update

radiant.basics/DESCRIPTION

@@ -26,7 +26,8 @@ Imports:
     shiny.i18n,
     rlang (>= 1.0.6),
     ggpp,
-    nortest
+    nortest,
+    naniar
 Suggests:
     testthat (>= 2.0.0),
     pkgdown (>= 1.1.0),

radiant.basics/NAMESPACE

@@ -8,6 +8,7 @@ S3method(plot,cross_tabs)
 S3method(plot,goodness)
 S3method(plot,homo_variance_test)
 S3method(plot,mda)
+S3method(plot,missing)
 S3method(plot,normality_test)
 S3method(plot,outlier)
 S3method(plot,prob_binom)
@@ -30,6 +31,7 @@ S3method(summary,cross_tabs)
 S3method(summary,goodness)
 S3method(summary,homo_variance_test)
 S3method(summary,mda)
+S3method(summary,missing)
 S3method(summary,normality_test)
 S3method(summary,outlier)
 S3method(summary,prob_binom)
@@ -54,6 +56,7 @@ export(get_single_norm)
 export(goodness)
 export(homo_variance_test)
 export(mda)
+export(missing)
 export(normality_test)
 export(outlier)
 export(prob_binom)

radiant.basics/inst/app/tools/analysis/missing_ui.R

+############################################
+##  Missing Value Analysis - UI
+############################################
+## 1. 标签
+missing_plots <- c("heatmap", "barplot")
+names(missing_plots) <- c(
+  i18n$t("Missing Heatmap"),
+  i18n$t("Missing Barplot")
+)
+
+## 2. 函数形参
+missing_args <- as.list(formals(missing))
+missing_args <- missing_args[names(missing_args) %in% c("dataset", "vars", "data_filter")]
+
+## 3. 输入收集
+missing_inputs <- reactive({
+  req(input$dataset)
+  inputs <- list(
+    dataset = input$dataset,
+    vars = input$missing_vars,  # 用户选择的变量
+    data_filter = if (input$show_filter) input$data_filter else "",
+    envir = r_data
+  )
+  # 校验参数完整性
+  for (arg in names(missing_args)) {
+    if (is.null(inputs[[arg]]) || length(inputs[[arg]]) == 0) {
+      inputs[[arg]] <- missing_args[[arg]]
+    }
+  }
+  inputs
+})
+
+## 4. 变量选择UI
+output$ui_missing_vars <- renderUI({
+  req(input$dataset)
+  current_data <- get_data(input$dataset, envir = r_data)
+  all_vars <- colnames(current_data)
+  
+  if (length(all_vars) == 0) {
+    return(div(class = "alert alert-warning", i18n$t("No variables in dataset. Please select another dataset.")))
+  }
+  
+  # 显示变量类型（数值型/分类型）
+  var_types <- sapply(current_data[, all_vars, drop = FALSE], function(col) class(col)[1])
+  choices <- setNames(nm = paste0(all_vars, " {", var_types, "}"), object = all_vars)
+  
+  selectizeInput(
+    inputId = "missing_vars",
+    label = i18n$t("Select variables to analyze:"),
+    choices = choices,
+    selected = state_multiple("missing_vars", character(0)),
+    multiple = TRUE,
+    options = list(placeholder = i18n$t("Select one or more variables"), plugins = list("remove_button", "drag_drop"))
+  )
+})
+
+## 5. 主UI（Summary + Plot标签页）
+output$ui_missing <- renderUI({
+  req(input$dataset)
+  tagList(
+    wellPanel(
+      # Summary标签页：变量选择 + 统计结果
+      conditionalPanel(
+        condition = "input.tabs_missing == 'Summary'",
+        uiOutput("ui_missing_vars")
+      ),
+      # Plot标签页：可视化类型选择
+      conditionalPanel(
+        condition = "input.tabs_missing == 'Plot'",
+        selectizeInput(
+          inputId = "missing_plots",
+          label = i18n$t("Select plots:"),
+          choices = missing_plots,
+          selected = state_multiple("missing_plots", missing_plots, "barplot"),  # 默认选中条形图
+          multiple = TRUE,
+          options = list(placeholder = i18n$t("Select plot types"), plugins = list("remove_button", "drag_drop"))
+        )
+      )
+    ),
+    # 帮助与报告（和离群值分析保持一致）
+    help_and_report(
+      modal_title = i18n$t("Missing Value Analysis"),
+      fun_name = "missing",
+      help_file = inclMD(file.path(getOption("radiant.path.basics"), "app/tools/help/missing.md"))
+    )
+  )
+})
+
+## 6. 图表尺寸计算
+missing_plot_dims <- reactive({
+  req(.missing())
+  plot_count <- length(input$missing_plots)
+  var_count <- length(.missing()$vars)
+  
+  # 基础高度
+  base_height_px <- if ("heatmap" %in% input$missing_plots) {
+    min(500 + (nrow(.missing()$raw_data) * 0.5), 1200)  # 样本越多，热图越高
+  } else {
+    400
+  }
+  total_height_px <- base_height_px * plot_count
+  
+  # 限制最大/最小高度
+  total_height_px <- min(total_height_px, 2000)
+  total_height_px <- max(total_height_px, 500)
+  
+  list(
+    width = 800,
+    height = total_height_px
+  )
+})
+missing_plot_width <- function() missing_plot_dims()$width
+missing_plot_height <- function() missing_plot_dims()$height
+
+## 7. 输出面板
+output$missing <- renderUI({
+  # 注册输出组件
+  register_print_output("summary_missing", ".summary_missing")
+  register_plot_output("plot_missing", ".plot_missing",
+                       height_fun = "missing_plot_height")
+  
+  # 标签页布局（Summary + Plot）
+  missing_panels <- tabsetPanel(
+    id = "tabs_missing",
+    tabPanel(
+      title = i18n$t("Summary"),
+      value = "Summary",
+      verbatimTextOutput("summary_missing", placeholder = TRUE)
+    ),
+    tabPanel(
+      title = i18n$t("Plot"),
+      value = "Plot",
+      download_link("dlp_missing"),  # 下载按钮
+      plotOutput("plot_missing", height = "100%"),
+      style = "margin-top: 10px;"
+    )
+  )
+  
+  # 集成到Data Quality菜单下
+  stat_tab_panel(
+    menu = i18n$t("Basics > Data Quality"),
+    tool = i18n$t("Missing Value Analysis"),
+    tool_ui = "ui_missing",
+    output_panels = missing_panels
+  )
+})
+
+## 8. 可用性检验
+missing_available <- reactive({
+  req(input$dataset)
+  current_data <- get_data(input$dataset, envir = r_data)
+  
+  # 校验是否选择变量：未选则返回提示，阻止后续计算
+  if (not_available(input$missing_vars)) {
+    return(i18n$t("Please select at least one variable to analyze."))
+  }
+  
+  # 校验变量是否存在
+  invalid_vars <- input$missing_vars[!input$missing_vars %in% colnames(current_data)]
+  if (length(invalid_vars) > 0) {
+    return(i18n$t(paste("Invalid variables: ", paste(invalid_vars, collapse = ", "), ". Please reselect.", sep = "")))
+  }
+  
+  "available"
+})
+
+## 9. 计算核心
+.missing <- reactive({
+  req(missing_available() == "available")
+  do.call(missing, missing_inputs())
+})
+
+## 10. Summary输出
+.summary_missing <- reactive({
+  req(missing_available() == "available")
+  summary(.missing())
+})
+
+## 11. Plot输出
+.plot_missing <- reactive({
+  req(missing_available() == "available")
+  validate(need(input$missing_plots, i18n$t("Please select at least one plot type first.")))
+  withProgress(message = i18n$t("Generating missing value plots..."), value = 0.5, {
+    p <- plot(.missing(), plots = input$missing_plots, shiny = TRUE)
+    setProgress(value = 1)
+  })
+  p
+})
+
+## 12. 下载功能
+download_handler(
+  id = "dlp_missing",
+  fun = function(file) {
+    plot_obj <- .plot_missing()
+    width_in <- missing_plot_width() / 96
+    height_in <- missing_plot_height() / 96
+    
+    ggsave(
+      filename = file,
+      plot = plot_obj,
+      width = width_in,
+      height = height_in,
+      device = "png",
+      dpi = 300,
+      limitsize = FALSE,
+      bg = "white"
+    )
+  },
+  fn = function() paste0(input$dataset, "_missing_value_analysis"),
+  type = "png",
+  caption = i18n$t("Save missing value plots")
+)
+
+## 13. 报告生成
+missing_report <- function() {
+  req(missing_available() == "available")
+  figs <- length(input$missing_plots) > 0
+  update_report(
+    inp_main = clean_args(missing_inputs(), missing_args),
+    fun_name = "missing",
+    inp_out = if (figs) list("", list(plots = input$missing_plots)) else list(""),
+    outputs = if (figs) c("summary", "plot") else "summary",
+    figs = figs,
+    fig.width = missing_plot_width(),
+    fig.height = missing_plot_height()
+  )
+}
+
+## 14. 截图功能
+observeEvent(input$missing_report, {
+  r_info[["latest_screenshot"]] <- NULL
+  missing_report()
+})
+observeEvent(input$missing_screenshot, {
+  r_info[["latest_screenshot"]] <- NULL
+  radiant_screenshot_modal("modal_missing_screenshot")
+})
+observeEvent(input$modal_missing_screenshot, {
+  missing_report()
+  removeModal()
+})