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FreesearchR/R/data_plots.R

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# source(here::here("functions.R"))
#' Data correlations evaluation module
#'
#' @param id Module id. (Use 'ns("id")')
#'
#' @name data-correlations
#' @returns Shiny ui module
#' @export
#'
data_visuals_ui <- function(id, tab_title = "Plots", ...) {
ns <- shiny::NS(id)
# bslib::navset_bar(
list(
# Sidebar with a slider input
sidebar = bslib::sidebar(
bslib::accordion(
multiple = FALSE,
bslib::accordion_panel(
title = "Creating plot",
icon = bsicons::bs_icon("graph-up"),
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")
),
bslib::accordion_panel(
title = "Download",
icon = bsicons::bs_icon("download"),
shinyWidgets::noUiSliderInput(
inputId = ns("height"),
label = "Plot height (mm)",
min = 50,
max = 300,
value = 100,
step = 1,
format = shinyWidgets::wNumbFormat(decimals = 0),
color = datamods:::get_primary_color()
),
shinyWidgets::noUiSliderInput(
inputId = ns("width"),
label = "Plot width (mm)",
min = 50,
max = 300,
value = 100,
step = 1,
format = shinyWidgets::wNumbFormat(decimals = 0),
color = datamods:::get_primary_color()
),
shiny::selectInput(
inputId = ns("plot_type"),
label = "File format",
choices = list(
"png",
"tiff",
"eps",
"pdf",
"jpeg",
"svg"
)
),
shiny::br(),
# Button
shiny::downloadButton(
outputId = ns("download_plot"),
label = "Download plot",
icon = shiny::icon("download")
)
)
)
),
bslib::nav_panel(
title = tab_title,
shiny::plotOutput(ns("plot"))
)
)
}
#'
#' @param data data
#' @param ... ignored
#'
#' @name data-correlations
#' @returns shiny server module
#' @export
data_visuals_server <- function(id,
data,
...) {
shiny::moduleServer(
id = id,
module = function(input, output, session) {
ns <- session$ns
rv <- shiny::reactiveValues(
plot.params = NULL,
plot = NULL
)
output$primary <- shiny::renderUI({
columnSelectInput(
inputId = ns("primary"),
data = data,
placeholder = "Select variable",
label = "Response variable",
multiple = FALSE
)
})
output$type <- shiny::renderUI({
shiny::req(input$primary)
# browser()
if (!input$primary %in% names(data())) {
plot_data <- data()[1]
} else {
plot_data <- data()[input$primary]
}
plots <- possible_plots(
data = plot_data
)
shiny::selectizeInput(
inputId = ns("type"),
selected = NULL,
label = shiny::h4("Plot type"),
choices = plots,
multiple = FALSE
)
})
rv$plot.params <- shiny::reactive({
get_plot_options(input$type)
})
output$secondary <- shiny::renderUI({
shiny::req(input$type)
# browser()
columnSelectInput(
inputId = ns("secondary"),
data = data,
placeholder = "Select variable",
label = "Secondary/group variable",
multiple = FALSE,
col_subset = c(
purrr::pluck(rv$plot.params(), 1)[["secondary.extra"]],
all_but(
colnames(subset_types(
data(),
purrr::pluck(rv$plot.params(), 1)[["secondary.type"]]
)),
input$primary
)
),
none_label = "No variable"
)
})
output$tertiary <- shiny::renderUI({
shiny::req(input$type)
columnSelectInput(
inputId = ns("tertiary"),
data = data,
placeholder = "Select variable",
label = "Strata variable",
multiple = FALSE,
col_subset = c(
"none",
all_but(
colnames(subset_types(
data(),
purrr::pluck(rv$plot.params(), 1)[["tertiary.type"]]
)),
input$primary,
input$secondary
)
),
none_label = "No stratification"
)
})
rv$plot <- shiny::reactive({
shiny::req(input$primary)
shiny::req(input$type)
shiny::req(input$secondary)
shiny::req(input$tertiary)
create_plot(
data = data(),
type = names(rv$plot.params()),
x = input$primary,
y = input$secondary,
z = input$tertiary
)
})
output$plot <- shiny::renderPlot({
rv$plot()
})
output$download_plot <- shiny::downloadHandler(
filename = shiny::reactive({
paste0("plot.", input$plot_type)
}),
content = function(file) {
shiny::withProgress(message = "Drawing the plot. Hold on for a moment..", {
ggplot2::ggsave(
filename = file,
plot = rv$plot(),
width = input$width,
height = input$height,
dpi = 300,
units = "mm", scale = 2
)
})
}
)
shiny::observe(
return(rv$plot)
)
}
)
}
#' Select all from vector but
#'
#' @param data vector
#' @param ... exclude
#'
#' @returns vector
#' @export
#'
#' @examples
#' all_but(1:10, c(2, 3), 11, 5)
all_but <- function(data, ...) {
data[!data %in% c(...)]
}
#' Easily subset by data type function
#'
#' @param data data
#' @param types desired types
#' @param type.fun function to get type. Default is outcome_type
#'
#' @returns vector
#' @export
#'
#' @examples
#' default_parsing(mtcars) |> subset_types("ordinal")
#' default_parsing(mtcars) |> subset_types(c("dichotomous", "ordinal"))
#' #' default_parsing(mtcars) |> subset_types("factor",class)
subset_types <- function(data, types, type.fun = outcome_type) {
data[sapply(data, type.fun) %in% types]
}
#' Implemented functions
#'
#' @description
#' Library of supported functions. The list name and "descr" element should be
#' unique for each element on list.
#'
#' - descr: Plot description
#'
#' - primary.type: Primary variable data type (continuous, dichotomous or ordinal)
#'
#' - secondary.type: Secondary variable data type (continuous, dichotomous or ordinal)
#'
#' - secondary.extra: "none" or NULL to have option to choose none.
#'
#' - tertiary.type: Tertiary variable data type (continuous, dichotomous or ordinal)
#'
#'
#' @returns list
#' @export
#'
#' @examples
#' supported_plots() |> str()
supported_plots <- function() {
list(
plot_hbars = list(
descr = "Stacked horizontal bars",
note = "A classical way of visualising the distribution of an ordinal scale like the modified Ranking Scale and known as Grotta bars",
primary.type = c("dichotomous", "ordinal"),
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = "none"
),
plot_violin = list(
descr = "Violin plot",
note = "A modern alternative to the classic boxplot to visualise data distribution",
primary.type = c("continuous", "dichotomous", "ordinal"),
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = "none"
),
plot_ridge = list(
descr = "Ridge plot",
note = "An alternative option to visualise data distribution",
primary.type = "continuous",
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = NULL
),
plot_sankey = list(
descr = "Sankey plot",
note = "A way of visualising change between groups",
primary.type = c("dichotomous", "ordinal"),
secondary.type = c("dichotomous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = NULL
),
plot_scatter = list(
descr = "Scatter plot",
note = "A classic way of showing the association between to variables",
primary.type = "continuous",
secondary.type = c("continuous", "ordinal"),
tertiary.type = c("dichotomous", "ordinal"),
secondary.extra = NULL
)
)
}
#' Title
#'
#' @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
#'
#' @returns character vector
#' @export
#'
#' @examples
#' mtcars |>
#' default_parsing() |>
#' dplyr::pull("cyl") |>
#' possible_plots()
#'
#' mtcars |>
#' default_parsing() |>
#' dplyr::select("mpg") |>
#' possible_plots()
possible_plots <- function(data) {
# browser()
if (is.data.frame(data)) {
data <- data[[1]]
}
type <- outcome_type(data)
if (type == "unknown") {
out <- type
} else {
out <- supported_plots() |>
lapply(\(.x){
if (type %in% .x$primary.type) {
.x$descr
}
}) |>
unlist()
}
unname(out)
}
#' Get the function options based on the selected function description
#'
#' @param data vector
#'
#' @returns list
#' @export
#'
#' @examples
#' ls <- mtcars |>
#' default_parsing() |>
#' dplyr::pull(mpg) |>
#' possible_plots() |>
#' (\(.x){
#' .x[[1]]
#' })() |>
#' get_plot_options()
get_plot_options <- function(data) {
descrs <- supported_plots() |>
lapply(\(.x){
.x$descr
}) |>
unlist()
supported_plots() |>
(\(.x){
.x[match(data, descrs)]
})()
}
#' Wrapper to create plot based on provided type
#'
#' @param type plot type (derived from possible_plots() and matches custom function)
#' @param ... ignored for now
#'
#' @name data-plots
#'
#' @returns ggplot2 object
#' @export
#'
#' @examples
#' create_plot(mtcars, "plot_violin", "mpg", "cyl")
create_plot <- function(data, type, x, y, z = NULL, ...) {
if (!y %in% names(data)) {
y <- NULL
}
if (!z %in% names(data)) {
z <- NULL
}
do.call(
type,
list(data, x, y, z, ...)
)
}
#' 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
#' @param score
#' @param group
#' @param strata
#' @param t.size
#'
#' @return
#' @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
#'
#' @returns character string
#' @export
#'
#' @examples
#' mtcars |> get_label(var = "mpg")
#' mtcars |> get_label()
#' mtcars$mpg |> get_label()
#' gtsummary::trial |> get_label(var = "trt")
#' 1:10 |> get_label()
get_label <- function(data, var = NULL) {
if (!is.null(var)) {
data <- data[[var]]
}
out <- REDCapCAST::get_attr(data = data, attr = "label")
if (is.na(out)) {
if (is.null(var)) {
out <- deparse(substitute(data))
} else {
if (is.symbol(var)) {
out <- gsub('\"', "", deparse(substitute(var)))
} else {
out <- var
}
}
}
out
}
#' 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 = x), ytitle = get_label(data, var = y)
)
})
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 = x), ytitle = get_label(data, var = y)
)
} else {
rempsyc::nice_scatter(
data = data,
predictor = y,
response = x,
group = z
)
}
}
#' Readying data for sankey plot
#'
#' @param data
#' @param x
#' @param y
#' @param z
#'
#' @returns
#' @export
#'
#' @examples
#' ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = sample(c(letters[1:4], NA), 100, TRUE, prob = c(rep(.23, 4), .08)))
#' ds |> sankey_ready("first", "last")
#' ds |> sankey_ready("first", "last", numbers = "percentage")
sankey_ready <- function(data, x, y, z = NULL, numbers = "count") {
## TODO: Ensure ordering x and y
if (is.null(z)) {
out <- dplyr::count(data, !!dplyr::sym(x), !!dplyr::sym(y))
} else {
out <- dplyr::count(data, !!dplyr::sym(x), !!dplyr::sym(y), !!dplyr::sym(z))
}
out <- out |>
dplyr::group_by(!!dplyr::sym(x)) |>
dplyr::mutate(gx.sum = sum(n)) |>
dplyr::ungroup() |>
dplyr::group_by(!!dplyr::sym(y)) |>
dplyr::mutate(gy.sum = sum(n)) |>
dplyr::ungroup()
if (numbers == "count") {
out <- out |> dplyr::mutate(
lx = factor(paste0(!!dplyr::sym(x), "\n(n=", gx.sum, ")")),
ly = factor(paste0(!!dplyr::sym(y), "\n(n=", gy.sum, ")"))
)
} else if (numbers == "percentage") {
out <- out |> dplyr::mutate(
lx = factor(paste0(!!dplyr::sym(x), "\n(", round((gx.sum / sum(n)) * 100, 1), "%)")),
ly = factor(paste0(!!dplyr::sym(y), "\n(", round((gy.sum / sum(n)) * 100, 1), "%)"))
)
}
if (is.factor(data[[x]])){
index <- match(levels(data[[x]]),str_remove_last(levels(out$lx),"\n"))
out$lx <- factor(out$lx,levels=levels(out$lx)[index])
}
if (is.factor(data[[y]])){
index <- match(levels(data[[y]]),str_remove_last(levels(out$ly),"\n"))
out$ly <- factor(out$ly,levels=levels(out$ly)[index])
}
out
}
str_remove_last <- function(data,pattern="\n"){
strsplit(data,split = pattern) |>
lapply(\(.x)paste(unlist(.x[[-length(.x)]]),collapse=pattern)) |>
unlist()
}
#' Line breaking at given number of characters for nicely plotting labels
#'
#' @param data
#' @param lineLength
#'
#' @returns
#' @export
#'
#' @examples
line_break <- function(data, lineLength = 20) {
# gsub(paste0('(.{1,',lineLength,'})(\\s)'), '\\1\n', data)
paste(strwrap(data, lineLength), collapse = "\n")
## https://stackoverflow.com/a/29847221
}
#' Beautiful sankey plot with option to split by a tertiary group
#'
#' @returns ggplot2 object
#' @export
#'
#' @name data-plots
#'
#' @examples
#' ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)))
#' ds |> plot_sankey("first", "last")
#' ds |> plot_sankey("first", "last", color.group = "y")
#' ds |> plot_sankey("first", "last", z = "g", color.group = "y")
plot_sankey <- function(data, x, y, z = NULL, color.group = "x", colors = NULL) {
if (!is.null(z)) {
ds <- split(data, data[z])
} else {
ds <- list(data)
}
out <- lapply(ds, \(.ds){
plot_sankey_single(.ds,x = x, y = y,color.group = color.group, colors = colors)
})
patchwork::wrap_plots(out)
}
default_theme <- function() {
theme_void()
}
#' Beautiful sankey plot
#'
#' @param color.group
#' @param colors
#'
#' @returns ggplot2 object
#' @export
#'
#' @examples
#' ds <- data.frame(g = sample(LETTERS[1:2], 100, TRUE), first = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)), last = REDCapCAST::as_factor(sample(letters[1:4], 100, TRUE)))
#' ds |> plot_sankey_single("first", "last")
#' ds |> plot_sankey_single("first", "last", color.group = "y")
plot_sankey_single <- function(data,x,y, color.group = "x", colors = NULL){
data <- data |> sankey_ready(x = x, y = y)
# browser()
library(ggalluvial)
na.color <- "#2986cc"
box.color <- "#1E4B66"
if (is.null(colors)) {
if (color.group == "y") {
main.colors <- viridisLite::viridis(n = length(levels(data[[y]])))
secondary.colors <- rep(na.color, length(levels(data[[x]])))
label.colors <- Reduce(c, lapply(list(secondary.colors, rev(main.colors)), contrast_text))
} else {
main.colors <- viridisLite::viridis(n = length(levels(data[[x]])))
secondary.colors <- rep(na.color, length(levels(data[[y]])))
label.colors <- Reduce(c, lapply(list(rev(main.colors), secondary.colors), contrast_text))
}
colors <- c(na.color, main.colors, secondary.colors)
} else {
label.colors <- contrast_text(colors)
}
group_labels <- c(get_label(data, x), get_label(data, y)) |>
sapply(line_break) |>
unname()
p <- ggplot2::ggplot(data, ggplot2::aes(y = n, axis1 = lx, axis2 = ly))
if (color.group == "y") {
p <- p +
ggalluvial::geom_alluvium(
ggplot2::aes(fill = !!dplyr::sym(y), color = !!dplyr::sym(y)),
width = 1 / 16,
alpha = .8,
knot.pos = 0.4,
curve_type = "sigmoid"
) + ggalluvial::geom_stratum(ggplot2::aes(fill = !!dplyr::sym(y)),
size = 2,
width = 1 / 3.4
)
} else {
p <- p +
ggalluvial::geom_alluvium(
ggplot2::aes(fill = !!dplyr::sym(x), color = !!dplyr::sym(x)),
width = 1 / 16,
alpha = .8,
knot.pos = 0.4,
curve_type = "sigmoid"
) + ggalluvial::geom_stratum(ggplot2::aes(fill = !!dplyr::sym(x)),
size = 2,
width = 1 / 3.4
)
}
p +
ggplot2::geom_text(
stat = "stratum",
ggplot2::aes(label = after_stat(stratum)),
colour = label.colors,
size = 8,
lineheight = 1
) +
ggplot2::scale_x_continuous(
breaks = 1:2,
labels = group_labels
) +
ggplot2::scale_fill_manual(values = colors[-1], na.value = colors[1]) +
ggplot2::scale_color_manual(values = main.colors) +
ggplot2::theme_void() +
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(),
axis.text.x = ggplot2::element_text(size = 20),
# text = element_text(size = 5),
# plot.title = element_blank(),
# panel.background = ggplot2::element_rect(fill = "white"),
plot.background = ggplot2::element_rect(fill = "white"),
panel.border = ggplot2::element_blank()
)
}
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