organising plotting functions - nicer plot wrapping - merge mulitple workbook sheets

This commit is contained in:
Andreas Gammelgaard Damsbo 2025-03-13 12:41:50 +01:00
commit 49016a4aa8
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20 changed files with 1615 additions and 910 deletions

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@ -4,7 +4,7 @@
#'
#' @param id Module id. (Use 'ns("id")')
#'
#' @name data-correlations
#' @name data-plots
#' @returns Shiny ui module
#' @export
#'
@ -24,12 +24,21 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
shiny::uiOutput(outputId = ns("primary")),
shiny::uiOutput(outputId = ns("type")),
shiny::uiOutput(outputId = ns("secondary")),
shiny::uiOutput(outputId = ns("tertiary"))
),
bslib::accordion_panel(
title = "Advanced",
icon = bsicons::bs_icon("gear")
shiny::uiOutput(outputId = ns("tertiary")),
shiny::br(),
shiny::actionButton(
inputId = ns("act_plot"),
label = "Plot",
width = "100%",
icon = shiny::icon("palette"),
disabled = FALSE
),
shiny::helpText('Adjust settings, then press "Plot".')
),
# bslib::accordion_panel(
# title = "Advanced",
# icon = bsicons::bs_icon("gear")
# ),
bslib::accordion_panel(
title = "Download",
icon = bsicons::bs_icon("download"),
@ -87,7 +96,7 @@ data_visuals_ui <- function(id, tab_title = "Plots", ...) {
#' @param data data
#' @param ... ignored
#'
#' @name data-correlations
#' @name data-plots
#' @returns shiny server module
#' @export
data_visuals_server <- function(id,
@ -130,14 +139,14 @@ data_visuals_server <- function(id,
plots_named <- get_plot_options(plots) |>
lapply(\(.x){
stats::setNames(.x$descr,.x$note)
stats::setNames(.x$descr, .x$note)
})
vectorSelectInput(
inputId = ns("type"),
selected = NULL,
label = shiny::h4("Plot type"),
choices = Reduce(c,plots_named),
choices = Reduce(c, plots_named),
multiple = FALSE
)
})
@ -148,7 +157,6 @@ data_visuals_server <- function(id,
output$secondary <- shiny::renderUI({
shiny::req(input$type)
# browser()
cols <- c(
rv$plot.params()[["secondary.extra"]],
@ -164,9 +172,9 @@ data_visuals_server <- function(id,
columnSelectInput(
inputId = ns("secondary"),
data = data,
selected = 1,
placeholder = "Select variable",
label = "Secondary/group variable",
selected = cols[1],
placeholder = "Please select",
label = if (isTRUE(rv$plot.params()[["secondary.multi"]])) "Additional variables" else "Secondary variable",
multiple = rv$plot.params()[["secondary.multi"]],
maxItems = rv$plot.params()[["secondary.max"]],
col_subset = cols,
@ -179,8 +187,8 @@ data_visuals_server <- function(id,
columnSelectInput(
inputId = ns("tertiary"),
data = data,
placeholder = "Select variable",
label = "Strata variable",
placeholder = "Please select",
label = "Grouping variable",
multiple = FALSE,
col_subset = c(
"none",
@ -197,25 +205,32 @@ data_visuals_server <- function(id,
)
})
rv$plot <- shiny::reactive({
shiny::req(input$primary)
shiny::req(input$type)
shiny::req(input$secondary)
shiny::req(input$tertiary)
# if (length(input$secondary)>1){
# browser()
# }
create_plot(
data = data(),
type = rv$plot.params()[["fun"]],
x = input$primary,
y = input$secondary,
z = input$tertiary
)
})
shiny::observeEvent(input$act_plot,
{
tryCatch(
{
rv$plot <- create_plot(
data = data(),
type = rv$plot.params()[["fun"]],
x = input$primary,
y = input$secondary,
z = input$tertiary
)
},
warning = function(warn) {
showNotification(paste0(warn), type = "warning")
},
error = function(err) {
showNotification(paste0(err), type = "err")
}
)
},
ignoreInit = TRUE
)
output$plot <- shiny::renderPlot({
rv$plot()
shiny::req(rv$plot)
rv$plot
})
output$download_plot <- shiny::downloadHandler(
@ -226,7 +241,7 @@ data_visuals_server <- function(id,
shiny::withProgress(message = "Drawing the plot. Hold on for a moment..", {
ggplot2::ggsave(
filename = file,
plot = rv$plot(),
plot = rv$plot,
width = input$width,
height = input$height,
dpi = 300,
@ -245,7 +260,6 @@ data_visuals_server <- function(id,
}
#' Select all from vector but
#'
#' @param data vector
@ -364,36 +378,6 @@ supported_plots <- function() {
)
}
#' Plot nice ridge plot
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |>
#' default_parsing() |>
#' plot_ridge(x = "mpg", y = "cyl")
#' mtcars |> plot_ridge(x = "mpg", y = "cyl", z = "gear")
plot_ridge <- function(data, x, y, z = NULL, ...) {
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
ds <- list(data)
}
out <- lapply(ds, \(.ds){
ggplot2::ggplot(.ds, ggplot2::aes(x = !!dplyr::sym(x), y = !!dplyr::sym(y), fill = !!dplyr::sym(y))) +
ggridges::geom_density_ridges() +
ggridges::theme_ridges() +
ggplot2::theme(legend.position = "none") |> rempsyc:::theme_apa()
})
patchwork::wrap_plots(out)
}
#' Get possible regression models
#'
#' @param data data
@ -494,104 +478,6 @@ create_plot <- function(data, type, x, y, z = NULL, ...) {
)
}
#' Nice horizontal stacked bars (Grotta bars)
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |> plot_hbars(x = "carb", y = "cyl")
#' mtcars |> plot_hbars(x = "carb", y = NULL)
plot_hbars <- function(data, x, y, z = NULL) {
out <- vertical_stacked_bars(data = data, score = x, group = y, strata = z)
out
}
#' Vertical stacked bar plot wrapper
#'
#' @param data data.frame
#' @param score outcome variable
#' @param group grouping variable
#' @param strata stratifying variable
#' @param t.size text size
#'
#' @return ggplot2 object
#' @export
#'
vertical_stacked_bars <- function(data,
score = "full_score",
group = "pase_0_q",
strata = NULL,
t.size = 10,
l.color = "black",
l.size = .5,
draw.lines = TRUE) {
if (is.null(group)) {
df.table <- data[c(score, group, strata)] |>
dplyr::mutate("All" = 1) |>
table()
group <- "All"
draw.lines <- FALSE
} else {
df.table <- data[c(score, group, strata)] |>
table()
}
p <- df.table |>
rankinPlot::grottaBar(
scoreName = score,
groupName = group,
textColor = c("black", "white"),
strataName = strata,
textCut = 6,
textSize = 20,
printNumbers = "none",
lineSize = l.size,
returnData = TRUE
)
colors <- viridisLite::viridis(nrow(df.table))
contrast_cut <-
sum(contrast_text(colors, threshold = .3) == "white")
score_label <- ifelse(is.na(REDCapCAST::get_attr(data$score, "label")), score, REDCapCAST::get_attr(data$score, "label"))
group_label <- ifelse(is.na(REDCapCAST::get_attr(data$group, "label")), group, REDCapCAST::get_attr(data$group, "label"))
p |>
(\(.x){
.x$plot +
ggplot2::geom_text(
data = .x$rectData[which(.x$rectData$n >
0), ],
size = t.size,
fontface = "plain",
ggplot2::aes(
x = group,
y = p_prev + 0.49 * p,
color = as.numeric(score) > contrast_cut,
# label = paste0(sprintf("%2.0f", 100 * p),"%"),
label = sprintf("%2.0f", 100 * p)
)
) +
ggplot2::labs(fill = score_label) +
ggplot2::scale_fill_manual(values = rev(colors)) +
ggplot2::theme(
legend.position = "bottom",
axis.title = ggplot2::element_text(),
) +
ggplot2::xlab(group_label) +
ggplot2::ylab(NULL)
# viridis::scale_fill_viridis(discrete = TRUE, direction = -1, option = "D")
})()
}
#' Print label, and if missing print variable name
#'
#' @param data vector or data frame
@ -626,62 +512,6 @@ get_label <- function(data, var = NULL) {
}
#' Beatiful violin plot
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |> plot_violin(x = "mpg", y = "cyl", z = "gear")
plot_violin <- function(data, x, y, z = NULL) {
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
ds <- list(data)
}
out <- lapply(ds, \(.ds){
rempsyc::nice_violin(
data = .ds,
group = y,
response = x, xtitle = get_label(data, var = y), ytitle = get_label(data, var = x)
)
})
patchwork::wrap_plots(out)
}
#' Beautiful violin plot
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' mtcars |> plot_scatter(x = "mpg", y = "wt")
plot_scatter <- function(data, x, y, z = NULL) {
if (is.null(z)) {
rempsyc::nice_scatter(
data = data,
predictor = y,
response = x, xtitle = get_label(data, var = y), ytitle = get_label(data, var = x)
)
} else {
rempsyc::nice_scatter(
data = data,
predictor = y,
response = x,
group = z, xtitle = get_label(data, var = y), ytitle = get_label(data, var = x)
)
}
}
#' Line breaking at given number of characters for nicely plotting labels
#'
#' @param data string
@ -705,3 +535,39 @@ 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")
} else {
out <- data
}
out
}
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
if (ggplot2::is.ggplot(..1)) {
p <- list(...)
} else if (is.list(..1)) {
p <- ..1
} else {
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()
xr <- purrr::map(p, ~ ggplot2::layer_scales(.x)$x$get_limits()) |>
unlist() |>
range() |>
unique()
p |> purrr::map(~ .x + ggplot2::xlim(xr) + ggplot2::ylim(yr))
}