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slimmed missings evaluation module
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1 changed files with 17 additions and 132 deletions
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@ -34,7 +34,8 @@ data_missings_server <- function(id,
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variabler <- if (is.reactive(variable)) variable else reactive(variable)
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rv <- shiny::reactiveValues(
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data = NULL
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data = NULL,
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table = NULL
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)
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rv$data <- shiny::reactive({
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@ -65,15 +66,25 @@ data_missings_server <- function(id,
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shiny::req(variabler)
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if (is.null(variabler()) || variabler() == "" || !variabler() %in% names(datar())) {
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if (anyNA(datar())){
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title <- "No variable chosen for analysis"
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} else {
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title <- "No missing observations"
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}
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} else {
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title <- paste("Missing vs non-missing observations in", variabler())
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title <- glue::glue("Missing vs non-missing observations in the variable **'{variabler()}'**")
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}
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rv$data() |>
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out <- rv$data() |>
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gtsummary::as_gt() |>
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gt::tab_header(title = gt::md(title))
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rv$table <- out
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out
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})
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return(reactive(rv$table))
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}
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)
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}
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@ -100,10 +111,12 @@ missing_demo_app <- function() {
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data_missings_server(id = "data", data = data_demo, variable = shiny::reactive(input$missings_var))
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visual_summary_server(id = "visual", data = data_demo)
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observeEvent(input$modal_missings, {
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tryCatch(
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{
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modal_visual_missings(data = data_demo, id = "modal_missings")
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modal_visual_summary(id = "visual")
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},
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error = function(err) {
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showNotification(paste0("We encountered the following error browsing your data: ", err), type = "err")
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@ -117,137 +130,9 @@ missing_demo_app <- function() {
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missing_demo_app()
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modal_visual_missings <- function(data,
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title = "Visual overview of data classes and missing observations",
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easyClose = TRUE,
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size = "xl",
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footer = NULL,
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...) {
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datar <- if (is.reactive(data)) data else reactive(data)
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showModal(modalDialog(
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title = tagList(title, datamods:::button_close_modal()),
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tags$div(
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# apexcharter::renderApexchart({
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# missings_apex_plot(datar(), ...)
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# })
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shiny::renderPlot({
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visdat::vis_dat(datar(),sort_type = FALSE) +
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ggplot2::guides(fill = ggplot2::guide_legend(title = "Data class")) +
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# ggplot2::theme_void() +
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ggplot2::theme(
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# legend.position = "none",
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panel.grid.major = ggplot2::element_blank(),
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panel.grid.minor = ggplot2::element_blank(),
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# axis.text.y = element_blank(),
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# axis.title.y = element_blank(),
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text = ggplot2::element_text(size = 18),
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# axis.text = ggplot2::element_blank(),
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# panel.background = ggplot2::element_rect(fill = "white"),
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# plot.background = ggplot2::element_rect(fill = "white"),
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# panel.border = ggplot2::element_blank()
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plot.title = ggplot2::element_blank()
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)
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})
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),
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easyClose = easyClose,
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size = size,
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footer = footer
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))
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}
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## Slow with many observations...
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#' Plot missings and class with apexcharter
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#'
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#' @param data data frame
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#'
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#' @returns An [apexchart()] `htmlwidget` object.
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#' @export
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#'
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#' @examples
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#' data_demo <- mtcars
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#' data_demo[2:4, "cyl"] <- NA
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#' rbind(data_demo, data_demo, data_demo, data_demo) |> missings_apex_plot()
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#' data_demo |> missings_apex_plot()
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#' mtcars |> missings_apex_plot(animation = TRUE)
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#' # dplyr::storms |> missings_apex_plot()
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#' visdat::vis_dat(dplyr::storms)
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missings_apex_plot <- function(data, animation = FALSE, ...) {
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browser()
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df_plot <- purrr::map_df(data, \(x){
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ifelse(is.na(x),
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yes = NA,
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no = glue::glue_collapse(class(x),
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sep = "\n"
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)
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)
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}) %>%
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dplyr::mutate(rows = dplyr::row_number()) %>%
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tidyr::pivot_longer(
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cols = -rows,
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names_to = "variable", values_to = "valueType", values_transform = list(valueType = as.character)
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) %>%
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dplyr::arrange(rows, variable, valueType)
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df_plot$valueType_num <- df_plot$valueType |>
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forcats::as_factor() |>
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as.numeric()
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df_plot$valueType[is.na(df_plot$valueType)] <- "NA"
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df_plot$valueType_num[is.na(df_plot$valueType_num)] <- max(df_plot$valueType_num, na.rm = TRUE) + 1
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labels <- setNames(unique(df_plot$valueType_num), unique(df_plot$valueType))
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if (any(df_plot$valueType == "NA")) {
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colors <- setNames(c(viridisLite::viridis(n = length(labels) - 1), "#999999"), names(labels))
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} else {
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colors <- setNames(viridisLite::viridis(n = length(labels)), names(labels))
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}
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label_list <- labels |>
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purrr::imap(\(.x, .i){
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list(
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from = .x,
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to = .x,
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color = colors[[.i]],
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name = .i
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)
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}) |>
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setNames(NULL)
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out <- apexcharter::apex(
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data = df_plot,
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type = "heatmap",
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mapping = apexcharter::aes(x = variable, y = rows, fill = valueType_num),
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...
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) %>%
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apexcharter::ax_stroke(width = NULL) |>
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apexcharter::ax_plotOptions(
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heatmap = apexcharter::heatmap_opts(
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radius = 0,
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enableShades = FALSE,
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colorScale = list(
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ranges = label_list
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),
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useFillColorAsStroke = TRUE
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)
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) %>%
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apexcharter::ax_dataLabels(enabled = FALSE) |>
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apexcharter::ax_tooltip(
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enabled = FALSE,
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intersect = FALSE
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)
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if (!isTRUE(animation)) {
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out <- out |>
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apexcharter::ax_chart(animations = list(enabled = FALSE))
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}
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out
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}
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