","","unknown"))[,c("rtreat","mrs_0","mrs_1","mrs_6","hypertension","diabetes","civil")] + +df <- dta |> select(c("rtreat","mrs_0","mrs_1","mrs_6")) |> pivot_longer(cols = -rtreat) + +x<-table(mRS=df$value, + Group=df$rtreat, + Time = df$name) + +grottaBar(x,groupName="Group", + scoreName = "mRS", + strataName="Time", + colourScheme ="custom" +) + + scale_fill_viridis_d(direction=-1) + +dta |> select(-c("mrs_0","mrs_1")) |> generic_stroke(group = "rtreat", score = "mrs_6", variables = c("hypertension","diabetes","civil")) + +library(stRoke) +cc<-dta[complete.cases(dta),] +talos <- cc[sample(1:nrow(cc),200),] |> select(-mrs_0) + +save(talos,file="talos.rda") diff --git a/2 Longterm/hr coef plot.R b/2 Longterm/hr coef plot.R new file mode 100644 index 0000000..5197f48 --- /dev/null +++ b/2 Longterm/hr coef plot.R @@ -0,0 +1,173 @@ +source(here::here("1 PA Decline/dst import.R")) + +file <- project.aid::docx2list("/Users/au301842/Library/CloudStorage/OneDrive-Personal/Research/PhD/2 TALOS opfølgning/Manuskript/Arkiv/Longterm risk_v1_0.docx", data.type = "table cell") + +ds <- file |> + purrr::pluck(3) |> + (\(.x){ + setNames(.x, letters[seq_len(ncol(.x))]) + })() |> + dplyr::filter(dplyr::row_number() <= dplyr::n() - 1) |> + setNames(c("var", purrr::map(1:3, \(.x)paste0(c("hr", "ci"), .x)) |> purrr::list_c())) |> + dplyr::mutate(dplyr::across(dplyr::everything(), ~ gsub("—", "", .x))) |> + (\(.x){ + split.default(.x, factor(project.aid::str_extract(names(.x), "\\d$"), labels = c("Univariable", "Multivariable", "Imputed"))) |> + purrr::imap(\(.y, .i){ + dplyr::bind_cols(var = .x[1], .y) |> + tidyr::separate_wider_delim( + col = dplyr::starts_with("ci"), + delim = ", ", + # names_sep = "_", + too_few = "align_start", + names = c("low", "high") + ) |> + (\(.z){ + names(.z)[2] <- "hr" + .z + # setNames(c("var","hr","low","high")) + })() |> + dplyr::mutate(model = .i) + }) + })() |> + dplyr::bind_rows() + +create_log_tics <- function(data) { + sort(round(unique(c(1 / data, data)), 2)) +} + +forest_plot <- function(data, + group.colors = viridisLite::viridis(3,option = "D"), + x.tics = create_log_tics(c(1, 1.5, 3, 6)), + point.shape = rep(23, 3), + legend.title = "", + dodge.width = .8, + wrap.col) { + + data |> + ggplot2::ggplot(ggplot2::aes(x = log(hr), y = labels, color = model, fill = model)) + + ggplot2::geom_vline(ggplot2::aes(xintercept = 0), linewidth = .5, linetype = "dashed") + + ggplot2::geom_point(ggplot2::aes(shape = model), + # position = ggplot2::position_dodge(width = dodge.width), + size = 7 + ) + + ggplot2::geom_errorbarh(ggplot2::aes(xmax = log(high), xmin = log(low)), + # position = ggplot2::position_dodge(width = dodge.width), + size = .5, + height = .2, + color = "gray50" + ) + + # ggplot2::position_dodge(width = 2, preserve = "total")+ + ggplot2::scale_x_continuous( + breaks = log(x.tics), + labels = x.tics, + limits = log(range(x.tics)) + ) + + ggplot2::scale_color_manual(values = group.colors) + + ggplot2::scale_fill_manual(values = group.colors) + + ggplot2::scale_shape_manual(values = point.shape) + + ggplot2::theme_bw() + + ggplot2::theme( + panel.grid.minor = ggplot2::element_blank(), + # legend.title = ggplot2::element_text(""), + legend.position = "bottom" + ) + + ggplot2::ylab("") + + ggplot2::xlab("Hazards ratio (log)") + + ggplot2::labs( + shape = legend.title, + color = legend.title, + fill = legend.title + ) + + ggplot2::facet_wrap(facets = ggplot2::vars(model), ncol = wrap.col) +} + +# LETTERS[1:8] |> purrr::map(\(.x){ +# viridisLite::viridis(3,option = .x)|> +# project.aid::color_plot(ncol = 3) +# }) |> patchwork::wrap_plots(ncol=1) + +headers <- c("Clinical data","Lifestyle factors","Socioeconomic factors","Assessments at follow-up") + +ds_new <- ds |> + dplyr::mutate(dplyr::across(c("hr", "low", "high"), ~ as.numeric(.x)), + var = factor(var, levels = rev(unique(var))), + model = factor(model, levels = unique(model)) + ) |> + dplyr::mutate(level=apply(is.na(dplyr::pick(hr,low,high))|dplyr::pick(hr,low,high) == "",1,sum), + labels=dplyr::case_when(level==3&var%in%headers~marquee::marquee_glue("**{var}**"), + level==3&var!="Body mass index"~marquee::marquee_glue("*{var}*"), + .default=marquee::marquee_glue("{var}")), + labels = factor(labels, levels = rev(unique(labels)))) + +(p1 <- ds_new |> (\(.x){ + forest_plot(.x, wrap.col = length(levels(.x$model))) + })()+ + ggplot2::theme(axis.text.y =marquee::element_marquee(vjust = .77, + # hjust = -1, + size=14))) + + +ggplot2::ggsave( + filename = here::here("2 Longterm/hr_plot_facets.png"), + plot = p1+ + ggplot2::theme( + strip.background = ggplot2::element_blank(), + strip.text.x = ggplot2::element_blank(), + legend.text = ggplot2::element_text(size=14), + axis.text.x=ggplot2::element_text(size=10), + axis.ticks.length.y = ggplot2::unit(3,"mm"), + axis.ticks.y = ggplot2::element_line(color = "white") + ), + units = "mm", + width = 250, + height = 300, + # pointsize = 10, + dpi = 300, +) + + +headers <- c("Clinical data","Lifestyle factors","Socioeconomic factors","Assessments at follow-up") + +(t <- ds |> + dplyr::mutate(dplyr::across(dplyr::everything(), ~ gsub("—", "", .x))) |> + dplyr::mutate(dplyr::across(c("hr", "low", "high"), ~ as.numeric(.x)), + var = factor(var, levels = rev(unique(var))), + model = factor(model, levels = unique(model)) + ) |> + dplyr::mutate(level=apply(is.na(dplyr::pick(hr,low,high))|dplyr::pick(hr,low,high) == "",1,sum), + labels=dplyr::case_when(level==3&var%in%headers~glue::glue("**{var}**"), + level==3~glue::glue("*{var}*"), + .default=glue::glue("{var}"))) |> + dplyr::filter(model=="Univariable") |> + ggplot2::ggplot( + # ggplot2::aes(x = x, y = var) + ) + + ggtext::geom_richtext(ggplot2::aes(x = 0, y = var,label = labels),fill = NA, + label.colour = NA, + hjust="right") + + ggplot2::scale_x_continuous( + limits = c(-.1,0) + )+ + ggplot2::theme_void()) + +(w <- patchwork::wrap_plots(list(t, + # patchwork::plot_spacer(), + p1+ + ggplot2::theme( + strip.background = ggplot2::element_blank(), + strip.text.x = ggplot2::element_blank(), + axis.text.y = ggplot2::element_blank(), + plot.margin = ggplot2::unit(c(1,1,1,0), 'cm'), + # axis.ticks.y.length = ggplot2::unit(0, "pt"), + # axis.ticks.y = ggplot2::element_blank(), + # panel.border=ggplot2::element_blank(), + panel.spacing = ggplot2::unit(0, "cm") + )),ncol=2,widths = c(1.2,3),axes = "collect")) + +ggplot2::ggsave( + filename = here::here("2 Longterm/hr_plot_facets_labels.png"),plot = w, + units = "mm", + width = 200, + height = 220, + dpi = 300, +) diff --git a/2 Longterm/hr_plot_facets.png b/2 Longterm/hr_plot_facets.png new file mode 100644 index 0000000..93998bd Binary files /dev/null and b/2 Longterm/hr_plot_facets.png differ diff --git a/2 Longterm/hr_plot_facets_labels.png b/2 Longterm/hr_plot_facets_labels.png new file mode 100644 index 0000000..3ad6f55 Binary files /dev/null and b/2 Longterm/hr_plot_facets_labels.png differ diff --git a/2 Longterm/import_mfi.R b/2 Longterm/import_mfi.R new file mode 100644 index 0000000..e69de29 diff --git a/2 Longterm/kamila-clustering.R b/2 Longterm/kamila-clustering.R new file mode 100644 index 0000000..3cde557 --- /dev/null +++ b/2 Longterm/kamila-clustering.R @@ -0,0 +1,84 @@ +## Not run: +# import and format a mixed-type data set +library(kamila) +data(Byar, package='clustMD') + +ds <- readr::read_csv("2 Longterm/assigndata.csv",na = c("","NA")) |> na.omit() + +ds <- ds |> dplyr::mutate(mrs_1 = mrs_1>2) + +cat_i <- lapply(ds,is.character) |> purrr::list_c() +con_i <- lapply(ds,is.double) |> purrr::list_c() + +xor(cat_i,con_i) + +clin_clust=2 +# Byar$logSpap <- log(Byar$Serum.prostatic.acid.phosphatase) + +# conInd <- c(5,6,8:10,16) +# conVars <- Byar[,conInd] +# conVars <- data.frame(scale(conVars)) + +conVars <- ds[,con_i] +conVars <- data.frame(scale(conVars)) + +# catVarsFac <- Byar[,-c(1:2,conInd,11,14,15)] +# catVarsFac[] <- lapply(catVarsFac, factor) +# catVarsDum <- dummyCodeFactorDf(catVarsFac) + +catVarsFac <- ds[,cat_i] +catVarsFac <- lapply(catVarsFac, factor) |> dplyr::bind_cols() |> as.data.frame() +catVarsDum <- dummyCodeFactorDf(catVarsFac) + +# Modha-Spangler clustering with kmeans default Hartigan-Wong algorithm +gmsResHw <- gmsClust(conVars, catVarsDum, nclust = clin_clust) + +# Modha-Spangler clustering with kmeans Forgy-Lloyd algorithm +# NOTE searchDensity should be >= 10 for optimal performance: +# this is just a syntax demo +gmsResLloyd <- gmsClust(conVars, catVarsDum, nclust = clin_clust, + algorithm = "Lloyd", searchDensity = 15) + +# KAMILA clustering +kamRes <- kamila(conVars, catVarsFac, numClust=2:7, numInit=10, calcNumClust="ps") + +# Plot results +# ternarySurvival <- factor(Byar$SurvStat) +# levels(ternarySurvival) <- c('Alive','DeadProst','DeadOther')[c(1,2,rep(3,8))] +plottingData <- cbind( + conVars, + catVarsFac, + KamilaCluster = factor(kamRes$finalMemb)) +# plottingData$Bone.metastases <- ifelse( +# plottingData$Bone.metastases == '1', yes='Yes',no='No') +# +# # Plot Modha-Spangler/Hartigan-Wong results +# msPlot <- ggplot( +# plottingData, +# aes( +# x=logSpap, +# y=Index.of.tumour.stage.and.histolic.grade, +# color=ternarySurvival, +# shape=MSCluster)) +# plotOpts <- function(pl) (pl + geom_point() + +# scale_shape_manual(values=c(2,3,7)) + geom_jitter()) +# plotOpts(msPlot) + +# Plot KAMILA results +kamPlot <- ggplot( + plottingData, + aes( + x=pase_0, + y=pase_6, + color=KamilaCluster, + shape=KamilaCluster)) +plotOpts(kamPlot) + + +plotting_ls <- tibble(KamilaCluster = factor(kamRes$finalMemb), + MSCluster = factor(gmsResHw$results$cluster)) |> + purrr::map(\(x) cbind(x, ds)) + +plotting_ls |> purrr::map(\(y) { + y |> gtsummary::tbl_summary(by=x) |> gtsummary::add_p() |> gtsummary::add_overall()}) |> + gtsummary::tbl_merge() diff --git a/2 Longterm/kmeans-clustering.R b/2 Longterm/kmeans-clustering.R new file mode 100644 index 0000000..cadcc26 --- /dev/null +++ b/2 Longterm/kmeans-clustering.R @@ -0,0 +1,114 @@ +ds <- readr::read_csv("2 Longterm/assigndata.csv", na = c("", "NA")) |> na.omit() + +ds <- ds |> dplyr::mutate(mrs_1 = mrs_1 > 2) + + +library(tidymodels) +library(tidyverse) + +ds |> + na.omit() |> + kmeans(centers = 3) + + +ds_num <- ds |> mutate( + across(where(is.double), ~ scale(.x)), + across(is.character, ~ as.numeric(factor(.x))) +) + +ds_num |> kmeans(centers = 3) + +set.seed(321) +kclusts <- + tibble(k = 1:9) %>% + mutate( + kclust = map(k, ~ kmeans(ds_num, .x)), + tidied = map(kclust, tidy), + glanced = map(kclust, glance), + augmented = map(kclust, augment, ds_num) + ) + +kclusts + +clusters <- + kclusts %>% + unnest(cols = c(tidied)) + +assignments <- + kclusts %>% + unnest(cols = c(augmented)) + +clusterings <- + kclusts %>% + unnest(cols = c(glanced)) + +ggplot(assignments, aes(x = pase_0, y = age)) + + geom_point(aes(color = .cluster), alpha = 0.8) + + facet_wrap(~k) + +ggplot(clusterings, aes(k, tot.withinss)) + + geom_line() + + geom_point() + +PCA + +pairs(assignments[-c(2:4)], + gap = 0, + bg = c("red", "yellow", "blue")[assignments$k], + pch = 21 +) + + +pc.out <- prcomp(ds_num, center = TRUE, scale = TRUE) + +pc.sum <- summary(pc.out) + +pscr <- tibble( + x = 1:dim(pc.sum$importance)[2], + Proportion = pc.sum$importance[2, ], + Cumulative = pc.sum$importance[3, ] +) %>% + pivot_longer(cols = -x) %>% + ggplot(aes(x = x, y = value, color = name)) + + geom_line() + + geom_point() + + ylim(0, 1) + + labs( + title = "Scree plot", + color = "Variance" + ) + + ylab("Variance") + + xlab("Principal components") + + +### +### + +install.packages("VarSelLCM") +library(VarSelLCM) + +# Please indicate the number of cores you wan to use for parallelization +nb.CPU <- 4 +# clustering without variable selection (about than 10/20 sec on 4 CPU) +res_without <- ds |> select(!mrs_1) |> + mutate(across(is.character, ~ factor(.x))) |> as.data.frame() |> + VarSelCluster( + gvals = 1:5, + crit.varsel = "BIC", + vbleSelec = FALSE, + nbcores = nb.CPU + ) + +summary(res_without) + +plot(res_without) + +plot(x=res_without, y="mdi_1") + +plot(x=res_without, y="sex") + +print(res_without) + +coef(res_without) + +VarSelShiny(res_without) diff --git a/2 Longterm/sankey events.R b/2 Longterm/sankey events.R new file mode 100644 index 0000000..951d398 --- /dev/null +++ b/2 Longterm/sankey events.R @@ -0,0 +1,191 @@ + +# source("1 PA Decline/data_format.R") + +# NEW QUARTILES + + +# Visuals - sankey +# https://stackoverflow.com/questions/50395027/beautifying-sankey-alluvial-visualization-using-r + + +## Painting + +df_raw <- readr::read_csv(here::here("2 Longterm/DDV 241031/event_sankey_data.csv")) + +df <- df_raw|> + dplyr::rename(pase_0_cut=pase_0_quartile, + pase_6_cut=pase_4_quartile) |> + dplyr::mutate(change=dplyr::case_when( + pase_0_cut==1 & pase_6_cut==1 ~ "ll", + pase_0_cut %in% 2:4 & pase_6_cut %in% 2:4 ~ "hh", + pase_0_cutpase_6_cut ~ "drop" + ), + dplyr::across(c(pase_0_cut,pase_6_cut,change),as.factor)) + + +df |> sankey_ready() + +sankey_ready <- function(data,change.var="change"){ + df <- data + # |> + # dplyr::count(dplyr::across(dplyr::all_of(c("pase_0_cut", "pase_6_cut",change.var)))) |> + # dplyr::mutate(dplyr::across(dplyr::starts_with("pase_"),\(.x) factor(.x))) |> + # setNames(c("pase_0_cut", "pase_6_cut","change","n")) + + lbs0 <- + c( + paste0("1st \n(n=", sum(df$n[df$pase_0_cut == "1"]), ")"), + paste0("2nd \n(n=", sum(df$n[df$pase_0_cut == "2"]), ")"), + paste0("3rd \n(n=", sum(df$n[df$pase_0_cut == "3"]), ")"), + paste0("4th \n(n=", sum(df$n[df$pase_0_cut == "4"]), ")") + ) + + + lbs6 <- + c( + paste0("1st \n(n=", sum(df$n[df$pase_6_cut == "1"]), ")"), + paste0("2nd \n(n=", sum(df$n[df$pase_6_cut == "2"]), ")"), + paste0("3rd \n(n=", sum(df$n[df$pase_6_cut == "3"]), ")"), + paste0("4th \n(n=", sum(df$n[df$pase_6_cut == "4"]), ")") + ) + + + levels(df$pase_0_cut) <- lbs0[1:length(levels(df$pase_0_cut))] + levels(df$pase_6_cut) <- lbs6[1:length(levels(df$pase_6_cut))] + + df$pase_0_cut <- factor(df$pase_0_cut, levels = rev(levels(df$pase_0_cut))) + df$pase_6_cut <- factor(df$pase_6_cut, levels = rev(levels(df$pase_6_cut))) + + df$change <- factor(df$change, levels = c("hh","hop", "drop", "ll")) + + if (change.var=="change"){ + df |> dplyr::mutate(first_grp=ifelse(substr(pase_0_cut,1,1)==1,"low","higher")) + } else if (change.var=="change_any"){ + df |> dplyr::mutate(first_grp=dplyr::case_when( + substr(pase_0_cut,1,1)==1 ~ "low", + substr(pase_0_cut,1,1) %in% 2:3 ~ "mid", + substr(pase_0_cut,1,1)==4 ~ "high")) + } +} + +# hops <- "#66c1a3" # grey +# # drops <- "#990033" # Midtrød +# drops <- "#CE0045" # Lighter Midtrød +# nos <- "grey80" # Light grey +# +# # border <- "#00596B" +# # box <- "#008099" +# +# border <- "#EA571D" +# box <- "#1E4B66" +# +# higher <- "yellow" +# low <- "purple" + +library(ggalluvial) + +library(ggplot2) + +# project.aid::color_plot(viridisLite::turbo(4)) + +plot_sankey <- function(data, + # palette=viridisLite::turbo(4), + hops = "#1AE4B6FF", + drops = "#FABA39FF", + hh = "#30123BFF", + ll = "#7A0403FF", + border = "#EA571D", + box = "#1E4B66", + higher = "#1E4B66", + mid = "#1E4B66", + low = "#1E4B66", + alpha = 0.8, + a1=pase_0_cut, + a2=pase_6_cut, + a1.grp=first_grp, + text.size = 4 +){ + + if (length(unique(data[[ncol(data)]]))>2) { + fills <- c(higher,low,mid) + } else { + fills <- c(higher,low) + } + + cls <- c(hh, hops, drops, ll) + # stratum.grp <- c(df[["first_grp"]],df[["last_grp"]]) + + # cls <- palette + # browser() + ggplot(data, aes(y = n, axis1 = {{a1}}, axis2 = {{a2}})) + + geom_alluvium( + aes(fill = change, color = change), + width = 1 / 16, + alpha = alpha, + knot.pos = 0.4, + curve_type ="sigmoid" + ) + + geom_stratum(aes(fill={{a1.grp}}), + # geom_stratum(aes(fill=stratum_grp), + size = 2, + width = 1 / 3.4, + # fill = box, + color = border + ) + + geom_text(stat = "stratum", + aes(label = after_stat(stratum)), + colour = "white", + size = text.size, + lineheight = 1) + + scale_x_continuous( + breaks = 1:2, + labels = c("Pre-stroke\nPASE quartile", "Six months\nPASE quartile") + ) + + scale_fill_manual(values = c(cls,fills),na.value = box) + + scale_color_manual(values = cls) + + ggtitle("PA level changes from \npre-stroke to post-stroke") +} + +# c("change","change_any") |> purrr::map(\(.x){ +# df_raw |> +# sankey_ready(change.var = .x) +# }) |> +# purrr::map(\(.x){ +# .x |> plot_sankey(text.size=4.5) +# }) |> +# patchwork::wrap_plots() + +pal <- viridisLite::turbo(12) + +project.aid::color_plot(pal) + +df |> + sankey_ready() |> + plot_sankey(text.size=4.5) + +p_delta <- df |> + sankey_ready() |> + plot_sankey(text.size=4.5) + +ggplot2::ggsave(filename = here::here("2 Longterm/sankey_event_stroke.png"), + p_delta + + theme_void() + + theme( + legend.position = "none", + # panel.grid.major = element_blank(), + # panel.grid.minor = element_blank(), + # axis.text.y = element_blank(), + # axis.title.y = element_blank(), + axis.text.x = element_text(), + # text = element_text(size = 5), + plot.title = element_blank(), + # panel.background = element_rect(fill = "white"), + plot.background = element_rect(fill="white"), + panel.border = element_blank() + ), + units = "mm", + width = 84, + height = 70, + # pointsize = 30, + dpi = 600) diff --git a/2 Longterm/sankey_event_stroke.png b/2 Longterm/sankey_event_stroke.png new file mode 100644 index 0000000..440eb27 Binary files /dev/null and b/2 Longterm/sankey_event_stroke.png differ diff --git a/2 Longterm/sdmt correction.R b/2 Longterm/sdmt correction.R new file mode 100644 index 0000000..99a15f1 --- /dev/null +++ b/2 Longterm/sdmt correction.R @@ -0,0 +1,24 @@ +# During study time, the SDMT was performed in a non-standardised way until around 2015-02-18, only giving subjects 60 seconds. +# Some filled assessments were marked and were all manually evaluated for correction. +# A decision was reached to multiply old results by a factor of 1.5 assuming a proportional increase in completed fields. + + + +sdmt_time<-openxlsx::read.xlsx("/Volumes/Data/source/tid.sdmt.xlsx") |> + tidyr::pivot_longer(cols = c(tid.1md, tid.6md)) |> mutate(deltager=as.character(deltager)) +sdmt_time$name <- as.double(as.character(factor(sdmt_time$name,labels = c("2","4")))) + +# Setting cut date +sdmt_cut<-as.Date("2015-02-18") + +# Joining datasets to have date of visit +sdmt_corr <- left_join(ls_nas$sdmt,sdmt_time |> mutate(name=as.character(name)), by = c("rnumb"="deltager","instance"="name")) + +# Modyfying old correction table to be complete +sdmt_corr$value <- if_else(sdmt_corr$talos_sdmt00 + ggsurvfit(linewidth = 1,) + + add_confidence_interval() + + add_risktable() + + add_quantile(y_value = 0.6, color = "gray50", linewidth = 0.75) + + # limit plot to show 8 years and less + coord_cartesian(xlim = c(0, 8)) + + # update figure labels/titles + labs( + y = "Percentage Survival", + title = "Recurrence by Time From Surgery to Randomization", + ) + + # reduce padding on edges of figure and format axes + scale_y_continuous(label = scales::percent, + breaks = seq(0, 1, by = 0.2), + expand = c(0.015, 0)) + + scale_x_continuous(breaks = 0:10, + expand = c(0.02, 0))) + + + +head(df_colon) + +library("survival") +library("survminer") + +variables <- c("sex", "age", "adhere", "extent", "surg") + +cox_model <- coxph(as.formula(paste("Surv(time, status) ~",paste(variables,collapse="+"))), data = df_colon) + +summary(cox_model) + +ggsurvplot(survfit(cox_model), data=df_colon, palette = "#2E9FDF", + ggtheme = theme_minimal()) + +# Checks + +(ph_check <- survival::cox.zph(cox_model)) + +survminer::ggcoxzph(ph_check, var=c("sex", "age", "adhere", "surg"), + font.main = 10, + font.x = 10, + font.y = 10) + + +## Below is the nest best solution +## Try at smoothing the survival curve + +## Working code to get satisfying object +df <- survfit2(Surv(time, status) ~ surg, data = df_colon) |> + tidy_survfit(type = "survival") + +df_split <- split(df,df$strata) + +df_smoothed <- purrr::reduce(lapply(c("estimate","conf.low", "conf.high"), function(j) { + do.call(rbind, + lapply(seq_along(df_split), function(i) { + nms <- names(df_split)[i] + y <- + predict(mgcv::gam(as.formula(paste0( + j[[1]], " ~ s(time, bs = 'cs')" + )), data = df_split[[i]])) + df <- data.frame(df_split[[i]]$time, y, nms) + names(df) <- c("time", paste0(j[[1]], ".smooth"), "strata") + df + })) +}),dplyr::full_join) |> full_join(df) + +ggplot(data=df_smoothed) + + geom_line(aes(x=time, y=estimate.smooth, color = strata))+ + geom_ribbon(aes(x=time, ymin = conf.low.smooth, ymax = conf.high.smooth, fill = strata), alpha = 0.50) + + # geom_smooth(aes(x=time, y=estimate, color = strata), method = "gam", formula = y ~ s(x, bs = "cs")) + + # reduce padding on edges of figure and format axes + scale_y_continuous(label = scales::percent, + breaks = seq(0, 1, by = 0.2), + expand = c(0.015, 0), limits = c(0,1)) + + scale_x_continuous(breaks = 0:10, + expand = c(0.02, 0))+ + labs( + y = "Percentage Survival", + title = "Recurrence by Time From Surgery to Randomization", + ) + + # limit plot to show 8 years and less + coord_cartesian(xlim = c(0, 8)) + + + +## Dendrogram + +df_colon[do.call(c, lapply(seq_len(ncol(df_colon)), function(i) { + is.double(df_colon[[i]]) +}))][-1] |> scale() |> dist() |> hclust(method="average") |> ggdendro::ggdendrogram() + + +## Better example?? +## + +library(tidyverse) +library(survival) +library(purrr) +library(ggsurvfit) +library(cobs) + +## Data +plot.type <- "survival" + +x <- survfit2(Surv(time, status) ~ surg, data = df_colon) +df <- + tidy_survfit(x, type = plot.type) %>% dplyr::mutate(survfit = c(list(x), + rep_len(list(), dplyr::n() - 1L))) +method <- "gam" + +df_split <- split(df,df$strata) + +df_smoothed <- purrr::reduce(lapply(c("estimate","conf.low", "conf.high"), function(j) { + do.call(rbind, + lapply(seq_along(df_split), function(i) { + nms <- names(df_split)[i] + x = df_split[[i]]$time + if (method=="loess"){ + y <- + predict(loess(as.formula(paste0( + j[[1]], " ~ time" + )), data = df_split[[i]])) + } else if (method=="gam"){ + y <- + predict(mgcv::gam(as.formula(paste0( + j[[1]], " ~ s(time, bs = 'cs')" + )), data = df_split[[i]])) + } else if (method=="cobs") { + + if (plot.type=="survival"){ + ## This will make the plot start in (0,1) + con <- rbind(c( 0,min(x),1)) + ## This ensures a monotonic decreasing slope + ## for the estimate, not the CIs + if (j[[1]]=="estimate"){ + direction="decrease" + } else {direction="none"} + + } else if (plot.type=="risk"){ + con <- rbind(c( 0,min(x),0)) + if (j[[1]]=="estimate"){ + direction="increase" + } else {direction="none"} + } + + m <- cobs(x,df_split[[i]][[j]], + constraint=direction, + nknots = 4, + pointwise= con, + degree = 2,) + y <- predict(m, x)[, 'fit'] + } + + df <- data.frame(x, y, nms) + names(df) <- c("time", paste0(j[[1]], ".smooth"), "strata") + df + })) +}),dplyr::full_join) |> full_join(df) + +## Plotting +ggplot(data=df_smoothed) + + geom_line(aes(x=time, y=estimate.smooth, color = strata))+ + geom_ribbon(aes(x=time, ymin = conf.low.smooth, ymax = conf.high.smooth, fill = strata), alpha = 0.50) + +## Weighted GAM approach +## https://stackoverflow.com/a/66705556/21019325 + +## It does not work. Gonna stop here due to lack of time. +## Apparantly + +dat_orig <- df_split[[1]][,c("time","estimate")] +x1=0 +y1=1 +# set.seed(123) +# N = 100 +# x <- sort(runif(N) * 4 - 1) +# f <- exp(4*x)/(1+exp(4*x)) +# y <- f + rnorm(N) * 0.1 +# x = c(-1, x) +# y = c(-0.1, y) +# dat = data.frame(x = x, y= y) +x <- do.call(c,c(x1,dat_orig[1])) +y <- do.call(c,c(y1,dat_orig[,2])) +dat <- data.frame(x=x,y=y) +k <- 13 + +library(mgcv) + +fit0 <- gam(y ~ s(x, k = k, bs = "cr"),data=dat) +# predict from unconstrained GAM fit + +newdata <- data.frame(x = x) +newdata$y_pred_fit0 <- predict(fit0, newdata = newdata) + +# Show regular spline fit (and save fitted object) +# f.ug <- gam(y~s(x,k=k,bs="cr")) + +# explicitly construct smooth term's design matrix +sm <- smoothCon(s(x,k=k,bs="cr"),dat,knots=NULL)[[1]] +# find linear constraints sufficient for monotonicity of a cubic regression spline +# it assumes "cr" is the basis and its knots are provided as input +f.mono <- mono.con(sm$xp,up = FALSE) + +G <- list( + X=sm$X, + C=matrix(0,0,0), # [0 x 0] matrix (no equality constraints) + sp=fit0$sp, # smoothing parameter estimates (taken from unconstrained model) + p=sm$xp, # array of feasible initial parameter estimates + y=dat[,2], + w= c(1e8, rep(1,nrow(dat_orig))), # weights for data + Ain=f.mono$A, # matrix for the inequality constraints + bin=f.mono$b, # vector for the inequality constraints + S=sm$S, # list of penalty matrices; The first parameter it penalizes is given by off[i]+1 + off=0 # Offset values locating the elements of M$S in the correct location within each penalty coefficient matrix. (Zero offset implies starting in first location) +) + +p <- pcls(G) # fit spline (using smoothing parameter estimates from unconstrained fit) + +# predict +newdata$y_pred_fit2 <- Predict.matrix(sm, data.frame(x = newdata$x)) %*% p +# plot +ggplot(data=newdata) + + geom_line(aes(x=x, y=y_pred_fit2)) + # geom_ribbon(aes(x=time, ymin = conf.low.smooth, ymax = conf.high.smooth, fill = strata), alpha = 0.50) +# +plot(y ~ x, data = dat) +lines(y_pred_fit0 ~ x, data = newdata, col = 2, lwd = 2) +lines(y_pred_fit2 ~ x, data = newdata, col = 4, lwd = 2) +abline(v = -1) +abline(h = -0.1) \ No newline at end of file diff --git a/2 Longterm/survival_two_groups.R b/2 Longterm/survival_two_groups.R new file mode 100644 index 0000000..3a3f52f --- /dev/null +++ b/2 Longterm/survival_two_groups.R @@ -0,0 +1,132 @@ +# ============================================================================= +# Retrospective Sample Size Evaluation — Event-Free Survival (2 Groups) +# Freedman Method | works directly from hazard ratio and observed events +# Appropriate for: unequal group sizes, single censored-at-event designs +# ============================================================================= + +# --- 0. Install & Load Packages ---------------------------------------------- + +# Uncomment to install if needed: +# install.packages("gsDesign") + +library(gsDesign) + + +# --- 1. Observed Data -------------------------------------------------------- + +# n : number of participants per group +# events : number of observed events per group +# person_time : total person-years of follow-up per group +# = sum of each individual's follow-up duration +# naturally accounts for censoring and variable follow-up +# max_follow_up : maximum follow-up time (years) + +n_A <- 323; events_A <- 80; person_time_A <- 2089 +n_B <- 56; events_B <- 32; person_time_B <- 294 +max_follow_up <- 9 # years + +# Derived quantities +hazard_A <- events_A / person_time_A # events per person-year +hazard_B <- events_B / person_time_B +hr <- hazard_B / hazard_A # observed hazard ratio (B vs A) + +overall_event_rate <- (events_A + events_B) / (n_A + n_B) +pi_A <- n_A / (n_A + n_B) +pi_B <- n_B / (n_A + n_B) + +cat("=== Observed Data Summary ===\n") +cat(sprintf("Group A : N=%-4d Events=%-3d Person-years=%6.0f Hazard=%.4f\n", + n_A, events_A, person_time_A, hazard_A)) +cat(sprintf("Group B : N=%-4d Events=%-3d Person-years=%6.0f Hazard=%.4f\n", + n_B, events_B, person_time_B, hazard_B)) +cat(sprintf("Hazard ratio (B vs A) : %.3f\n", hr)) +cat(sprintf("Allocation (A / B) : %.1f%% / %.1f%%\n", pi_A*100, pi_B*100)) +cat(sprintf("Overall event rate : %.3f\n", overall_event_rate)) + + +# --- 2. Required Events — Freedman Method ------------------------------------ +# Freedman (1982): works directly from the hazard ratio. +# More stable than Schoenfeld when group sizes are unequal, because it does +# not depend on a variance term V that collapses under unequal allocation. +# +# Formula: E = (z_alpha/2 + z_beta)^2 * (1 + hr)^2 / (hr - 1)^2 +# +# Note: hr must not equal 1 (no difference). If hr < 1, invert it so +# the formula always uses the ratio > 1. + +required_events_freedman <- function(alpha = 0.05, power = 0.80, hr) { + if (hr == 1) stop("Hazard ratio must not equal 1 (no detectable difference).") + hr <- ifelse(hr < 1, 1/hr, hr) # ensure hr > 1 + z_alpha <- qnorm(1 - alpha / 2) + z_beta <- qnorm(power) + E <- (z_alpha + z_beta)^2 * (1 + hr)^2 / (hr - 1)^2 + return(ceiling(E)) +} + +E_required <- required_events_freedman(alpha = 0.05, power = 0.80, hr = hr) +N_required <- ceiling(E_required / overall_event_rate) + +cat("\n=== Freedman Method (alpha=0.05, power=0.80) ===\n") +cat("Required total events :", E_required, "\n") +cat("Observed total events :", events_A + events_B, "\n") +cat("Required total N :", N_required, "\n") +cat("Observed total N :", n_A + n_B, "\n") +cat("Difference (req-obs) :", N_required - (n_A + n_B), "\n") + + +# --- 3. Validation via gsDesign::nSurv --------------------------------------- + +cat("\n=== gsDesign Validation ===\n") +tryCatch({ + gs <- nSurv( + lambdaC = hazard_A, + hr = hr, + sided = 2, + alpha = 0.05, + beta = 0.20, + T = max_follow_up, + minfup = 0 + ) + print(gs) +}, error = function(e) { + cat("gsDesign error:", conditionMessage(e), "\n") +}) + + +# --- 4. Sensitivity Analysis Across Power Levels ----------------------------- + +power_levels <- c(0.70, 0.75, 0.80, 0.85, 0.90) + +sensitivity <- data.frame( + power = power_levels, + events_req = sapply(power_levels, function(pw) + required_events_freedman(0.05, pw, hr)) +) +sensitivity$n_req <- ceiling(sensitivity$events_req / overall_event_rate) +sensitivity$adequate <- ifelse(sensitivity$n_req <= (n_A + n_B), "Yes", "No") + +cat("\n=== Sensitivity Analysis by Power Level ===\n") +print(sensitivity) + + +# --- 5. Interpretation ------------------------------------------------------- + +cat("\n=== Interpretation ===\n") +cat(sprintf("Hazard ratio: %.3f — Group B events occur %.1fx faster than Group A\n", + hr, hr)) + +if (E_required <= (events_A + events_B)) { + cat("Event count : ADEQUATE — observed events meet Freedman requirement.\n") +} else { + cat(sprintf("Event count : INSUFFICIENT — %d observed, %d required.\n", + events_A + events_B, E_required)) +} + +if (N_required <= (n_A + n_B)) { + cat("Sample size : ADEQUATELY POWERED at 80%.\n") +} else { + cat(sprintf(paste0("Sample size : UNDERPOWERED at 80%% — ", + "N=%d observed, N=%d required (shortfall: %d).\n"), + n_A + n_B, N_required, N_required - (n_A + n_B))) + cat("Type II error risk is elevated — interpret null results with caution.\n") +} \ No newline at end of file diff --git a/2 Longterm/table plotting tests.R b/2 Longterm/table plotting tests.R new file mode 100644 index 0000000..e69de29 diff --git a/2 Longterm/varsellcm-clustering.R b/2 Longterm/varsellcm-clustering.R new file mode 100644 index 0000000..1e44ac5 --- /dev/null +++ b/2 Longterm/varsellcm-clustering.R @@ -0,0 +1,33 @@ +ds <- readr::read_csv("2 Longterm/assigndata.csv", na = c("", "NA")) + +library(VarSelLCM) + +# Please indicate the number of cores you wan to use for parallelization +nb.CPU <- 4 +# clustering without variable selection (about than 10/20 sec on 4 CPU) +clusters <- ds |> select(!mrs_1) |> na.omit() |> + mutate(across(is.character, ~ factor(.x))) |> as.data.frame() |> + VarSelCluster( + gvals = 1:5, + crit.varsel = "BIC", + vbleSelec = FALSE, + nbcores = nb.CPU + ) + +summary(clusters) + +plot(clusters) + +plot(x=clusters, y="mdi_1") + +plot(x=clusters, y="sex") + +print(clusters) + +coef(clusters) + +VarSelShiny(clusters) + +clusters@partitions@zMAP + +gtsummary::tbl_summary(tibble(ds |> na.omit(), class=clusters@partitions@zMAP),by=class) |> gtsummary::add_p() diff --git a/2 Longterm/wsc-sankey.R b/2 Longterm/wsc-sankey.R new file mode 100644 index 0000000..9c18599 --- /dev/null +++ b/2 Longterm/wsc-sankey.R @@ -0,0 +1,58 @@ + +grps <- rbind(c("low0","low6","1"), + c("high0","low6","2"), + c("low0","high6","3"), + c("high0","high6","4")) + +tmes <- c(48,62,55,323) + +tbl <- data.frame(grps,tmes) + +colnames(tbl) <- c("t1","t2","grp","n") + +library(ggalluvial) + +cls <- viridisLite::turbo(4,direction=-1) + +cls +stRoke::color_plot(viridisLite::turbo(4,direction=-1)) + +p <- ggplot(tbl,aes(y = n, axis1 = t1, axis2 = t2)) + + geom_alluvium( + aes(fill = grp, color = grp), + width = 1 / 16, + alpha = .6, + knot.pos = 0.4 + ) + + # geom_stratum(aes(size=10),width = 1 / 4, + # fill = box, + # color = border) + + # geom_text(stat = "stratum", aes(label = after_stat(stratum)), colour = "white", size = 20) + + scale_x_continuous(breaks = 1:2, + labels = c("Pre-stroke\nquartile", "Six months\nquartile")) + + scale_y_continuous(breaks=c(0,103,488))+ + scale_fill_manual(values = cls) + + scale_color_manual(values = cls) + + # labs(title=NULL, legend=NULL)+ + theme_minimal() + +png( + filename = "sankey_change_WSC.png", + units = "mm", + width = 500, + height = 500, + pointsize = 15, + res = 300 +); p+ + theme_minimal() + + theme( + legend.position = "none", + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.text.y = element_blank(), + axis.title.y = element_blank(), + axis.text.x = element_blank(), + plot.title = element_blank(), + panel.background = element_rect(fill='transparent'), + plot.background = element_rect(fill='transparent', color=NA) + ); dev.off() diff --git a/3 Cognitive change/.DS_Store b/3 Cognitive change/.DS_Store new file mode 100644 index 0000000..516caf3 Binary files /dev/null and b/3 Cognitive change/.DS_Store differ diff --git a/3 Cognitive change/dsc_tekst.png b/3 Cognitive change/dsc_tekst.png new file mode 100755 index 0000000..7f088f2 Binary files /dev/null and b/3 Cognitive change/dsc_tekst.png differ diff --git a/3 Cognitive change/project-presentation.html b/3 Cognitive change/project-presentation.html new file mode 100644 index 0000000..a50a926 --- /dev/null +++ b/3 Cognitive change/project-presentation.html @@ -0,0 +1,2171 @@ + + + + + + + + + + + + Physical activity before stroke, small vessel disease and cognitive decline after stroke + + + + + + + + + + + + + + + + +
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Physical activity before stroke, small vessel disease and cognitive decline after stroke

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Hypothesis

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Higher level of PA before acute ischemic stroke (AIS) is associated with decreased risk of cognitive decline after stroke independently of stroke severity and cSVD burden.

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Data

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  • Physical Activity Scale in the Elderly (PASE) questionnaire
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  • Modified Rankin Scale (mRS)
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  • Level of education
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  • Informant Questionnaire on Cognitive Decline in the Elderly, short version (IQCODE)
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  • Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), Scandinavian version
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  • Wellbeing and depressive symptoms
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  • Cerebral Small vessel disease burden (cSVD score)
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Education

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CharacteristicOverall, N = 951female, N = 331male, N = 621
Level of education
    Folkeskole (9-10 år)20 (21%)11 (33%)9 (15%)
    Gymnasiale/erhvervsfaglige uddannelser (11-12 år)30 (32%)8 (24%)22 (36%)
    Erhvervsakademiuddannelser (13-14 år)16 (17%)5 (15%)11 (18%)
    Professionsbachelor/universitetsbachelor (15-17 år)18 (19%)8 (24%)10 (16%)
    Kandidatuddannelser (18-19 år)10 (11%)1 (3.0%)9 (15%)
    PhD ( > 20 år)0 (0%)0 (0%)0 (0%)
    Not classified101
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SVD scoring

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  • Number of microbleeds (SWI)
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  • Superficiel siderose (SWI)
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  • Lacunes (3D FLAIR)
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  • White matter hyperintensities (3D FLAIR)
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  • Enlarged Perivascular Space (3D FLAIR)
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  • Atrophy (3D T1)
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SVD score

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CharacteristicN = 201
svd_score
    02 (10%)
    14 (20%)
    23 (15%)
    35 (25%)
    41 (5.0%)
    52 (10%)
    81 (5.0%)
    91 (5.0%)
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IQCODE og RBANS

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CharacteristicOverall, N = 951female, N = 331male, N = 621
mRS at inclusion
    069 (73%)22 (67%)47 (76%)
    117 (18%)6 (18%)11 (18%)
    29 (9.5%)5 (15%)4 (6.5%)
IQCODE score49.0 (48.0, 54.0)49.5 (48.0, 53.0)49.0 (48.0, 54.0)
RBANS 3 months81 (73, 90)83 (73, 90)80 (74, 85)
RBANS 12 months84 (73, 94)86 (72, 92)78 (73, 93)
1 n (%); Median (IQR)
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RBANS plot

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Andreas

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+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/3 Cognitive change/project-presentation.qmd b/3 Cognitive change/project-presentation.qmd new file mode 100644 index 0000000..10f0c2e --- /dev/null +++ b/3 Cognitive change/project-presentation.qmd @@ -0,0 +1,160 @@ +--- +title: "Physical activity before stroke, small vessel disease and cognitive decline after stroke" +format: + revealjs: + footer: "Andreas" + slide-number: c/t + show-slide-number: all + toc: true + toc-title: "Overview" + logo: dsc_tekst.png + theme: serif + css: style.css +editor: visual +--- + +## Hypothesis + +```{r} +library(tidyverse) +# remotes::install_github("agdamsbo/REDCapRITS") + +library(gtsummary) +library(patchwork) +library(REDCapRITS) + +# REDCapR::redcap_instruments(redcap_uri = "https://redcap.au.dk/api/", token = keyring::key_get("enigma_api_key"))$data + +d_list <- REDCapRITS::read_redcap_tables( + uri = "https://redcap.au.dk/api/", + token = keyring::key_get("enigma_api_key"), + fields = c("record_id", "incl_by" , "incl_date"), + forms = c( + "klassifikation_af_primre_stroke", + "baseline_stroke", + "registrering", + "baseline_nihss", + "mrs", + "uddannelsesniveau", + "hjde_vgt_og_blodtryk", + "rygeanamnese", + "rbans", + "iqcode", + "eos" + ) +) +``` + +```{r message=FALSE} +d <- REDCapRITS::redcap_wider(d_list) +``` + +> Higher level of PA before acute ischemic stroke (AIS) is associated with decreased risk of cognitive decline after stroke independently of stroke severity and cSVD burden. + +## Data {.smaller} + +- Physical Activity Scale in the Elderly (**PASE**) questionnaire +- Modified Rankin Scale (**mRS**) +- Level of education +- Informant Questionnaire on Cognitive Decline in the Elderly, short version (**IQCODE**) +- Repeatable Battery for the Assessment of Neuropsychological Status (**RBANS**), Scandinavian version +- Wellbeing and depressive symptoms +- Cerebral Small vessel disease burden (**cSVD score**) + +## Education {.smaller} + +```{r education} +# dput(levels(factor(d$education_inclusion_long))) + +d |> + select(kon, education_inclusion_long, ) |> + mutate( + education_inclusion_long = factor( + education_inclusion_long, + levels = c( + "Folkeskole (9-10 år)", + "Gymnasiale/erhvervsfaglige uddannelser (11-12 år)", + "Erhvervsakademiuddannelser (13-14 år)", + "Professionsbachelor/universitetsbachelor (15-17 år)", + "Kandidatuddannelser (18-19 år)", + "PhD ( > 20 år)" + ) + )) |> + tbl_summary( + by = "kon", + missing = "ifany", + missing_text = "Not classified", + label = list(education_inclusion_long ~ "Level of education") + ) |> + add_overall( + ) +``` + +## SVD scoring + +- Number of microbleeds (SWI) +- Superficiel siderose (SWI) +- Lacunes (3D FLAIR) +- White matter hyperintensities (3D FLAIR) +- Enlarged Perivascular Space (3D FLAIR) +- Atrophy (3D T1) + +## SVD score {.scrollable} + +```{r} +REDCapTidieR::read_redcap( + redcap_uri = "https://redcap.au.dk/api/", + token = keyring::key_get("enigma_api_key"), + forms = c("svd_score")) |> REDCapTidieR::bind_tibbles() + +svd_score |> filter(svd_initials=="AGD",redcap_event=="inclusion") |> select(svd_score) |> na.omit() |> mutate(svd_score = as.numeric(svd_score)) |> tbl_summary() +``` + +## IQCODE og RBANS {.smaller} + +```{r} +d |> + select(kon, + mrs_score_inclusion_long, + iq_score, + rbans_e_is_3_months_long, + rbans_e_is_12_months_long + # , + # svd_score + ) |> + mutate(mrs_score_inclusion_long=factor(mrs_score_inclusion_long,labels = c("0","1","2"))) |> + tbl_summary( + by = "kon", + missing = "no", + label = list( + mrs_score_inclusion_long~"mRS at inclusion", + iq_score~"IQCODE score", + rbans_e_is_3_months_long~"RBANS 3 months", + rbans_e_is_12_months_long~"RBANS 12 months"#, + # svd_score~"SVD score at inclusion" + ) + ) |> + add_overall() +``` + +## RBANS plot + +```{r} +source("/Users/au301842/ENIGMAtrial_R/src/plot_index.R") + +rbans <- d_list$rbans |> + dplyr::select(c("record_id", + "redcap_event_name", + ends_with(c("_is","_lo","_up","_per")))) |> + na.omit()|> + dplyr::mutate(redcap_event=factor(redcap_event_name, + levels = c("3_months","12_months"), + labels = c("3 months","12 months"))) |> + dplyr::tibble() + +rbans |> + dplyr::filter(record_id %in% record_id[duplicated(record_id)]) |> # Only patients with both 3 and 12 month + dplyr::filter(record_id %in% sample(record_id,5)) |> # 5 random patients + # filter(record_id %in% 28:32) |> # Only specified number + plot_index(id="redcap_event_name",facet.by = "record_id") +``` diff --git a/3 Cognitive change/project-presentation_files/.DS_Store b/3 Cognitive change/project-presentation_files/.DS_Store new file mode 100644 index 0000000..74e05e0 Binary files /dev/null and b/3 Cognitive change/project-presentation_files/.DS_Store differ diff --git a/3 Cognitive change/project-presentation_files/figure-revealjs/unnamed-chunk-4-1.png b/3 Cognitive change/project-presentation_files/figure-revealjs/unnamed-chunk-4-1.png 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100644 index 0000000..c2857c3 --- /dev/null +++ b/3 Cognitive change/project-presentation_files/libs/quarto-html/quarto-html.min.css @@ -0,0 +1 @@ +/*# sourceMappingURL=0a6b880beb84f9b6f36107a76f82c5b1.css.map */ diff --git a/3 Cognitive change/project-presentation_files/libs/quarto-html/quarto-syntax-highlighting-dark.css b/3 Cognitive change/project-presentation_files/libs/quarto-html/quarto-syntax-highlighting-dark.css new file mode 100644 index 0000000..941c9df --- /dev/null +++ b/3 Cognitive change/project-presentation_files/libs/quarto-html/quarto-syntax-highlighting-dark.css @@ -0,0 +1,187 @@ +/* quarto syntax highlight colors */ +:root { + --quarto-hl-al-color: #f07178; + --quarto-hl-an-color: #d4d0ab; + --quarto-hl-at-color: #00e0e0; + --quarto-hl-bn-color: #d4d0ab; + --quarto-hl-bu-color: #abe338; + --quarto-hl-ch-color: #abe338; + --quarto-hl-co-color: #f8f8f2; + --quarto-hl-cv-color: #ffd700; + --quarto-hl-cn-color: #ffd700; + --quarto-hl-cf-color: #ffa07a; + --quarto-hl-dt-color: #ffa07a; + --quarto-hl-dv-color: #d4d0ab; + --quarto-hl-do-color: #f8f8f2; + --quarto-hl-er-color: #f07178; + --quarto-hl-ex-color: #00e0e0; + --quarto-hl-fl-color: #d4d0ab; + --quarto-hl-fu-color: #ffa07a; + --quarto-hl-im-color: #abe338; + --quarto-hl-in-color: #d4d0ab; + --quarto-hl-kw-color: #ffa07a; + --quarto-hl-op-color: #ffa07a; + --quarto-hl-ot-color: #00e0e0; + --quarto-hl-pp-color: #dcc6e0; + --quarto-hl-re-color: #00e0e0; + --quarto-hl-sc-color: #abe338; + --quarto-hl-ss-color: #abe338; + --quarto-hl-st-color: #abe338; + --quarto-hl-va-color: #00e0e0; + --quarto-hl-vs-color: #abe338; + --quarto-hl-wa-color: #dcc6e0; +} + +/* other quarto variables */ +:root { + --quarto-font-monospace: SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace; +} + +code span.al { + font-weight: bold; + color: #f07178; +} + +code span.an { + color: #d4d0ab; +} + +code span.at { + color: #00e0e0; +} + +code span.bn { + color: #d4d0ab; +} + +code span.bu { + color: #abe338; +} + +code span.ch { + color: #abe338; +} + +code span.co { + font-style: italic; + color: #f8f8f2; +} + +code span.cv { + color: #ffd700; +} + +code span.cn { + color: #ffd700; +} + +code span.cf { + font-weight: bold; + color: #ffa07a; +} + +code span.dt { + color: #ffa07a; +} + +code span.dv { + color: #d4d0ab; +} + +code span.do { + color: #f8f8f2; +} + +code span.er { + color: #f07178; + text-decoration: underline; +} + +code span.ex { + font-weight: bold; + color: #00e0e0; +} + +code span.fl { + color: #d4d0ab; +} + +code span.fu { + color: #ffa07a; +} + +code span.im { + color: #abe338; +} + +code span.in { + color: #d4d0ab; +} + +code span.kw { + font-weight: bold; + color: #ffa07a; +} + +pre > code.sourceCode > span { + color: #f8f8f2; +} + +code span { + color: #f8f8f2; +} + +code.sourceCode > span { + color: #f8f8f2; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #f8f8f2; +} + +code span.op { + color: #ffa07a; +} + +code span.ot { + color: #00e0e0; +} + +code span.pp { + color: #dcc6e0; +} + +code span.re { + color: #00e0e0; +} + +code span.sc { + color: #abe338; +} + +code span.ss { + color: #abe338; +} + +code span.st { + color: #abe338; +} + +code span.va { + color: #00e0e0; +} + +code span.vs { + color: #abe338; +} + +code span.wa { + color: #dcc6e0; +} + +.prevent-inlining { + content: " code.sourceCode > span { + color: #003B4F; +} + +code span { + color: #003B4F; +} + +code.sourceCode > span { + color: #003B4F; +} + +div.sourceCode, +div.sourceCode pre.sourceCode { + color: #003B4F; +} + +code span.ot { + color: #003B4F; +} + +code span.at { + color: #657422; +} + +code span.ss { + color: #20794D; +} + +code span.an { + color: #5E5E5E; +} + +code span.fu { + color: #4758AB; +} + +code span.st { + color: #20794D; +} + +code span.cf { + color: #003B4F; +} + +code span.op { + color: #5E5E5E; +} + +code span.er { + color: #AD0000; +} + +code span.bn { + color: #AD0000; +} + +code span.al { + color: #AD0000; +} + +code span.va { + color: #111111; +} + +code span.pp { + color: #AD0000; +} + +code span.in { + color: #5E5E5E; +} + +code span.vs { + color: #20794D; +} + +code span.wa { + color: #5E5E5E; + font-style: italic; +} + +code span.do { + color: #5E5E5E; + font-style: italic; +} + +code span.im { + color: #00769E; +} + +code span.ch { + color: #20794D; +} + +code span.dt { + color: #AD0000; +} + +code span.fl { + color: #AD0000; +} + +code span.co { + color: #5E5E5E; +} + +code span.cv { + color: #5E5E5E; + font-style: italic; +} + +code span.cn { + color: #8f5902; +} + +code span.sc { + color: #5E5E5E; +} + +code span.dv { + color: #AD0000; +} + +code span.kw { + color: #003B4F; +} + +.prevent-inlining { + content: " !el.hasAttribute("disabled") && !el.getAttribute("aria-hidden") + ); + }; + + /** + * Remove roles and attributes from a tab and its content + * @param {Node} tab The tab + * @param {Node} content The tab content + * @param {Object} settings User settings and options + */ + var destroyTab = function (tab, content, settings) { + // Remove the generated ID + if (tab.id.slice(0, settings.idPrefix.length) === settings.idPrefix) { + tab.id = ""; + } + + // remove event listener + tab.removeEventListener("focus", focusHandler, true); + + // Remove roles + tab.removeAttribute("role"); + tab.removeAttribute("aria-controls"); + tab.removeAttribute("aria-selected"); + tab.removeAttribute("tabindex"); + tab.closest("li").removeAttribute("role"); + content.removeAttribute("role"); + content.removeAttribute("aria-labelledby"); + content.removeAttribute("hidden"); + }; + + /** + * Add the required roles and attributes to a tab and its content + * @param {Node} tab The tab + * @param {Node} content The tab content + * @param {Object} settings User settings and options + */ + var setupTab = function (tab, content, settings) { + // Give tab an ID if it doesn't already have one + if (!tab.id) { + tab.id = settings.idPrefix + content.id; + } + + // Add roles + tab.setAttribute("role", "tab"); + tab.setAttribute("aria-controls", content.id); + tab.closest("li").setAttribute("role", "presentation"); + content.setAttribute("role", "tabpanel"); + content.setAttribute("aria-labelledby", tab.id); + + // Add selected state + if (tab.matches(settings.default)) { + tab.setAttribute("aria-selected", "true"); + } else { + tab.setAttribute("aria-selected", "false"); + content.setAttribute("hidden", "hidden"); + } + + // add focus event listender + tab.addEventListener("focus", focusHandler); + }; + + /** + * Hide a tab and its content + * @param {Node} newTab The new tab that's replacing it + */ + var hide = function (newTab) { + // Variables + var tabGroup = newTab.closest('[role="tablist"]'); + if (!tabGroup) return {}; + var tab = tabGroup.querySelector('[role="tab"][aria-selected="true"]'); + if (!tab) return {}; + var content = document.querySelector(tab.hash); + + // Hide the tab + tab.setAttribute("aria-selected", "false"); + + // Hide the content + if (!content) return { previousTab: tab }; + content.setAttribute("hidden", "hidden"); + + // Return the hidden tab and content + return { + previousTab: tab, + previousContent: content, + }; + }; + + /** + * Show a tab and its content + * @param {Node} tab The tab + * @param {Node} content The tab content + */ + var show = function (tab, content) { + tab.setAttribute("aria-selected", "true"); + content.removeAttribute("hidden"); + tab.focus(); + }; + + /** + * Toggle a new tab + * @param {Node} tab The tab to show + */ + var toggle = function (tab) { + // Make sure there's a tab to toggle and it's not already active + if (!tab || tab.getAttribute("aria-selected") == "true") return; + + // Variables + var content = document.querySelector(tab.hash); + if (!content) return; + + // Hide active tab and content + var details = hide(tab); + + // Show new tab and content + show(tab, content); + + // Add event details + details.tab = tab; + details.content = content; + + // Emit a custom event + emitEvent(tab, details); + }; + + /** + * Get all of the tabs in a tablist + * @param {Node} tab A tab from the list + * @return {Object} The tabs and the index of the currently active one + */ + var getTabsMap = function (tab) { + var tabGroup = tab.closest('[role="tablist"]'); + var tabs = tabGroup ? tabGroup.querySelectorAll('[role="tab"]') : null; + if (!tabs) return; + return { + tabs: tabs, + index: Array.prototype.indexOf.call(tabs, tab), + }; + }; + + /** + * Switch the active tab based on keyboard activity + * @param {Node} tab The currently active tab + * @param {Key} key The key that was pressed + */ + var switchTabs = function (tab, key) { + // Get a map of tabs + var map = getTabsMap(tab); + if (!map) return; + var length = map.tabs.length - 1; + var index; + + // Go to previous tab + if (["ArrowUp", "ArrowLeft", "Up", "Left"].indexOf(key) > -1) { + index = map.index < 1 ? length : map.index - 1; + } + + // Go to next tab + else if (["ArrowDown", "ArrowRight", "Down", "Right"].indexOf(key) > -1) { + index = map.index === length ? 0 : map.index + 1; + } + + // Go to home + else if (key === "Home") { + index = 0; + } + + // Go to end + else if (key === "End") { + index = length; + } + + // Toggle the tab + toggle(map.tabs[index]); + }; + + /** + * Create the Constructor object + */ + var Constructor = function (selector, options) { + // + // Variables + // + + var publicAPIs = {}; + var settings, tabWrapper; + + // + // Methods + // + + publicAPIs.destroy = function () { + // Get all tabs + var tabs = tabWrapper.querySelectorAll("a"); + + // Add roles to tabs + Array.prototype.forEach.call(tabs, function (tab) { + // Get the tab content + var content = document.querySelector(tab.hash); + if (!content) return; + + // Setup the tab + destroyTab(tab, content, settings); + }); + + // Remove role from wrapper + tabWrapper.removeAttribute("role"); + + // Remove event listeners + document.documentElement.removeEventListener( + "click", + clickHandler, + true + ); + tabWrapper.removeEventListener("keydown", keyHandler, true); + + // Reset variables + settings = null; + tabWrapper = null; + }; + + /** + * Setup the DOM with the proper attributes + */ + publicAPIs.setup = function () { + // Variables + tabWrapper = document.querySelector(selector); + if (!tabWrapper) return; + var tabs = tabWrapper.querySelectorAll("a"); + + // Add role to wrapper + tabWrapper.setAttribute("role", "tablist"); + + // Add roles to tabs. provide dynanmic tab indexes if we are within reveal + var contentTabindexes = + window.document.body.classList.contains("reveal-viewport"); + var nextTabindex = 1; + Array.prototype.forEach.call(tabs, function (tab) { + if (contentTabindexes) { + tab.setAttribute("tabindex", "" + nextTabindex++); + } else { + tab.setAttribute("tabindex", "0"); + } + + // Get the tab content + var content = document.querySelector(tab.hash); + if (!content) return; + + // set tab indexes for content + if (contentTabindexes) { + getKeyboardFocusableElements(content).forEach(function (el) { + el.setAttribute("tabindex", "" + nextTabindex++); + }); + } + + // Setup the tab + setupTab(tab, content, settings); + }); + }; + + /** + * Toggle a tab based on an ID + * @param {String|Node} id The tab to toggle + */ + publicAPIs.toggle = function (id) { + // Get the tab + var tab = id; + if (typeof id === "string") { + tab = document.querySelector( + selector + ' [role="tab"][href*="' + id + '"]' + ); + } + + // Toggle the tab + toggle(tab); + }; + + /** + * Handle click events + */ + var clickHandler = function (event) { + // Only run on toggles + var tab = event.target.closest(selector + ' [role="tab"]'); + if (!tab) return; + + // Prevent link behavior + event.preventDefault(); + + // Toggle the tab + toggle(tab); + }; + + /** + * Handle keydown events + */ + var keyHandler = function (event) { + // Only run if a tab is in focus + var tab = document.activeElement; + if (!tab.matches(selector + ' [role="tab"]')) 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+{"version":3,"file":"reveal.esm.js","sources":["../js/utils/util.js","../js/utils/device.js","../node_modules/fitty/dist/fitty.module.js","../js/controllers/slidecontent.js","../js/controllers/slidenumber.js","../js/utils/color.js","../js/controllers/backgrounds.js","../js/utils/constants.js","../js/controllers/autoanimate.js","../js/controllers/fragments.js","../js/controllers/overview.js","../js/controllers/keyboard.js","../js/controllers/location.js","../js/controllers/controls.js","../js/controllers/progress.js","../js/controllers/pointer.js","../js/utils/loader.js","../js/controllers/plugins.js","../js/controllers/print.js","../js/controllers/touch.js","../js/controllers/focus.js","../js/controllers/notes.js","../js/components/playback.js","../js/config.js","../js/reveal.js","../js/index.js"],"sourcesContent":["/**\n * Extend object a with the properties of object b.\n * If there's a conflict, object b takes precedence.\n *\n * @param {object} a\n * @param {object} b\n */\nexport const extend = ( a, b ) => {\n\n\tfor( let i in b ) {\n\t\ta[ i ] = b[ i ];\n\t}\n\n\treturn a;\n\n}\n\n/**\n * querySelectorAll but returns an Array.\n */\nexport const queryAll = ( el, selector ) => {\n\n\treturn Array.from( el.querySelectorAll( selector ) );\n\n}\n\n/**\n * classList.toggle() with cross browser support\n */\nexport const toggleClass = ( el, className, value ) => {\n\tif( value ) {\n\t\tel.classList.add( className );\n\t}\n\telse {\n\t\tel.classList.remove( className );\n\t}\n}\n\n/**\n * Utility for deserializing a value.\n *\n * @param {*} value\n * @return {*}\n */\nexport const deserialize = ( value ) => {\n\n\tif( typeof value === 'string' ) {\n\t\tif( value === 'null' ) return null;\n\t\telse if( value === 'true' ) return true;\n\t\telse if( value === 'false' ) return false;\n\t\telse if( value.match( /^-?[\\d\\.]+$/ ) ) return parseFloat( value );\n\t}\n\n\treturn value;\n\n}\n\n/**\n * Measures the distance in pixels between point a\n * and point b.\n *\n * @param {object} a point with x/y properties\n * @param {object} b point with x/y properties\n *\n * @return {number}\n */\nexport const distanceBetween = ( a, b ) => {\n\n\tlet dx = a.x - b.x,\n\t\tdy = a.y - b.y;\n\n\treturn Math.sqrt( dx*dx + dy*dy );\n\n}\n\n/**\n * Applies a CSS transform to the target element.\n *\n * @param {HTMLElement} element\n * @param {string} transform\n */\nexport const transformElement = ( element, transform ) => {\n\n\telement.style.transform = transform;\n\n}\n\n/**\n * Element.matches with IE support.\n *\n * @param {HTMLElement} target The element to match\n * @param {String} selector The CSS selector to match\n * the element against\n *\n * @return {Boolean}\n */\nexport const matches = ( target, selector ) => {\n\n\tlet matchesMethod = target.matches || target.matchesSelector || target.msMatchesSelector;\n\n\treturn !!( matchesMethod && matchesMethod.call( target, selector ) );\n\n}\n\n/**\n * Find the closest parent that matches the given\n * selector.\n *\n * @param {HTMLElement} target The child element\n * @param {String} selector The CSS selector to match\n * the parents against\n *\n * @return {HTMLElement} The matched parent or null\n * if no matching parent was found\n */\nexport const closest = ( target, selector ) => {\n\n\t// Native Element.closest\n\tif( typeof target.closest === 'function' ) {\n\t\treturn target.closest( selector );\n\t}\n\n\t// Polyfill\n\twhile( target ) {\n\t\tif( matches( target, selector ) ) {\n\t\t\treturn target;\n\t\t}\n\n\t\t// Keep searching\n\t\ttarget = target.parentNode;\n\t}\n\n\treturn null;\n\n}\n\n/**\n * Handling the fullscreen functionality via the fullscreen API\n *\n * @see http://fullscreen.spec.whatwg.org/\n * @see https://developer.mozilla.org/en-US/docs/DOM/Using_fullscreen_mode\n */\nexport const enterFullscreen = element => {\n\n\telement = element || document.documentElement;\n\n\t// Check which implementation is available\n\tlet requestMethod = element.requestFullscreen ||\n\t\t\t\t\t\telement.webkitRequestFullscreen ||\n\t\t\t\t\t\telement.webkitRequestFullScreen ||\n\t\t\t\t\t\telement.mozRequestFullScreen ||\n\t\t\t\t\t\telement.msRequestFullscreen;\n\n\tif( requestMethod ) {\n\t\trequestMethod.apply( element );\n\t}\n\n}\n\n/**\n * Creates an HTML element and returns a reference to it.\n * If the element already exists the existing instance will\n * be returned.\n *\n * @param {HTMLElement} container\n * @param {string} tagname\n * @param {string} classname\n * @param {string} innerHTML\n *\n * @return {HTMLElement}\n */\nexport const createSingletonNode = ( container, tagname, classname, innerHTML='' ) => {\n\n\t// Find all nodes matching the description\n\tlet nodes = container.querySelectorAll( '.' + classname );\n\n\t// Check all matches to find one which is a direct child of\n\t// the specified container\n\tfor( let i = 0; i < nodes.length; i++ ) {\n\t\tlet testNode = nodes[i];\n\t\tif( testNode.parentNode === container ) {\n\t\t\treturn testNode;\n\t\t}\n\t}\n\n\t// If no node was found, create it now\n\tlet node = document.createElement( tagname );\n\tnode.className = classname;\n\tnode.innerHTML = innerHTML;\n\tcontainer.appendChild( node );\n\n\treturn node;\n\n}\n\n/**\n * Injects the given CSS styles into the DOM.\n *\n * @param {string} value\n */\nexport const createStyleSheet = ( value ) => {\n\n\tlet tag = document.createElement( 'style' );\n\ttag.type = 'text/css';\n\n\tif( value && value.length > 0 ) {\n\t\tif( tag.styleSheet ) {\n\t\t\ttag.styleSheet.cssText = value;\n\t\t}\n\t\telse {\n\t\t\ttag.appendChild( document.createTextNode( value ) );\n\t\t}\n\t}\n\n\tdocument.head.appendChild( tag );\n\n\treturn tag;\n\n}\n\n/**\n * Returns a key:value hash of all query params.\n */\nexport const getQueryHash = () => {\n\n\tlet query = {};\n\n\tlocation.search.replace( /[A-Z0-9]+?=([\\w\\.%-]*)/gi, a => {\n\t\tquery[ a.split( '=' ).shift() ] = a.split( '=' ).pop();\n\t} );\n\n\t// Basic deserialization\n\tfor( let i in query ) {\n\t\tlet value = query[ i ];\n\n\t\tquery[ i ] = deserialize( unescape( value ) );\n\t}\n\n\t// Do not accept new dependencies via query config to avoid\n\t// the potential of malicious script injection\n\tif( typeof query['dependencies'] !== 'undefined' ) delete query['dependencies'];\n\n\treturn query;\n\n}\n\n/**\n * Returns the remaining height within the parent of the\n * target element.\n *\n * remaining height = [ configured parent height ] - [ current parent height ]\n *\n * @param {HTMLElement} element\n * @param {number} [height]\n */\nexport const getRemainingHeight = ( element, height = 0 ) => {\n\n\tif( element ) {\n\t\tlet newHeight, oldHeight = element.style.height;\n\n\t\t// Change the .stretch element height to 0 in order find the height of all\n\t\t// the other elements\n\t\telement.style.height = '0px';\n\n\t\t// In Overview mode, the parent (.slide) height is set of 700px.\n\t\t// Restore it temporarily to its natural height.\n\t\telement.parentNode.style.height = 'auto';\n\n\t\tnewHeight = height - element.parentNode.offsetHeight;\n\n\t\t// Restore the old height, just in case\n\t\telement.style.height = oldHeight + 'px';\n\n\t\t// Clear the parent (.slide) height. .removeProperty works in IE9+\n\t\telement.parentNode.style.removeProperty('height');\n\n\t\treturn newHeight;\n\t}\n\n\treturn height;\n\n}\n\nconst fileExtensionToMimeMap = {\n\t'mp4': 'video/mp4',\n\t'm4a': 'video/mp4',\n\t'ogv': 'video/ogg',\n\t'mpeg': 'video/mpeg',\n\t'webm': 'video/webm'\n}\n\n/**\n * Guess the MIME type for common file formats.\n */\nexport const getMimeTypeFromFile = ( filename='' ) => {\n\treturn fileExtensionToMimeMap[filename.split('.').pop()]\n}","const UA = navigator.userAgent;\n\nexport const isMobile = /(iphone|ipod|ipad|android)/gi.test( UA ) ||\n\t\t\t\t\t\t( navigator.platform === 'MacIntel' && navigator.maxTouchPoints > 1 ); // iPadOS\n\nexport const isChrome = /chrome/i.test( UA ) && !/edge/i.test( UA );\n\nexport const isAndroid = /android/gi.test( UA );","/*\n * fitty v2.3.3 - Snugly resizes text to fit its parent container\n * Copyright (c) 2020 Rik Schennink (https://pqina.nl/)\n */\n'use strict';\n\nObject.defineProperty(exports, \"__esModule\", {\n value: true\n});\n\nvar _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };\n\nexports.default = function (w) {\n\n // no window, early exit\n if (!w) return;\n\n // node list to array helper method\n var toArray = function toArray(nl) {\n return [].slice.call(nl);\n };\n\n // states\n var DrawState = {\n IDLE: 0,\n DIRTY_CONTENT: 1,\n DIRTY_LAYOUT: 2,\n DIRTY: 3\n };\n\n // all active fitty elements\n var fitties = [];\n\n // group all redraw calls till next frame, we cancel each frame request when a new one comes in. If no support for request animation frame, this is an empty function and supports for fitty stops.\n var redrawFrame = null;\n var requestRedraw = 'requestAnimationFrame' in w ? function () {\n w.cancelAnimationFrame(redrawFrame);\n redrawFrame = w.requestAnimationFrame(function () {\n return redraw(fitties.filter(function (f) {\n return f.dirty && f.active;\n }));\n });\n } : function () {};\n\n // sets all fitties to dirty so they are redrawn on the next redraw loop, then calls redraw\n var redrawAll = function redrawAll(type) {\n return function () {\n fitties.forEach(function (f) {\n return f.dirty = type;\n });\n requestRedraw();\n };\n };\n\n // redraws fitties so they nicely fit their parent container\n var redraw = function redraw(fitties) {\n\n // getting info from the DOM at this point should not trigger a reflow, let's gather as much intel as possible before triggering a reflow\n\n // check if styles of all fitties have been computed\n fitties.filter(function (f) {\n return !f.styleComputed;\n }).forEach(function (f) {\n f.styleComputed = computeStyle(f);\n });\n\n // restyle elements that require pre-styling, this triggers a reflow, please try to prevent by adding CSS rules (see docs)\n fitties.filter(shouldPreStyle).forEach(applyStyle);\n\n // we now determine which fitties should be redrawn\n var fittiesToRedraw = fitties.filter(shouldRedraw);\n\n // we calculate final styles for these fitties\n fittiesToRedraw.forEach(calculateStyles);\n\n // now we apply the calculated styles from our previous loop\n fittiesToRedraw.forEach(function (f) {\n applyStyle(f);\n markAsClean(f);\n });\n\n // now we dispatch events for all restyled fitties\n fittiesToRedraw.forEach(dispatchFitEvent);\n };\n\n var markAsClean = function markAsClean(f) {\n return f.dirty = DrawState.IDLE;\n };\n\n var calculateStyles = function calculateStyles(f) {\n\n // get available width from parent node\n f.availableWidth = f.element.parentNode.clientWidth;\n\n // the space our target element uses\n f.currentWidth = f.element.scrollWidth;\n\n // remember current font size\n f.previousFontSize = f.currentFontSize;\n\n // let's calculate the new font size\n f.currentFontSize = Math.min(Math.max(f.minSize, f.availableWidth / f.currentWidth * f.previousFontSize), f.maxSize);\n\n // if allows wrapping, only wrap when at minimum font size (otherwise would break container)\n f.whiteSpace = f.multiLine && f.currentFontSize === f.minSize ? 'normal' : 'nowrap';\n };\n\n // should always redraw if is not dirty layout, if is dirty layout, only redraw if size has changed\n var shouldRedraw = function shouldRedraw(f) {\n return f.dirty !== DrawState.DIRTY_LAYOUT || f.dirty === DrawState.DIRTY_LAYOUT && f.element.parentNode.clientWidth !== f.availableWidth;\n };\n\n // every fitty element is tested for invalid styles\n var computeStyle = function computeStyle(f) {\n\n // get style properties\n var style = w.getComputedStyle(f.element, null);\n\n // get current font size in pixels (if we already calculated it, use the calculated version)\n f.currentFontSize = parseFloat(style.getPropertyValue('font-size'));\n\n // get display type and wrap mode\n f.display = style.getPropertyValue('display');\n f.whiteSpace = style.getPropertyValue('white-space');\n };\n\n // determines if this fitty requires initial styling, can be prevented by applying correct styles through CSS\n var shouldPreStyle = function shouldPreStyle(f) {\n\n var preStyle = false;\n\n // if we already tested for prestyling we don't have to do it again\n if (f.preStyleTestCompleted) return false;\n\n // should have an inline style, if not, apply\n if (!/inline-/.test(f.display)) {\n preStyle = true;\n f.display = 'inline-block';\n }\n\n // to correctly calculate dimensions the element should have whiteSpace set to nowrap\n if (f.whiteSpace !== 'nowrap') {\n preStyle = true;\n f.whiteSpace = 'nowrap';\n }\n\n // we don't have to do this twice\n f.preStyleTestCompleted = true;\n\n return preStyle;\n };\n\n // apply styles to single fitty\n var applyStyle = function applyStyle(f) {\n f.element.style.whiteSpace = f.whiteSpace;\n f.element.style.display = f.display;\n f.element.style.fontSize = f.currentFontSize + 'px';\n };\n\n // dispatch a fit event on a fitty\n var dispatchFitEvent = function dispatchFitEvent(f) {\n f.element.dispatchEvent(new CustomEvent('fit', {\n detail: {\n oldValue: f.previousFontSize,\n newValue: f.currentFontSize,\n scaleFactor: f.currentFontSize / f.previousFontSize\n }\n }));\n };\n\n // fit method, marks the fitty as dirty and requests a redraw (this will also redraw any other fitty marked as dirty)\n var fit = function fit(f, type) {\n return function () {\n f.dirty = type;\n if (!f.active) return;\n requestRedraw();\n };\n };\n\n var init = function init(f) {\n\n // save some of the original CSS properties before we change them\n f.originalStyle = {\n whiteSpace: f.element.style.whiteSpace,\n display: f.element.style.display,\n fontSize: f.element.style.fontSize\n };\n\n // should we observe DOM mutations\n observeMutations(f);\n\n // this is a new fitty so we need to validate if it's styles are in order\n f.newbie = true;\n\n // because it's a new fitty it should also be dirty, we want it to redraw on the first loop\n f.dirty = true;\n\n // we want to be able to update this fitty\n fitties.push(f);\n };\n\n var destroy = function destroy(f) {\n return function () {\n\n // remove from fitties array\n fitties = fitties.filter(function (_) {\n return _.element !== f.element;\n });\n\n // stop observing DOM\n if (f.observeMutations) f.observer.disconnect();\n\n // reset the CSS properties we changes\n f.element.style.whiteSpace = f.originalStyle.whiteSpace;\n f.element.style.display = f.originalStyle.display;\n f.element.style.fontSize = f.originalStyle.fontSize;\n };\n };\n\n // add a new fitty, does not redraw said fitty\n var subscribe = function subscribe(f) {\n return function () {\n if (f.active) return;\n f.active = true;\n requestRedraw();\n };\n };\n\n // remove an existing fitty\n var unsubscribe = function unsubscribe(f) {\n return function () {\n return f.active = false;\n };\n };\n\n var observeMutations = function observeMutations(f) {\n\n // no observing?\n if (!f.observeMutations) return;\n\n // start observing mutations\n f.observer = new MutationObserver(fit(f, DrawState.DIRTY_CONTENT));\n\n // start observing\n f.observer.observe(f.element, f.observeMutations);\n };\n\n // default mutation observer settings\n var mutationObserverDefaultSetting = {\n subtree: true,\n childList: true,\n characterData: true\n };\n\n // default fitty options\n var defaultOptions = {\n minSize: 16,\n maxSize: 512,\n multiLine: true,\n observeMutations: 'MutationObserver' in w ? mutationObserverDefaultSetting : false\n };\n\n // array of elements in, fitty instances out\n function fittyCreate(elements, options) {\n\n // set options object\n var fittyOptions = _extends({}, defaultOptions, options);\n\n // create fitties\n var publicFitties = elements.map(function (element) {\n\n // create fitty instance\n var f = _extends({}, fittyOptions, {\n\n // internal options for this fitty\n element: element,\n active: true\n });\n\n // initialise this fitty\n init(f);\n\n // expose API\n return {\n element: element,\n fit: fit(f, DrawState.DIRTY),\n unfreeze: subscribe(f),\n freeze: unsubscribe(f),\n unsubscribe: destroy(f)\n };\n });\n\n // call redraw on newly initiated fitties\n requestRedraw();\n\n // expose fitties\n return publicFitties;\n }\n\n // fitty creation function\n function fitty(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {};\n\n\n // if target is a string\n return typeof target === 'string' ?\n\n // treat it as a querySelector\n fittyCreate(toArray(document.querySelectorAll(target)), options) :\n\n // create single fitty\n fittyCreate([target], options)[0];\n }\n\n // handles viewport changes, redraws all fitties, but only does so after a timeout\n var resizeDebounce = null;\n var onWindowResized = function onWindowResized() {\n w.clearTimeout(resizeDebounce);\n resizeDebounce = w.setTimeout(redrawAll(DrawState.DIRTY_LAYOUT), fitty.observeWindowDelay);\n };\n\n // define observe window property, so when we set it to true or false events are automatically added and removed\n var events = ['resize', 'orientationchange'];\n Object.defineProperty(fitty, 'observeWindow', {\n set: function set(enabled) {\n var method = (enabled ? 'add' : 'remove') + 'EventListener';\n events.forEach(function (e) {\n w[method](e, onWindowResized);\n });\n }\n });\n\n // fitty global properties (by setting observeWindow to true the events above get added)\n fitty.observeWindow = true;\n fitty.observeWindowDelay = 100;\n\n // public fit all method, will force redraw no matter what\n fitty.fitAll = redrawAll(DrawState.DIRTY);\n\n // export our fitty function, we don't want to keep it to our selves\n return fitty;\n}(typeof window === 'undefined' ? null : window);","import { extend, queryAll, closest, getMimeTypeFromFile } from '../utils/util.js'\nimport { isMobile } from '../utils/device.js'\n\nimport fitty from 'fitty';\n\n/**\n * Handles loading, unloading and playback of slide\n * content such as images, videos and iframes.\n */\nexport default class SlideContent {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.startEmbeddedIframe = this.startEmbeddedIframe.bind( this );\n\n\t}\n\n\t/**\n\t * Should the given element be preloaded?\n\t * Decides based on local element attributes and global config.\n\t *\n\t * @param {HTMLElement} element\n\t */\n\tshouldPreload( element ) {\n\n\t\t// Prefer an explicit global preload setting\n\t\tlet preload = this.Reveal.getConfig().preloadIframes;\n\n\t\t// If no global setting is available, fall back on the element's\n\t\t// own preload setting\n\t\tif( typeof preload !== 'boolean' ) {\n\t\t\tpreload = element.hasAttribute( 'data-preload' );\n\t\t}\n\n\t\treturn preload;\n\t}\n\n\t/**\n\t * Called when the given slide is within the configured view\n\t * distance. Shows the slide element and loads any content\n\t * that is set to load lazily (data-src).\n\t *\n\t * @param {HTMLElement} slide Slide to show\n\t */\n\tload( slide, options = {} ) {\n\n\t\t// Show the slide element\n\t\tslide.style.display = this.Reveal.getConfig().display;\n\n\t\t// Media elements with data-src attributes\n\t\tqueryAll( slide, 'img[data-src], video[data-src], audio[data-src], iframe[data-src]' ).forEach( element => {\n\t\t\tif( element.tagName !== 'IFRAME' || this.shouldPreload( element ) ) {\n\t\t\t\telement.setAttribute( 'src', element.getAttribute( 'data-src' ) );\n\t\t\t\telement.setAttribute( 'data-lazy-loaded', '' );\n\t\t\t\telement.removeAttribute( 'data-src' );\n\t\t\t}\n\t\t} );\n\n\t\t// Media elements with children\n\t\tqueryAll( slide, 'video, audio' ).forEach( media => {\n\t\t\tlet sources = 0;\n\n\t\t\tqueryAll( media, 'source[data-src]' ).forEach( source => {\n\t\t\t\tsource.setAttribute( 'src', source.getAttribute( 'data-src' ) );\n\t\t\t\tsource.removeAttribute( 'data-src' );\n\t\t\t\tsource.setAttribute( 'data-lazy-loaded', '' );\n\t\t\t\tsources += 1;\n\t\t\t} );\n\n\t\t\t// Enable inline video playback in mobile Safari\n\t\t\tif( isMobile && media.tagName === 'VIDEO' ) {\n\t\t\t\tmedia.setAttribute( 'playsinline', '' );\n\t\t\t}\n\n\t\t\t// If we rewrote sources for this video/audio element, we need\n\t\t\t// to manually tell it to load from its new origin\n\t\t\tif( sources > 0 ) {\n\t\t\t\tmedia.load();\n\t\t\t}\n\t\t} );\n\n\n\t\t// Show the corresponding background element\n\t\tlet background = slide.slideBackgroundElement;\n\t\tif( background ) {\n\t\t\tbackground.style.display = 'block';\n\n\t\t\tlet backgroundContent = slide.slideBackgroundContentElement;\n\t\t\tlet backgroundIframe = slide.getAttribute( 'data-background-iframe' );\n\n\t\t\t// If the background contains media, load it\n\t\t\tif( background.hasAttribute( 'data-loaded' ) === false ) {\n\t\t\t\tbackground.setAttribute( 'data-loaded', 'true' );\n\n\t\t\t\tlet backgroundImage = slide.getAttribute( 'data-background-image' ),\n\t\t\t\t\tbackgroundVideo = slide.getAttribute( 'data-background-video' ),\n\t\t\t\t\tbackgroundVideoLoop = slide.hasAttribute( 'data-background-video-loop' ),\n\t\t\t\t\tbackgroundVideoMuted = slide.hasAttribute( 'data-background-video-muted' );\n\n\t\t\t\t// Images\n\t\t\t\tif( backgroundImage ) {\n\t\t\t\t\t// base64\n\t\t\t\t\tif( /^data:/.test( backgroundImage.trim() ) ) {\n\t\t\t\t\t\tbackgroundContent.style.backgroundImage = `url(${backgroundImage.trim()})`;\n\t\t\t\t\t}\n\t\t\t\t\t// URL(s)\n\t\t\t\t\telse {\n\t\t\t\t\t\tbackgroundContent.style.backgroundImage = backgroundImage.split( ',' ).map( background => {\n\t\t\t\t\t\t\treturn `url(${encodeURI(background.trim())})`;\n\t\t\t\t\t\t}).join( ',' );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Videos\n\t\t\t\telse if ( backgroundVideo && !this.Reveal.isSpeakerNotes() ) {\n\t\t\t\t\tlet video = document.createElement( 'video' );\n\n\t\t\t\t\tif( backgroundVideoLoop ) {\n\t\t\t\t\t\tvideo.setAttribute( 'loop', '' );\n\t\t\t\t\t}\n\n\t\t\t\t\tif( backgroundVideoMuted ) {\n\t\t\t\t\t\tvideo.muted = true;\n\t\t\t\t\t}\n\n\t\t\t\t\t// Enable inline playback in mobile Safari\n\t\t\t\t\t//\n\t\t\t\t\t// Mute is required for video to play when using\n\t\t\t\t\t// swipe gestures to navigate since they don't\n\t\t\t\t\t// count as direct user actions :'(\n\t\t\t\t\tif( isMobile ) {\n\t\t\t\t\t\tvideo.muted = true;\n\t\t\t\t\t\tvideo.setAttribute( 'playsinline', '' );\n\t\t\t\t\t}\n\n\t\t\t\t\t// Support comma separated lists of video sources\n\t\t\t\t\tbackgroundVideo.split( ',' ).forEach( source => {\n\t\t\t\t\t\tlet type = getMimeTypeFromFile( source );\n\t\t\t\t\t\tif( type ) {\n\t\t\t\t\t\t\tvideo.innerHTML += ``;\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tvideo.innerHTML += ``;\n\t\t\t\t\t\t}\n\t\t\t\t\t} );\n\n\t\t\t\t\tbackgroundContent.appendChild( video );\n\t\t\t\t}\n\t\t\t\t// Iframes\n\t\t\t\telse if( backgroundIframe && options.excludeIframes !== true ) {\n\t\t\t\t\tlet iframe = document.createElement( 'iframe' );\n\t\t\t\t\tiframe.setAttribute( 'allowfullscreen', '' );\n\t\t\t\t\tiframe.setAttribute( 'mozallowfullscreen', '' );\n\t\t\t\t\tiframe.setAttribute( 'webkitallowfullscreen', '' );\n\t\t\t\t\tiframe.setAttribute( 'allow', 'autoplay' );\n\n\t\t\t\t\tiframe.setAttribute( 'data-src', backgroundIframe );\n\n\t\t\t\t\tiframe.style.width = '100%';\n\t\t\t\t\tiframe.style.height = '100%';\n\t\t\t\t\tiframe.style.maxHeight = '100%';\n\t\t\t\t\tiframe.style.maxWidth = '100%';\n\n\t\t\t\t\tbackgroundContent.appendChild( iframe );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// Start loading preloadable iframes\n\t\t\tlet backgroundIframeElement = backgroundContent.querySelector( 'iframe[data-src]' );\n\t\t\tif( backgroundIframeElement ) {\n\n\t\t\t\t// Check if this iframe is eligible to be preloaded\n\t\t\t\tif( this.shouldPreload( background ) && !/autoplay=(1|true|yes)/gi.test( backgroundIframe ) ) {\n\t\t\t\t\tif( backgroundIframeElement.getAttribute( 'src' ) !== backgroundIframe ) {\n\t\t\t\t\t\tbackgroundIframeElement.setAttribute( 'src', backgroundIframe );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t\tthis.layout( slide );\n\n\t}\n\n\t/**\n\t * Applies JS-dependent layout helpers for the given slide,\n\t * if there are any.\n\t */\n\tlayout( slide ) {\n\n\t\t// Autosize text with the r-fit-text class based on the\n\t\t// size of its container. This needs to happen after the\n\t\t// slide is visible in order to measure the text.\n\t\tArray.from( slide.querySelectorAll( '.r-fit-text' ) ).forEach( element => {\n\t\t\tfitty( element, {\n\t\t\t\tminSize: 24,\n\t\t\t\tmaxSize: this.Reveal.getConfig().height * 0.8,\n\t\t\t\tobserveMutations: false,\n\t\t\t\tobserveWindow: false\n\t\t\t} );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Unloads and hides the given slide. This is called when the\n\t * slide is moved outside of the configured view distance.\n\t *\n\t * @param {HTMLElement} slide\n\t */\n\tunload( slide ) {\n\n\t\t// Hide the slide element\n\t\tslide.style.display = 'none';\n\n\t\t// Hide the corresponding background element\n\t\tlet background = this.Reveal.getSlideBackground( slide );\n\t\tif( background ) {\n\t\t\tbackground.style.display = 'none';\n\n\t\t\t// Unload any background iframes\n\t\t\tqueryAll( background, 'iframe[src]' ).forEach( element => {\n\t\t\t\telement.removeAttribute( 'src' );\n\t\t\t} );\n\t\t}\n\n\t\t// Reset lazy-loaded media elements with src attributes\n\t\tqueryAll( slide, 'video[data-lazy-loaded][src], audio[data-lazy-loaded][src], iframe[data-lazy-loaded][src]' ).forEach( element => {\n\t\t\telement.setAttribute( 'data-src', element.getAttribute( 'src' ) );\n\t\t\telement.removeAttribute( 'src' );\n\t\t} );\n\n\t\t// Reset lazy-loaded media elements with children\n\t\tqueryAll( slide, 'video[data-lazy-loaded] source[src], audio source[src]' ).forEach( source => {\n\t\t\tsource.setAttribute( 'data-src', source.getAttribute( 'src' ) );\n\t\t\tsource.removeAttribute( 'src' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Enforces origin-specific format rules for embedded media.\n\t */\n\tformatEmbeddedContent() {\n\n\t\tlet _appendParamToIframeSource = ( sourceAttribute, sourceURL, param ) => {\n\t\t\tqueryAll( this.Reveal.getSlidesElement(), 'iframe['+ sourceAttribute +'*=\"'+ sourceURL +'\"]' ).forEach( el => {\n\t\t\t\tlet src = el.getAttribute( sourceAttribute );\n\t\t\t\tif( src && src.indexOf( param ) === -1 ) {\n\t\t\t\t\tel.setAttribute( sourceAttribute, src + ( !/\\?/.test( src ) ? '?' : '&' ) + param );\n\t\t\t\t}\n\t\t\t});\n\t\t};\n\n\t\t// YouTube frames must include \"?enablejsapi=1\"\n\t\t_appendParamToIframeSource( 'src', 'youtube.com/embed/', 'enablejsapi=1' );\n\t\t_appendParamToIframeSource( 'data-src', 'youtube.com/embed/', 'enablejsapi=1' );\n\n\t\t// Vimeo frames must include \"?api=1\"\n\t\t_appendParamToIframeSource( 'src', 'player.vimeo.com/', 'api=1' );\n\t\t_appendParamToIframeSource( 'data-src', 'player.vimeo.com/', 'api=1' );\n\n\t}\n\n\t/**\n\t * Start playback of any embedded content inside of\n\t * the given element.\n\t *\n\t * @param {HTMLElement} element\n\t */\n\tstartEmbeddedContent( element ) {\n\n\t\tif( element && !this.Reveal.isSpeakerNotes() ) {\n\n\t\t\t// Restart GIFs\n\t\t\tqueryAll( element, 'img[src$=\".gif\"]' ).forEach( el => {\n\t\t\t\t// Setting the same unchanged source like this was confirmed\n\t\t\t\t// to work in Chrome, FF & Safari\n\t\t\t\tel.setAttribute( 'src', el.getAttribute( 'src' ) );\n\t\t\t} );\n\n\t\t\t// HTML5 media elements\n\t\t\tqueryAll( element, 'video, audio' ).forEach( el => {\n\t\t\t\tif( closest( el, '.fragment' ) && !closest( el, '.fragment.visible' ) ) {\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\t// Prefer an explicit global autoplay setting\n\t\t\t\tlet autoplay = this.Reveal.getConfig().autoPlayMedia;\n\n\t\t\t\t// If no global setting is available, fall back on the element's\n\t\t\t\t// own autoplay setting\n\t\t\t\tif( typeof autoplay !== 'boolean' ) {\n\t\t\t\t\tautoplay = el.hasAttribute( 'data-autoplay' ) || !!closest( el, '.slide-background' );\n\t\t\t\t}\n\n\t\t\t\tif( autoplay && typeof el.play === 'function' ) {\n\n\t\t\t\t\t// If the media is ready, start playback\n\t\t\t\t\tif( el.readyState > 1 ) {\n\t\t\t\t\t\tthis.startEmbeddedMedia( { target: el } );\n\t\t\t\t\t}\n\t\t\t\t\t// Mobile devices never fire a loaded event so instead\n\t\t\t\t\t// of waiting, we initiate playback\n\t\t\t\t\telse if( isMobile ) {\n\t\t\t\t\t\tlet promise = el.play();\n\n\t\t\t\t\t\t// If autoplay does not work, ensure that the controls are visible so\n\t\t\t\t\t\t// that the viewer can start the media on their own\n\t\t\t\t\t\tif( promise && typeof promise.catch === 'function' && el.controls === false ) {\n\t\t\t\t\t\t\tpromise.catch( () => {\n\t\t\t\t\t\t\t\tel.controls = true;\n\n\t\t\t\t\t\t\t\t// Once the video does start playing, hide the controls again\n\t\t\t\t\t\t\t\tel.addEventListener( 'play', () => {\n\t\t\t\t\t\t\t\t\tel.controls = false;\n\t\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\t// If the media isn't loaded, wait before playing\n\t\t\t\t\telse {\n\t\t\t\t\t\tel.removeEventListener( 'loadeddata', this.startEmbeddedMedia ); // remove first to avoid dupes\n\t\t\t\t\t\tel.addEventListener( 'loadeddata', this.startEmbeddedMedia );\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\t// Normal iframes\n\t\t\tqueryAll( element, 'iframe[src]' ).forEach( el => {\n\t\t\t\tif( closest( el, '.fragment' ) && !closest( el, '.fragment.visible' ) ) {\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\tthis.startEmbeddedIframe( { target: el } );\n\t\t\t} );\n\n\t\t\t// Lazy loading iframes\n\t\t\tqueryAll( element, 'iframe[data-src]' ).forEach( el => {\n\t\t\t\tif( closest( el, '.fragment' ) && !closest( el, '.fragment.visible' ) ) {\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\tif( el.getAttribute( 'src' ) !== el.getAttribute( 'data-src' ) ) {\n\t\t\t\t\tel.removeEventListener( 'load', this.startEmbeddedIframe ); // remove first to avoid dupes\n\t\t\t\t\tel.addEventListener( 'load', this.startEmbeddedIframe );\n\t\t\t\t\tel.setAttribute( 'src', el.getAttribute( 'data-src' ) );\n\t\t\t\t}\n\t\t\t} );\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Starts playing an embedded video/audio element after\n\t * it has finished loading.\n\t *\n\t * @param {object} event\n\t */\n\tstartEmbeddedMedia( event ) {\n\n\t\tlet isAttachedToDOM = !!closest( event.target, 'html' ),\n\t\t\tisVisible \t\t= !!closest( event.target, '.present' );\n\n\t\tif( isAttachedToDOM && isVisible ) {\n\t\t\tevent.target.currentTime = 0;\n\t\t\tevent.target.play();\n\t\t}\n\n\t\tevent.target.removeEventListener( 'loadeddata', this.startEmbeddedMedia );\n\n\t}\n\n\t/**\n\t * \"Starts\" the content of an embedded iframe using the\n\t * postMessage API.\n\t *\n\t * @param {object} event\n\t */\n\tstartEmbeddedIframe( event ) {\n\n\t\tlet iframe = event.target;\n\n\t\tif( iframe && iframe.contentWindow ) {\n\n\t\t\tlet isAttachedToDOM = !!closest( event.target, 'html' ),\n\t\t\t\tisVisible \t\t= !!closest( event.target, '.present' );\n\n\t\t\tif( isAttachedToDOM && isVisible ) {\n\n\t\t\t\t// Prefer an explicit global autoplay setting\n\t\t\t\tlet autoplay = this.Reveal.getConfig().autoPlayMedia;\n\n\t\t\t\t// If no global setting is available, fall back on the element's\n\t\t\t\t// own autoplay setting\n\t\t\t\tif( typeof autoplay !== 'boolean' ) {\n\t\t\t\t\tautoplay = iframe.hasAttribute( 'data-autoplay' ) || !!closest( iframe, '.slide-background' );\n\t\t\t\t}\n\n\t\t\t\t// YouTube postMessage API\n\t\t\t\tif( /youtube\\.com\\/embed\\//.test( iframe.getAttribute( 'src' ) ) && autoplay ) {\n\t\t\t\t\tiframe.contentWindow.postMessage( '{\"event\":\"command\",\"func\":\"playVideo\",\"args\":\"\"}', '*' );\n\t\t\t\t}\n\t\t\t\t// Vimeo postMessage API\n\t\t\t\telse if( /player\\.vimeo\\.com\\//.test( iframe.getAttribute( 'src' ) ) && autoplay ) {\n\t\t\t\t\tiframe.contentWindow.postMessage( '{\"method\":\"play\"}', '*' );\n\t\t\t\t}\n\t\t\t\t// Generic postMessage API\n\t\t\t\telse {\n\t\t\t\t\tiframe.contentWindow.postMessage( 'slide:start', '*' );\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Stop playback of any embedded content inside of\n\t * the targeted slide.\n\t *\n\t * @param {HTMLElement} element\n\t */\n\tstopEmbeddedContent( element, options = {} ) {\n\n\t\toptions = extend( {\n\t\t\t// Defaults\n\t\t\tunloadIframes: true\n\t\t}, options );\n\n\t\tif( element && element.parentNode ) {\n\t\t\t// HTML5 media elements\n\t\t\tqueryAll( element, 'video, audio' ).forEach( el => {\n\t\t\t\tif( !el.hasAttribute( 'data-ignore' ) && typeof el.pause === 'function' ) {\n\t\t\t\t\tel.setAttribute('data-paused-by-reveal', '');\n\t\t\t\t\tel.pause();\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\t// Generic postMessage API for non-lazy loaded iframes\n\t\t\tqueryAll( element, 'iframe' ).forEach( el => {\n\t\t\t\tif( el.contentWindow ) el.contentWindow.postMessage( 'slide:stop', '*' );\n\t\t\t\tel.removeEventListener( 'load', this.startEmbeddedIframe );\n\t\t\t});\n\n\t\t\t// YouTube postMessage API\n\t\t\tqueryAll( element, 'iframe[src*=\"youtube.com/embed/\"]' ).forEach( el => {\n\t\t\t\tif( !el.hasAttribute( 'data-ignore' ) && el.contentWindow && typeof el.contentWindow.postMessage === 'function' ) {\n\t\t\t\t\tel.contentWindow.postMessage( '{\"event\":\"command\",\"func\":\"pauseVideo\",\"args\":\"\"}', '*' );\n\t\t\t\t}\n\t\t\t});\n\n\t\t\t// Vimeo postMessage API\n\t\t\tqueryAll( element, 'iframe[src*=\"player.vimeo.com/\"]' ).forEach( el => {\n\t\t\t\tif( !el.hasAttribute( 'data-ignore' ) && el.contentWindow && typeof el.contentWindow.postMessage === 'function' ) {\n\t\t\t\t\tel.contentWindow.postMessage( '{\"method\":\"pause\"}', '*' );\n\t\t\t\t}\n\t\t\t});\n\n\t\t\tif( options.unloadIframes === true ) {\n\t\t\t\t// Unload lazy-loaded iframes\n\t\t\t\tqueryAll( element, 'iframe[data-src]' ).forEach( el => {\n\t\t\t\t\t// Only removing the src doesn't actually unload the frame\n\t\t\t\t\t// in all browsers (Firefox) so we set it to blank first\n\t\t\t\t\tel.setAttribute( 'src', 'about:blank' );\n\t\t\t\t\tel.removeAttribute( 'src' );\n\t\t\t\t} );\n\t\t\t}\n\t\t}\n\n\t}\n\n}\n","/**\n * Handles the display of reveal.js' optional slide number.\n */\nexport default class SlideNumber {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'slide-number';\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tlet slideNumberDisplay = 'none';\n\t\tif( config.slideNumber && !this.Reveal.isPrintingPDF() ) {\n\t\t\tif( config.showSlideNumber === 'all' ) {\n\t\t\t\tslideNumberDisplay = 'block';\n\t\t\t}\n\t\t\telse if( config.showSlideNumber === 'speaker' && this.Reveal.isSpeakerNotes() ) {\n\t\t\t\tslideNumberDisplay = 'block';\n\t\t\t}\n\t\t}\n\n\t\tthis.element.style.display = slideNumberDisplay;\n\n\t}\n\n\t/**\n\t * Updates the slide number to match the current slide.\n\t */\n\tupdate() {\n\n\t\t// Update slide number if enabled\n\t\tif( this.Reveal.getConfig().slideNumber && this.element ) {\n\t\t\tthis.element.innerHTML = this.getSlideNumber();\n\t\t}\n\n\t}\n\n\t/**\n\t * Returns the HTML string corresponding to the current slide\n\t * number, including formatting.\n\t */\n\tgetSlideNumber( slide = this.Reveal.getCurrentSlide() ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\t\tlet value;\n\t\tlet format = 'h.v';\n\n\t\tif ( typeof config.slideNumber === 'function' ) {\n\t\t\tvalue = config.slideNumber( slide );\n\t\t} else {\n\t\t\t// Check if a custom number format is available\n\t\t\tif( typeof config.slideNumber === 'string' ) {\n\t\t\t\tformat = config.slideNumber;\n\t\t\t}\n\n\t\t\t// If there are ONLY vertical slides in this deck, always use\n\t\t\t// a flattened slide number\n\t\t\tif( !/c/.test( format ) && this.Reveal.getHorizontalSlides().length === 1 ) {\n\t\t\t\tformat = 'c';\n\t\t\t}\n\n\t\t\t// Offset the current slide number by 1 to make it 1-indexed\n\t\t\tlet horizontalOffset = slide && slide.dataset.visibility === 'uncounted' ? 0 : 1;\n\n\t\t\tvalue = [];\n\t\t\tswitch( format ) {\n\t\t\t\tcase 'c':\n\t\t\t\t\tvalue.push( this.Reveal.getSlidePastCount( slide ) + horizontalOffset );\n\t\t\t\t\tbreak;\n\t\t\t\tcase 'c/t':\n\t\t\t\t\tvalue.push( this.Reveal.getSlidePastCount( slide ) + horizontalOffset, '/', this.Reveal.getTotalSlides() );\n\t\t\t\t\tbreak;\n\t\t\t\tdefault:\n\t\t\t\t\tlet indices = this.Reveal.getIndices( slide );\n\t\t\t\t\tvalue.push( indices.h + horizontalOffset );\n\t\t\t\t\tlet sep = format === 'h/v' ? '/' : '.';\n\t\t\t\t\tif( this.Reveal.isVerticalSlide( slide ) ) value.push( sep, indices.v + 1 );\n\t\t\t}\n\t\t}\n\n\t\tlet url = '#' + this.Reveal.location.getHash( slide );\n\t\treturn this.formatNumber( value[0], value[1], value[2], url );\n\n\t}\n\n\t/**\n\t * Applies HTML formatting to a slide number before it's\n\t * written to the DOM.\n\t *\n\t * @param {number} a Current slide\n\t * @param {string} delimiter Character to separate slide numbers\n\t * @param {(number|*)} b Total slides\n\t * @param {HTMLElement} [url='#'+locationHash()] The url to link to\n\t * @return {string} HTML string fragment\n\t */\n\tformatNumber( a, delimiter, b, url = '#' + this.Reveal.location.getHash() ) {\n\n\t\tif( typeof b === 'number' && !isNaN( b ) ) {\n\t\t\treturn `\n\t\t\t\t\t${a}\n\t\t\t\t\t${delimiter}\n\t\t\t\t\t${b}\n\t\t\t\t\t`;\n\t\t}\n\t\telse {\n\t\t\treturn `\n\t\t\t\t\t${a}\n\t\t\t\t\t`;\n\t\t}\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.element.remove();\n\n\t}\n\n}","/**\n * Converts various color input formats to an {r:0,g:0,b:0} object.\n *\n * @param {string} color The string representation of a color\n * @example\n * colorToRgb('#000');\n * @example\n * colorToRgb('#000000');\n * @example\n * colorToRgb('rgb(0,0,0)');\n * @example\n * colorToRgb('rgba(0,0,0)');\n *\n * @return {{r: number, g: number, b: number, [a]: number}|null}\n */\nexport const colorToRgb = ( color ) => {\n\n\tlet hex3 = color.match( /^#([0-9a-f]{3})$/i );\n\tif( hex3 && hex3[1] ) {\n\t\thex3 = hex3[1];\n\t\treturn {\n\t\t\tr: parseInt( hex3.charAt( 0 ), 16 ) * 0x11,\n\t\t\tg: parseInt( hex3.charAt( 1 ), 16 ) * 0x11,\n\t\t\tb: parseInt( hex3.charAt( 2 ), 16 ) * 0x11\n\t\t};\n\t}\n\n\tlet hex6 = color.match( /^#([0-9a-f]{6})$/i );\n\tif( hex6 && hex6[1] ) {\n\t\thex6 = hex6[1];\n\t\treturn {\n\t\t\tr: parseInt( hex6.slice( 0, 2 ), 16 ),\n\t\t\tg: parseInt( hex6.slice( 2, 4 ), 16 ),\n\t\t\tb: parseInt( hex6.slice( 4, 6 ), 16 )\n\t\t};\n\t}\n\n\tlet rgb = color.match( /^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)$/i );\n\tif( rgb ) {\n\t\treturn {\n\t\t\tr: parseInt( rgb[1], 10 ),\n\t\t\tg: parseInt( rgb[2], 10 ),\n\t\t\tb: parseInt( rgb[3], 10 )\n\t\t};\n\t}\n\n\tlet rgba = color.match( /^rgba\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\,\\s*([\\d]+|[\\d]*.[\\d]+)\\s*\\)$/i );\n\tif( rgba ) {\n\t\treturn {\n\t\t\tr: parseInt( rgba[1], 10 ),\n\t\t\tg: parseInt( rgba[2], 10 ),\n\t\t\tb: parseInt( rgba[3], 10 ),\n\t\t\ta: parseFloat( rgba[4] )\n\t\t};\n\t}\n\n\treturn null;\n\n}\n\n/**\n * Calculates brightness on a scale of 0-255.\n *\n * @param {string} color See colorToRgb for supported formats.\n * @see {@link colorToRgb}\n */\nexport const colorBrightness = ( color ) => {\n\n\tif( typeof color === 'string' ) color = colorToRgb( color );\n\n\tif( color ) {\n\t\treturn ( color.r * 299 + color.g * 587 + color.b * 114 ) / 1000;\n\t}\n\n\treturn null;\n\n}","import { queryAll } from '../utils/util.js'\nimport { colorToRgb, colorBrightness } from '../utils/color.js'\n\n/**\n * Creates and updates slide backgrounds.\n */\nexport default class Backgrounds {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'backgrounds';\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t}\n\n\t/**\n\t * Creates the slide background elements and appends them\n\t * to the background container. One element is created per\n\t * slide no matter if the given slide has visible background.\n\t */\n\tcreate() {\n\n\t\t// Clear prior backgrounds\n\t\tthis.element.innerHTML = '';\n\t\tthis.element.classList.add( 'no-transition' );\n\n\t\t// Iterate over all horizontal slides\n\t\tthis.Reveal.getHorizontalSlides().forEach( slideh => {\n\n\t\t\tlet backgroundStack = this.createBackground( slideh, this.element );\n\n\t\t\t// Iterate over all vertical slides\n\t\t\tqueryAll( slideh, 'section' ).forEach( slidev => {\n\n\t\t\t\tthis.createBackground( slidev, backgroundStack );\n\n\t\t\t\tbackgroundStack.classList.add( 'stack' );\n\n\t\t\t} );\n\n\t\t} );\n\n\t\t// Add parallax background if specified\n\t\tif( this.Reveal.getConfig().parallaxBackgroundImage ) {\n\n\t\t\tthis.element.style.backgroundImage = 'url(\"' + this.Reveal.getConfig().parallaxBackgroundImage + '\")';\n\t\t\tthis.element.style.backgroundSize = this.Reveal.getConfig().parallaxBackgroundSize;\n\t\t\tthis.element.style.backgroundRepeat = this.Reveal.getConfig().parallaxBackgroundRepeat;\n\t\t\tthis.element.style.backgroundPosition = this.Reveal.getConfig().parallaxBackgroundPosition;\n\n\t\t\t// Make sure the below properties are set on the element - these properties are\n\t\t\t// needed for proper transitions to be set on the element via CSS. To remove\n\t\t\t// annoying background slide-in effect when the presentation starts, apply\n\t\t\t// these properties after short time delay\n\t\t\tsetTimeout( () => {\n\t\t\t\tthis.Reveal.getRevealElement().classList.add( 'has-parallax-background' );\n\t\t\t}, 1 );\n\n\t\t}\n\t\telse {\n\n\t\t\tthis.element.style.backgroundImage = '';\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'has-parallax-background' );\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Creates a background for the given slide.\n\t *\n\t * @param {HTMLElement} slide\n\t * @param {HTMLElement} container The element that the background\n\t * should be appended to\n\t * @return {HTMLElement} New background div\n\t */\n\tcreateBackground( slide, container ) {\n\n\t\t// Main slide background element\n\t\tlet element = document.createElement( 'div' );\n\t\telement.className = 'slide-background ' + slide.className.replace( /present|past|future/, '' );\n\n\t\t// Inner background element that wraps images/videos/iframes\n\t\tlet contentElement = document.createElement( 'div' );\n\t\tcontentElement.className = 'slide-background-content';\n\n\t\telement.appendChild( contentElement );\n\t\tcontainer.appendChild( element );\n\n\t\tslide.slideBackgroundElement = element;\n\t\tslide.slideBackgroundContentElement = contentElement;\n\n\t\t// Syncs the background to reflect all current background settings\n\t\tthis.sync( slide );\n\n\t\treturn element;\n\n\t}\n\n\t/**\n\t * Renders all of the visual properties of a slide background\n\t * based on the various background attributes.\n\t *\n\t * @param {HTMLElement} slide\n\t */\n\tsync( slide ) {\n\n\t\tconst element = slide.slideBackgroundElement,\n\t\t\tcontentElement = slide.slideBackgroundContentElement;\n\n\t\tconst data = {\n\t\t\tbackground: slide.getAttribute( 'data-background' ),\n\t\t\tbackgroundSize: slide.getAttribute( 'data-background-size' ),\n\t\t\tbackgroundImage: slide.getAttribute( 'data-background-image' ),\n\t\t\tbackgroundVideo: slide.getAttribute( 'data-background-video' ),\n\t\t\tbackgroundIframe: slide.getAttribute( 'data-background-iframe' ),\n\t\t\tbackgroundColor: slide.getAttribute( 'data-background-color' ),\n\t\t\tbackgroundRepeat: slide.getAttribute( 'data-background-repeat' ),\n\t\t\tbackgroundPosition: slide.getAttribute( 'data-background-position' ),\n\t\t\tbackgroundTransition: slide.getAttribute( 'data-background-transition' ),\n\t\t\tbackgroundOpacity: slide.getAttribute( 'data-background-opacity' ),\n\t\t};\n\n\t\tconst dataPreload = slide.hasAttribute( 'data-preload' );\n\n\t\t// Reset the prior background state in case this is not the\n\t\t// initial sync\n\t\tslide.classList.remove( 'has-dark-background' );\n\t\tslide.classList.remove( 'has-light-background' );\n\n\t\telement.removeAttribute( 'data-loaded' );\n\t\telement.removeAttribute( 'data-background-hash' );\n\t\telement.removeAttribute( 'data-background-size' );\n\t\telement.removeAttribute( 'data-background-transition' );\n\t\telement.style.backgroundColor = '';\n\n\t\tcontentElement.style.backgroundSize = '';\n\t\tcontentElement.style.backgroundRepeat = '';\n\t\tcontentElement.style.backgroundPosition = '';\n\t\tcontentElement.style.backgroundImage = '';\n\t\tcontentElement.style.opacity = '';\n\t\tcontentElement.innerHTML = '';\n\n\t\tif( data.background ) {\n\t\t\t// Auto-wrap image urls in url(...)\n\t\t\tif( /^(http|file|\\/\\/)/gi.test( data.background ) || /\\.(svg|png|jpg|jpeg|gif|bmp)([?#\\s]|$)/gi.test( data.background ) ) {\n\t\t\t\tslide.setAttribute( 'data-background-image', data.background );\n\t\t\t}\n\t\t\telse {\n\t\t\t\telement.style.background = data.background;\n\t\t\t}\n\t\t}\n\n\t\t// Create a hash for this combination of background settings.\n\t\t// This is used to determine when two slide backgrounds are\n\t\t// the same.\n\t\tif( data.background || data.backgroundColor || data.backgroundImage || data.backgroundVideo || data.backgroundIframe ) {\n\t\t\telement.setAttribute( 'data-background-hash', data.background +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundSize +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundImage +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundVideo +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundIframe +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundColor +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundRepeat +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundPosition +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundTransition +\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tdata.backgroundOpacity );\n\t\t}\n\n\t\t// Additional and optional background properties\n\t\tif( data.backgroundSize ) element.setAttribute( 'data-background-size', data.backgroundSize );\n\t\tif( data.backgroundColor ) element.style.backgroundColor = data.backgroundColor;\n\t\tif( data.backgroundTransition ) element.setAttribute( 'data-background-transition', data.backgroundTransition );\n\n\t\tif( dataPreload ) element.setAttribute( 'data-preload', '' );\n\n\t\t// Background image options are set on the content wrapper\n\t\tif( data.backgroundSize ) contentElement.style.backgroundSize = data.backgroundSize;\n\t\tif( data.backgroundRepeat ) contentElement.style.backgroundRepeat = data.backgroundRepeat;\n\t\tif( data.backgroundPosition ) contentElement.style.backgroundPosition = data.backgroundPosition;\n\t\tif( data.backgroundOpacity ) contentElement.style.opacity = data.backgroundOpacity;\n\n\t\t// If this slide has a background color, we add a class that\n\t\t// signals if it is light or dark. If the slide has no background\n\t\t// color, no class will be added\n\t\tlet contrastColor = data.backgroundColor;\n\n\t\t// If no bg color was found, or it cannot be converted by colorToRgb, check the computed background\n\t\tif( !contrastColor || !colorToRgb( contrastColor ) ) {\n\t\t\tlet computedBackgroundStyle = window.getComputedStyle( element );\n\t\t\tif( computedBackgroundStyle && computedBackgroundStyle.backgroundColor ) {\n\t\t\t\tcontrastColor = computedBackgroundStyle.backgroundColor;\n\t\t\t}\n\t\t}\n\n\t\tif( contrastColor ) {\n\t\t\tconst rgb = colorToRgb( contrastColor );\n\n\t\t\t// Ignore fully transparent backgrounds. Some browsers return\n\t\t\t// rgba(0,0,0,0) when reading the computed background color of\n\t\t\t// an element with no background\n\t\t\tif( rgb && rgb.a !== 0 ) {\n\t\t\t\tif( colorBrightness( contrastColor ) < 128 ) {\n\t\t\t\t\tslide.classList.add( 'has-dark-background' );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tslide.classList.add( 'has-light-background' );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the background elements to reflect the current\n\t * slide.\n\t *\n\t * @param {boolean} includeAll If true, the backgrounds of\n\t * all vertical slides (not just the present) will be updated.\n\t */\n\tupdate( includeAll = false ) {\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tlet indices = this.Reveal.getIndices();\n\n\t\tlet currentBackground = null;\n\n\t\t// Reverse past/future classes when in RTL mode\n\t\tlet horizontalPast = this.Reveal.getConfig().rtl ? 'future' : 'past',\n\t\t\thorizontalFuture = this.Reveal.getConfig().rtl ? 'past' : 'future';\n\n\t\t// Update the classes of all backgrounds to match the\n\t\t// states of their slides (past/present/future)\n\t\tArray.from( this.element.childNodes ).forEach( ( backgroundh, h ) => {\n\n\t\t\tbackgroundh.classList.remove( 'past', 'present', 'future' );\n\n\t\t\tif( h < indices.h ) {\n\t\t\t\tbackgroundh.classList.add( horizontalPast );\n\t\t\t}\n\t\t\telse if ( h > indices.h ) {\n\t\t\t\tbackgroundh.classList.add( horizontalFuture );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tbackgroundh.classList.add( 'present' );\n\n\t\t\t\t// Store a reference to the current background element\n\t\t\t\tcurrentBackground = backgroundh;\n\t\t\t}\n\n\t\t\tif( includeAll || h === indices.h ) {\n\t\t\t\tqueryAll( backgroundh, '.slide-background' ).forEach( ( backgroundv, v ) => {\n\n\t\t\t\t\tbackgroundv.classList.remove( 'past', 'present', 'future' );\n\n\t\t\t\t\tif( v < indices.v ) {\n\t\t\t\t\t\tbackgroundv.classList.add( 'past' );\n\t\t\t\t\t}\n\t\t\t\t\telse if ( v > indices.v ) {\n\t\t\t\t\t\tbackgroundv.classList.add( 'future' );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tbackgroundv.classList.add( 'present' );\n\n\t\t\t\t\t\t// Only if this is the present horizontal and vertical slide\n\t\t\t\t\t\tif( h === indices.h ) currentBackground = backgroundv;\n\t\t\t\t\t}\n\n\t\t\t\t} );\n\t\t\t}\n\n\t\t} );\n\n\t\t// Stop content inside of previous backgrounds\n\t\tif( this.previousBackground ) {\n\n\t\t\tthis.Reveal.slideContent.stopEmbeddedContent( this.previousBackground, { unloadIframes: !this.Reveal.slideContent.shouldPreload( this.previousBackground ) } );\n\n\t\t}\n\n\t\t// Start content in the current background\n\t\tif( currentBackground ) {\n\n\t\t\tthis.Reveal.slideContent.startEmbeddedContent( currentBackground );\n\n\t\t\tlet currentBackgroundContent = currentBackground.querySelector( '.slide-background-content' );\n\t\t\tif( currentBackgroundContent ) {\n\n\t\t\t\tlet backgroundImageURL = currentBackgroundContent.style.backgroundImage || '';\n\n\t\t\t\t// Restart GIFs (doesn't work in Firefox)\n\t\t\t\tif( /\\.gif/i.test( backgroundImageURL ) ) {\n\t\t\t\t\tcurrentBackgroundContent.style.backgroundImage = '';\n\t\t\t\t\twindow.getComputedStyle( currentBackgroundContent ).opacity;\n\t\t\t\t\tcurrentBackgroundContent.style.backgroundImage = backgroundImageURL;\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\t// Don't transition between identical backgrounds. This\n\t\t\t// prevents unwanted flicker.\n\t\t\tlet previousBackgroundHash = this.previousBackground ? this.previousBackground.getAttribute( 'data-background-hash' ) : null;\n\t\t\tlet currentBackgroundHash = currentBackground.getAttribute( 'data-background-hash' );\n\t\t\tif( currentBackgroundHash && currentBackgroundHash === previousBackgroundHash && currentBackground !== this.previousBackground ) {\n\t\t\t\tthis.element.classList.add( 'no-transition' );\n\t\t\t}\n\n\t\t\tthis.previousBackground = currentBackground;\n\n\t\t}\n\n\t\t// If there's a background brightness flag for this slide,\n\t\t// bubble it to the .reveal container\n\t\tif( currentSlide ) {\n\t\t\t[ 'has-light-background', 'has-dark-background' ].forEach( classToBubble => {\n\t\t\t\tif( currentSlide.classList.contains( classToBubble ) ) {\n\t\t\t\t\tthis.Reveal.getRevealElement().classList.add( classToBubble );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.getRevealElement().classList.remove( classToBubble );\n\t\t\t\t}\n\t\t\t}, this );\n\t\t}\n\n\t\t// Allow the first background to apply without transition\n\t\tsetTimeout( () => {\n\t\t\tthis.element.classList.remove( 'no-transition' );\n\t\t}, 1 );\n\n\t}\n\n\t/**\n\t * Updates the position of the parallax background based\n\t * on the current slide index.\n\t */\n\tupdateParallax() {\n\n\t\tlet indices = this.Reveal.getIndices();\n\n\t\tif( this.Reveal.getConfig().parallaxBackgroundImage ) {\n\n\t\t\tlet horizontalSlides = this.Reveal.getHorizontalSlides(),\n\t\t\t\tverticalSlides = this.Reveal.getVerticalSlides();\n\n\t\t\tlet backgroundSize = this.element.style.backgroundSize.split( ' ' ),\n\t\t\t\tbackgroundWidth, backgroundHeight;\n\n\t\t\tif( backgroundSize.length === 1 ) {\n\t\t\t\tbackgroundWidth = backgroundHeight = parseInt( backgroundSize[0], 10 );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tbackgroundWidth = parseInt( backgroundSize[0], 10 );\n\t\t\t\tbackgroundHeight = parseInt( backgroundSize[1], 10 );\n\t\t\t}\n\n\t\t\tlet slideWidth = this.element.offsetWidth,\n\t\t\t\thorizontalSlideCount = horizontalSlides.length,\n\t\t\t\thorizontalOffsetMultiplier,\n\t\t\t\thorizontalOffset;\n\n\t\t\tif( typeof this.Reveal.getConfig().parallaxBackgroundHorizontal === 'number' ) {\n\t\t\t\thorizontalOffsetMultiplier = this.Reveal.getConfig().parallaxBackgroundHorizontal;\n\t\t\t}\n\t\t\telse {\n\t\t\t\thorizontalOffsetMultiplier = horizontalSlideCount > 1 ? ( backgroundWidth - slideWidth ) / ( horizontalSlideCount-1 ) : 0;\n\t\t\t}\n\n\t\t\thorizontalOffset = horizontalOffsetMultiplier * indices.h * -1;\n\n\t\t\tlet slideHeight = this.element.offsetHeight,\n\t\t\t\tverticalSlideCount = verticalSlides.length,\n\t\t\t\tverticalOffsetMultiplier,\n\t\t\t\tverticalOffset;\n\n\t\t\tif( typeof this.Reveal.getConfig().parallaxBackgroundVertical === 'number' ) {\n\t\t\t\tverticalOffsetMultiplier = this.Reveal.getConfig().parallaxBackgroundVertical;\n\t\t\t}\n\t\t\telse {\n\t\t\t\tverticalOffsetMultiplier = ( backgroundHeight - slideHeight ) / ( verticalSlideCount-1 );\n\t\t\t}\n\n\t\t\tverticalOffset = verticalSlideCount > 0 ? verticalOffsetMultiplier * indices.v : 0;\n\n\t\t\tthis.element.style.backgroundPosition = horizontalOffset + 'px ' + -verticalOffset + 'px';\n\n\t\t}\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.element.remove();\n\n\t}\n\n}\n","\nexport const SLIDES_SELECTOR = '.slides section';\nexport const HORIZONTAL_SLIDES_SELECTOR = '.slides>section';\nexport const VERTICAL_SLIDES_SELECTOR = '.slides>section.present>section';\n\n// Methods that may not be invoked via the postMessage API\nexport const POST_MESSAGE_METHOD_BLACKLIST = /registerPlugin|registerKeyboardShortcut|addKeyBinding|addEventListener/;\n\n// Regex for retrieving the fragment style from a class attribute\nexport const FRAGMENT_STYLE_REGEX = /fade-(down|up|right|left|out|in-then-out|in-then-semi-out)|semi-fade-out|current-visible|shrink|grow/;","import { queryAll, extend, createStyleSheet, matches, closest } from '../utils/util.js'\nimport { FRAGMENT_STYLE_REGEX } from '../utils/constants.js'\n\n// Counter used to generate unique IDs for auto-animated elements\nlet autoAnimateCounter = 0;\n\n/**\n * Automatically animates matching elements across\n * slides with the [data-auto-animate] attribute.\n */\nexport default class AutoAnimate {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\t/**\n\t * Runs an auto-animation between the given slides.\n\t *\n\t * @param {HTMLElement} fromSlide\n\t * @param {HTMLElement} toSlide\n\t */\n\trun( fromSlide, toSlide ) {\n\n\t\t// Clean up after prior animations\n\t\tthis.reset();\n\n\t\tlet allSlides = this.Reveal.getSlides();\n\t\tlet toSlideIndex = allSlides.indexOf( toSlide );\n\t\tlet fromSlideIndex = allSlides.indexOf( fromSlide );\n\n\t\t// Ensure that both slides are auto-animate targets with the same data-auto-animate-id value\n\t\t// (including null if absent on both) and that data-auto-animate-restart isn't set on the\n\t\t// physically latter slide (independent of slide direction)\n\t\tif( fromSlide.hasAttribute( 'data-auto-animate' ) && toSlide.hasAttribute( 'data-auto-animate' )\n\t\t\t\t&& fromSlide.getAttribute( 'data-auto-animate-id' ) === toSlide.getAttribute( 'data-auto-animate-id' ) \n\t\t\t\t&& !( toSlideIndex > fromSlideIndex ? toSlide : fromSlide ).hasAttribute( 'data-auto-animate-restart' ) ) {\n\n\t\t\t// Create a new auto-animate sheet\n\t\t\tthis.autoAnimateStyleSheet = this.autoAnimateStyleSheet || createStyleSheet();\n\n\t\t\tlet animationOptions = this.getAutoAnimateOptions( toSlide );\n\n\t\t\t// Set our starting state\n\t\t\tfromSlide.dataset.autoAnimate = 'pending';\n\t\t\ttoSlide.dataset.autoAnimate = 'pending';\n\n\t\t\t// Flag the navigation direction, needed for fragment buildup\n\t\t\tanimationOptions.slideDirection = toSlideIndex > fromSlideIndex ? 'forward' : 'backward';\n\n\t\t\t// Inject our auto-animate styles for this transition\n\t\t\tlet css = this.getAutoAnimatableElements( fromSlide, toSlide ).map( elements => {\n\t\t\t\treturn this.autoAnimateElements( elements.from, elements.to, elements.options || {}, animationOptions, autoAnimateCounter++ );\n\t\t\t} );\n\n\t\t\t// Animate unmatched elements, if enabled\n\t\t\tif( toSlide.dataset.autoAnimateUnmatched !== 'false' && this.Reveal.getConfig().autoAnimateUnmatched === true ) {\n\n\t\t\t\t// Our default timings for unmatched elements\n\t\t\t\tlet defaultUnmatchedDuration = animationOptions.duration * 0.8,\n\t\t\t\t\tdefaultUnmatchedDelay = animationOptions.duration * 0.2;\n\n\t\t\t\tthis.getUnmatchedAutoAnimateElements( toSlide ).forEach( unmatchedElement => {\n\n\t\t\t\t\tlet unmatchedOptions = this.getAutoAnimateOptions( unmatchedElement, animationOptions );\n\t\t\t\t\tlet id = 'unmatched';\n\n\t\t\t\t\t// If there is a duration or delay set specifically for this\n\t\t\t\t\t// element our unmatched elements should adhere to those\n\t\t\t\t\tif( unmatchedOptions.duration !== animationOptions.duration || unmatchedOptions.delay !== animationOptions.delay ) {\n\t\t\t\t\t\tid = 'unmatched-' + autoAnimateCounter++;\n\t\t\t\t\t\tcss.push( `[data-auto-animate=\"running\"] [data-auto-animate-target=\"${id}\"] { transition: opacity ${unmatchedOptions.duration}s ease ${unmatchedOptions.delay}s; }` );\n\t\t\t\t\t}\n\n\t\t\t\t\tunmatchedElement.dataset.autoAnimateTarget = id;\n\n\t\t\t\t}, this );\n\n\t\t\t\t// Our default transition for unmatched elements\n\t\t\t\tcss.push( `[data-auto-animate=\"running\"] [data-auto-animate-target=\"unmatched\"] { transition: opacity ${defaultUnmatchedDuration}s ease ${defaultUnmatchedDelay}s; }` );\n\n\t\t\t}\n\n\t\t\t// Setting the whole chunk of CSS at once is the most\n\t\t\t// efficient way to do this. Using sheet.insertRule\n\t\t\t// is multiple factors slower.\n\t\t\tthis.autoAnimateStyleSheet.innerHTML = css.join( '' );\n\n\t\t\t// Start the animation next cycle\n\t\t\trequestAnimationFrame( () => {\n\t\t\t\tif( this.autoAnimateStyleSheet ) {\n\t\t\t\t\t// This forces our newly injected styles to be applied in Firefox\n\t\t\t\t\tgetComputedStyle( this.autoAnimateStyleSheet ).fontWeight;\n\n\t\t\t\t\ttoSlide.dataset.autoAnimate = 'running';\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\ttype: 'autoanimate',\n\t\t\t\tdata: {\n\t\t\t\t\tfromSlide,\n\t\t\t\t\ttoSlide,\n\t\t\t\t\tsheet: this.autoAnimateStyleSheet\n\t\t\t\t}\n\t\t\t});\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Rolls back all changes that we've made to the DOM so\n\t * that as part of animating.\n\t */\n\treset() {\n\n\t\t// Reset slides\n\t\tqueryAll( this.Reveal.getRevealElement(), '[data-auto-animate]:not([data-auto-animate=\"\"])' ).forEach( element => {\n\t\t\telement.dataset.autoAnimate = '';\n\t\t} );\n\n\t\t// Reset elements\n\t\tqueryAll( this.Reveal.getRevealElement(), '[data-auto-animate-target]' ).forEach( element => {\n\t\t\tdelete element.dataset.autoAnimateTarget;\n\t\t} );\n\n\t\t// Remove the animation sheet\n\t\tif( this.autoAnimateStyleSheet && this.autoAnimateStyleSheet.parentNode ) {\n\t\t\tthis.autoAnimateStyleSheet.parentNode.removeChild( this.autoAnimateStyleSheet );\n\t\t\tthis.autoAnimateStyleSheet = null;\n\t\t}\n\n\t}\n\n\t/**\n\t * Creates a FLIP animation where the `to` element starts out\n\t * in the `from` element position and animates to its original\n\t * state.\n\t *\n\t * @param {HTMLElement} from\n\t * @param {HTMLElement} to\n\t * @param {Object} elementOptions Options for this element pair\n\t * @param {Object} animationOptions Options set at the slide level\n\t * @param {String} id Unique ID that we can use to identify this\n\t * auto-animate element in the DOM\n\t */\n\tautoAnimateElements( from, to, elementOptions, animationOptions, id ) {\n\n\t\t// 'from' elements are given a data-auto-animate-target with no value,\n\t\t// 'to' elements are are given a data-auto-animate-target with an ID\n\t\tfrom.dataset.autoAnimateTarget = '';\n\t\tto.dataset.autoAnimateTarget = id;\n\n\t\t// Each element may override any of the auto-animate options\n\t\t// like transition easing, duration and delay via data-attributes\n\t\tlet options = this.getAutoAnimateOptions( to, animationOptions );\n\n\t\t// If we're using a custom element matcher the element options\n\t\t// may contain additional transition overrides\n\t\tif( typeof elementOptions.delay !== 'undefined' ) options.delay = elementOptions.delay;\n\t\tif( typeof elementOptions.duration !== 'undefined' ) options.duration = elementOptions.duration;\n\t\tif( typeof elementOptions.easing !== 'undefined' ) options.easing = elementOptions.easing;\n\n\t\tlet fromProps = this.getAutoAnimatableProperties( 'from', from, elementOptions ),\n\t\t\ttoProps = this.getAutoAnimatableProperties( 'to', to, elementOptions );\n\n\t\t// Maintain fragment visibility for matching elements when\n\t\t// we're navigating forwards, this way the viewer won't need\n\t\t// to step through the same fragments twice\n\t\tif( to.classList.contains( 'fragment' ) ) {\n\n\t\t\t// Don't auto-animate the opacity of fragments to avoid\n\t\t\t// conflicts with fragment animations\n\t\t\tdelete toProps.styles['opacity'];\n\n\t\t\tif( from.classList.contains( 'fragment' ) ) {\n\n\t\t\t\tlet fromFragmentStyle = ( from.className.match( FRAGMENT_STYLE_REGEX ) || [''] )[0];\n\t\t\t\tlet toFragmentStyle = ( to.className.match( FRAGMENT_STYLE_REGEX ) || [''] )[0];\n\n\t\t\t\t// Only skip the fragment if the fragment animation style\n\t\t\t\t// remains unchanged\n\t\t\t\tif( fromFragmentStyle === toFragmentStyle && animationOptions.slideDirection === 'forward' ) {\n\t\t\t\t\tto.classList.add( 'visible', 'disabled' );\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t\t// If translation and/or scaling are enabled, css transform\n\t\t// the 'to' element so that it matches the position and size\n\t\t// of the 'from' element\n\t\tif( elementOptions.translate !== false || elementOptions.scale !== false ) {\n\n\t\t\tlet presentationScale = this.Reveal.getScale();\n\n\t\t\tlet delta = {\n\t\t\t\tx: ( fromProps.x - toProps.x ) / presentationScale,\n\t\t\t\ty: ( fromProps.y - toProps.y ) / presentationScale,\n\t\t\t\tscaleX: fromProps.width / toProps.width,\n\t\t\t\tscaleY: fromProps.height / toProps.height\n\t\t\t};\n\n\t\t\t// Limit decimal points to avoid 0.0001px blur and stutter\n\t\t\tdelta.x = Math.round( delta.x * 1000 ) / 1000;\n\t\t\tdelta.y = Math.round( delta.y * 1000 ) / 1000;\n\t\t\tdelta.scaleX = Math.round( delta.scaleX * 1000 ) / 1000;\n\t\t\tdelta.scaleX = Math.round( delta.scaleX * 1000 ) / 1000;\n\n\t\t\tlet translate = elementOptions.translate !== false && ( delta.x !== 0 || delta.y !== 0 ),\n\t\t\t\tscale = elementOptions.scale !== false && ( delta.scaleX !== 0 || delta.scaleY !== 0 );\n\n\t\t\t// No need to transform if nothing's changed\n\t\t\tif( translate || scale ) {\n\n\t\t\t\tlet transform = [];\n\n\t\t\t\tif( translate ) transform.push( `translate(${delta.x}px, ${delta.y}px)` );\n\t\t\t\tif( scale ) transform.push( `scale(${delta.scaleX}, ${delta.scaleY})` );\n\n\t\t\t\tfromProps.styles['transform'] = transform.join( ' ' );\n\t\t\t\tfromProps.styles['transform-origin'] = 'top left';\n\n\t\t\t\ttoProps.styles['transform'] = 'none';\n\n\t\t\t}\n\n\t\t}\n\n\t\t// Delete all unchanged 'to' styles\n\t\tfor( let propertyName in toProps.styles ) {\n\t\t\tconst toValue = toProps.styles[propertyName];\n\t\t\tconst fromValue = fromProps.styles[propertyName];\n\n\t\t\tif( toValue === fromValue ) {\n\t\t\t\tdelete toProps.styles[propertyName];\n\t\t\t}\n\t\t\telse {\n\t\t\t\t// If these property values were set via a custom matcher providing\n\t\t\t\t// an explicit 'from' and/or 'to' value, we always inject those values.\n\t\t\t\tif( toValue.explicitValue === true ) {\n\t\t\t\t\ttoProps.styles[propertyName] = toValue.value;\n\t\t\t\t}\n\n\t\t\t\tif( fromValue.explicitValue === true ) {\n\t\t\t\t\tfromProps.styles[propertyName] = fromValue.value;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tlet css = '';\n\n\t\tlet toStyleProperties = Object.keys( toProps.styles );\n\n\t\t// Only create animate this element IF at least one style\n\t\t// property has changed\n\t\tif( toStyleProperties.length > 0 ) {\n\n\t\t\t// Instantly move to the 'from' state\n\t\t\tfromProps.styles['transition'] = 'none';\n\n\t\t\t// Animate towards the 'to' state\n\t\t\ttoProps.styles['transition'] = `all ${options.duration}s ${options.easing} ${options.delay}s`;\n\t\t\ttoProps.styles['transition-property'] = toStyleProperties.join( ', ' );\n\t\t\ttoProps.styles['will-change'] = toStyleProperties.join( ', ' );\n\n\t\t\t// Build up our custom CSS. We need to override inline styles\n\t\t\t// so we need to make our styles vErY IMPORTANT!1!!\n\t\t\tlet fromCSS = Object.keys( fromProps.styles ).map( propertyName => {\n\t\t\t\treturn propertyName + ': ' + fromProps.styles[propertyName] + ' !important;';\n\t\t\t} ).join( '' );\n\n\t\t\tlet toCSS = Object.keys( toProps.styles ).map( propertyName => {\n\t\t\t\treturn propertyName + ': ' + toProps.styles[propertyName] + ' !important;';\n\t\t\t} ).join( '' );\n\n\t\t\tcss = \t'[data-auto-animate-target=\"'+ id +'\"] {'+ fromCSS +'}' +\n\t\t\t\t\t'[data-auto-animate=\"running\"] [data-auto-animate-target=\"'+ id +'\"] {'+ toCSS +'}';\n\n\t\t}\n\n\t\treturn css;\n\n\t}\n\n\t/**\n\t * Returns the auto-animate options for the given element.\n\t *\n\t * @param {HTMLElement} element Element to pick up options\n\t * from, either a slide or an animation target\n\t * @param {Object} [inheritedOptions] Optional set of existing\n\t * options\n\t */\n\tgetAutoAnimateOptions( element, inheritedOptions ) {\n\n\t\tlet options = {\n\t\t\teasing: this.Reveal.getConfig().autoAnimateEasing,\n\t\t\tduration: this.Reveal.getConfig().autoAnimateDuration,\n\t\t\tdelay: 0\n\t\t};\n\n\t\toptions = extend( options, inheritedOptions );\n\n\t\t// Inherit options from parent elements\n\t\tif( element.parentNode ) {\n\t\t\tlet autoAnimatedParent = closest( element.parentNode, '[data-auto-animate-target]' );\n\t\t\tif( autoAnimatedParent ) {\n\t\t\t\toptions = this.getAutoAnimateOptions( autoAnimatedParent, options );\n\t\t\t}\n\t\t}\n\n\t\tif( element.dataset.autoAnimateEasing ) {\n\t\t\toptions.easing = element.dataset.autoAnimateEasing;\n\t\t}\n\n\t\tif( element.dataset.autoAnimateDuration ) {\n\t\t\toptions.duration = parseFloat( element.dataset.autoAnimateDuration );\n\t\t}\n\n\t\tif( element.dataset.autoAnimateDelay ) {\n\t\t\toptions.delay = parseFloat( element.dataset.autoAnimateDelay );\n\t\t}\n\n\t\treturn options;\n\n\t}\n\n\t/**\n\t * Returns an object containing all of the properties\n\t * that can be auto-animated for the given element and\n\t * their current computed values.\n\t *\n\t * @param {String} direction 'from' or 'to'\n\t */\n\tgetAutoAnimatableProperties( direction, element, elementOptions ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\n\t\tlet properties = { styles: [] };\n\n\t\t// Position and size\n\t\tif( elementOptions.translate !== false || elementOptions.scale !== false ) {\n\t\t\tlet bounds;\n\n\t\t\t// Custom auto-animate may optionally return a custom tailored\n\t\t\t// measurement function\n\t\t\tif( typeof elementOptions.measure === 'function' ) {\n\t\t\t\tbounds = elementOptions.measure( element );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tif( config.center ) {\n\t\t\t\t\t// More precise, but breaks when used in combination\n\t\t\t\t\t// with zoom for scaling the deck ¯\\_(ツ)_/¯\n\t\t\t\t\tbounds = element.getBoundingClientRect();\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tlet scale = this.Reveal.getScale();\n\t\t\t\t\tbounds = {\n\t\t\t\t\t\tx: element.offsetLeft * scale,\n\t\t\t\t\t\ty: element.offsetTop * scale,\n\t\t\t\t\t\twidth: element.offsetWidth * scale,\n\t\t\t\t\t\theight: element.offsetHeight * scale\n\t\t\t\t\t};\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tproperties.x = bounds.x;\n\t\t\tproperties.y = bounds.y;\n\t\t\tproperties.width = bounds.width;\n\t\t\tproperties.height = bounds.height;\n\t\t}\n\n\t\tconst computedStyles = getComputedStyle( element );\n\n\t\t// CSS styles\n\t\t( elementOptions.styles || config.autoAnimateStyles ).forEach( style => {\n\t\t\tlet value;\n\n\t\t\t// `style` is either the property name directly, or an object\n\t\t\t// definition of a style property\n\t\t\tif( typeof style === 'string' ) style = { property: style };\n\n\t\t\tif( typeof style.from !== 'undefined' && direction === 'from' ) {\n\t\t\t\tvalue = { value: style.from, explicitValue: true };\n\t\t\t}\n\t\t\telse if( typeof style.to !== 'undefined' && direction === 'to' ) {\n\t\t\t\tvalue = { value: style.to, explicitValue: true };\n\t\t\t}\n\t\t\telse {\n\t\t\t\tvalue = computedStyles[style.property];\n\t\t\t}\n\n\t\t\tif( value !== '' ) {\n\t\t\t\tproperties.styles[style.property] = value;\n\t\t\t}\n\t\t} );\n\n\t\treturn properties;\n\n\t}\n\n\t/**\n\t * Get a list of all element pairs that we can animate\n\t * between the given slides.\n\t *\n\t * @param {HTMLElement} fromSlide\n\t * @param {HTMLElement} toSlide\n\t *\n\t * @return {Array} Each value is an array where [0] is\n\t * the element we're animating from and [1] is the\n\t * element we're animating to\n\t */\n\tgetAutoAnimatableElements( fromSlide, toSlide ) {\n\n\t\tlet matcher = typeof this.Reveal.getConfig().autoAnimateMatcher === 'function' ? this.Reveal.getConfig().autoAnimateMatcher : this.getAutoAnimatePairs;\n\n\t\tlet pairs = matcher.call( this, fromSlide, toSlide );\n\n\t\tlet reserved = [];\n\n\t\t// Remove duplicate pairs\n\t\treturn pairs.filter( ( pair, index ) => {\n\t\t\tif( reserved.indexOf( pair.to ) === -1 ) {\n\t\t\t\treserved.push( pair.to );\n\t\t\t\treturn true;\n\t\t\t}\n\t\t} );\n\n\t}\n\n\t/**\n\t * Identifies matching elements between slides.\n\t *\n\t * You can specify a custom matcher function by using\n\t * the `autoAnimateMatcher` config option.\n\t */\n\tgetAutoAnimatePairs( fromSlide, toSlide ) {\n\n\t\tlet pairs = [];\n\n\t\tconst codeNodes = 'pre';\n\t\tconst textNodes = 'h1, h2, h3, h4, h5, h6, p, li';\n\t\tconst mediaNodes = 'img, video, iframe';\n\n\t\t// Eplicit matches via data-id\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, '[data-id]', node => {\n\t\t\treturn node.nodeName + ':::' + node.getAttribute( 'data-id' );\n\t\t} );\n\n\t\t// Text\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, textNodes, node => {\n\t\t\treturn node.nodeName + ':::' + node.innerText;\n\t\t} );\n\n\t\t// Media\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, mediaNodes, node => {\n\t\t\treturn node.nodeName + ':::' + ( node.getAttribute( 'src' ) || node.getAttribute( 'data-src' ) );\n\t\t} );\n\n\t\t// Code\n\t\tthis.findAutoAnimateMatches( pairs, fromSlide, toSlide, codeNodes, node => {\n\t\t\treturn node.nodeName + ':::' + node.innerText;\n\t\t} );\n\n\t\tpairs.forEach( pair => {\n\n\t\t\t// Disable scale transformations on text nodes, we transition\n\t\t\t// each individual text property instead\n\t\t\tif( matches( pair.from, textNodes ) ) {\n\t\t\t\tpair.options = { scale: false };\n\t\t\t}\n\t\t\t// Animate individual lines of code\n\t\t\telse if( matches( pair.from, codeNodes ) ) {\n\n\t\t\t\t// Transition the code block's width and height instead of scaling\n\t\t\t\t// to prevent its content from being squished\n\t\t\t\tpair.options = { scale: false, styles: [ 'width', 'height' ] };\n\n\t\t\t\t// Lines of code\n\t\t\t\tthis.findAutoAnimateMatches( pairs, pair.from, pair.to, '.hljs .hljs-ln-code', node => {\n\t\t\t\t\treturn node.textContent;\n\t\t\t\t}, {\n\t\t\t\t\tscale: false,\n\t\t\t\t\tstyles: [],\n\t\t\t\t\tmeasure: this.getLocalBoundingBox.bind( this )\n\t\t\t\t} );\n\n\t\t\t\t// Line numbers\n\t\t\t\tthis.findAutoAnimateMatches( pairs, pair.from, pair.to, '.hljs .hljs-ln-line[data-line-number]', node => {\n\t\t\t\t\treturn node.getAttribute( 'data-line-number' );\n\t\t\t\t}, {\n\t\t\t\t\tscale: false,\n\t\t\t\t\tstyles: [ 'width' ],\n\t\t\t\t\tmeasure: this.getLocalBoundingBox.bind( this )\n\t\t\t\t} );\n\n\t\t\t}\n\n\t\t}, this );\n\n\t\treturn pairs;\n\n\t}\n\n\t/**\n\t * Helper method which returns a bounding box based on\n\t * the given elements offset coordinates.\n\t *\n\t * @param {HTMLElement} element\n\t * @return {Object} x, y, width, height\n\t */\n\tgetLocalBoundingBox( element ) {\n\n\t\tconst presentationScale = this.Reveal.getScale();\n\n\t\treturn {\n\t\t\tx: Math.round( ( element.offsetLeft * presentationScale ) * 100 ) / 100,\n\t\t\ty: Math.round( ( element.offsetTop * presentationScale ) * 100 ) / 100,\n\t\t\twidth: Math.round( ( element.offsetWidth * presentationScale ) * 100 ) / 100,\n\t\t\theight: Math.round( ( element.offsetHeight * presentationScale ) * 100 ) / 100\n\t\t};\n\n\t}\n\n\t/**\n\t * Finds matching elements between two slides.\n\t *\n\t * @param {Array} pairs \tList of pairs to push matches to\n\t * @param {HTMLElement} fromScope Scope within the from element exists\n\t * @param {HTMLElement} toScope Scope within the to element exists\n\t * @param {String} selector CSS selector of the element to match\n\t * @param {Function} serializer A function that accepts an element and returns\n\t * a stringified ID based on its contents\n\t * @param {Object} animationOptions Optional config options for this pair\n\t */\n\tfindAutoAnimateMatches( pairs, fromScope, toScope, selector, serializer, animationOptions ) {\n\n\t\tlet fromMatches = {};\n\t\tlet toMatches = {};\n\n\t\t[].slice.call( fromScope.querySelectorAll( selector ) ).forEach( ( element, i ) => {\n\t\t\tconst key = serializer( element );\n\t\t\tif( typeof key === 'string' && key.length ) {\n\t\t\t\tfromMatches[key] = fromMatches[key] || [];\n\t\t\t\tfromMatches[key].push( element );\n\t\t\t}\n\t\t} );\n\n\t\t[].slice.call( toScope.querySelectorAll( selector ) ).forEach( ( element, i ) => {\n\t\t\tconst key = serializer( element );\n\t\t\ttoMatches[key] = toMatches[key] || [];\n\t\t\ttoMatches[key].push( element );\n\n\t\t\tlet fromElement;\n\n\t\t\t// Retrieve the 'from' element\n\t\t\tif( fromMatches[key] ) {\n\t\t\t\tconst pimaryIndex = toMatches[key].length - 1;\n\t\t\t\tconst secondaryIndex = fromMatches[key].length - 1;\n\n\t\t\t\t// If there are multiple identical from elements, retrieve\n\t\t\t\t// the one at the same index as our to-element.\n\t\t\t\tif( fromMatches[key][ pimaryIndex ] ) {\n\t\t\t\t\tfromElement = fromMatches[key][ pimaryIndex ];\n\t\t\t\t\tfromMatches[key][ pimaryIndex ] = null;\n\t\t\t\t}\n\t\t\t\t// If there are no matching from-elements at the same index,\n\t\t\t\t// use the last one.\n\t\t\t\telse if( fromMatches[key][ secondaryIndex ] ) {\n\t\t\t\t\tfromElement = fromMatches[key][ secondaryIndex ];\n\t\t\t\t\tfromMatches[key][ secondaryIndex ] = null;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t// If we've got a matching pair, push it to the list of pairs\n\t\t\tif( fromElement ) {\n\t\t\t\tpairs.push({\n\t\t\t\t\tfrom: fromElement,\n\t\t\t\t\tto: element,\n\t\t\t\t\toptions: animationOptions\n\t\t\t\t});\n\t\t\t}\n\t\t} );\n\n\t}\n\n\t/**\n\t * Returns a all elements within the given scope that should\n\t * be considered unmatched in an auto-animate transition. If\n\t * fading of unmatched elements is turned on, these elements\n\t * will fade when going between auto-animate slides.\n\t *\n\t * Note that parents of auto-animate targets are NOT considerd\n\t * unmatched since fading them would break the auto-animation.\n\t *\n\t * @param {HTMLElement} rootElement\n\t * @return {Array}\n\t */\n\tgetUnmatchedAutoAnimateElements( rootElement ) {\n\n\t\treturn [].slice.call( rootElement.children ).reduce( ( result, element ) => {\n\n\t\t\tconst containsAnimatedElements = element.querySelector( '[data-auto-animate-target]' );\n\n\t\t\t// The element is unmatched if\n\t\t\t// - It is not an auto-animate target\n\t\t\t// - It does not contain any auto-animate targets\n\t\t\tif( !element.hasAttribute( 'data-auto-animate-target' ) && !containsAnimatedElements ) {\n\t\t\t\tresult.push( element );\n\t\t\t}\n\n\t\t\tif( element.querySelector( '[data-auto-animate-target]' ) ) {\n\t\t\t\tresult = result.concat( this.getUnmatchedAutoAnimateElements( element ) );\n\t\t\t}\n\n\t\t\treturn result;\n\n\t\t}, [] );\n\n\t}\n\n}\n","import { extend, queryAll } from '../utils/util.js'\n\n/**\n * Handles sorting and navigation of slide fragments.\n * Fragments are elements within a slide that are\n * revealed/animated incrementally.\n */\nexport default class Fragments {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.fragments === false ) {\n\t\t\tthis.disable();\n\t\t}\n\t\telse if( oldConfig.fragments === false ) {\n\t\t\tthis.enable();\n\t\t}\n\n\t}\n\n\t/**\n\t * If fragments are disabled in the deck, they should all be\n\t * visible rather than stepped through.\n\t */\n\tdisable() {\n\n\t\tqueryAll( this.Reveal.getSlidesElement(), '.fragment' ).forEach( element => {\n\t\t\telement.classList.add( 'visible' );\n\t\t\telement.classList.remove( 'current-fragment' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Reverse of #disable(). Only called if fragments have\n\t * previously been disabled.\n\t */\n\tenable() {\n\n\t\tqueryAll( this.Reveal.getSlidesElement(), '.fragment' ).forEach( element => {\n\t\t\telement.classList.remove( 'visible' );\n\t\t\telement.classList.remove( 'current-fragment' );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Returns an object describing the available fragment\n\t * directions.\n\t *\n\t * @return {{prev: boolean, next: boolean}}\n\t */\n\tavailableRoutes() {\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide && this.Reveal.getConfig().fragments ) {\n\t\t\tlet fragments = currentSlide.querySelectorAll( '.fragment:not(.disabled)' );\n\t\t\tlet hiddenFragments = currentSlide.querySelectorAll( '.fragment:not(.disabled):not(.visible)' );\n\n\t\t\treturn {\n\t\t\t\tprev: fragments.length - hiddenFragments.length > 0,\n\t\t\t\tnext: !!hiddenFragments.length\n\t\t\t};\n\t\t}\n\t\telse {\n\t\t\treturn { prev: false, next: false };\n\t\t}\n\n\t}\n\n\t/**\n\t * Return a sorted fragments list, ordered by an increasing\n\t * \"data-fragment-index\" attribute.\n\t *\n\t * Fragments will be revealed in the order that they are returned by\n\t * this function, so you can use the index attributes to control the\n\t * order of fragment appearance.\n\t *\n\t * To maintain a sensible default fragment order, fragments are presumed\n\t * to be passed in document order. This function adds a \"fragment-index\"\n\t * attribute to each node if such an attribute is not already present,\n\t * and sets that attribute to an integer value which is the position of\n\t * the fragment within the fragments list.\n\t *\n\t * @param {object[]|*} fragments\n\t * @param {boolean} grouped If true the returned array will contain\n\t * nested arrays for all fragments with the same index\n\t * @return {object[]} sorted Sorted array of fragments\n\t */\n\tsort( fragments, grouped = false ) {\n\n\t\tfragments = Array.from( fragments );\n\n\t\tlet ordered = [],\n\t\t\tunordered = [],\n\t\t\tsorted = [];\n\n\t\t// Group ordered and unordered elements\n\t\tfragments.forEach( fragment => {\n\t\t\tif( fragment.hasAttribute( 'data-fragment-index' ) ) {\n\t\t\t\tlet index = parseInt( fragment.getAttribute( 'data-fragment-index' ), 10 );\n\n\t\t\t\tif( !ordered[index] ) {\n\t\t\t\t\tordered[index] = [];\n\t\t\t\t}\n\n\t\t\t\tordered[index].push( fragment );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tunordered.push( [ fragment ] );\n\t\t\t}\n\t\t} );\n\n\t\t// Append fragments without explicit indices in their\n\t\t// DOM order\n\t\tordered = ordered.concat( unordered );\n\n\t\t// Manually count the index up per group to ensure there\n\t\t// are no gaps\n\t\tlet index = 0;\n\n\t\t// Push all fragments in their sorted order to an array,\n\t\t// this flattens the groups\n\t\tordered.forEach( group => {\n\t\t\tgroup.forEach( fragment => {\n\t\t\t\tsorted.push( fragment );\n\t\t\t\tfragment.setAttribute( 'data-fragment-index', index );\n\t\t\t} );\n\n\t\t\tindex ++;\n\t\t} );\n\n\t\treturn grouped === true ? ordered : sorted;\n\n\t}\n\n\t/**\n\t * Sorts and formats all of fragments in the\n\t * presentation.\n\t */\n\tsortAll() {\n\n\t\tthis.Reveal.getHorizontalSlides().forEach( horizontalSlide => {\n\n\t\t\tlet verticalSlides = queryAll( horizontalSlide, 'section' );\n\t\t\tverticalSlides.forEach( ( verticalSlide, y ) => {\n\n\t\t\t\tthis.sort( verticalSlide.querySelectorAll( '.fragment' ) );\n\n\t\t\t}, this );\n\n\t\t\tif( verticalSlides.length === 0 ) this.sort( horizontalSlide.querySelectorAll( '.fragment' ) );\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Refreshes the fragments on the current slide so that they\n\t * have the appropriate classes (.visible + .current-fragment).\n\t *\n\t * @param {number} [index] The index of the current fragment\n\t * @param {array} [fragments] Array containing all fragments\n\t * in the current slide\n\t *\n\t * @return {{shown: array, hidden: array}}\n\t */\n\tupdate( index, fragments ) {\n\n\t\tlet changedFragments = {\n\t\t\tshown: [],\n\t\t\thidden: []\n\t\t};\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide && this.Reveal.getConfig().fragments ) {\n\n\t\t\tfragments = fragments || this.sort( currentSlide.querySelectorAll( '.fragment' ) );\n\n\t\t\tif( fragments.length ) {\n\n\t\t\t\tlet maxIndex = 0;\n\n\t\t\t\tif( typeof index !== 'number' ) {\n\t\t\t\t\tlet currentFragment = this.sort( currentSlide.querySelectorAll( '.fragment.visible' ) ).pop();\n\t\t\t\t\tif( currentFragment ) {\n\t\t\t\t\t\tindex = parseInt( currentFragment.getAttribute( 'data-fragment-index' ) || 0, 10 );\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tArray.from( fragments ).forEach( ( el, i ) => {\n\n\t\t\t\t\tif( el.hasAttribute( 'data-fragment-index' ) ) {\n\t\t\t\t\t\ti = parseInt( el.getAttribute( 'data-fragment-index' ), 10 );\n\t\t\t\t\t}\n\n\t\t\t\t\tmaxIndex = Math.max( maxIndex, i );\n\n\t\t\t\t\t// Visible fragments\n\t\t\t\t\tif( i <= index ) {\n\t\t\t\t\t\tlet wasVisible = el.classList.contains( 'visible' )\n\t\t\t\t\t\tel.classList.add( 'visible' );\n\t\t\t\t\t\tel.classList.remove( 'current-fragment' );\n\n\t\t\t\t\t\tif( i === index ) {\n\t\t\t\t\t\t\t// Announce the fragments one by one to the Screen Reader\n\t\t\t\t\t\t\tthis.Reveal.announceStatus( this.Reveal.getStatusText( el ) );\n\n\t\t\t\t\t\t\tel.classList.add( 'current-fragment' );\n\t\t\t\t\t\t\tthis.Reveal.slideContent.startEmbeddedContent( el );\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tif( !wasVisible ) {\n\t\t\t\t\t\t\tchangedFragments.shown.push( el )\n\t\t\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\t\t\ttarget: el,\n\t\t\t\t\t\t\t\ttype: 'visible',\n\t\t\t\t\t\t\t\tbubbles: false\n\t\t\t\t\t\t\t});\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\t// Hidden fragments\n\t\t\t\t\telse {\n\t\t\t\t\t\tlet wasVisible = el.classList.contains( 'visible' )\n\t\t\t\t\t\tel.classList.remove( 'visible' );\n\t\t\t\t\t\tel.classList.remove( 'current-fragment' );\n\n\t\t\t\t\t\tif( wasVisible ) {\n\t\t\t\t\t\t\tthis.Reveal.slideContent.stopEmbeddedContent( el );\n\t\t\t\t\t\t\tchangedFragments.hidden.push( el );\n\t\t\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\t\t\ttarget: el,\n\t\t\t\t\t\t\t\ttype: 'hidden',\n\t\t\t\t\t\t\t\tbubbles: false\n\t\t\t\t\t\t\t});\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t} );\n\n\t\t\t\t// Write the current fragment index to the slide
.\n\t\t\t\t// This can be used by end users to apply styles based on\n\t\t\t\t// the current fragment index.\n\t\t\t\tindex = typeof index === 'number' ? index : -1;\n\t\t\t\tindex = Math.max( Math.min( index, maxIndex ), -1 );\n\t\t\t\tcurrentSlide.setAttribute( 'data-fragment', index );\n\n\t\t\t}\n\n\t\t}\n\n\t\treturn changedFragments;\n\n\t}\n\n\t/**\n\t * Formats the fragments on the given slide so that they have\n\t * valid indices. Call this if fragments are changed in the DOM\n\t * after reveal.js has already initialized.\n\t *\n\t * @param {HTMLElement} slide\n\t * @return {Array} a list of the HTML fragments that were synced\n\t */\n\tsync( slide = this.Reveal.getCurrentSlide() ) {\n\n\t\treturn this.sort( slide.querySelectorAll( '.fragment' ) );\n\n\t}\n\n\t/**\n\t * Navigate to the specified slide fragment.\n\t *\n\t * @param {?number} index The index of the fragment that\n\t * should be shown, -1 means all are invisible\n\t * @param {number} offset Integer offset to apply to the\n\t * fragment index\n\t *\n\t * @return {boolean} true if a change was made in any\n\t * fragments visibility as part of this call\n\t */\n\tgoto( index, offset = 0 ) {\n\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide && this.Reveal.getConfig().fragments ) {\n\n\t\t\tlet fragments = this.sort( currentSlide.querySelectorAll( '.fragment:not(.disabled)' ) );\n\t\t\tif( fragments.length ) {\n\n\t\t\t\t// If no index is specified, find the current\n\t\t\t\tif( typeof index !== 'number' ) {\n\t\t\t\t\tlet lastVisibleFragment = this.sort( currentSlide.querySelectorAll( '.fragment:not(.disabled).visible' ) ).pop();\n\n\t\t\t\t\tif( lastVisibleFragment ) {\n\t\t\t\t\t\tindex = parseInt( lastVisibleFragment.getAttribute( 'data-fragment-index' ) || 0, 10 );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tindex = -1;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// Apply the offset if there is one\n\t\t\t\tindex += offset;\n\n\t\t\t\tlet changedFragments = this.update( index, fragments );\n\n\t\t\t\tif( changedFragments.hidden.length ) {\n\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\ttype: 'fragmenthidden',\n\t\t\t\t\t\tdata: {\n\t\t\t\t\t\t\tfragment: changedFragments.hidden[0],\n\t\t\t\t\t\t\tfragments: changedFragments.hidden\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\n\t\t\t\tif( changedFragments.shown.length ) {\n\t\t\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\t\t\ttype: 'fragmentshown',\n\t\t\t\t\t\tdata: {\n\t\t\t\t\t\t\tfragment: changedFragments.shown[0],\n\t\t\t\t\t\t\tfragments: changedFragments.shown\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\n\t\t\t\tthis.Reveal.controls.update();\n\t\t\t\tthis.Reveal.progress.update();\n\n\t\t\t\tif( this.Reveal.getConfig().fragmentInURL ) {\n\t\t\t\t\tthis.Reveal.location.writeURL();\n\t\t\t\t}\n\n\t\t\t\treturn !!( changedFragments.shown.length || changedFragments.hidden.length );\n\n\t\t\t}\n\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\t/**\n\t * Navigate to the next slide fragment.\n\t *\n\t * @return {boolean} true if there was a next fragment,\n\t * false otherwise\n\t */\n\tnext() {\n\n\t\treturn this.goto( null, 1 );\n\n\t}\n\n\t/**\n\t * Navigate to the previous slide fragment.\n\t *\n\t * @return {boolean} true if there was a previous fragment,\n\t * false otherwise\n\t */\n\tprev() {\n\n\t\treturn this.goto( null, -1 );\n\n\t}\n\n}","import { SLIDES_SELECTOR } from '../utils/constants.js'\nimport { extend, queryAll, transformElement } from '../utils/util.js'\n\n/**\n * Handles all logic related to the overview mode\n * (birds-eye view of all slides).\n */\nexport default class Overview {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.active = false;\n\n\t\tthis.onSlideClicked = this.onSlideClicked.bind( this );\n\n\t}\n\n\t/**\n\t * Displays the overview of slides (quick nav) by scaling\n\t * down and arranging all slide elements.\n\t */\n\tactivate() {\n\n\t\t// Only proceed if enabled in config\n\t\tif( this.Reveal.getConfig().overview && !this.isActive() ) {\n\n\t\t\tthis.active = true;\n\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'overview' );\n\n\t\t\t// Don't auto-slide while in overview mode\n\t\t\tthis.Reveal.cancelAutoSlide();\n\n\t\t\t// Move the backgrounds element into the slide container to\n\t\t\t// that the same scaling is applied\n\t\t\tthis.Reveal.getSlidesElement().appendChild( this.Reveal.getBackgroundsElement() );\n\n\t\t\t// Clicking on an overview slide navigates to it\n\t\t\tqueryAll( this.Reveal.getRevealElement(), SLIDES_SELECTOR ).forEach( slide => {\n\t\t\t\tif( !slide.classList.contains( 'stack' ) ) {\n\t\t\t\t\tslide.addEventListener( 'click', this.onSlideClicked, true );\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\t// Calculate slide sizes\n\t\t\tconst margin = 70;\n\t\t\tconst slideSize = this.Reveal.getComputedSlideSize();\n\t\t\tthis.overviewSlideWidth = slideSize.width + margin;\n\t\t\tthis.overviewSlideHeight = slideSize.height + margin;\n\n\t\t\t// Reverse in RTL mode\n\t\t\tif( this.Reveal.getConfig().rtl ) {\n\t\t\t\tthis.overviewSlideWidth = -this.overviewSlideWidth;\n\t\t\t}\n\n\t\t\tthis.Reveal.updateSlidesVisibility();\n\n\t\t\tthis.layout();\n\t\t\tthis.update();\n\n\t\t\tthis.Reveal.layout();\n\n\t\t\tconst indices = this.Reveal.getIndices();\n\n\t\t\t// Notify observers of the overview showing\n\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\ttype: 'overviewshown',\n\t\t\t\tdata: {\n\t\t\t\t\t'indexh': indices.h,\n\t\t\t\t\t'indexv': indices.v,\n\t\t\t\t\t'currentSlide': this.Reveal.getCurrentSlide()\n\t\t\t\t}\n\t\t\t});\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Uses CSS transforms to position all slides in a grid for\n\t * display inside of the overview mode.\n\t */\n\tlayout() {\n\n\t\t// Layout slides\n\t\tthis.Reveal.getHorizontalSlides().forEach( ( hslide, h ) => {\n\t\t\thslide.setAttribute( 'data-index-h', h );\n\t\t\ttransformElement( hslide, 'translate3d(' + ( h * this.overviewSlideWidth ) + 'px, 0, 0)' );\n\n\t\t\tif( hslide.classList.contains( 'stack' ) ) {\n\n\t\t\t\tqueryAll( hslide, 'section' ).forEach( ( vslide, v ) => {\n\t\t\t\t\tvslide.setAttribute( 'data-index-h', h );\n\t\t\t\t\tvslide.setAttribute( 'data-index-v', v );\n\n\t\t\t\t\ttransformElement( vslide, 'translate3d(0, ' + ( v * this.overviewSlideHeight ) + 'px, 0)' );\n\t\t\t\t} );\n\n\t\t\t}\n\t\t} );\n\n\t\t// Layout slide backgrounds\n\t\tArray.from( this.Reveal.getBackgroundsElement().childNodes ).forEach( ( hbackground, h ) => {\n\t\t\ttransformElement( hbackground, 'translate3d(' + ( h * this.overviewSlideWidth ) + 'px, 0, 0)' );\n\n\t\t\tqueryAll( hbackground, '.slide-background' ).forEach( ( vbackground, v ) => {\n\t\t\t\ttransformElement( vbackground, 'translate3d(0, ' + ( v * this.overviewSlideHeight ) + 'px, 0)' );\n\t\t\t} );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Moves the overview viewport to the current slides.\n\t * Called each time the current slide changes.\n\t */\n\tupdate() {\n\n\t\tconst vmin = Math.min( window.innerWidth, window.innerHeight );\n\t\tconst scale = Math.max( vmin / 5, 150 ) / vmin;\n\t\tconst indices = this.Reveal.getIndices();\n\n\t\tthis.Reveal.transformSlides( {\n\t\t\toverview: [\n\t\t\t\t'scale('+ scale +')',\n\t\t\t\t'translateX('+ ( -indices.h * this.overviewSlideWidth ) +'px)',\n\t\t\t\t'translateY('+ ( -indices.v * this.overviewSlideHeight ) +'px)'\n\t\t\t].join( ' ' )\n\t\t} );\n\n\t}\n\n\t/**\n\t * Exits the slide overview and enters the currently\n\t * active slide.\n\t */\n\tdeactivate() {\n\n\t\t// Only proceed if enabled in config\n\t\tif( this.Reveal.getConfig().overview ) {\n\n\t\t\tthis.active = false;\n\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'overview' );\n\n\t\t\t// Temporarily add a class so that transitions can do different things\n\t\t\t// depending on whether they are exiting/entering overview, or just\n\t\t\t// moving from slide to slide\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'overview-deactivating' );\n\n\t\t\tsetTimeout( () => {\n\t\t\t\tthis.Reveal.getRevealElement().classList.remove( 'overview-deactivating' );\n\t\t\t}, 1 );\n\n\t\t\t// Move the background element back out\n\t\t\tthis.Reveal.getRevealElement().appendChild( this.Reveal.getBackgroundsElement() );\n\n\t\t\t// Clean up changes made to slides\n\t\t\tqueryAll( this.Reveal.getRevealElement(), SLIDES_SELECTOR ).forEach( slide => {\n\t\t\t\ttransformElement( slide, '' );\n\n\t\t\t\tslide.removeEventListener( 'click', this.onSlideClicked, true );\n\t\t\t} );\n\n\t\t\t// Clean up changes made to backgrounds\n\t\t\tqueryAll( this.Reveal.getBackgroundsElement(), '.slide-background' ).forEach( background => {\n\t\t\t\ttransformElement( background, '' );\n\t\t\t} );\n\n\t\t\tthis.Reveal.transformSlides( { overview: '' } );\n\n\t\t\tconst indices = this.Reveal.getIndices();\n\n\t\t\tthis.Reveal.slide( indices.h, indices.v );\n\t\t\tthis.Reveal.layout();\n\t\t\tthis.Reveal.cueAutoSlide();\n\n\t\t\t// Notify observers of the overview hiding\n\t\t\tthis.Reveal.dispatchEvent({\n\t\t\t\ttype: 'overviewhidden',\n\t\t\t\tdata: {\n\t\t\t\t\t'indexh': indices.h,\n\t\t\t\t\t'indexv': indices.v,\n\t\t\t\t\t'currentSlide': this.Reveal.getCurrentSlide()\n\t\t\t\t}\n\t\t\t});\n\n\t\t}\n\t}\n\n\t/**\n\t * Toggles the slide overview mode on and off.\n\t *\n\t * @param {Boolean} [override] Flag which overrides the\n\t * toggle logic and forcibly sets the desired state. True means\n\t * overview is open, false means it's closed.\n\t */\n\ttoggle( override ) {\n\n\t\tif( typeof override === 'boolean' ) {\n\t\t\toverride ? this.activate() : this.deactivate();\n\t\t}\n\t\telse {\n\t\t\tthis.isActive() ? this.deactivate() : this.activate();\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if the overview is currently active.\n\t *\n\t * @return {Boolean} true if the overview is active,\n\t * false otherwise\n\t */\n\tisActive() {\n\n\t\treturn this.active;\n\n\t}\n\n\t/**\n\t * Invoked when a slide is and we're in the overview.\n\t *\n\t * @param {object} event\n\t */\n\tonSlideClicked( event ) {\n\n\t\tif( this.isActive() ) {\n\t\t\tevent.preventDefault();\n\n\t\t\tlet element = event.target;\n\n\t\t\twhile( element && !element.nodeName.match( /section/gi ) ) {\n\t\t\t\telement = element.parentNode;\n\t\t\t}\n\n\t\t\tif( element && !element.classList.contains( 'disabled' ) ) {\n\n\t\t\t\tthis.deactivate();\n\n\t\t\t\tif( element.nodeName.match( /section/gi ) ) {\n\t\t\t\t\tlet h = parseInt( element.getAttribute( 'data-index-h' ), 10 ),\n\t\t\t\t\t\tv = parseInt( element.getAttribute( 'data-index-v' ), 10 );\n\n\t\t\t\t\tthis.Reveal.slide( h, v );\n\t\t\t\t}\n\n\t\t\t}\n\t\t}\n\n\t}\n\n}","import { enterFullscreen } from '../utils/util.js'\n\n/**\n * Handles all reveal.js keyboard interactions.\n */\nexport default class Keyboard {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// A key:value map of keyboard keys and descriptions of\n\t\t// the actions they trigger\n\t\tthis.shortcuts = {};\n\n\t\t// Holds custom key code mappings\n\t\tthis.bindings = {};\n\n\t\tthis.onDocumentKeyDown = this.onDocumentKeyDown.bind( this );\n\t\tthis.onDocumentKeyPress = this.onDocumentKeyPress.bind( this );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.navigationMode === 'linear' ) {\n\t\t\tthis.shortcuts['→ , ↓ , SPACE , N , L , J'] = 'Next slide';\n\t\t\tthis.shortcuts['← , ↑ , P , H , K'] = 'Previous slide';\n\t\t}\n\t\telse {\n\t\t\tthis.shortcuts['N , SPACE'] = 'Next slide';\n\t\t\tthis.shortcuts['P , Shift SPACE'] = 'Previous slide';\n\t\t\tthis.shortcuts['← , H'] = 'Navigate left';\n\t\t\tthis.shortcuts['→ , L'] = 'Navigate right';\n\t\t\tthis.shortcuts['↑ , K'] = 'Navigate up';\n\t\t\tthis.shortcuts['↓ , J'] = 'Navigate down';\n\t\t}\n\n\t\tthis.shortcuts['Alt + ←/↑/→/↓'] = 'Navigate without fragments';\n\t\tthis.shortcuts['Shift + ←/↑/→/↓'] = 'Jump to first/last slide';\n\t\tthis.shortcuts['B , .'] = 'Pause';\n\t\tthis.shortcuts['F'] = 'Fullscreen';\n\t\tthis.shortcuts['ESC, O'] = 'Slide overview';\n\n\t}\n\n\t/**\n\t * Starts listening for keyboard events.\n\t */\n\tbind() {\n\n\t\tdocument.addEventListener( 'keydown', this.onDocumentKeyDown, false );\n\t\tdocument.addEventListener( 'keypress', this.onDocumentKeyPress, false );\n\n\t}\n\n\t/**\n\t * Stops listening for keyboard events.\n\t */\n\tunbind() {\n\n\t\tdocument.removeEventListener( 'keydown', this.onDocumentKeyDown, false );\n\t\tdocument.removeEventListener( 'keypress', this.onDocumentKeyPress, false );\n\n\t}\n\n\t/**\n\t * Add a custom key binding with optional description to\n\t * be added to the help screen.\n\t */\n\taddKeyBinding( binding, callback ) {\n\n\t\tif( typeof binding === 'object' && binding.keyCode ) {\n\t\t\tthis.bindings[binding.keyCode] = {\n\t\t\t\tcallback: callback,\n\t\t\t\tkey: binding.key,\n\t\t\t\tdescription: binding.description\n\t\t\t};\n\t\t}\n\t\telse {\n\t\t\tthis.bindings[binding] = {\n\t\t\t\tcallback: callback,\n\t\t\t\tkey: null,\n\t\t\t\tdescription: null\n\t\t\t};\n\t\t}\n\n\t}\n\n\t/**\n\t * Removes the specified custom key binding.\n\t */\n\tremoveKeyBinding( keyCode ) {\n\n\t\tdelete this.bindings[keyCode];\n\n\t}\n\n\t/**\n\t * Programmatically triggers a keyboard event\n\t *\n\t * @param {int} keyCode\n\t */\n\ttriggerKey( keyCode ) {\n\n\t\tthis.onDocumentKeyDown( { keyCode } );\n\n\t}\n\n\t/**\n\t * Registers a new shortcut to include in the help overlay\n\t *\n\t * @param {String} key\n\t * @param {String} value\n\t */\n\tregisterKeyboardShortcut( key, value ) {\n\n\t\tthis.shortcuts[key] = value;\n\n\t}\n\n\tgetShortcuts() {\n\n\t\treturn this.shortcuts;\n\n\t}\n\n\tgetBindings() {\n\n\t\treturn this.bindings;\n\n\t}\n\n\t/**\n\t * Handler for the document level 'keypress' event.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentKeyPress( event ) {\n\n\t\t// Check if the pressed key is question mark\n\t\tif( event.shiftKey && event.charCode === 63 ) {\n\t\t\tthis.Reveal.toggleHelp();\n\t\t}\n\n\t}\n\n\t/**\n\t * Handler for the document level 'keydown' event.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentKeyDown( event ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\n\t\t// If there's a condition specified and it returns false,\n\t\t// ignore this event\n\t\tif( typeof config.keyboardCondition === 'function' && config.keyboardCondition(event) === false ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// If keyboardCondition is set, only capture keyboard events\n\t\t// for embedded decks when they are focused\n\t\tif( config.keyboardCondition === 'focused' && !this.Reveal.isFocused() ) {\n\t\t\treturn true;\n\t\t}\n\n\t\t// Shorthand\n\t\tlet keyCode = event.keyCode;\n\n\t\t// Remember if auto-sliding was paused so we can toggle it\n\t\tlet autoSlideWasPaused = !this.Reveal.isAutoSliding();\n\n\t\tthis.Reveal.onUserInput( event );\n\n\t\t// Is there a focused element that could be using the keyboard?\n\t\tlet activeElementIsCE = document.activeElement && document.activeElement.isContentEditable === true;\n\t\tlet activeElementIsInput = document.activeElement && document.activeElement.tagName && /input|textarea/i.test( document.activeElement.tagName );\n\t\tlet activeElementIsNotes = document.activeElement && document.activeElement.className && /speaker-notes/i.test( document.activeElement.className);\n\n\t\t// Whitelist certain modifiers for slide navigation shortcuts\n\t\tlet isNavigationKey = [32, 37, 38, 39, 40, 78, 80].indexOf( event.keyCode ) !== -1;\n\n\t\t// Prevent all other events when a modifier is pressed\n\t\tlet unusedModifier = \t!( isNavigationKey && event.shiftKey || event.altKey ) &&\n\t\t\t\t\t\t\t\t( event.shiftKey || event.altKey || event.ctrlKey || event.metaKey );\n\n\t\t// Disregard the event if there's a focused element or a\n\t\t// keyboard modifier key is present\n\t\tif( activeElementIsCE || activeElementIsInput || activeElementIsNotes || unusedModifier ) return;\n\n\t\t// While paused only allow resume keyboard events; 'b', 'v', '.'\n\t\tlet resumeKeyCodes = [66,86,190,191];\n\t\tlet key;\n\n\t\t// Custom key bindings for togglePause should be able to resume\n\t\tif( typeof config.keyboard === 'object' ) {\n\t\t\tfor( key in config.keyboard ) {\n\t\t\t\tif( config.keyboard[key] === 'togglePause' ) {\n\t\t\t\t\tresumeKeyCodes.push( parseInt( key, 10 ) );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif( this.Reveal.isPaused() && resumeKeyCodes.indexOf( keyCode ) === -1 ) {\n\t\t\treturn false;\n\t\t}\n\n\t\t// Use linear navigation if we're configured to OR if\n\t\t// the presentation is one-dimensional\n\t\tlet useLinearMode = config.navigationMode === 'linear' || !this.Reveal.hasHorizontalSlides() || !this.Reveal.hasVerticalSlides();\n\n\t\tlet triggered = false;\n\n\t\t// 1. User defined key bindings\n\t\tif( typeof config.keyboard === 'object' ) {\n\n\t\t\tfor( key in config.keyboard ) {\n\n\t\t\t\t// Check if this binding matches the pressed key\n\t\t\t\tif( parseInt( key, 10 ) === keyCode ) {\n\n\t\t\t\t\tlet value = config.keyboard[ key ];\n\n\t\t\t\t\t// Callback function\n\t\t\t\t\tif( typeof value === 'function' ) {\n\t\t\t\t\t\tvalue.apply( null, [ event ] );\n\t\t\t\t\t}\n\t\t\t\t\t// String shortcuts to reveal.js API\n\t\t\t\t\telse if( typeof value === 'string' && typeof this.Reveal[ value ] === 'function' ) {\n\t\t\t\t\t\tthis.Reveal[ value ].call();\n\t\t\t\t\t}\n\n\t\t\t\t\ttriggered = true;\n\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t\t// 2. Registered custom key bindings\n\t\tif( triggered === false ) {\n\n\t\t\tfor( key in this.bindings ) {\n\n\t\t\t\t// Check if this binding matches the pressed key\n\t\t\t\tif( parseInt( key, 10 ) === keyCode ) {\n\n\t\t\t\t\tlet action = this.bindings[ key ].callback;\n\n\t\t\t\t\t// Callback function\n\t\t\t\t\tif( typeof action === 'function' ) {\n\t\t\t\t\t\taction.apply( null, [ event ] );\n\t\t\t\t\t}\n\t\t\t\t\t// String shortcuts to reveal.js API\n\t\t\t\t\telse if( typeof action === 'string' && typeof this.Reveal[ action ] === 'function' ) {\n\t\t\t\t\t\tthis.Reveal[ action ].call();\n\t\t\t\t\t}\n\n\t\t\t\t\ttriggered = true;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\t// 3. System defined key bindings\n\t\tif( triggered === false ) {\n\n\t\t\t// Assume true and try to prove false\n\t\t\ttriggered = true;\n\n\t\t\t// P, PAGE UP\n\t\t\tif( keyCode === 80 || keyCode === 33 ) {\n\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t}\n\t\t\t// N, PAGE DOWN\n\t\t\telse if( keyCode === 78 || keyCode === 34 ) {\n\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t}\n\t\t\t// H, LEFT\n\t\t\telse if( keyCode === 72 || keyCode === 37 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( 0 );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.left({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// L, RIGHT\n\t\t\telse if( keyCode === 76 || keyCode === 39 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( this.Reveal.getHorizontalSlides().length - 1 );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.right({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// K, UP\n\t\t\telse if( keyCode === 75 || keyCode === 38 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( undefined, 0 );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.up({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// J, DOWN\n\t\t\telse if( keyCode === 74 || keyCode === 40 ) {\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.slide( undefined, Number.MAX_VALUE );\n\t\t\t\t}\n\t\t\t\telse if( !this.Reveal.overview.isActive() && useLinearMode ) {\n\t\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.down({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// HOME\n\t\t\telse if( keyCode === 36 ) {\n\t\t\t\tthis.Reveal.slide( 0 );\n\t\t\t}\n\t\t\t// END\n\t\t\telse if( keyCode === 35 ) {\n\t\t\t\tthis.Reveal.slide( this.Reveal.getHorizontalSlides().length - 1 );\n\t\t\t}\n\t\t\t// SPACE\n\t\t\telse if( keyCode === 32 ) {\n\t\t\t\tif( this.Reveal.overview.isActive() ) {\n\t\t\t\t\tthis.Reveal.overview.deactivate();\n\t\t\t\t}\n\t\t\t\tif( event.shiftKey ) {\n\t\t\t\t\tthis.Reveal.prev({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.Reveal.next({skipFragments: event.altKey});\n\t\t\t\t}\n\t\t\t}\n\t\t\t// TWO-SPOT, SEMICOLON, B, V, PERIOD, LOGITECH PRESENTER TOOLS \"BLACK SCREEN\" BUTTON\n\t\t\telse if( keyCode === 58 || keyCode === 59 || keyCode === 66 || keyCode === 86 || keyCode === 190 || keyCode === 191 ) {\n\t\t\t\tthis.Reveal.togglePause();\n\t\t\t}\n\t\t\t// F\n\t\t\telse if( keyCode === 70 ) {\n\t\t\t\tenterFullscreen( config.embedded ? this.Reveal.getViewportElement() : document.documentElement );\n\t\t\t}\n\t\t\t// A\n\t\t\telse if( keyCode === 65 ) {\n\t\t\t\tif ( config.autoSlideStoppable ) {\n\t\t\t\t\tthis.Reveal.toggleAutoSlide( autoSlideWasPaused );\n\t\t\t\t}\n\t\t\t}\n\t\t\telse {\n\t\t\t\ttriggered = false;\n\t\t\t}\n\n\t\t}\n\n\t\t// If the input resulted in a triggered action we should prevent\n\t\t// the browsers default behavior\n\t\tif( triggered ) {\n\t\t\tevent.preventDefault && event.preventDefault();\n\t\t}\n\t\t// ESC or O key\n\t\telse if( keyCode === 27 || keyCode === 79 ) {\n\t\t\tif( this.Reveal.closeOverlay() === false ) {\n\t\t\t\tthis.Reveal.overview.toggle();\n\t\t\t}\n\n\t\t\tevent.preventDefault && event.preventDefault();\n\t\t}\n\n\t\t// If auto-sliding is enabled we need to cue up\n\t\t// another timeout\n\t\tthis.Reveal.cueAutoSlide();\n\n\t}\n\n}","/**\n * Reads and writes the URL based on reveal.js' current state.\n */\nexport default class Location {\n\n\t// The minimum number of milliseconds that must pass between\n\t// calls to history.replaceState\n\tMAX_REPLACE_STATE_FREQUENCY = 1000\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// Delays updates to the URL due to a Chrome thumbnailer bug\n\t\tthis.writeURLTimeout = 0;\n\n\t\tthis.replaceStateTimestamp = 0;\n\n\t\tthis.onWindowHashChange = this.onWindowHashChange.bind( this );\n\n\t}\n\n\tbind() {\n\n\t\twindow.addEventListener( 'hashchange', this.onWindowHashChange, false );\n\n\t}\n\n\tunbind() {\n\n\t\twindow.removeEventListener( 'hashchange', this.onWindowHashChange, false );\n\n\t}\n\n\t/**\n\t * Returns the slide indices for the given hash link.\n\t *\n\t * @param {string} [hash] the hash string that we want to\n\t * find the indices for\n\t *\n\t * @returns slide indices or null\n\t */\n\tgetIndicesFromHash( hash=window.location.hash ) {\n\n\t\t// Attempt to parse the hash as either an index or name\n\t\tlet name = hash.replace( /^#\\/?/, '' );\n\t\tlet bits = name.split( '/' );\n\n\t\t// If the first bit is not fully numeric and there is a name we\n\t\t// can assume that this is a named link\n\t\tif( !/^[0-9]*$/.test( bits[0] ) && name.length ) {\n\t\t\tlet element;\n\n\t\t\tlet f;\n\n\t\t\t// Parse named links with fragments (#/named-link/2)\n\t\t\tif( /\\/[-\\d]+$/g.test( name ) ) {\n\t\t\t\tf = parseInt( name.split( '/' ).pop(), 10 );\n\t\t\t\tf = isNaN(f) ? undefined : f;\n\t\t\t\tname = name.split( '/' ).shift();\n\t\t\t}\n\n\t\t\t// Ensure the named link is a valid HTML ID attribute\n\t\t\ttry {\n\t\t\t\telement = document.getElementById( decodeURIComponent( name ) );\n\t\t\t}\n\t\t\tcatch ( error ) { }\n\n\t\t\tif( element ) {\n\t\t\t\treturn { ...this.Reveal.getIndices( element ), f };\n\t\t\t}\n\t\t}\n\t\telse {\n\t\t\tconst config = this.Reveal.getConfig();\n\t\t\tlet hashIndexBase = config.hashOneBasedIndex ? 1 : 0;\n\n\t\t\t// Read the index components of the hash\n\t\t\tlet h = ( parseInt( bits[0], 10 ) - hashIndexBase ) || 0,\n\t\t\t\tv = ( parseInt( bits[1], 10 ) - hashIndexBase ) || 0,\n\t\t\t\tf;\n\n\t\t\tif( config.fragmentInURL ) {\n\t\t\t\tf = parseInt( bits[2], 10 );\n\t\t\t\tif( isNaN( f ) ) {\n\t\t\t\t\tf = undefined;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\treturn { h, v, f };\n\t\t}\n\n\t\t// The hash couldn't be parsed or no matching named link was found\n\t\treturn null\n\n\t}\n\n\t/**\n\t * Reads the current URL (hash) and navigates accordingly.\n\t */\n\treadURL() {\n\n\t\tconst currentIndices = this.Reveal.getIndices();\n\t\tconst newIndices = this.getIndicesFromHash();\n\n\t\tif( newIndices ) {\n\t\t\tif( ( newIndices.h !== currentIndices.h || newIndices.v !== currentIndices.v || newIndices.f !== undefined ) ) {\n\t\t\t\t\tthis.Reveal.slide( newIndices.h, newIndices.v, newIndices.f );\n\t\t\t}\n\t\t}\n\t\t// If no new indices are available, we're trying to navigate to\n\t\t// a slide hash that does not exist\n\t\telse {\n\t\t\tthis.Reveal.slide( currentIndices.h || 0, currentIndices.v || 0 );\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the page URL (hash) to reflect the current\n\t * state.\n\t *\n\t * @param {number} delay The time in ms to wait before\n\t * writing the hash\n\t */\n\twriteURL( delay ) {\n\n\t\tlet config = this.Reveal.getConfig();\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\n\t\t// Make sure there's never more than one timeout running\n\t\tclearTimeout( this.writeURLTimeout );\n\n\t\t// If a delay is specified, timeout this call\n\t\tif( typeof delay === 'number' ) {\n\t\t\tthis.writeURLTimeout = setTimeout( this.writeURL, delay );\n\t\t}\n\t\telse if( currentSlide ) {\n\n\t\t\tlet hash = this.getHash();\n\n\t\t\t// If we're configured to push to history OR the history\n\t\t\t// API is not avaialble.\n\t\t\tif( config.history ) {\n\t\t\t\twindow.location.hash = hash;\n\t\t\t}\n\t\t\t// If we're configured to reflect the current slide in the\n\t\t\t// URL without pushing to history.\n\t\t\telse if( config.hash ) {\n\t\t\t\t// If the hash is empty, don't add it to the URL\n\t\t\t\tif( hash === '/' ) {\n\t\t\t\t\tthis.debouncedReplaceState( window.location.pathname + window.location.search );\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthis.debouncedReplaceState( '#' + hash );\n\t\t\t\t}\n\t\t\t}\n\t\t\t// UPDATE: The below nuking of all hash changes breaks\n\t\t\t// anchors on pages where reveal.js is running. Removed\n\t\t\t// in 4.0. Why was it here in the first place? ¯\\_(ツ)_/¯\n\t\t\t//\n\t\t\t// If history and hash are both disabled, a hash may still\n\t\t\t// be added to the URL by clicking on a href with a hash\n\t\t\t// target. Counter this by always removing the hash.\n\t\t\t// else {\n\t\t\t// \twindow.history.replaceState( null, null, window.location.pathname + window.location.search );\n\t\t\t// }\n\n\t\t}\n\n\t}\n\n\treplaceState( url ) {\n\n\t\twindow.history.replaceState( null, null, url );\n\t\tthis.replaceStateTimestamp = Date.now();\n\n\t}\n\n\tdebouncedReplaceState( url ) {\n\n\t\tclearTimeout( this.replaceStateTimeout );\n\n\t\tif( Date.now() - this.replaceStateTimestamp > this.MAX_REPLACE_STATE_FREQUENCY ) {\n\t\t\tthis.replaceState( url );\n\t\t}\n\t\telse {\n\t\t\tthis.replaceStateTimeout = setTimeout( () => this.replaceState( url ), this.MAX_REPLACE_STATE_FREQUENCY );\n\t\t}\n\n\t}\n\n\t/**\n\t * Return a hash URL that will resolve to the given slide location.\n\t *\n\t * @param {HTMLElement} [slide=currentSlide] The slide to link to\n\t */\n\tgetHash( slide ) {\n\n\t\tlet url = '/';\n\n\t\t// Attempt to create a named link based on the slide's ID\n\t\tlet s = slide || this.Reveal.getCurrentSlide();\n\t\tlet id = s ? s.getAttribute( 'id' ) : null;\n\t\tif( id ) {\n\t\t\tid = encodeURIComponent( id );\n\t\t}\n\n\t\tlet index = this.Reveal.getIndices( slide );\n\t\tif( !this.Reveal.getConfig().fragmentInURL ) {\n\t\t\tindex.f = undefined;\n\t\t}\n\n\t\t// If the current slide has an ID, use that as a named link,\n\t\t// but we don't support named links with a fragment index\n\t\tif( typeof id === 'string' && id.length ) {\n\t\t\turl = '/' + id;\n\n\t\t\t// If there is also a fragment, append that at the end\n\t\t\t// of the named link, like: #/named-link/2\n\t\t\tif( index.f >= 0 ) url += '/' + index.f;\n\t\t}\n\t\t// Otherwise use the /h/v index\n\t\telse {\n\t\t\tlet hashIndexBase = this.Reveal.getConfig().hashOneBasedIndex ? 1 : 0;\n\t\t\tif( index.h > 0 || index.v > 0 || index.f >= 0 ) url += index.h + hashIndexBase;\n\t\t\tif( index.v > 0 || index.f >= 0 ) url += '/' + (index.v + hashIndexBase );\n\t\t\tif( index.f >= 0 ) url += '/' + index.f;\n\t\t}\n\n\t\treturn url;\n\n\t}\n\n\t/**\n\t * Handler for the window level 'hashchange' event.\n\t *\n\t * @param {object} [event]\n\t */\n\tonWindowHashChange( event ) {\n\n\t\tthis.readURL();\n\n\t}\n\n}","import { queryAll } from '../utils/util.js'\nimport { isAndroid } from '../utils/device.js'\n\n/**\n * Manages our presentation controls. This includes both\n * the built-in control arrows as well as event monitoring\n * of any elements within the presentation with either of the\n * following helper classes:\n * - .navigate-up\n * - .navigate-right\n * - .navigate-down\n * - .navigate-left\n * - .navigate-next\n * - .navigate-prev\n */\nexport default class Controls {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.onNavigateLeftClicked = this.onNavigateLeftClicked.bind( this );\n\t\tthis.onNavigateRightClicked = this.onNavigateRightClicked.bind( this );\n\t\tthis.onNavigateUpClicked = this.onNavigateUpClicked.bind( this );\n\t\tthis.onNavigateDownClicked = this.onNavigateDownClicked.bind( this );\n\t\tthis.onNavigatePrevClicked = this.onNavigatePrevClicked.bind( this );\n\t\tthis.onNavigateNextClicked = this.onNavigateNextClicked.bind( this );\n\n\t}\n\n\trender() {\n\n\t\tconst rtl = this.Reveal.getConfig().rtl;\n\t\tconst revealElement = this.Reveal.getRevealElement();\n\n\t\tthis.element = document.createElement( 'aside' );\n\t\tthis.element.className = 'controls';\n\t\tthis.element.innerHTML =\n\t\t\t`\n\t\t\t\n\t\t\t\n\t\t\t`;\n\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t\t// There can be multiple instances of controls throughout the page\n\t\tthis.controlsLeft = queryAll( revealElement, '.navigate-left' );\n\t\tthis.controlsRight = queryAll( revealElement, '.navigate-right' );\n\t\tthis.controlsUp = queryAll( revealElement, '.navigate-up' );\n\t\tthis.controlsDown = queryAll( revealElement, '.navigate-down' );\n\t\tthis.controlsPrev = queryAll( revealElement, '.navigate-prev' );\n\t\tthis.controlsNext = queryAll( revealElement, '.navigate-next' );\n\n\t\t// The left, right and down arrows in the standard reveal.js controls\n\t\tthis.controlsRightArrow = this.element.querySelector( '.navigate-right' );\n\t\tthis.controlsLeftArrow = this.element.querySelector( '.navigate-left' );\n\t\tthis.controlsDownArrow = this.element.querySelector( '.navigate-down' );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tthis.element.style.display = config.controls ? 'block' : 'none';\n\n\t\tthis.element.setAttribute( 'data-controls-layout', config.controlsLayout );\n\t\tthis.element.setAttribute( 'data-controls-back-arrows', config.controlsBackArrows );\n\n\t}\n\n\tbind() {\n\n\t\t// Listen to both touch and click events, in case the device\n\t\t// supports both\n\t\tlet pointerEvents = [ 'touchstart', 'click' ];\n\n\t\t// Only support touch for Android, fixes double navigations in\n\t\t// stock browser\n\t\tif( isAndroid ) {\n\t\t\tpointerEvents = [ 'touchstart' ];\n\t\t}\n\n\t\tpointerEvents.forEach( eventName => {\n\t\t\tthis.controlsLeft.forEach( el => el.addEventListener( eventName, this.onNavigateLeftClicked, false ) );\n\t\t\tthis.controlsRight.forEach( el => el.addEventListener( eventName, this.onNavigateRightClicked, false ) );\n\t\t\tthis.controlsUp.forEach( el => el.addEventListener( eventName, this.onNavigateUpClicked, false ) );\n\t\t\tthis.controlsDown.forEach( el => el.addEventListener( eventName, this.onNavigateDownClicked, false ) );\n\t\t\tthis.controlsPrev.forEach( el => el.addEventListener( eventName, this.onNavigatePrevClicked, false ) );\n\t\t\tthis.controlsNext.forEach( el => el.addEventListener( eventName, this.onNavigateNextClicked, false ) );\n\t\t} );\n\n\t}\n\n\tunbind() {\n\n\t\t[ 'touchstart', 'click' ].forEach( eventName => {\n\t\t\tthis.controlsLeft.forEach( el => el.removeEventListener( eventName, this.onNavigateLeftClicked, false ) );\n\t\t\tthis.controlsRight.forEach( el => el.removeEventListener( eventName, this.onNavigateRightClicked, false ) );\n\t\t\tthis.controlsUp.forEach( el => el.removeEventListener( eventName, this.onNavigateUpClicked, false ) );\n\t\t\tthis.controlsDown.forEach( el => el.removeEventListener( eventName, this.onNavigateDownClicked, false ) );\n\t\t\tthis.controlsPrev.forEach( el => el.removeEventListener( eventName, this.onNavigatePrevClicked, false ) );\n\t\t\tthis.controlsNext.forEach( el => el.removeEventListener( eventName, this.onNavigateNextClicked, false ) );\n\t\t} );\n\n\t}\n\n\t/**\n\t * Updates the state of all control/navigation arrows.\n\t */\n\tupdate() {\n\n\t\tlet routes = this.Reveal.availableRoutes();\n\n\t\t// Remove the 'enabled' class from all directions\n\t\t[...this.controlsLeft, ...this.controlsRight, ...this.controlsUp, ...this.controlsDown, ...this.controlsPrev, ...this.controlsNext].forEach( node => {\n\t\t\tnode.classList.remove( 'enabled', 'fragmented' );\n\n\t\t\t// Set 'disabled' attribute on all directions\n\t\t\tnode.setAttribute( 'disabled', 'disabled' );\n\t\t} );\n\n\t\t// Add the 'enabled' class to the available routes; remove 'disabled' attribute to enable buttons\n\t\tif( routes.left ) this.controlsLeft.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.right ) this.controlsRight.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.up ) this.controlsUp.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.down ) this.controlsDown.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\n\t\t// Prev/next buttons\n\t\tif( routes.left || routes.up ) this.controlsPrev.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\tif( routes.right || routes.down ) this.controlsNext.forEach( el => { el.classList.add( 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\n\t\t// Highlight fragment directions\n\t\tlet currentSlide = this.Reveal.getCurrentSlide();\n\t\tif( currentSlide ) {\n\n\t\t\tlet fragmentsRoutes = this.Reveal.fragments.availableRoutes();\n\n\t\t\t// Always apply fragment decorator to prev/next buttons\n\t\t\tif( fragmentsRoutes.prev ) this.controlsPrev.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\tif( fragmentsRoutes.next ) this.controlsNext.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\n\t\t\t// Apply fragment decorators to directional buttons based on\n\t\t\t// what slide axis they are in\n\t\t\tif( this.Reveal.isVerticalSlide( currentSlide ) ) {\n\t\t\t\tif( fragmentsRoutes.prev ) this.controlsUp.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t\tif( fragmentsRoutes.next ) this.controlsDown.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tif( fragmentsRoutes.prev ) this.controlsLeft.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t\tif( fragmentsRoutes.next ) this.controlsRight.forEach( el => { el.classList.add( 'fragmented', 'enabled' ); el.removeAttribute( 'disabled' ); } );\n\t\t\t}\n\n\t\t}\n\n\t\tif( this.Reveal.getConfig().controlsTutorial ) {\n\n\t\t\tlet indices = this.Reveal.getIndices();\n\n\t\t\t// Highlight control arrows with an animation to ensure\n\t\t\t// that the viewer knows how to navigate\n\t\t\tif( !this.Reveal.hasNavigatedVertically() && routes.down ) {\n\t\t\t\tthis.controlsDownArrow.classList.add( 'highlight' );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tthis.controlsDownArrow.classList.remove( 'highlight' );\n\n\t\t\t\tif( this.Reveal.getConfig().rtl ) {\n\n\t\t\t\t\tif( !this.Reveal.hasNavigatedHorizontally() && routes.left && indices.v === 0 ) {\n\t\t\t\t\t\tthis.controlsLeftArrow.classList.add( 'highlight' );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.controlsLeftArrow.classList.remove( 'highlight' );\n\t\t\t\t\t}\n\n\t\t\t\t} else {\n\n\t\t\t\t\tif( !this.Reveal.hasNavigatedHorizontally() && routes.right && indices.v === 0 ) {\n\t\t\t\t\t\tthis.controlsRightArrow.classList.add( 'highlight' );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.controlsRightArrow.classList.remove( 'highlight' );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tdestroy() {\n\n\t\tthis.unbind();\n\t\tthis.element.remove();\n\n\t}\n\n\t/**\n\t * Event handlers for navigation control buttons.\n\t */\n\tonNavigateLeftClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tif( this.Reveal.getConfig().navigationMode === 'linear' ) {\n\t\t\tthis.Reveal.prev();\n\t\t}\n\t\telse {\n\t\t\tthis.Reveal.left();\n\t\t}\n\n\t}\n\n\tonNavigateRightClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tif( this.Reveal.getConfig().navigationMode === 'linear' ) {\n\t\t\tthis.Reveal.next();\n\t\t}\n\t\telse {\n\t\t\tthis.Reveal.right();\n\t\t}\n\n\t}\n\n\tonNavigateUpClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.up();\n\n\t}\n\n\tonNavigateDownClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.down();\n\n\t}\n\n\tonNavigatePrevClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.prev();\n\n\t}\n\n\tonNavigateNextClicked( event ) {\n\n\t\tevent.preventDefault();\n\t\tthis.Reveal.onUserInput();\n\n\t\tthis.Reveal.next();\n\n\t}\n\n\n}","/**\n * Creates a visual progress bar for the presentation.\n */\nexport default class Progress {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.onProgressClicked = this.onProgressClicked.bind( this );\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'progress';\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t\tthis.bar = document.createElement( 'span' );\n\t\tthis.element.appendChild( this.bar );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tthis.element.style.display = config.progress ? 'block' : 'none';\n\n\t}\n\n\tbind() {\n\n\t\tif( this.Reveal.getConfig().progress && this.element ) {\n\t\t\tthis.element.addEventListener( 'click', this.onProgressClicked, false );\n\t\t}\n\n\t}\n\n\tunbind() {\n\n\t\tif ( this.Reveal.getConfig().progress && this.element ) {\n\t\t\tthis.element.removeEventListener( 'click', this.onProgressClicked, false );\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the progress bar to reflect the current slide.\n\t */\n\tupdate() {\n\n\t\t// Update progress if enabled\n\t\tif( this.Reveal.getConfig().progress && this.bar ) {\n\n\t\t\tlet scale = this.Reveal.getProgress();\n\n\t\t\t// Don't fill the progress bar if there's only one slide\n\t\t\tif( this.Reveal.getTotalSlides() < 2 ) {\n\t\t\t\tscale = 0;\n\t\t\t}\n\n\t\t\tthis.bar.style.transform = 'scaleX('+ scale +')';\n\n\t\t}\n\n\t}\n\n\tgetMaxWidth() {\n\n\t\treturn this.Reveal.getRevealElement().offsetWidth;\n\n\t}\n\n\t/**\n\t * Clicking on the progress bar results in a navigation to the\n\t * closest approximate horizontal slide using this equation:\n\t *\n\t * ( clickX / presentationWidth ) * numberOfSlides\n\t *\n\t * @param {object} event\n\t */\n\tonProgressClicked( event ) {\n\n\t\tthis.Reveal.onUserInput( event );\n\n\t\tevent.preventDefault();\n\n\t\tlet slides = this.Reveal.getSlides();\n\t\tlet slidesTotal = slides.length;\n\t\tlet slideIndex = Math.floor( ( event.clientX / this.getMaxWidth() ) * slidesTotal );\n\n\t\tif( this.Reveal.getConfig().rtl ) {\n\t\t\tslideIndex = slidesTotal - slideIndex;\n\t\t}\n\n\t\tlet targetIndices = this.Reveal.getIndices(slides[slideIndex]);\n\t\tthis.Reveal.slide( targetIndices.h, targetIndices.v );\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.element.remove();\n\n\t}\n\n}","/**\n * Handles hiding of the pointer/cursor when inactive.\n */\nexport default class Pointer {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// Throttles mouse wheel navigation\n\t\tthis.lastMouseWheelStep = 0;\n\n\t\t// Is the mouse pointer currently hidden from view\n\t\tthis.cursorHidden = false;\n\n\t\t// Timeout used to determine when the cursor is inactive\n\t\tthis.cursorInactiveTimeout = 0;\n\n\t\tthis.onDocumentCursorActive = this.onDocumentCursorActive.bind( this );\n\t\tthis.onDocumentMouseScroll = this.onDocumentMouseScroll.bind( this );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.mouseWheel ) {\n\t\t\tdocument.addEventListener( 'DOMMouseScroll', this.onDocumentMouseScroll, false ); // FF\n\t\t\tdocument.addEventListener( 'mousewheel', this.onDocumentMouseScroll, false );\n\t\t}\n\t\telse {\n\t\t\tdocument.removeEventListener( 'DOMMouseScroll', this.onDocumentMouseScroll, false ); // FF\n\t\t\tdocument.removeEventListener( 'mousewheel', this.onDocumentMouseScroll, false );\n\t\t}\n\n\t\t// Auto-hide the mouse pointer when its inactive\n\t\tif( config.hideInactiveCursor ) {\n\t\t\tdocument.addEventListener( 'mousemove', this.onDocumentCursorActive, false );\n\t\t\tdocument.addEventListener( 'mousedown', this.onDocumentCursorActive, false );\n\t\t}\n\t\telse {\n\t\t\tthis.showCursor();\n\n\t\t\tdocument.removeEventListener( 'mousemove', this.onDocumentCursorActive, false );\n\t\t\tdocument.removeEventListener( 'mousedown', this.onDocumentCursorActive, false );\n\t\t}\n\n\t}\n\n\t/**\n\t * Shows the mouse pointer after it has been hidden with\n\t * #hideCursor.\n\t */\n\tshowCursor() {\n\n\t\tif( this.cursorHidden ) {\n\t\t\tthis.cursorHidden = false;\n\t\t\tthis.Reveal.getRevealElement().style.cursor = '';\n\t\t}\n\n\t}\n\n\t/**\n\t * Hides the mouse pointer when it's on top of the .reveal\n\t * container.\n\t */\n\thideCursor() {\n\n\t\tif( this.cursorHidden === false ) {\n\t\t\tthis.cursorHidden = true;\n\t\t\tthis.Reveal.getRevealElement().style.cursor = 'none';\n\t\t}\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.showCursor();\n\n\t\tdocument.removeEventListener( 'DOMMouseScroll', this.onDocumentMouseScroll, false );\n\t\tdocument.removeEventListener( 'mousewheel', this.onDocumentMouseScroll, false );\n\t\tdocument.removeEventListener( 'mousemove', this.onDocumentCursorActive, false );\n\t\tdocument.removeEventListener( 'mousedown', this.onDocumentCursorActive, false );\n\n\t}\n\n\t/**\n\t * Called whenever there is mouse input at the document level\n\t * to determine if the cursor is active or not.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentCursorActive( event ) {\n\n\t\tthis.showCursor();\n\n\t\tclearTimeout( this.cursorInactiveTimeout );\n\n\t\tthis.cursorInactiveTimeout = setTimeout( this.hideCursor.bind( this ), this.Reveal.getConfig().hideCursorTime );\n\n\t}\n\n\t/**\n\t * Handles mouse wheel scrolling, throttled to avoid skipping\n\t * multiple slides.\n\t *\n\t * @param {object} event\n\t */\n\tonDocumentMouseScroll( event ) {\n\n\t\tif( Date.now() - this.lastMouseWheelStep > 1000 ) {\n\n\t\t\tthis.lastMouseWheelStep = Date.now();\n\n\t\t\tlet delta = event.detail || -event.wheelDelta;\n\t\t\tif( delta > 0 ) {\n\t\t\t\tthis.Reveal.next();\n\t\t\t}\n\t\t\telse if( delta < 0 ) {\n\t\t\t\tthis.Reveal.prev();\n\t\t\t}\n\n\t\t}\n\n\t}\n\n}","/**\n * Loads a JavaScript file from the given URL and executes it.\n *\n * @param {string} url Address of the .js file to load\n * @param {function} callback Method to invoke when the script\n * has loaded and executed\n */\nexport const loadScript = ( url, callback ) => {\n\n\tconst script = document.createElement( 'script' );\n\tscript.type = 'text/javascript';\n\tscript.async = false;\n\tscript.defer = false;\n\tscript.src = url;\n\n\tif( typeof callback === 'function' ) {\n\n\t\t// Success callback\n\t\tscript.onload = script.onreadystatechange = event => {\n\t\t\tif( event.type === 'load' || /loaded|complete/.test( script.readyState ) ) {\n\n\t\t\t\t// Kill event listeners\n\t\t\t\tscript.onload = script.onreadystatechange = script.onerror = null;\n\n\t\t\t\tcallback();\n\n\t\t\t}\n\t\t};\n\n\t\t// Error callback\n\t\tscript.onerror = err => {\n\n\t\t\t// Kill event listeners\n\t\t\tscript.onload = script.onreadystatechange = script.onerror = null;\n\n\t\t\tcallback( new Error( 'Failed loading script: ' + script.src + '\\n' + err ) );\n\n\t\t};\n\n\t}\n\n\t// Append the script at the end of \n\tconst head = document.querySelector( 'head' );\n\thead.insertBefore( script, head.lastChild );\n\n}","import { loadScript } from '../utils/loader.js'\n\n/**\n * Manages loading and registering of reveal.js plugins.\n */\nexport default class Plugins {\n\n\tconstructor( reveal ) {\n\n\t\tthis.Reveal = reveal;\n\n\t\t// Flags our current state (idle -> loading -> loaded)\n\t\tthis.state = 'idle';\n\n\t\t// An id:instance map of currently registed plugins\n\t\tthis.registeredPlugins = {};\n\n\t\tthis.asyncDependencies = [];\n\n\t}\n\n\t/**\n\t * Loads reveal.js dependencies, registers and\n\t * initializes plugins.\n\t *\n\t * Plugins are direct references to a reveal.js plugin\n\t * object that we register and initialize after any\n\t * synchronous dependencies have loaded.\n\t *\n\t * Dependencies are defined via the 'dependencies' config\n\t * option and will be loaded prior to starting reveal.js.\n\t * Some dependencies may have an 'async' flag, if so they\n\t * will load after reveal.js has been started up.\n\t */\n\tload( plugins, dependencies ) {\n\n\t\tthis.state = 'loading';\n\n\t\tplugins.forEach( this.registerPlugin.bind( this ) );\n\n\t\treturn new Promise( resolve => {\n\n\t\t\tlet scripts = [],\n\t\t\t\tscriptsToLoad = 0;\n\n\t\t\tdependencies.forEach( s => {\n\t\t\t\t// Load if there's no condition or the condition is truthy\n\t\t\t\tif( !s.condition || s.condition() ) {\n\t\t\t\t\tif( s.async ) {\n\t\t\t\t\t\tthis.asyncDependencies.push( s );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tscripts.push( s );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\tif( scripts.length ) {\n\t\t\t\tscriptsToLoad = scripts.length;\n\n\t\t\t\tconst scriptLoadedCallback = (s) => {\n\t\t\t\t\tif( s && typeof s.callback === 'function' ) s.callback();\n\n\t\t\t\t\tif( --scriptsToLoad === 0 ) {\n\t\t\t\t\t\tthis.initPlugins().then( resolve );\n\t\t\t\t\t}\n\t\t\t\t};\n\n\t\t\t\t// Load synchronous scripts\n\t\t\t\tscripts.forEach( s => {\n\t\t\t\t\tif( typeof s.id === 'string' ) {\n\t\t\t\t\t\tthis.registerPlugin( s );\n\t\t\t\t\t\tscriptLoadedCallback( s );\n\t\t\t\t\t}\n\t\t\t\t\telse if( typeof s.src === 'string' ) {\n\t\t\t\t\t\tloadScript( s.src, () => scriptLoadedCallback(s) );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tconsole.warn( 'Unrecognized plugin format', s );\n\t\t\t\t\t\tscriptLoadedCallback();\n\t\t\t\t\t}\n\t\t\t\t} );\n\t\t\t}\n\t\t\telse {\n\t\t\t\tthis.initPlugins().then( resolve );\n\t\t\t}\n\n\t\t} );\n\n\t}\n\n\t/**\n\t * Initializes our plugins and waits for them to be ready\n\t * before proceeding.\n\t */\n\tinitPlugins() {\n\n\t\treturn new Promise( resolve => {\n\n\t\t\tlet pluginValues = Object.values( this.registeredPlugins );\n\t\t\tlet pluginsToInitialize = pluginValues.length;\n\n\t\t\t// If there are no plugins, skip this step\n\t\t\tif( pluginsToInitialize === 0 ) {\n\t\t\t\tthis.loadAsync().then( resolve );\n\t\t\t}\n\t\t\t// ... otherwise initialize plugins\n\t\t\telse {\n\n\t\t\t\tlet initNextPlugin;\n\n\t\t\t\tlet afterPlugInitialized = () => {\n\t\t\t\t\tif( --pluginsToInitialize === 0 ) {\n\t\t\t\t\t\tthis.loadAsync().then( resolve );\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tinitNextPlugin();\n\t\t\t\t\t}\n\t\t\t\t};\n\n\t\t\t\tlet i = 0;\n\n\t\t\t\t// Initialize plugins serially\n\t\t\t\tinitNextPlugin = () => {\n\n\t\t\t\t\tlet plugin = pluginValues[i++];\n\n\t\t\t\t\t// If the plugin has an 'init' method, invoke it\n\t\t\t\t\tif( typeof plugin.init === 'function' ) {\n\t\t\t\t\t\tlet promise = plugin.init( this.Reveal );\n\n\t\t\t\t\t\t// If the plugin returned a Promise, wait for it\n\t\t\t\t\t\tif( promise && typeof promise.then === 'function' ) {\n\t\t\t\t\t\t\tpromise.then( afterPlugInitialized );\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tafterPlugInitialized();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tafterPlugInitialized();\n\t\t\t\t\t}\n\n\t\t\t\t}\n\n\t\t\t\tinitNextPlugin();\n\n\t\t\t}\n\n\t\t} )\n\n\t}\n\n\t/**\n\t * Loads all async reveal.js dependencies.\n\t */\n\tloadAsync() {\n\n\t\tthis.state = 'loaded';\n\n\t\tif( this.asyncDependencies.length ) {\n\t\t\tthis.asyncDependencies.forEach( s => {\n\t\t\t\tloadScript( s.src, s.callback );\n\t\t\t} );\n\t\t}\n\n\t\treturn Promise.resolve();\n\n\t}\n\n\t/**\n\t * Registers a new plugin with this reveal.js instance.\n\t *\n\t * reveal.js waits for all regisered plugins to initialize\n\t * before considering itself ready, as long as the plugin\n\t * is registered before calling `Reveal.initialize()`.\n\t */\n\tregisterPlugin( plugin ) {\n\n\t\t// Backwards compatibility to make reveal.js ~3.9.0\n\t\t// plugins work with reveal.js 4.0.0\n\t\tif( arguments.length === 2 && typeof arguments[0] === 'string' ) {\n\t\t\tplugin = arguments[1];\n\t\t\tplugin.id = arguments[0];\n\t\t}\n\t\t// Plugin can optionally be a function which we call\n\t\t// to create an instance of the plugin\n\t\telse if( typeof plugin === 'function' ) {\n\t\t\tplugin = plugin();\n\t\t}\n\n\t\tlet id = plugin.id;\n\n\t\tif( typeof id !== 'string' ) {\n\t\t\tconsole.warn( 'Unrecognized plugin format; can\\'t find plugin.id', plugin );\n\t\t}\n\t\telse if( this.registeredPlugins[id] === undefined ) {\n\t\t\tthis.registeredPlugins[id] = plugin;\n\n\t\t\t// If a plugin is registered after reveal.js is loaded,\n\t\t\t// initialize it right away\n\t\t\tif( this.state === 'loaded' && typeof plugin.init === 'function' ) {\n\t\t\t\tplugin.init( this.Reveal );\n\t\t\t}\n\t\t}\n\t\telse {\n\t\t\tconsole.warn( 'reveal.js: \"'+ id +'\" plugin has already been registered' );\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if a specific plugin has been registered.\n\t *\n\t * @param {String} id Unique plugin identifier\n\t */\n\thasPlugin( id ) {\n\n\t\treturn !!this.registeredPlugins[id];\n\n\t}\n\n\t/**\n\t * Returns the specific plugin instance, if a plugin\n\t * with the given ID has been registered.\n\t *\n\t * @param {String} id Unique plugin identifier\n\t */\n\tgetPlugin( id ) {\n\n\t\treturn this.registeredPlugins[id];\n\n\t}\n\n\tgetRegisteredPlugins() {\n\n\t\treturn this.registeredPlugins;\n\n\t}\n\n\tdestroy() {\n\n\t\tObject.values( this.registeredPlugins ).forEach( plugin => {\n\t\t\tif( typeof plugin.destroy === 'function' ) {\n\t\t\t\tplugin.destroy();\n\t\t\t}\n\t\t} );\n\n\t\tthis.registeredPlugins = {};\n\t\tthis.asyncDependencies = [];\n\n\t}\n\n}\n","import { SLIDES_SELECTOR } from '../utils/constants.js'\nimport { queryAll, createStyleSheet } from '../utils/util.js'\n\n/**\n * Setups up our presentation for printing/exporting to PDF.\n */\nexport default class Print {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\t/**\n\t * Configures the presentation for printing to a static\n\t * PDF.\n\t */\n\tasync setupPDF() {\n\n\t\tconst config = this.Reveal.getConfig();\n\t\tconst slides = queryAll( this.Reveal.getRevealElement(), SLIDES_SELECTOR )\n\n\t\t// Compute slide numbers now, before we start duplicating slides\n\t\tconst doingSlideNumbers = config.slideNumber && /all|print/i.test( config.showSlideNumber );\n\n\t\tconst slideSize = this.Reveal.getComputedSlideSize( window.innerWidth, window.innerHeight );\n\n\t\t// Dimensions of the PDF pages\n\t\tconst pageWidth = Math.floor( slideSize.width * ( 1 + config.margin ) ),\n\t\t\tpageHeight = Math.floor( slideSize.height * ( 1 + config.margin ) );\n\n\t\t// Dimensions of slides within the pages\n\t\tconst slideWidth = slideSize.width,\n\t\t\tslideHeight = slideSize.height;\n\n\t\tawait new Promise( requestAnimationFrame );\n\n\t\t// Let the browser know what page size we want to print\n\t\tcreateStyleSheet( '@page{size:'+ pageWidth +'px '+ pageHeight +'px; margin: 0px;}' );\n\n\t\t// Limit the size of certain elements to the dimensions of the slide\n\t\tcreateStyleSheet( '.reveal section>img, .reveal section>video, .reveal section>iframe{max-width: '+ slideWidth +'px; max-height:'+ slideHeight +'px}' );\n\n\t\tdocument.documentElement.classList.add( 'print-pdf' );\n\t\tdocument.body.style.width = pageWidth + 'px';\n\t\tdocument.body.style.height = pageHeight + 'px';\n\n\t\tconst viewportElement = document.querySelector( '.reveal-viewport' );\n\t\tlet presentationBackground;\n\t\tif( viewportElement ) {\n\t\t\tconst viewportStyles = window.getComputedStyle( viewportElement );\n\t\t\tif( viewportStyles && viewportStyles.background ) {\n\t\t\t\tpresentationBackground = viewportStyles.background;\n\t\t\t}\n\t\t}\n\n\t\t// Make sure stretch elements fit on slide\n\t\tawait new Promise( requestAnimationFrame );\n\t\tthis.Reveal.layoutSlideContents( slideWidth, slideHeight );\n\n\t\t// Batch scrollHeight access to prevent layout thrashing\n\t\tawait new Promise( requestAnimationFrame );\n\n\t\tconst slideScrollHeights = slides.map( slide => slide.scrollHeight );\n\n\t\tconst pages = [];\n\t\tconst pageContainer = slides[0].parentNode;\n\n\t\t// Slide and slide background layout\n\t\tslides.forEach( function( slide, index ) {\n\n\t\t\t// Vertical stacks are not centred since their section\n\t\t\t// children will be\n\t\t\tif( slide.classList.contains( 'stack' ) === false ) {\n\t\t\t\t// Center the slide inside of the page, giving the slide some margin\n\t\t\t\tlet left = ( pageWidth - slideWidth ) / 2;\n\t\t\t\tlet top = ( pageHeight - slideHeight ) / 2;\n\n\t\t\t\tconst contentHeight = slideScrollHeights[ index ];\n\t\t\t\tlet numberOfPages = Math.max( Math.ceil( contentHeight / pageHeight ), 1 );\n\n\t\t\t\t// Adhere to configured pages per slide limit\n\t\t\t\tnumberOfPages = Math.min( numberOfPages, config.pdfMaxPagesPerSlide );\n\n\t\t\t\t// Center slides vertically\n\t\t\t\tif( numberOfPages === 1 && config.center || slide.classList.contains( 'center' ) ) {\n\t\t\t\t\ttop = Math.max( ( pageHeight - contentHeight ) / 2, 0 );\n\t\t\t\t}\n\n\t\t\t\t// Wrap the slide in a page element and hide its overflow\n\t\t\t\t// so that no page ever flows onto another\n\t\t\t\tconst page = document.createElement( 'div' );\n\t\t\t\tpages.push( page );\n\n\t\t\t\tpage.className = 'pdf-page';\n\t\t\t\tpage.style.height = ( ( pageHeight + config.pdfPageHeightOffset ) * numberOfPages ) + 'px';\n\n\t\t\t\t// Copy the presentation-wide background to each individual\n\t\t\t\t// page when printing\n\t\t\t\tif( presentationBackground ) {\n\t\t\t\t\tpage.style.background = presentationBackground;\n\t\t\t\t}\n\n\t\t\t\tpage.appendChild( slide );\n\n\t\t\t\t// Position the slide inside of the page\n\t\t\t\tslide.style.left = left + 'px';\n\t\t\t\tslide.style.top = top + 'px';\n\t\t\t\tslide.style.width = slideWidth + 'px';\n\n\t\t\t\t// Re-run the slide layout so that r-fit-text is applied based on\n\t\t\t\t// the printed slide size\n\t\t\t\tthis.Reveal.slideContent.layout( slide )\n\n\t\t\t\tif( slide.slideBackgroundElement ) {\n\t\t\t\t\tpage.insertBefore( slide.slideBackgroundElement, slide );\n\t\t\t\t}\n\n\t\t\t\t// Inject notes if `showNotes` is enabled\n\t\t\t\tif( config.showNotes ) {\n\n\t\t\t\t\t// Are there notes for this slide?\n\t\t\t\t\tconst notes = this.Reveal.getSlideNotes( slide );\n\t\t\t\t\tif( notes ) {\n\n\t\t\t\t\t\tconst notesSpacing = 8;\n\t\t\t\t\t\tconst notesLayout = typeof config.showNotes === 'string' ? config.showNotes : 'inline';\n\t\t\t\t\t\tconst notesElement = document.createElement( 'div' );\n\t\t\t\t\t\tnotesElement.classList.add( 'speaker-notes' );\n\t\t\t\t\t\tnotesElement.classList.add( 'speaker-notes-pdf' );\n\t\t\t\t\t\tnotesElement.setAttribute( 'data-layout', notesLayout );\n\t\t\t\t\t\tnotesElement.innerHTML = notes;\n\n\t\t\t\t\t\tif( notesLayout === 'separate-page' ) {\n\t\t\t\t\t\t\tpages.push( notesElement );\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tnotesElement.style.left = notesSpacing + 'px';\n\t\t\t\t\t\t\tnotesElement.style.bottom = notesSpacing + 'px';\n\t\t\t\t\t\t\tnotesElement.style.width = ( pageWidth - notesSpacing*2 ) + 'px';\n\t\t\t\t\t\t\tpage.appendChild( notesElement );\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\n\t\t\t\t}\n\n\t\t\t\t// Inject slide numbers if `slideNumbers` are enabled\n\t\t\t\tif( doingSlideNumbers ) {\n\t\t\t\t\tconst slideNumber = index + 1;\n\t\t\t\t\tconst numberElement = document.createElement( 'div' );\n\t\t\t\t\tnumberElement.classList.add( 'slide-number' );\n\t\t\t\t\tnumberElement.classList.add( 'slide-number-pdf' );\n\t\t\t\t\tnumberElement.innerHTML = slideNumber;\n\t\t\t\t\tpage.appendChild( numberElement );\n\t\t\t\t}\n\n\t\t\t\t// Copy page and show fragments one after another\n\t\t\t\tif( config.pdfSeparateFragments ) {\n\n\t\t\t\t\t// Each fragment 'group' is an array containing one or more\n\t\t\t\t\t// fragments. Multiple fragments that appear at the same time\n\t\t\t\t\t// are part of the same group.\n\t\t\t\t\tconst fragmentGroups = this.Reveal.fragments.sort( page.querySelectorAll( '.fragment' ), true );\n\n\t\t\t\t\tlet previousFragmentStep;\n\n\t\t\t\t\tfragmentGroups.forEach( function( fragments ) {\n\n\t\t\t\t\t\t// Remove 'current-fragment' from the previous group\n\t\t\t\t\t\tif( previousFragmentStep ) {\n\t\t\t\t\t\t\tpreviousFragmentStep.forEach( function( fragment ) {\n\t\t\t\t\t\t\t\tfragment.classList.remove( 'current-fragment' );\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\t// Show the fragments for the current index\n\t\t\t\t\t\tfragments.forEach( function( fragment ) {\n\t\t\t\t\t\t\tfragment.classList.add( 'visible', 'current-fragment' );\n\t\t\t\t\t\t}, this );\n\n\t\t\t\t\t\t// Create a separate page for the current fragment state\n\t\t\t\t\t\tconst clonedPage = page.cloneNode( true );\n\t\t\t\t\t\tpages.push( clonedPage );\n\n\t\t\t\t\t\tpreviousFragmentStep = fragments;\n\n\t\t\t\t\t}, this );\n\n\t\t\t\t\t// Reset the first/original page so that all fragments are hidden\n\t\t\t\t\tfragmentGroups.forEach( function( fragments ) {\n\t\t\t\t\t\tfragments.forEach( function( fragment ) {\n\t\t\t\t\t\t\tfragment.classList.remove( 'visible', 'current-fragment' );\n\t\t\t\t\t\t} );\n\t\t\t\t\t} );\n\n\t\t\t\t}\n\t\t\t\t// Show all fragments\n\t\t\t\telse {\n\t\t\t\t\tqueryAll( page, '.fragment:not(.fade-out)' ).forEach( function( fragment ) {\n\t\t\t\t\t\tfragment.classList.add( 'visible' );\n\t\t\t\t\t} );\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}, this );\n\n\t\tawait new Promise( requestAnimationFrame );\n\n\t\tpages.forEach( page => pageContainer.appendChild( page ) );\n\n\t\t// Notify subscribers that the PDF layout is good to go\n\t\tthis.Reveal.dispatchEvent({ type: 'pdf-ready' });\n\n\t}\n\n\t/**\n\t * Checks if this instance is being used to print a PDF.\n\t */\n\tisPrintingPDF() {\n\n\t\treturn ( /print-pdf/gi ).test( window.location.search );\n\n\t}\n\n}\n","import { isAndroid } from '../utils/device.js'\nimport { matches } from '../utils/util.js'\n\nconst SWIPE_THRESHOLD = 40;\n\n/**\n * Controls all touch interactions and navigations for\n * a presentation.\n */\nexport default class Touch {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\t// Holds information about the currently ongoing touch interaction\n\t\tthis.touchStartX = 0;\n\t\tthis.touchStartY = 0;\n\t\tthis.touchStartCount = 0;\n\t\tthis.touchCaptured = false;\n\n\t\tthis.onPointerDown = this.onPointerDown.bind( this );\n\t\tthis.onPointerMove = this.onPointerMove.bind( this );\n\t\tthis.onPointerUp = this.onPointerUp.bind( this );\n\t\tthis.onTouchStart = this.onTouchStart.bind( this );\n\t\tthis.onTouchMove = this.onTouchMove.bind( this );\n\t\tthis.onTouchEnd = this.onTouchEnd.bind( this );\n\n\t}\n\n\t/**\n\t *\n\t */\n\tbind() {\n\n\t\tlet revealElement = this.Reveal.getRevealElement();\n\n\t\tif( 'onpointerdown' in window ) {\n\t\t\t// Use W3C pointer events\n\t\t\trevealElement.addEventListener( 'pointerdown', this.onPointerDown, false );\n\t\t\trevealElement.addEventListener( 'pointermove', this.onPointerMove, false );\n\t\t\trevealElement.addEventListener( 'pointerup', this.onPointerUp, false );\n\t\t}\n\t\telse if( window.navigator.msPointerEnabled ) {\n\t\t\t// IE 10 uses prefixed version of pointer events\n\t\t\trevealElement.addEventListener( 'MSPointerDown', this.onPointerDown, false );\n\t\t\trevealElement.addEventListener( 'MSPointerMove', this.onPointerMove, false );\n\t\t\trevealElement.addEventListener( 'MSPointerUp', this.onPointerUp, false );\n\t\t}\n\t\telse {\n\t\t\t// Fall back to touch events\n\t\t\trevealElement.addEventListener( 'touchstart', this.onTouchStart, false );\n\t\t\trevealElement.addEventListener( 'touchmove', this.onTouchMove, false );\n\t\t\trevealElement.addEventListener( 'touchend', this.onTouchEnd, false );\n\t\t}\n\n\t}\n\n\t/**\n\t *\n\t */\n\tunbind() {\n\n\t\tlet revealElement = this.Reveal.getRevealElement();\n\n\t\trevealElement.removeEventListener( 'pointerdown', this.onPointerDown, false );\n\t\trevealElement.removeEventListener( 'pointermove', this.onPointerMove, false );\n\t\trevealElement.removeEventListener( 'pointerup', this.onPointerUp, false );\n\n\t\trevealElement.removeEventListener( 'MSPointerDown', this.onPointerDown, false );\n\t\trevealElement.removeEventListener( 'MSPointerMove', this.onPointerMove, false );\n\t\trevealElement.removeEventListener( 'MSPointerUp', this.onPointerUp, false );\n\n\t\trevealElement.removeEventListener( 'touchstart', this.onTouchStart, false );\n\t\trevealElement.removeEventListener( 'touchmove', this.onTouchMove, false );\n\t\trevealElement.removeEventListener( 'touchend', this.onTouchEnd, false );\n\n\t}\n\n\t/**\n\t * Checks if the target element prevents the triggering of\n\t * swipe navigation.\n\t */\n\tisSwipePrevented( target ) {\n\n\t\t// Prevent accidental swipes when scrubbing timelines\n\t\tif( matches( target, 'video, audio' ) ) return true;\n\n\t\twhile( target && typeof target.hasAttribute === 'function' ) {\n\t\t\tif( target.hasAttribute( 'data-prevent-swipe' ) ) return true;\n\t\t\ttarget = target.parentNode;\n\t\t}\n\n\t\treturn false;\n\n\t}\n\n\t/**\n\t * Handler for the 'touchstart' event, enables support for\n\t * swipe and pinch gestures.\n\t *\n\t * @param {object} event\n\t */\n\tonTouchStart( event ) {\n\n\t\tif( this.isSwipePrevented( event.target ) ) return true;\n\n\t\tthis.touchStartX = event.touches[0].clientX;\n\t\tthis.touchStartY = event.touches[0].clientY;\n\t\tthis.touchStartCount = event.touches.length;\n\n\t}\n\n\t/**\n\t * Handler for the 'touchmove' event.\n\t *\n\t * @param {object} event\n\t */\n\tonTouchMove( event ) {\n\n\t\tif( this.isSwipePrevented( event.target ) ) return true;\n\n\t\tlet config = this.Reveal.getConfig();\n\n\t\t// Each touch should only trigger one action\n\t\tif( !this.touchCaptured ) {\n\t\t\tthis.Reveal.onUserInput( event );\n\n\t\t\tlet currentX = event.touches[0].clientX;\n\t\t\tlet currentY = event.touches[0].clientY;\n\n\t\t\t// There was only one touch point, look for a swipe\n\t\t\tif( event.touches.length === 1 && this.touchStartCount !== 2 ) {\n\n\t\t\t\tlet availableRoutes = this.Reveal.availableRoutes({ includeFragments: true });\n\n\t\t\t\tlet deltaX = currentX - this.touchStartX,\n\t\t\t\t\tdeltaY = currentY - this.touchStartY;\n\n\t\t\t\tif( deltaX > SWIPE_THRESHOLD && Math.abs( deltaX ) > Math.abs( deltaY ) ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tif( config.rtl ) {\n\t\t\t\t\t\t\tthis.Reveal.next();\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tthis.Reveal.prev();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.left();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if( deltaX < -SWIPE_THRESHOLD && Math.abs( deltaX ) > Math.abs( deltaY ) ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tif( config.rtl ) {\n\t\t\t\t\t\t\tthis.Reveal.prev();\n\t\t\t\t\t\t}\n\t\t\t\t\t\telse {\n\t\t\t\t\t\t\tthis.Reveal.next();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.right();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if( deltaY > SWIPE_THRESHOLD && availableRoutes.up ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tthis.Reveal.prev();\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.up();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if( deltaY < -SWIPE_THRESHOLD && availableRoutes.down ) {\n\t\t\t\t\tthis.touchCaptured = true;\n\t\t\t\t\tif( config.navigationMode === 'linear' ) {\n\t\t\t\t\t\tthis.Reveal.next();\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tthis.Reveal.down();\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\t// If we're embedded, only block touch events if they have\n\t\t\t\t// triggered an action\n\t\t\t\tif( config.embedded ) {\n\t\t\t\t\tif( this.touchCaptured || this.Reveal.isVerticalSlide() ) {\n\t\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Not embedded? Block them all to avoid needless tossing\n\t\t\t\t// around of the viewport in iOS\n\t\t\t\telse {\n\t\t\t\t\tevent.preventDefault();\n\t\t\t\t}\n\n\t\t\t}\n\t\t}\n\t\t// There's a bug with swiping on some Android devices unless\n\t\t// the default action is always prevented\n\t\telse if( isAndroid ) {\n\t\t\tevent.preventDefault();\n\t\t}\n\n\t}\n\n\t/**\n\t * Handler for the 'touchend' event.\n\t *\n\t * @param {object} event\n\t */\n\tonTouchEnd( event ) {\n\n\t\tthis.touchCaptured = false;\n\n\t}\n\n\t/**\n\t * Convert pointer down to touch start.\n\t *\n\t * @param {object} event\n\t */\n\tonPointerDown( event ) {\n\n\t\tif( event.pointerType === event.MSPOINTER_TYPE_TOUCH || event.pointerType === \"touch\" ) {\n\t\t\tevent.touches = [{ clientX: event.clientX, clientY: event.clientY }];\n\t\t\tthis.onTouchStart( event );\n\t\t}\n\n\t}\n\n\t/**\n\t * Convert pointer move to touch move.\n\t *\n\t * @param {object} event\n\t */\n\tonPointerMove( event ) {\n\n\t\tif( event.pointerType === event.MSPOINTER_TYPE_TOUCH || event.pointerType === \"touch\" ) {\n\t\t\tevent.touches = [{ clientX: event.clientX, clientY: event.clientY }];\n\t\t\tthis.onTouchMove( event );\n\t\t}\n\n\t}\n\n\t/**\n\t * Convert pointer up to touch end.\n\t *\n\t * @param {object} event\n\t */\n\tonPointerUp( event ) {\n\n\t\tif( event.pointerType === event.MSPOINTER_TYPE_TOUCH || event.pointerType === \"touch\" ) {\n\t\t\tevent.touches = [{ clientX: event.clientX, clientY: event.clientY }];\n\t\t\tthis.onTouchEnd( event );\n\t\t}\n\n\t}\n\n}","import { closest } from '../utils/util.js'\n\n/**\n * Manages focus when a presentation is embedded. This\n * helps us only capture keyboard from the presentation\n * a user is currently interacting with in a page where\n * multiple presentations are embedded.\n */\n\nconst STATE_FOCUS = 'focus';\nconst STATE_BLUR = 'blur';\n\nexport default class Focus {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t\tthis.onRevealPointerDown = this.onRevealPointerDown.bind( this );\n\t\tthis.onDocumentPointerDown = this.onDocumentPointerDown.bind( this );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.embedded ) {\n\t\t\tthis.blur();\n\t\t}\n\t\telse {\n\t\t\tthis.focus();\n\t\t\tthis.unbind();\n\t\t}\n\n\t}\n\n\tbind() {\n\n\t\tif( this.Reveal.getConfig().embedded ) {\n\t\t\tthis.Reveal.getRevealElement().addEventListener( 'pointerdown', this.onRevealPointerDown, false );\n\t\t}\n\n\t}\n\n\tunbind() {\n\n\t\tthis.Reveal.getRevealElement().removeEventListener( 'pointerdown', this.onRevealPointerDown, false );\n\t\tdocument.removeEventListener( 'pointerdown', this.onDocumentPointerDown, false );\n\n\t}\n\n\tfocus() {\n\n\t\tif( this.state !== STATE_FOCUS ) {\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'focused' );\n\t\t\tdocument.addEventListener( 'pointerdown', this.onDocumentPointerDown, false );\n\t\t}\n\n\t\tthis.state = STATE_FOCUS;\n\n\t}\n\n\tblur() {\n\n\t\tif( this.state !== STATE_BLUR ) {\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'focused' );\n\t\t\tdocument.removeEventListener( 'pointerdown', this.onDocumentPointerDown, false );\n\t\t}\n\n\t\tthis.state = STATE_BLUR;\n\n\t}\n\n\tisFocused() {\n\n\t\treturn this.state === STATE_FOCUS;\n\n\t}\n\n\tdestroy() {\n\n\t\tthis.Reveal.getRevealElement().classList.remove( 'focused' );\n\n\t}\n\n\tonRevealPointerDown( event ) {\n\n\t\tthis.focus();\n\n\t}\n\n\tonDocumentPointerDown( event ) {\n\n\t\tlet revealElement = closest( event.target, '.reveal' );\n\t\tif( !revealElement || revealElement !== this.Reveal.getRevealElement() ) {\n\t\t\tthis.blur();\n\t\t}\n\n\t}\n\n}","/**\n * Handles the showing and \n */\nexport default class Notes {\n\n\tconstructor( Reveal ) {\n\n\t\tthis.Reveal = Reveal;\n\n\t}\n\n\trender() {\n\n\t\tthis.element = document.createElement( 'div' );\n\t\tthis.element.className = 'speaker-notes';\n\t\tthis.element.setAttribute( 'data-prevent-swipe', '' );\n\t\tthis.element.setAttribute( 'tabindex', '0' );\n\t\tthis.Reveal.getRevealElement().appendChild( this.element );\n\n\t}\n\n\t/**\n\t * Called when the reveal.js config is updated.\n\t */\n\tconfigure( config, oldConfig ) {\n\n\t\tif( config.showNotes ) {\n\t\t\tthis.element.setAttribute( 'data-layout', typeof config.showNotes === 'string' ? config.showNotes : 'inline' );\n\t\t}\n\n\t}\n\n\t/**\n\t * Pick up notes from the current slide and display them\n\t * to the viewer.\n\t *\n\t * @see {@link config.showNotes}\n\t */\n\tupdate() {\n\n\t\tif( this.Reveal.getConfig().showNotes && this.element && this.Reveal.getCurrentSlide() && !this.Reveal.print.isPrintingPDF() ) {\n\n\t\t\tthis.element.innerHTML = this.getSlideNotes() || 'No notes on this slide.';\n\n\t\t}\n\n\t}\n\n\t/**\n\t * Updates the visibility of the speaker notes sidebar that\n\t * is used to share annotated slides. The notes sidebar is\n\t * only visible if showNotes is true and there are notes on\n\t * one or more slides in the deck.\n\t */\n\tupdateVisibility() {\n\n\t\tif( this.Reveal.getConfig().showNotes && this.hasNotes() && !this.Reveal.print.isPrintingPDF() ) {\n\t\t\tthis.Reveal.getRevealElement().classList.add( 'show-notes' );\n\t\t}\n\t\telse {\n\t\t\tthis.Reveal.getRevealElement().classList.remove( 'show-notes' );\n\t\t}\n\n\t}\n\n\t/**\n\t * Checks if there are speaker notes for ANY slide in the\n\t * presentation.\n\t */\n\thasNotes() {\n\n\t\treturn this.Reveal.getSlidesElement().querySelectorAll( '[data-notes], aside.notes' ).length > 0;\n\n\t}\n\n\t/**\n\t * Checks if this presentation is running inside of the\n\t * speaker notes window.\n\t *\n\t * @return {boolean}\n\t */\n\tisSpeakerNotesWindow() {\n\n\t\treturn !!window.location.search.match( /receiver/gi );\n\n\t}\n\n\t/**\n\t * Retrieves the speaker notes from a slide. Notes can be\n\t * defined in two ways:\n\t * 1. As a data-notes attribute on the slide
\n\t * 2. As an