update:更新了离群值分析功能

radiant.basics/NAMESPACE

@@ -9,6 +9,7 @@ S3method(plot,goodness)
 S3method(plot,homo_variance_test)
 S3method(plot,mda)
 S3method(plot,normality_test)
+S3method(plot,outlier)
 S3method(plot,prob_binom)
 S3method(plot,prob_chisq)
 S3method(plot,prob_disc)
@@ -30,6 +31,7 @@ S3method(summary,goodness)
 S3method(summary,homo_variance_test)
 S3method(summary,mda)
 S3method(summary,normality_test)
+S3method(summary,outlier)
 S3method(summary,prob_binom)
 S3method(summary,prob_chisq)
 S3method(summary,prob_disc)
@@ -53,6 +55,7 @@ export(goodness)
 export(homo_variance_test)
 export(mda)
 export(normality_test)
+export(outlier)
 export(prob_binom)
 export(prob_chisq)
 export(prob_disc)

radiant.basics/R/outlier.R

+############################################
+##  Outlier Analysis (IQR/Z-score Method)
+############################################
+#' @export
+outlier <- function(dataset,
+                    vars,
+                    method = c("iqr", "zscore"),
+                    iqr_multiplier = 1.5,
+                    z_threshold = 3,
+                    data_filter = "",
+                    envir = parent.frame()) {
+  
+  # 1. 基础参数处理
+  method <- match.arg(method, choices = c("iqr", "zscore"))
+  df_name <- if (is_string(dataset)) dataset else deparse(substitute(dataset))
+  
+  # 2. 数据提取：仅保留选择的变量+过滤数据
+  dataset <- get_data(
+    dataset,
+    vars = vars,
+    filt = data_filter,
+    na.rm = FALSE,  # 保留缺失值，后续标记为非离群值
+    envir = envir
+  )
+  
+  # 3. 数据校验
+  if (length(vars) == 0) stop("Please select at least one numeric variable.", call. = FALSE)
+  if (!all(vars %in% colnames(dataset))) {
+    stop(paste("Variables not found in dataset:", paste(setdiff(vars, colnames(dataset)), collapse = ", ")), call. = FALSE)
+  }
+  
+  # 4. 离群值计算
+  outlier_results <- list()
+  for (var in vars) {
+    var_data <- dataset[[var]]
+    valid_data <- var_data[!is.na(var_data)]  # 仅对非缺失值计算
+    
+    if (length(valid_data) < 5) {
+      outlier_results[[var]] <- list(
+        overview = tibble::tibble(
+          var = var,
+          n_total = length(var_data),
+          n_valid = length(valid_data),
+          n_outlier = 0,
+          outlier_pct = 0,
+          lower_bound = NA_real_,
+          upper_bound = NA_real_
+        ),
+        # 强制deviation为character(0)
+        details = tibble::tibble(
+          row_idx = integer(0),
+          value = numeric(0),
+          deviation = character(0)  # 明确指定字符型空向量
+        )
+      )
+      next
+    }
+    
+    # 调用辅助函数计算离群值
+    if (method == "iqr") {
+      res <- calc_outlier_iqr(valid_data, multiplier = iqr_multiplier)
+    } else {
+      res <- calc_outlier_zscore(valid_data, threshold = z_threshold)
+    }
+    
+    # 匹配原始行索引
+    outlier_idx <- which(!is.na(var_data) & var_data %in% res$outlier_values)
+    # 当无离群值时，deviation仍为character(0)
+    deviation <- if (length(outlier_idx) == 0) {
+      character(0)
+    } else {
+      ifelse(var_data[outlier_idx] < res$lower_bound, "Below Lower Bound", "Above Upper Bound")
+    }
+    
+    # 整理结果
+    outlier_results[[var]] <- list(
+      overview = tibble::tibble(
+        var = var,
+        n_total = length(var_data),
+        n_valid = length(valid_data),
+        n_outlier = length(res$outlier_values),
+        outlier_pct = round(length(res$outlier_values)/length(valid_data)*100, 2),
+        lower_bound = round(res$lower_bound, 3),
+        upper_bound = round(res$upper_bound, 3)
+      ),
+      details = tibble::tibble(
+        row_idx = outlier_idx,
+        value = var_data[outlier_idx],
+        deviation = deviation 
+      )
+    )
+  }
+  
+  # 5. 结果打包
+  out <- structure(
+    list(
+      df_name = df_name,
+      vars = vars,
+      method = method,
+      params = list(
+        iqr_multiplier = if (method == "iqr") iqr_multiplier else NA,
+        z_threshold = if (method == "zscore") z_threshold else NA
+      ),
+      data_filter = if (data_filter == "") "None" else data_filter,
+      results = outlier_results,  # 每个变量的离群值结果
+      raw_data = dataset  # 原始数据
+    ),
+    class = "outlier"
+  )
+  out
+}
+
+# ------------------------------
+# 辅助函数1：IQR法计算离群值
+# ------------------------------
+calc_outlier_iqr <- function(data, multiplier = 1.5) {
+  q1 <- stats::quantile(data, 0.25, na.rm = TRUE)
+  q3 <- stats::quantile(data, 0.75, na.rm = TRUE)
+  iqr_val <- q3 - q1
+  lower <- q1 - multiplier * iqr_val
+  upper <- q3 + multiplier * iqr_val
+  outlier_values <- data[data < lower | data > upper]
+  
+  list(
+    lower_bound = lower,
+    upper_bound = upper,
+    outlier_values = outlier_values
+  )
+}
+
+# ------------------------------
+# 辅助函数2：Z-score法计算离群值
+# ------------------------------
+calc_outlier_zscore <- function(data, threshold = 3) {
+  mean_val <- mean(data, na.rm = TRUE)
+  sd_val <- stats::sd(data, na.rm = TRUE)
+  z_scores <- (data - mean_val) / sd_val
+  lower <- mean_val - threshold * sd_val
+  upper <- mean_val + threshold * sd_val
+  outlier_values <- data[abs(z_scores) > threshold]
+  
+  list(
+    lower_bound = lower,
+    upper_bound = upper,
+    outlier_values = outlier_values,
+    mean = mean_val,
+    sd = sd_val
+  )
+}
+
+# ------------------------------
+# Summary方法：展示离群值概览
+# ------------------------------
+#' @export
+summary.outlier <- function(object, dec = 3, ...) {
+  # 1. 基础信息
+  cat("Outlier Analysis Results\n")
+  cat("Data         :", object$df_name, "\n")
+  cat("Variables    :", paste(object$vars, collapse = ", "), "(numeric)\n")
+  cat("Method       :", if (object$method == "iqr") paste("IQR Method (Multiplier =", object$params$iqr_multiplier, ")") else paste("Z-score Method (Threshold =", object$params$z_threshold, ")"), "\n")
+  cat("Filter       :", object$data_filter, "\n\n")
+  
+  # 2. 离群值概览表
+  cat("=== 1. Outlier Overview ===\n")
+  overview_df <- purrr::map_dfr(object$results, ~ .x$overview)
+  overview_formatted <- overview_df %>%
+    dplyr::mutate(
+      outlier_pct = paste0(outlier_pct, "%"),
+      lower_bound = as.character(round(lower_bound, dec)),
+      upper_bound = as.character(round(upper_bound, dec))
+    ) %>%
+    dplyr::rename(
+      "Variable" = var,
+      "Total Samples" = n_total,
+      "Valid Samples" = n_valid,
+      "Outlier Count" = n_outlier,
+      "Outlier %" = outlier_pct,
+      "Lower Bound" = lower_bound,
+      "Upper Bound" = upper_bound
+    ) %>%
+    as.data.frame(stringsAsFactors = FALSE)
+  
+  print(overview_formatted, row.names = FALSE, right = FALSE)
+  cat("\n")
+  
+  # 3. 离群值明细提示
+  cat("=== 2. Outlier Details (By Variable) ===\n")
+  for (var in object$vars) {
+    details <- object$results[[var]]$details
+    if (nrow(details) == 0) {
+      cat(paste("•", var, ": No outliers detected\n"))
+    } else {
+      cat(paste("•", var, ":", nrow(details), "outliers\n"))
+      details_formatted <- details %>%
+        dplyr::mutate(
+          value = round(value, dec)
+        ) %>%
+        dplyr::rename(
+          "Row Index" = row_idx,
+          "Value" = value,
+          "Deviation" = deviation
+        )
+      print(details_formatted, row.names = FALSE, right = FALSE)
+      cat("\n")
+    }
+  }
+  
+  # 4. 方法说明
+  cat("=== 3. Method Explanation ===\n")
+  if (object$method == "iqr") {
+    cat("• IQR Method: Outliers are values outside [Q1 - k×IQR, Q3 + k×IQR] (k =", object$params$iqr_multiplier, ")\n")
+    cat("• Q1 = 25th percentile, Q3 = 75th percentile, IQR = Q3 - Q1\n")
+  } else {
+    cat("• Z-score Method: Outliers are values with |Z-score| >", object$params$z_threshold, "\n")
+    cat("• Z-score = (Value - Mean) / Standard Deviation\n")
+  }
+  
+  invisible(object)
+}
+
+# ------------------------------
+# Plot方法：可视化离群值
+# ------------------------------
+#' @export
+plot.outlier <- function(x,
+                         plots = c("boxplot", "histogram", "scatter"),
+                         shiny = FALSE, custom = FALSE, ...) {
+  # 1. 基础校验
+  if (length(plots) == 0) {
+    return(ggplot2::ggplot() +
+             ggplot2::annotate("text", x = 1, y = 1, label = i18n$t("No plots selected")) +
+             ggplot2::theme_void())
+  }
+  
+  plot_list <- list()
+  vars <- x$vars
+  raw_data <- x$raw_data %>%
+    tidyr::pivot_longer(cols = all_of(vars), names_to = "variable", values_to = "value")  # 长格式便于分面
+  
+  # 2. 箱线图
+  if ("boxplot" %in% plots) {
+    raw_data <- raw_data %>%
+      dplyr::mutate(variable = as.factor(variable))
+    
+    p <- ggplot2::ggplot(raw_data, ggplot2::aes(x = 1, y = value, fill = variable)) +  
+      ggplot2::geom_boxplot(alpha = 0.7, outlier.color = "red", outlier.size = 2, show.legend = FALSE) +
+      ggplot2::scale_fill_brewer(palette = "Set2") +
+      ggplot2::facet_wrap(~variable, scales = "free_y", ncol = 2) +  
+      ggplot2::labs(
+        x = "", 
+        y = i18n$t("Value"),  
+        title = i18n$t("Outlier Detection: Boxplot (Per Variable)")  
+      ) +
+      ggplot2::theme_minimal() +
+      ggplot2::theme(
+        axis.text.x = ggplot2::element_blank(),  
+        strip.text = ggplot2::element_text(size = 11),  
+        plot.title = ggplot2::element_text(hjust = 0.5, size = 12),  
+        panel.spacing = ggplot2::unit(1, "cm")  
+      )
+    plot_list[["boxplot"]] <- p
+  }
+  
+  # 3. 直方图
+  if ("histogram" %in% plots) {
+    # 合并所有变量的界值
+    bound_data <- purrr::map_dfr(x$results, function(res) {
+      tibble::tibble(
+        variable = res$overview$var,
+        lower_bound = res$overview$lower_bound,
+        upper_bound = res$overview$upper_bound
+      )
+    })
+    
+    p <- ggplot2::ggplot(raw_data, ggplot2::aes(x = value)) +
+      ggplot2::geom_histogram(fill = "#4287f5", alpha = 0.7, bins = 30) +
+      ggplot2::geom_vline(
+        data = bound_data,
+        ggplot2::aes(xintercept = lower_bound),
+        color = "red", linetype = "dashed", linewidth = 1
+      ) +
+      ggplot2::geom_vline(
+        data = bound_data,
+        ggplot2::aes(xintercept = upper_bound),
+        color = "red", linetype = "dashed", linewidth = 1
+      ) +
+      ggplot2::facet_wrap(~variable, scales = "free") +
+      ggplot2::labs(x = i18n$t("Value"), y = i18n$t("Count"),
+                    title = i18n$t("Outlier Detection: Histogram (Red Dashed = Thresholds)")) +
+      ggplot2::theme_minimal() +
+      ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5, size = 12))
+    plot_list[["histogram"]] <- p
+  }
+  
+  # 4. 散点图
+  if ("scatter" %in% plots) {
+    if (length(vars) >= 2) {
+      # 变量数≥2：正常生成散点图
+      outlier_row_idx <- purrr::map(x$results, ~ .x$details$row_idx)
+      outlier_row_idx <- unique(unlist(outlier_row_idx))  
+      
+      # 取前两个变量做散点图
+      var1 <- vars[1]
+      var2 <- vars[2]
+      # 构建散点图数据
+      scatter_data <- x$raw_data %>%
+        dplyr::mutate(
+          row_idx = dplyr::row_number(),  
+          is_outlier = row_idx %in% outlier_row_idx  
+        ) %>%
+        dplyr::select(row_idx, all_of(c(var1, var2)), is_outlier)  
+      
+      p <- ggplot2::ggplot(scatter_data, ggplot2::aes(x = .data[[var1]], y = .data[[var2]], color = is_outlier)) +
+        ggplot2::geom_point(alpha = 0.7, size = 1.5) +
+        ggplot2::scale_color_manual(values = c("black", "red"), labels = c("Normal", "Outlier")) +
+        ggplot2::labs(x = var1, y = var2, color = i18n$t("Type"),
+                      title = i18n$t("Outlier Detection: Scatter Plot")) +
+        ggplot2::theme_minimal() +
+        ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5, size = 12))
+    } else {
+      p <- ggplot2::ggplot() +
+        ggplot2::annotate("text", 
+                          x = 1, y = 1, 
+                          label = i18n$t("Scatter Plot requires at least 2 numeric variables.\nPlease select more variables."),
+                          size = 4.5, color = "#666666") +
+        ggplot2::labs(title = i18n$t("Outlier Detection: Scatter Plot")) +
+        ggplot2::theme_minimal() +
+        ggplot2::theme(
+          plot.title = ggplot2::element_text(hjust = 0.5, size = 12),
+          axis.text = ggplot2::element_blank(),
+          axis.title = ggplot2::element_blank(),
+          panel.grid = ggplot2::element_blank()
+        )
+    }
+    plot_list[["scatter"]] <- p 
+  }
+  
+  
+  # 5. 组合图表
+  combined_plot <- patchwork::wrap_plots(plot_list[plots], ncol = 1, guides = "collect")
+  
+  # 6. 输出
+  if (shiny) {
+    print(combined_plot)
+    return(invisible(combined_plot))
+  } else {
+    return(combined_plot)
+  }
+}

radiant.basics/inst/app/init.R

@@ -31,6 +31,9 @@ options(
         tags$head(
           tags$script(src = "www_basics/js/run_return.js")
         ),
+        i18n$t("Data Quality"),
+        tabPanel(i18n$t("Missing Value Analysis"), uiOutput("missing")),
+        tabPanel(i18n$t("Outlier Analysis"), uiOutput("outlier")),
         i18n$t("Probability"),
         tabPanel(i18n$t("Probability calculator"), uiOutput("prob_calc")),
         tabPanel(i18n$t("Central Limit Theorem"), uiOutput("clt")),

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

 ############################################
 ##  Multigroup Difference Analysis (ANOVA/KW) - UI
-##  对齐单独检验的UI设计：简洁+严格校验+统一风格
 ############################################
-## 1. 翻译标签（对齐单独检验的i18n逻辑，保持术语一致）
+## 1. 翻译标签
 mda_norm_type <- c("overall", "by_group")
 names(mda_norm_type) <- c(i18n$t("Overall (Whole variable)"),
                           i18n$t("By Group (Each level separately)"))

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

+############################################
+##  Outlier Analysis - UI
+############################################
+## 1. 翻译标签
+outlier_methods <- c("iqr", "zscore")
+names(outlier_methods) <- c(i18n$t("IQR Method (1.5×IQR)"),
+                            i18n$t("Z-score Method (±3σ)"))
+outlier_plots <- c("boxplot", "histogram", "scatter")
+names(outlier_plots) <- c(i18n$t("Boxplot (Mark Outliers)"),
+                          i18n$t("Histogram (With Thresholds)"),
+                          i18n$t("Scatter Plot (Variable Pairs)"))
+
+## 2. 函数形参
+outlier_args <- as.list(formals(outlier))
+outlier_args <- outlier_args[names(outlier_args) %in% c("dataset", "vars", "method", "iqr_multiplier", "z_threshold", "data_filter")]
+
+## 3. 输入收集
+outlier_inputs <- reactive({
+  req(input$dataset)
+  inputs <- list(
+    dataset = input$dataset,
+    vars = input$outlier_vars,  
+    method = input$outlier_method,
+    iqr_multiplier = input$outlier_iqr_multiplier,
+    z_threshold = input$outlier_z_threshold,
+    data_filter = if (input$show_filter) input$data_filter else "",
+    envir = r_data
+  )
+  # 校验参数完整性
+  for (arg in names(outlier_args)) {
+    if (is.null(inputs[[arg]]) || length(inputs[[arg]]) == 0) {
+      inputs[[arg]] <- outlier_args[[arg]]
+    }
+  }
+  inputs
+})
+
+## 4. 变量选择UI
+output$ui_outlier_vars <- renderUI({
+  req(input$dataset)
+  current_data <- get_data(input$dataset, envir = r_data)
+  is_num <- sapply(current_data, function(col) is.numeric(col) || is.ts(col))
+  num_vars <- names(is_num)[is_num]
+  
+  if (length(num_vars) == 0) {
+    return(div(class = "alert alert-warning", i18n$t("No numeric variables in dataset. Please select another dataset.")))
+  }
+  
+  # 提取变量类型并显示
+  var_types <- sapply(current_data[, num_vars, drop = FALSE], function(col) class(col)[1])
+  choices <- setNames(nm = paste0(num_vars, " {", var_types, "}"), object = num_vars)
+  
+  selectizeInput(
+    inputId = "outlier_vars",
+    label = i18n$t("Select numeric variable:"),
+    choices = choices,
+    selected = state_multiple("outlier_vars", num_vars),
+    multiple = TRUE,
+    options = list(placeholder = i18n$t("Select one or more variables"), plugins = list("remove_button", "drag_drop"))
+  )
+})
+
+## 5. 方法参数调整UI
+output$ui_outlier_params <- renderUI({
+  req(input$outlier_method)
+  tagList(
+    # IQR方法：调整倍数（默认1.5）
+    conditionalPanel(
+      condition = "input.outlier_method == 'iqr'",
+      numericInput(
+        inputId = "outlier_iqr_multiplier",
+        label = i18n$t("IQR Multiplier:"),
+        value = state_init("outlier_iqr_multiplier", 1.5),
+        min = 0.5, max = 5, step = 0.5
+      )
+    ),
+    # Z-score方法：调整阈值（默认3）
+    conditionalPanel(
+      condition = "input.outlier_method == 'zscore'",
+      numericInput(
+        inputId = "outlier_z_threshold",
+        label = i18n$t("Z-score Threshold:"),
+        value = state_init("outlier_z_threshold", 3),
+        min = 1.5, max = 5, step = 0.5
+      )
+    )
+  )
+})
+
+## 6. 主UI
+output$ui_outlier <- renderUI({
+  req(input$dataset)
+  tagList(
+    wellPanel(
+      # Summary标签页：变量选择+方法选择+参数调整
+      conditionalPanel(
+        condition = "input.tabs_outlier == 'Summary'",
+        uiOutput("ui_outlier_vars"),
+        radioButtons(
+          inputId = "outlier_method",
+          label = i18n$t("Select outlier detection method:"),
+          choices = outlier_methods,
+          selected = state_single("outlier_method", outlier_methods, "iqr"),
+          inline = FALSE
+        ),
+        uiOutput("ui_outlier_params")  # 动态参数面板
+      ),
+      # Plot标签页：图表选择
+      conditionalPanel(
+        condition = "input.tabs_outlier == 'Plot'",
+        selectizeInput(
+          inputId = "outlier_plots",
+          label = i18n$t("Select plots:"),
+          choices = outlier_plots,
+          selected = state_multiple("outlier_plots", outlier_plots, "boxplot"),
+          multiple = TRUE,
+          options = list(placeholder = i18n$t("Select plot types"), plugins = list("remove_button", "drag_drop"))
+        )
+      )
+    ),
+    # 帮助与报告
+    help_and_report(
+      modal_title = i18n$t("Outlier Analysis"),
+      fun_name = "outlier",
+      help_file = inclMD(file.path(getOption("radiant.path.basics"), "app/tools/help/outlier.md"))
+    )
+  )
+})
+
+## 7. 图表尺寸
+outlier_plot_dims <- reactive({
+  req(.outlier())
+  plot_count <- length(input$outlier_plots)
+  var_count <- length(.outlier()$vars)  # 选择的变量数
+  
+  # 每个子图基础高度（像素）
+  base_height_px <- 300
+  total_height_px <- base_height_px * plot_count * ceiling(var_count / 2)  # 2列布局
+  
+  # 限制最大/最小高度
+  total_height_px <- min(total_height_px, 2500)
+  total_height_px <- max(total_height_px, 500)
+  
+  list(
+    width = 800,  # 宽屏适配多变量
+    height = total_height_px
+  )
+})
+outlier_plot_width <- function() outlier_plot_dims()$width
+outlier_plot_height <- function() outlier_plot_dims()$height
+
+## 8. 输出面板
+output$outlier<- renderUI({
+  # 注册输出组件
+  register_print_output("summary_outlier", ".summary_outlier")
+  register_plot_output("plot_outlier", ".plot_outlier",
+                       height_fun = "outlier_plot_height")
+  
+  # 标签页布局
+  outlier_panels <- tabsetPanel(
+    id = "tabs_outlier",
+    tabPanel(
+      title = i18n$t("Summary"),
+      value = "Summary",
+      verbatimTextOutput("summary_outlier", placeholder = TRUE)
+    ),
+    tabPanel(
+      title = i18n$t("Plot"),
+      value = "Plot",
+      download_link("dlp_outlier"),  # 下载按钮
+      plotOutput("plot_outlier", height = "100%"),
+      style = "margin-top: 10px;"
+    )
+  )
+  
+  stat_tab_panel(
+    menu = i18n$t("Basics > Data Quality"),
+    tool = i18n$t("Outlier Analysis"),
+    tool_ui = "ui_outlier",
+    output_panels = outlier_panels
+  )
+})
+
+## 9. 可用性检验
+outlier_available <- reactive({
+  req(input$dataset)
+  current_data <- get_data(input$dataset, envir = r_data)
+  
+  # 校验是否选择变量
+  if (not_available(input$outlier_vars)) {
+    return(i18n$t("Please select at least one numeric variable."))
+  }
+  
+  # 校验变量是否存在且为数值型
+  invalid_vars <- input$outlier_vars[!input$outlier_vars %in% colnames(current_data)]
+  if (length(invalid_vars) > 0) {
+    return(i18n$t(paste("Invalid variables: ", paste(invalid_vars, collapse = ", "), ". Please reselect.", sep = "")))
+  }
+  
+  # 校验变量是否为数值型
+  non_num_vars <- input$outlier_vars[!sapply(current_data[, input$outlier_vars, drop = FALSE], is.numeric)]
+  if (length(non_num_vars) > 0) {
+    return(i18n$t(paste("Non-numeric variables: ", paste(non_num_vars, collapse = ", "), ". Please select numeric variables.", sep = "")))
+  }
+  
+  "available"
+})
+
+## 10. 计算核心
+.outlier <- reactive({
+  req(outlier_available() == "available")
+  do.call(outlier, outlier_inputs())
+})
+
+## 11. Summary输出
+.summary_outlier <- reactive({
+  req(outlier_available() == "available")
+  summary(.outlier())
+})
+
+## 12. Plot输出
+.plot_outlier <- reactive({
+  req(outlier_available() == "available")
+  validate(need(input$outlier_plots, i18n$t("Please select at least one plot type first.")))
+  withProgress(message = i18n$t("Generating outlier plots..."), value = 0.5, {
+    p <- plot(.outlier(), plots = input$outlier_plots, shiny = TRUE)
+    setProgress(value = 1)
+  })
+  p
+})
+
+## 13. 下载与截图
+download_handler(
+  id = "dlp_outlier",
+  fun = function(file) {
+    plot_obj <- .plot_outlier()
+    width_in <- outlier_plot_width() / 96
+    height_in <- outlier_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$dataset, "_outlier_analysis"),
+  type = "png",
+  caption = i18n$t("Save outlier plots")
+)
+
+## 14. 报告生成
+outlier_report <- function() {
+  req(outlier_available() == "available")
+  figs <- length(input$outlier_plots) > 0
+  update_report(
+    inp_main = clean_args(outlier_inputs(), outlier_args),
+    fun_name = "outlier",
+    inp_out = if (figs) list("", list(plots = input$outlier_plots)) else list(""),
+    outputs = if (figs) c("summary", "plot") else "summary",
+    figs = figs,
+    fig.width = outlier_plot_width(),
+    fig.height = outlier_plot_height()
+  )
+}
+
+## 15. 截图功能
+observeEvent(input$outlier_report, {
+  r_info[["latest_screenshot"]] <- NULL
+  outlier_report()
+})
+observeEvent(input$outlier_screenshot, {
+  r_info[["latest_screenshot"]] <- NULL
+  radiant_screenshot_modal("modal_outlier_screenshot")
+})
+observeEvent(input$modal_outlier_screenshot, {
+  outlier_report()
+  removeModal()
+})

radiant.basics/inst/app/tools/help/missing.md

+xxxxxmiss
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radiant.basics/inst/app/tools/help/outlier.md

+xxxxxout
\ No newline at end of file