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291
defence.R
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291
defence.R
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source(here::here("1 PA Decline/dst import.R"))
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source(here::here("fun/functions.R"))
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################################################################################
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############ Study 1
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################################################################################
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# pak::pak("agdamsbo/project.aid")
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############ MMRM
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ls <- project.aid::docx2list(path = here::here("1 PA Decline/Fra DDV/241002/mmrm.docx"))
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df_mmrm_coef <- setNames(ls[[1]][c(1, 4, 5)], c("var", "beta", "ci"))
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df_mmrm_coef <- df_mmrm_coef[-nrow(df_mmrm_coef), ]
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df_mmrm_coef_clean <- apply(df_mmrm_coef, 2, \(.x) {
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.x[.x == "—"] <- "0, 0"
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.x
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}) |>
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as.data.frame() |>
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tidyr::separate(
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col = "ci", into = c("lo", "hi"), sep = ", ", convert = TRUE
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) |>
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apply(2, \(.x) {
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.x[.x == "0, 0"] <- "0"
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.x[.x == ""] <- NA
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.x
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}) |>
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as.data.frame() |>
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tibble::as_tibble() |>
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dplyr::mutate(dplyr::across(tidyselect::all_of(c("beta", "lo", "hi")), as.numeric))
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## Manual cleaning
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df_mmrm_coef_clean$var <- c(
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"Age", "Female sex", "Admission NIHSS", "Treated with IVT",
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"Treated with EVT", "Placebo trial treatment", "Living alone",
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"Current smoker", "High alcohol consumption", "Hypertension",
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"Diabetes", "Previous TIA", "Atrial fibrillation", "Previous MI",
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"Peripheral arterial disease", "Not employed", "Lower family income",
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"High family income", "Medium family income", "Low family income", "Lower educational level",
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"High educational level", "Medium educational level", "Low educational level", "Pre-stroke WHO-5 score",
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"Pre-stroke mRS > 0"
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)
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df_mmrm_coef_clean <- df_mmrm_coef_clean[!df_mmrm_coef_clean$var %in% c("Lower family income", "Lower educational level"), ]
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## Plot
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p_list <- lapply(list("all" = list(), "sign" = list(only_sign = TRUE)), \(.x){
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# browser()
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do.call(
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forest_plot,
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modifyList(.x, list(data = df_mmrm_coef_clean))
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)
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})
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purrr::imap(p_list[2], \(.x, .i){
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ggplot2::ggsave(
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filename = glue::glue("mmrm_coef_{.i}.png"),
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plot = .x,
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height = 14,
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width = 14,
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units = "cm",
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dpi = 300,
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scale = .8
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)
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})
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############ Prediction model
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df_coefs_raw <- get_coefs(path = here::here("1 PA Decline/Fra DDV/241002/mmrm.docx"), index.table = 1) |>
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dplyr::filter(variable != "(Intercept)") |>
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dplyr::select(variable, hop_median, drop_median) |>
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setNames(c("variable", "INCREASE", "DECREASE"))
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df_coefs_raw <- get_coefs(path = here::here("1 PA Decline/Fra DDV/240624/pa_change_analyses.docx"), index.table = 2) |>
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dplyr::filter(variable != "(Intercept)") |>
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dplyr::select(variable, hop_median, drop_median) |>
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setNames(c("variable", "INCREASE", "DECREASE"))
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## Real work
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df_coefs <- df_coefs_raw |>
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dplyr::mutate(dplyr::across(
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tidyselect::all_of(c("INCREASE", "DECREASE")),
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function(.x) {
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# signif(
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as.numeric(.x) # ,
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# 3
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# )
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}
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)) |>
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dplyr::mutate(variable = dplyr::if_else(variable == "Alcohol consumption above recommendations",
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"High alcohol consumption", variable
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)) |>
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## Important step to keep the data ordered for ggplot
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(function(.y) {
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.y |> dplyr::mutate(variable = factor(variable, levels = rev(.y$variable)))
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})()
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## Highest ORs
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ls_max <- list(df_coefs[c(1, 2)], df_coefs[c(1, 3)]) |>
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setNames(names(df_coefs)[2:3]) |>
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purrr::map(function(.x) {
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.x |>
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setNames(c("var", "val")) |>
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dplyr::mutate(sorting = abs(log(val))) |>
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dplyr::arrange(1 - sorting) |>
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dplyr::mutate(dplyr::across(dplyr::where(is.numeric), ~ signif(.x, 2))) |>
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head(5)
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})
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p_ls_a <- lapply(ls_max, simple_shap_plot)
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purrr::imap(p_ls_a, \(.x, .i){
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ggplot2::ggsave(
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filename = glue::glue("pred_coef_{.i}.png"),
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plot = .x,
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height = 8,
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width = 14,
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units = "cm",
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dpi = 300,
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scale = 1
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)
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})
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################################################################################
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############ Study 2
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################################################################################
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ls_2_supp <- project.aid::docx2list(path = "/Users/au301842/Library/CloudStorage/OneDrive-Personal/Research/PhD/2 TALOS opfølgning/Manuskript/Supplementary material_v2_0.docx")
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pase_0 <- ls_2_supp[["30"]][4:7, -2]
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pase_4 <- ls_2_supp[["30"]][10:13, -2]
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ls_2_main <- project.aid::docx2list(path = "/Users/au301842/Library/CloudStorage/OneDrive-Personal/Research/PhD/2 TALOS opfølgning/Manuskript/SR/Manuscript_2.docx")
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pase_change <- ls_2_main[["169"]][2:5, -2]
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pase_clean <- list(
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pase_change,
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pase_0,
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pase_4
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) |>
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lapply(pase_table_clean)
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pase_clean_plots <- purrr::map2(
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pase_clean,
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list(
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list(
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rev_y = TRUE,
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group.colors = c(
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"#30123BFF",
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"#FABA39FF",
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"#1AE4B6FF",
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"#7A0403FF"
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)
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),
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list(
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rev_y = FALSE,
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group.colors = viridisLite::viridis(6, option = "B")[2:5]
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),
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list(
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rev_y = FALSE,
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group.colors = viridisLite::viridis(6, option = "B")[2:5]
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)
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), \(.x, .y){
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do.call(
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forest_plot_grp,
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modifyList(
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list(
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data = .x
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),
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.y
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)
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)
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}
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)
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pase_clean_plots[2]
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purrr::imap(setNames(
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pase_clean_plots,
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c(
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"pase_change",
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"pase_0",
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"pase_4"
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)
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), \(.x, .i){
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ggplot2::ggsave(
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filename = glue::glue("pase_risk_{.i}.png"),
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plot = .x,
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height = 10,
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width = 12,
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units = "cm",
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dpi = 300,
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scale = 1.2
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)
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})
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############ Only mRS 0
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############
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pase_change_mrs0_clean <- ls_2_supp[["39"]][3:6,-2] |> pase_table_clean()
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pase_change_mrs0_plot <- forest_plot_grp(pase_change_mrs0_clean,
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rev_y = TRUE,
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group.colors = c(
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"#30123BFF",
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"#FABA39FF",
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"#1AE4B6FF",
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"#7A0403FF"
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))
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ggplot2::ggsave(
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filename = "pase_risk_mrs0.png",
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plot = pase_change_mrs0_plot,
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height = 10,
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width = 12,
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units = "cm",
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dpi = 300,
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scale = 1.2
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)
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################################################################################
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############ Study 3
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################################################################################
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ls_3_main <- project.aid::docx2list(path = "/Users/au301842/Library/CloudStorage/OneDrive-Personal/Research/PhD/3 SVD and stroke/Manuskript/EJN/SVD burden_v4_0.docx")
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svd <- ls_3_main[["138"]][3:6, ]
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svd_clean <- purrr::imap(svd[2:4], \(.x, .i){
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# browser()
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out <- data.frame(or_ci = .x) |>
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tidyr::separate(
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col = "or_ci", into = c("val", "ci"), sep = " \\("
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) |>
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tidyr::separate(
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col = "ci", into = c("lo", "hi"), sep = " to "
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) |>
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apply(2, \(.s){
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gsub("\\)", "", .s)
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}) |>
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as.data.frame() |>
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dplyr::mutate(dplyr::across(tidyselect::all_of(c("val", "lo", "hi")), as.numeric))
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out[1, 1] <- 1
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# browser()
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out$var <- sapply(svd[[1]], \(.s){
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l <- nchar(.s)
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substr(.s, 5, l)
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})
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# out[1,2:4] <- 1
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out$model <- .i
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# out$var <- sapply(out$var,\(.x){
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# paste(strwrap(.x, 25), collapse = "\n")})
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out
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}) |> dplyr::bind_rows()
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svd_plot <- svd_clean |>
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forest_plot_grp(rev_y = FALSE, group.colors = viridisLite::viridis(6, option = "B")[2:5])
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ggplot2::ggsave(
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filename = "svd_olr_coef.png",
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plot = svd_plot,
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height = 10,
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width = 12,
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units = "cm",
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dpi = 300,
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scale = 1.2
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)
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# p_colors <- lapply(LETTERS[1:8], \(.x){
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# viridisLite::viridis(6, option = .x)[2:5]
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# })
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# project.aid::color_plot(p_colors[[2]])
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#
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# lapply(p_colors, project.aid::color_plot)
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