feat(ssa): Complete Phase 2A frontend integration - multi-step workflow end-to-end

Phase 2A: WorkflowPlannerService, WorkflowExecutorService, Python data quality, 6 bug fixes, DescriptiveResultView, multi-step R code/Word export, MVP UI reuse. V11 UI: Gemini-style, multi-task, single-page scroll, Word export. Architecture: Block-based rendering consensus (4 block types). New R tools: chi_square, correlation, descriptive, logistic_binary, mann_whitney, t_test_paired. Docs: dev summary, block-based plan, status updates, task list v2.0. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-02-20 23:09:27 +08:00
parent 23b422f758
commit 428a22adf2
62 changed files with 15416 additions and 299 deletions
--- a/r-statistics-service/tools/mann_whitney.R
+++ b/r-statistics-service/tools/mann_whitney.R
@@ -0,0 +1,235 @@
+#' @tool_code ST_MANN_WHITNEY
+#' @name Mann-Whitney U 检验
+#' @version 1.0.0
+#' @description 两组独立样本非参数比较（Wilcoxon秩和检验）
+#' @author SSA-Pro Team
+
+library(glue)
+library(ggplot2)
+library(base64enc)
+
+run_analysis <- function(input) {
+  # ===== 初始化 =====
+  logs <- c()
+  log_add <- function(msg) { logs <<- c(logs, paste0("[", Sys.time(), "] ", msg)) }
+  
+  on.exit({}, add = TRUE)
+  
+  # ===== 数据加载 =====
+  log_add("开始加载输入数据")
+  df <- tryCatch(
+    load_input_data(input),
+    error = function(e) {
+      log_add(paste("数据加载失败:", e$message))
+      return(NULL)
+    }
+  )
+  
+  if (is.null(df)) {
+    return(make_error(ERROR_CODES$E100_INTERNAL_ERROR, details = "数据加载失败"))
+  }
+  log_add(glue("数据加载成功: {nrow(df)} 行, {ncol(df)} 列"))
+  
+  p <- input$params
+  group_var <- p$group_var
+  value_var <- p$value_var
+  
+  # ===== 参数校验 =====
+  if (!(group_var %in% names(df))) {
+    return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = group_var))
+  }
+  if (!(value_var %in% names(df))) {
+    return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = value_var))
+  }
+  
+  # ===== 数据清洗 =====
+  original_rows <- nrow(df)
+  df <- df[!is.na(df[[group_var]]) & trimws(as.character(df[[group_var]])) != "", ]
+  df <- df[!is.na(df[[value_var]]), ]
+  
+  removed_rows <- original_rows - nrow(df)
+  if (removed_rows > 0) {
+    log_add(glue("数据清洗: 移除 {removed_rows} 行缺失值 (剩余 {nrow(df)} 行)"))
+  }
+  
+  # 分组检查
+  groups <- unique(df[[group_var]])
+  if (length(groups) != 2) {
+    return(make_error(ERROR_CODES$E003_INSUFFICIENT_GROUPS, 
+                      col = group_var, expected = 2, actual = length(groups)))
+  }
+  
+  # 提取两组数据
+  g1_vals <- df[df[[group_var]] == groups[1], value_var]
+  g2_vals <- df[df[[group_var]] == groups[2], value_var]
+  
+  # ===== 护栏检查 =====
+  guardrail_results <- list()
+  warnings_list <- c()
+  
+  # 样本量检查（每组至少5个）
+  min_n <- min(length(g1_vals), length(g2_vals))
+  sample_check <- check_sample_size(min_n, min_required = 5, action = ACTION_BLOCK)
+  guardrail_results <- c(guardrail_results, list(sample_check))
+  log_add(glue("样本量检查: 每组最小 {min_n}, {sample_check$reason}"))
+  
+  guardrail_status <- run_guardrail_chain(guardrail_results)
+  
+  if (guardrail_status$status == "blocked") {
+    return(list(
+      status = "blocked",
+      message = guardrail_status$reason,
+      trace_log = logs
+    ))
+  }
+  
+  if (length(guardrail_status$warnings) > 0) {
+    warnings_list <- c(warnings_list, guardrail_status$warnings)
+  }
+  
+  # ===== 核心计算 =====
+  log_add("执行 Mann-Whitney U 检验 (Wilcoxon rank-sum test)")
+  
+  result <- tryCatch({
+    wilcox.test(g1_vals, g2_vals, exact = FALSE, correct = TRUE)
+  }, error = function(e) {
+    log_add(paste("Mann-Whitney U 检验失败:", e$message))
+    return(NULL)
+  })
+  
+  if (is.null(result)) {
+    return(make_error(ERROR_CODES$E100_INTERNAL_ERROR, details = "Mann-Whitney U 检验执行失败"))
+  }
+  
+  # 计算效应量 r = Z / sqrt(N)
+  n1 <- length(g1_vals)
+  n2 <- length(g2_vals)
+  N <- n1 + n2
+  
+  # 从 U 统计量计算 Z 值
+  U <- result$statistic
+  mu <- n1 * n2 / 2
+  sigma <- sqrt(n1 * n2 * (n1 + n2 + 1) / 12)
+  z_value <- (U - mu) / sigma
+  effect_r <- abs(z_value) / sqrt(N)
+  
+  # 效应量解释
+  effect_interpretation <- if (effect_r < 0.1) "微小" else if (effect_r < 0.3) "小" else if (effect_r < 0.5) "中等" else "大"
+  
+  log_add(glue("U = {round(U, 2)}, Z = {round(z_value, 3)}, p = {round(result$p.value, 4)}, r = {round(effect_r, 3)}"))
+  
+  # ===== 生成图表 =====
+  log_add("生成箱线图")
+  plot_base64 <- tryCatch({
+    generate_boxplot(df, group_var, value_var)
+  }, error = function(e) {
+    log_add(paste("图表生成失败:", e$message))
+    NULL
+  })
+  
+  # ===== 生成可复现代码 =====
+  original_filename <- if (!is.null(input$original_filename) && nchar(input$original_filename) > 0) {
+    input$original_filename
+  } else {
+    "data.csv"
+  }
+  
+  reproducible_code <- glue('
+# SSA-Pro 自动生成代码
+# 工具: Mann-Whitney U 检验
+# 时间: {Sys.time()}
+# ================================
+
+library(ggplot2)
+
+# 数据准备
+df <- read.csv("{original_filename}")
+group_var <- "{group_var}"
+value_var <- "{value_var}"
+
+# 数据清洗
+df <- df[!is.na(df[[group_var]]) & !is.na(df[[value_var]]), ]
+
+# Mann-Whitney U 检验
+g1_vals <- df[df[[group_var]] == "{groups[1]}", value_var]
+g2_vals <- df[df[[group_var]] == "{groups[2]}", value_var]
+result <- wilcox.test(g1_vals, g2_vals, exact = FALSE, correct = TRUE)
+print(result)
+
+# 计算效应量 r
+n1 <- length(g1_vals)
+n2 <- length(g2_vals)
+U <- result$statistic
+mu <- n1 * n2 / 2
+sigma <- sqrt(n1 * n2 * (n1 + n2 + 1) / 12)
+z_value <- (U - mu) / sigma
+effect_r <- abs(z_value) / sqrt(n1 + n2)
+cat("Effect size r =", round(effect_r, 3), "\\n")
+
+# 可视化
+ggplot(df, aes(x = .data[[group_var]], y = .data[[value_var]])) +
+  geom_boxplot(fill = "#8b5cf6", alpha = 0.6) +
+  theme_minimal() +
+  labs(title = paste("Distribution of", value_var, "by", group_var))
+')
+  
+  # ===== 返回结果 =====
+  log_add("分析完成")
+  
+  return(list(
+    status = "success",
+    message = "分析完成",
+    warnings = if (length(warnings_list) > 0) warnings_list else NULL,
+    results = list(
+      method = "Wilcoxon rank sum test with continuity correction",
+      statistic_U = jsonlite::unbox(as.numeric(U)),
+      z_value = jsonlite::unbox(round(z_value, 4)),
+      p_value = jsonlite::unbox(as.numeric(result$p.value)),
+      p_value_fmt = format_p_value(result$p.value),
+      effect_size = list(
+        r = jsonlite::unbox(round(effect_r, 4)),
+        interpretation = effect_interpretation
+      ),
+      group_stats = list(
+        list(
+          group = as.character(groups[1]), 
+          n = n1, 
+          median = median(g1_vals),
+          iqr = IQR(g1_vals),
+          min = min(g1_vals),
+          max = max(g1_vals)
+        ),
+        list(
+          group = as.character(groups[2]), 
+          n = n2, 
+          median = median(g2_vals),
+          iqr = IQR(g2_vals),
+          min = min(g2_vals),
+          max = max(g2_vals)
+        )
+      )
+    ),
+    plots = if (!is.null(plot_base64)) list(plot_base64) else list(),
+    trace_log = logs,
+    reproducible_code = as.character(reproducible_code)
+  ))
+}
+
+# 辅助函数：生成箱线图
+generate_boxplot <- function(df, group_var, value_var) {
+  p <- ggplot(df, aes(x = .data[[group_var]], y = .data[[value_var]])) +
+    geom_boxplot(fill = "#8b5cf6", alpha = 0.6) +
+    geom_jitter(width = 0.2, alpha = 0.3, size = 1) +
+    theme_minimal() +
+    labs(
+      title = paste("Distribution of", value_var, "by", group_var),
+      subtitle = "Mann-Whitney U Test"
+    )
+  
+  tmp_file <- tempfile(fileext = ".png")
+  ggsave(tmp_file, p, width = 6, height = 4, dpi = 100)
+  base64_str <- base64encode(tmp_file)
+  unlink(tmp_file)
+  
+  return(paste0("data:image/png;base64,", base64_str))
+}