updated docs + boxplot

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Andreas Gammelgaard Damsbo 2025-03-19 13:10:56 +01:00
commit 111393c73f
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23 changed files with 908 additions and 306 deletions

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@ -1 +1 @@
app_version <- function()'250318_0827'
app_version <- function()'250319_1306'

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@ -102,13 +102,16 @@ library(shiny)
#' f <- d_t |> cut(2)
#' readr::parse_time(c("01:00:20", "03:00:20", "01:20:20", "03:02:20", NA)) |> cut(breaks = lubridate::as_datetime(c(hms::as_hms(levels(f)), hms::as_hms(max(d_t, na.rm = TRUE) + 1))), right = FALSE)
cut.hms <- function(x, breaks, ...) {
## as_hms keeps returning warnings on tz(); ignored
suppressWarnings({
if (hms::is_hms(breaks)) {
breaks <- lubridate::as_datetime(breaks, tz = "UTC")
breaks <- lubridate::as_datetime(breaks)
}
x <- lubridate::as_datetime(x, tz = "UTC")
x <- lubridate::as_datetime(x)
out <- cut.POSIXt(x, breaks = breaks, ...)
attr(out, which = "brks") <- hms::as_hms(lubridate::as_datetime(attr(out, which = "brks")))
attr(out, which = "levels") <- as.character(hms::as_hms(lubridate::as_datetime(attr(out, which = "levels"))))
})
out
}

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@ -59,27 +59,7 @@ data_import_server <- function(id) {
id = ns("file_import"),
show_data_in = "popup",
trigger_return = "change",
return_class = "data.frame",
read_fns = list(
ods = import_ods,
dta = function(file) {
haven::read_dta(
file = file,
.name_repair = "unique_quiet"
)
},
csv = import_delim,
tsv = import_delim,
txt = import_delim,
xls = import_xls,
xlsx = import_xls,
rds = function(file) {
readr::read_rds(
file = file,
name_repair = "unique_quiet"
)
}
)
return_class = "data.frame"
)
shiny::observeEvent(data_file$data(), {

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@ -112,6 +112,99 @@ data_visuals_server <- function(id,
plot = NULL
)
# ## --- New attempt
#
# rv$plot.params <- shiny::reactive({
# get_plot_options(input$type) |> purrr::pluck(1)
# })
#
# c(output,
# list(shiny::renderUI({
# columnSelectInput(
# inputId = ns("primary"),
# data = data,
# placeholder = "Select variable",
# label = "Response variable",
# multiple = FALSE
# )
# }),
# shiny::renderUI({
# shiny::req(input$primary)
# # browser()
#
# if (!input$primary %in% names(data())) {
# plot_data <- data()[1]
# } else {
# plot_data <- data()[input$primary]
# }
#
# plots <- possible_plots(
# data = plot_data
# )
#
# plots_named <- get_plot_options(plots) |>
# lapply(\(.x){
# stats::setNames(.x$descr, .x$note)
# })
#
# vectorSelectInput(
# inputId = ns("type"),
# selected = NULL,
# label = shiny::h4("Plot type"),
# choices = Reduce(c, plots_named),
# multiple = FALSE
# )
# }),
# shiny::renderUI({
# shiny::req(input$type)
#
# cols <- c(
# rv$plot.params()[["secondary.extra"]],
# all_but(
# colnames(subset_types(
# data(),
# rv$plot.params()[["secondary.type"]]
# )),
# input$primary
# )
# )
#
# columnSelectInput(
# inputId = ns("secondary"),
# data = data,
# selected = cols[1],
# placeholder = "Please select",
# label = if (isTRUE(rv$plot.params()[["secondary.multi"]])) "Additional variables" else "Secondary variable",
# multiple = rv$plot.params()[["secondary.multi"]],
# maxItems = rv$plot.params()[["secondary.max"]],
# col_subset = cols,
# none_label = "No variable"
# )
# }),
# shiny::renderUI({
# shiny::req(input$type)
# columnSelectInput(
# inputId = ns("tertiary"),
# data = data,
# placeholder = "Please select",
# label = "Grouping variable",
# multiple = FALSE,
# col_subset = c(
# "none",
# all_but(
# colnames(subset_types(
# data(),
# rv$plot.params()[["tertiary.type"]]
# )),
# input$primary,
# input$secondary
# )
# ),
# none_label = "No stratification"
# )
# })
# )|> setNames(c("primary","type","secondary","tertiary")),keep.null = TRUE)
output$primary <- shiny::renderUI({
columnSelectInput(
inputId = ns("primary"),
@ -364,6 +457,16 @@ supported_plots <- function() {
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = NULL
),
plot_box = list(
fun = "plot_box",
descr = "Box plot",
note = "A classic way to plot data distribution by groups",
primary.type = c("continuous", "dichotomous", "ordinal"),
secondary.type = c("dichotomous", "ordinal"),
secondary.multi = FALSE,
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = "none"
),
plot_euler = list(
fun = "plot_euler",
descr = "Euler diagram",
@ -535,18 +638,49 @@ line_break <- function(data, lineLength = 20, fixed = FALSE) {
}
wrap_plot_list <- function(data) {
if (length(data) > 1) {
out <- data |>
allign_axes() |>
patchwork::wrap_plots(guides = "collect", axes = "collect", axis_titles = "collect")
#' Wrapping
#'
#' @param data list of ggplot2 objects
#' @param tag_levels passed to patchwork::plot_annotation if given. Default is NULL
#'
#' @returns list of ggplot2 objects
#' @export
#'
wrap_plot_list <- function(data, tag_levels = NULL) {
if (ggplot2::is.ggplot(data[[1]])) {
if (length(data) > 1) {
out <- data |>
(\(.x){
if (rlang::is_named(.x)) {
purrr::imap(.x, \(.y, .i){
.y + ggplot2::ggtitle(.i)
})
} else {
.x
}
})() |>
allign_axes() |>
patchwork::wrap_plots(guides = "collect", axes = "collect", axis_titles = "collect")
if (!is.null(tag_levels)) {
out <- out + patchwork::plot_annotation(tag_levels = tag_levels)
}
} else {
out <- data
}
} else {
out <- data
cli::cli_abort("Can only wrap lists of {.cls ggplot} objects")
}
out
}
#' Alligns axes between plots
#'
#' @param ... ggplot2 objects or list of ggplot2 objects
#'
#' @returns list of ggplot2 objects
#' @export
#'
allign_axes <- function(...) {
# https://stackoverflow.com/questions/62818776/get-axis-limits-from-ggplot-object
# https://github.com/thomasp85/patchwork/blob/main/R/plot_multipage.R#L150
@ -558,16 +692,30 @@ allign_axes <- function(...) {
cli::cli_abort("Can only align {.cls ggplot} objects or a list of them")
}
# browser()
yr <- purrr::map(p, ~ ggplot2::layer_scales(.x)$y$get_limits()) |>
unlist() |>
range() |>
unique()
yr <- clean_common_axis(p, "y")
xr <- purrr::map(p, ~ ggplot2::layer_scales(.x)$x$get_limits()) |>
unlist() |>
range() |>
unique()
xr <- clean_common_axis(p, "x")
p |> purrr::map(~ .x + ggplot2::xlim(xr) + ggplot2::ylim(yr))
}
#' Extract and clean axis ranges
#'
#' @param p plot
#' @param axis axis. x or y.
#'
#' @returns vector
#' @export
#'
clean_common_axis <- function(p, axis) {
purrr::map(p, ~ ggplot2::layer_scales(.x)[[axis]]$get_limits()) |>
unlist() |>
(\(.x){
if (is.numeric(.x)) {
range(.x)
} else {
.x
}
})() |>
unique()
}

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@ -215,7 +215,9 @@ default_parsing <- function(data) {
out <- data |>
REDCapCAST::parse_data() |>
REDCapCAST::as_factor() |>
REDCapCAST::numchar2fct()
REDCapCAST::numchar2fct(numeric.threshold = 8,character.throshold = 10) |>
REDCapCAST::as_logical() |>
REDCapCAST::fct_drop()
purrr::map2(out,name_labels,\(.x,.l){
if (!(is.na(.l) | .l=="")) {
@ -275,6 +277,7 @@ remove_empty_cols <- function(data,cutoff=.7){
#' @param index index name
#'
#' @returns list
#' @export
#'
#' @examples
#' ls_d <- list(test=c(1:20))

80
R/plot_box.R Normal file
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@ -0,0 +1,80 @@
#' Beautiful box plot(s)
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |> plot_box(x = "mpg", y = "cyl", z = "gear")
#' mtcars |>
#' default_parsing() |>
#' plot_box(x = "mpg", y = "cyl", z = "gear")
plot_box <- function(data, x, y, z = NULL) {
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
ds <- list(data)
}
out <- lapply(ds, \(.ds){
plot_box_single(
data = .ds,
x = x,
y = y
)
})
wrap_plot_list(out)
# patchwork::wrap_plots(out,guides = "collect")
}
#' Create nice box-plots
#'
#' @name data-plots
#'
#' @returns
#' @export
#'
#' @examples
#' mtcars |> plot_box_single("mpg","cyl")
plot_box_single <- function(data, x, y=NULL, seed = 2103) {
set.seed(seed)
if (is.null(y)) {
y <- "All"
data[[y]] <- y
}
discrete <- !outcome_type(data[[y]]) %in% "continuous"
data |>
ggplot2::ggplot(ggplot2::aes(x = !!dplyr::sym(x), y = !!dplyr::sym(y), fill = !!dplyr::sym(y), group = !!dplyr::sym(y))) +
ggplot2::geom_boxplot(linewidth = 1.8, outliers = FALSE) +
## THis could be optional in future
ggplot2::geom_jitter(color = "black", size = 2, alpha = 0.9) +
ggplot2::coord_flip() +
viridis::scale_fill_viridis(discrete = discrete, option = "D") +
# ggplot2::theme_void() +
ggplot2::theme_bw(base_size = 24) +
ggplot2::theme(
legend.position = "none",
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# axis.text.y = element_blank(),
# axis.title.y = element_blank(),
# text = ggplot2::element_text(size = 20),
# axis.text = ggplot2::element_blank(),
# plot.title = element_blank(),
panel.background = ggplot2::element_rect(fill = "white"),
plot.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),
panel.grid.minor = ggplot2::element_blank(),
axis.line = ggplot2::element_line(colour = "black"),
axis.ticks = ggplot2::element_line(colour = "black")
)
}

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@ -78,9 +78,6 @@ ggeulerr <- function(
#' mtcars |> plot_euler("vs", "am", seed = 1)
plot_euler <- function(data, x, y, z = NULL, seed = 2103) {
set.seed(seed = seed)
# data <- data[c(...,z)]
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
@ -93,6 +90,7 @@ plot_euler <- function(data, x, y, z = NULL, seed = 2103) {
plot_euler_single()
})
# names(out)
wrap_plot_list(out)
# patchwork::wrap_plots(out, guides = "collect")
}
@ -116,7 +114,7 @@ plot_euler_single <- function(data) {
ggeulerr(shape = "circle") +
ggplot2::theme_void() +
ggplot2::theme(
legend.position = "right",
legend.position = "none",
# panel.grid.major = element_blank(),
# panel.grid.minor = element_blank(),
# axis.text.y = element_blank(),

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@ -33,7 +33,7 @@ plot.tbl_regression <- function(x,
# gtsummary:::check_scalar_logical(remove_reference_rows)
df_coefs <- x$table_body
browser()
if (isTRUE(remove_header_rows)) {
df_coefs <- df_coefs |> dplyr::filter(!header_row %in% TRUE)
}
@ -48,22 +48,16 @@ plot.tbl_regression <- function(x,
if (plot_ref == TRUE){
df_coefs[df_coefs$var_type == "categorical" & is.na(df_coefs$reference_row),"estimate"] <- if (x$inputs$exponentiate) 1 else 0}
df_coefs |>
p <- df_coefs |>
ggstats::ggcoef_plot(exponentiate = x$inputs$exponentiate, ...)
if (x$inputs$exponentiate){
p <- symmetrical_scale_x_log10(p)
}
p
}
# default_parsing(mtcars) |> lapply(class)
#
# purrr::imap(mtcars,\(.x,.i){
# if (.i %in% c("vs","am","gear","carb")){
# as.factor(.x)
# } else .x
# }) |> dplyr::bind_cols()
#
#
#' Wrapper to pivot gtsummary table data to long for plotting
#'
#' @param list a custom regression models list
@ -103,3 +97,47 @@ merge_long <- function(list, model.names) {
l_merged
}
#' Easily round log scale limits for nice plots
#'
#' @param data data
#' @param fun rounding function (floor/ceiling)
#' @param ... ignored
#'
#' @returns numeric vector
#' @export
#'
#' @examples
#' limit_log(-.1,floor)
#' limit_log(.1,ceiling)
#' limit_log(-2.1,ceiling)
#' limit_log(2.1,ceiling)
limit_log <- function(data,fun,...){
fun(10^-floor(data)*10^data)/10^-floor(data)
}
#' Ensure symmetrical plot around 1 on a logarithmic x scale for ratio plots
#'
#' @param plot ggplot2 plot
#' @param breaks breaks used and mirrored
#' @param ... ignored
#'
#' @returns ggplot2 object
#' @export
#'
symmetrical_scale_x_log10 <- function(plot,breaks=c(1,2,3,5,10),...){
rx <- ggplot2::layer_scales(plot)$x$get_limits()
x_min <- floor(10*rx[1])/10
x_max <- ceiling(10*rx[2])/10
rx_min <- limit_log(rx[1],floor)
rx_max <- limit_log(rx[2],ceiling)
max_abs_x <- max(abs(c(x_min,x_max)))
ticks <- log10(breaks)+(ceiling(max_abs_x)-1)
browser()
plot + ggplot2::scale_x_log10(limits=c(rx_min,rx_max),breaks=create_log_tics(10^ticks[ticks<=max_abs_x]))
}