AIclinicalresearch/r-statistics-service/tools/mann_whitney.R

#' @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))
}