feat(ssa): Complete T-test end-to-end testing with 9 bug fixes - Phase 1 core 85% complete. R service: missing value auto-filter. Backend: error handling, variable matching, dynamic filename. Frontend: module activation, session isolation, error propagation. Full flow verified.

Co-authored-by: Cursor <cursoragent@cursor.com>

r-statistics-service/utils/data_loader.R

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+# utils/data_loader.R
+# 混合数据协议：自动识别 inline 数据 vs 预签名 URL
+# 
+# 架构说明：
+# - R 服务不持有 OSS 密钥，遵循平台 OSS 存储规范
+# - Node.js 后端通过 storage.getUrl() 生成预签名 URL
+# - R 服务直接访问预签名 URL 下载数据
+# - 开发环境使用 ai-clinical-data-dev bucket，无需 Mock
+
+library(httr)
+library(jsonlite)
+library(glue)
+
+# 统一数据加载入口
+load_input_data <- function(input) {
+  # 检查输入结构
+  if (is.null(input$data_source)) {
+    stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR, 
+                    details = "请求缺少 data_source 字段"))
+  }
+  
+  source_type <- input$data_source$type  # "inline" | "oss"
+  
+  if (source_type == "inline") {
+    # 方式1：内联 JSON 数据（< 2MB）
+    message("[DataLoader] 使用 inline 数据模式")
+    
+    raw_data <- input$data_source$data
+    
+    # 调试：打印原始数据结构
+    message(glue("[DataLoader] 原始数据类型: {class(raw_data)}"))
+    message(glue("[DataLoader] 原始数据字段: {paste(names(raw_data), collapse=', ')}"))
+    
+    # 安全转换：处理不同的 JSON 解析结果
+    if (is.data.frame(raw_data)) {
+      df <- raw_data
+    } else if (is.list(raw_data)) {
+      # JSON 对象 {"col1": [...], "col2": [...]} -> data.frame
+      # JSON 数组可能被解析为 list 而非 vector，需要先 unlist
+      df <- data.frame(
+        lapply(raw_data, function(x) {
+          if (is.list(x)) unlist(x) else x
+        }),
+        stringsAsFactors = FALSE
+      )
+    } else {
+      stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR,
+                      details = paste("无法解析的数据类型:", class(raw_data))))
+    }
+    
+    message(glue("[DataLoader] 转换后: {nrow(df)} 行, {ncol(df)} 列, 列名: {paste(names(df), collapse=', ')}"))
+    return(df)
+    
+  } else if (source_type == "oss") {
+    # 方式2：从预签名 URL 下载（2MB - 20MB）
+    # 注意：oss_url 是由 Node.js 后端生成的预签名 URL，不是 oss_key
+    oss_url <- input$data_source$oss_url
+    
+    if (is.null(oss_url) || oss_url == "") {
+      stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR,
+                      details = "OSS 模式缺少 oss_url 字段"))
+    }
+    
+    return(load_from_signed_url(oss_url))
+    
+  } else {
+    stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR,
+                    details = paste("未知的 data_source.type:", source_type)))
+  }
+}
+
+# 从预签名 URL 下载数据
+# 
+# @param url 预签名 URL（由 Node.js storage.getUrl() 生成）
+# @return data.frame
+# 
+# 说明：开发环境和生产环境都使用真实 OSS
+# - 开发环境：ai-clinical-data-dev bucket
+# - 生产环境：ai-clinical-data bucket
+load_from_signed_url <- function(url) {
+  
+  message(glue("[DataLoader] 从预签名 URL 下载数据"))
+  
+  temp_file <- tempfile(fileext = ".csv")
+  on.exit(unlink(temp_file))
+  
+  tryCatch({
+    # 预签名 URL 自带认证信息，直接 GET 即可
+    response <- GET(url, write_disk(temp_file, overwrite = TRUE))
+    
+    status <- status_code(response)
+    if (status != 200) {
+      # 403 通常表示签名过期
+      if (status == 403) {
+        stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR,
+                        details = "预签名 URL 已过期，请重新上传数据"))
+      }
+      stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR,
+                      details = paste("OSS 下载失败，HTTP 状态码:", status)))
+    }
+    
+    # 检测文件类型并读取
+    content_type <- headers(response)$`content-type`
+    
+    if (grepl("csv", content_type, ignore.case = TRUE) || 
+        grepl("\\.csv", url, ignore.case = TRUE)) {
+      return(read.csv(temp_file, stringsAsFactors = FALSE))
+    } else if (grepl("excel|xlsx", content_type, ignore.case = TRUE) ||
+               grepl("\\.xlsx?", url, ignore.case = TRUE)) {
+      # 需要 readxl 包
+      if (!requireNamespace("readxl", quietly = TRUE)) {
+        stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR,
+                        details = "Excel 文件需要 readxl 包"))
+      }
+      return(as.data.frame(readxl::read_excel(temp_file)))
+    } else {
+      # 默认尝试 CSV
+      return(read.csv(temp_file, stringsAsFactors = FALSE))
+    }
+    
+  }, error = function(e) {
+    if (grepl("make_error", deparse(e$call))) {
+      stop(e)  # 重新抛出已格式化的错误
+    }
+    stop(make_error(ERROR_CODES$E100_INTERNAL_ERROR,
+                    details = paste("OSS 网络错误:", e$message)))
+  })
+}

r-statistics-service/utils/error_codes.R

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+# utils/error_codes.R
+# 结构化错误码，便于 LLM 自愈
+
+ERROR_CODES <- list(
+  # 业务错误（可被 Planner 修复）
+  E001_COLUMN_NOT_FOUND = list(
+    code = "E001",
+    type = "business",
+    message_template = "列名 '{col}' 在数据中不存在",
+    user_hint = "请检查变量名是否拼写正确"
+  ),
+  E002_TYPE_MISMATCH = list(
+    code = "E002",
+    type = "business",
+    message_template = "列 '{col}' 类型应为 {expected}，实际为 {actual}",
+    user_hint = "该列包含非数值数据，请检查数据格式"
+  ),
+  E003_INSUFFICIENT_GROUPS = list(
+    code = "E003",
+    type = "business",
+    message_template = "分组变量 '{col}' 应有 {expected} 个水平，实际有 {actual} 个",
+    user_hint = "分组变量的取值个数不符合要求"
+  ),
+  E004_SAMPLE_TOO_SMALL = list(
+    code = "E004",
+    type = "business",
+    message_template = "样本量 {n} 不足，至少需要 {min_required}",
+    user_hint = "数据量太少，无法进行统计分析"
+  ),
+  
+  # 统计计算错误（用户友好映射）
+  E005_SINGULAR_MATRIX = list(
+    code = "E005",
+    type = "business",
+    message_template = "矩阵计算异常: {details}",
+    user_hint = "变量之间可能存在多重共线性，建议移除高度相关的变量"
+  ),
+  E006_CONVERGENCE_FAILED = list(
+    code = "E006",
+    type = "business",
+    message_template = "模型未能收敛: {details}",
+    user_hint = "算法未能找到稳定解，可能需要调整参数或检查数据"
+  ),
+  E007_VARIANCE_ZERO = list(
+    code = "E007",
+    type = "business",
+    message_template = "变量 '{col}' 方差为零",
+    user_hint = "该列的所有值都相同，无法进行比较"
+  ),
+  
+  # 系统错误（需人工介入）
+  E100_INTERNAL_ERROR = list(
+    code = "E100",
+    type = "system",
+    message_template = "内部错误: {details}",
+    user_hint = "系统繁忙，请稍后重试"
+  ),
+  E101_PACKAGE_MISSING = list(
+    code = "E101",
+    type = "system",
+    message_template = "缺少依赖包: {package}",
+    user_hint = "请联系管理员"
+  )
+)
+
+# R 原始错误到错误码的映射字典
+R_ERROR_MAPPING <- list(
+  "system is computationally singular" = "E005_SINGULAR_MATRIX",
+  "did not converge" = "E006_CONVERGENCE_FAILED",
+  "constant" = "E007_VARIANCE_ZERO"
+)
+
+# 构造错误响应（含用户友好提示）
+make_error <- function(error_def, ...) {
+  params <- list(...)
+  msg <- error_def$message_template
+  for (name in names(params)) {
+    msg <- gsub(paste0("\\{", name, "\\}"), as.character(params[[name]]), msg)
+  }
+  return(list(
+    status = "error",
+    error_code = error_def$code,
+    error_type = error_def$type,
+    message = msg,
+    user_hint = error_def$user_hint
+  ))
+}
+
+# 尝试将 R 原始错误映射为友好错误码
+map_r_error <- function(raw_error_msg) {
+  for (pattern in names(R_ERROR_MAPPING)) {
+    if (grepl(pattern, raw_error_msg, ignore.case = TRUE)) {
+      error_key <- R_ERROR_MAPPING[[pattern]]
+      return(make_error(ERROR_CODES[[error_key]], details = raw_error_msg))
+    }
+  }
+  # 无法映射，返回通用内部错误
+  return(make_error(ERROR_CODES$E100_INTERNAL_ERROR, details = raw_error_msg))
+}

r-statistics-service/utils/guardrails.R

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+# utils/guardrails.R
+# 统计护栏函数库
+
+library(glue)
+
+# 大样本优化阈值
+LARGE_SAMPLE_THRESHOLD <- 5000
+
+# 护栏 Action 类型
+ACTION_BLOCK <- "Block"    # 阻止执行
+ACTION_WARN <- "Warn"      # 警告但继续
+ACTION_SWITCH <- "Switch"  # 切换到备选方法
+
+# 正态性检验（支持三种 Action）
+check_normality <- function(values, alpha = 0.05, action = ACTION_SWITCH, action_target = NULL) {
+  n <- length(values)
+  
+  # 样本量过小
+  if (n < 3) {
+    return(list(
+      passed = TRUE, 
+      action = NULL,
+      action_target = NULL,
+      reason = "样本量过小，跳过正态性检验", 
+      skipped = TRUE
+    ))
+  }
+  
+  # 大样本优化：N > 5000 时使用抽样检验
+  if (n > LARGE_SAMPLE_THRESHOLD) {
+    set.seed(42)
+    sampled_values <- sample(values, 1000)
+    test <- shapiro.test(sampled_values)
+    passed <- test$p.value >= alpha
+    
+    return(list(
+      passed = passed,
+      action = if (passed) NULL else action,
+      action_target = if (passed) NULL else action_target,
+      p_value = test$p.value,
+      reason = glue("大样本(N={n})抽样检验，{if (passed) '满足正态性' else '不满足正态性'}"),
+      sampled = TRUE,
+      sample_size = 1000
+    ))
+  }
+  
+  # 常规检验
+  test <- shapiro.test(values)
+  passed <- test$p.value >= alpha
+  
+  return(list(
+    passed = passed,
+    action = if (passed) NULL else action,
+    action_target = if (passed) NULL else action_target,
+    p_value = test$p.value,
+    reason = if (passed) "满足正态性" else "不满足正态性",
+    sampled = FALSE
+  ))
+}
+
+# 方差齐性检验 (Levene)
+check_homogeneity <- function(df, group_var, value_var, alpha = 0.05, action = ACTION_WARN) {
+  library(car)
+  
+  formula <- as.formula(paste(value_var, "~", group_var))
+  test <- leveneTest(formula, data = df)
+  p_val <- test$`Pr(>F)`[1]
+  passed <- p_val >= alpha
+  
+  return(list(
+    passed = passed,
+    action = if (passed) NULL else action,
+    p_value = p_val,
+    reason = if (passed) "方差齐性满足" else "方差不齐性"
+  ))
+}
+
+# 样本量检验
+check_sample_size <- function(n, min_required = 3, action = ACTION_BLOCK) {
+  passed <- n >= min_required
+  return(list(
+    passed = passed,
+    action = if (passed) NULL else action,
+    n = n,
+    reason = if (passed) "样本量充足" else paste0("样本量不足, 需要至少 ", min_required)
+  ))
+}
+
+# 执行护栏链（按 check_order 顺序执行）
+run_guardrail_chain <- function(guardrail_results) {
+  warnings <- c()
+  
+  for (result in guardrail_results) {
+    if (!result$passed) {
+      if (result$action == ACTION_BLOCK) {
+        return(list(
+          status = "blocked",
+          reason = result$reason
+        ))
+      } else if (result$action == ACTION_SWITCH) {
+        return(list(
+          status = "switch",
+          target_tool = result$action_target,
+          reason = result$reason
+        ))
+      } else if (result$action == ACTION_WARN) {
+        warnings <- c(warnings, result$reason)
+      }
+    }
+  }
+  
+  return(list(
+    status = "passed",
+    warnings = warnings
+  ))
+}

r-statistics-service/utils/result_formatter.R

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+# utils/result_formatter.R
+# 统计结果格式化，确保 p 值显示规范
+
+# 格式化 p 值（符合 APA 规范）
+format_p_value <- function(p) {
+  if (is.na(p)) return(NA)
+  
+  if (p < 0.001) {
+    return("< 0.001")
+  } else {
+    return(sprintf("%.3f", p))
+  }
+}
+
+# 构建标准化结果（包含 p_value_fmt）
+make_result <- function(p_value, statistic, method, ...) {
+  list(
+    p_value = p_value,
+    p_value_fmt = format_p_value(p_value),
+    statistic = statistic,
+    method = method,
+    ...
+  )
+}
+
+# 格式化置信区间
+format_ci <- function(lower, upper, digits = 2) {
+  sprintf("[%.2f, %.2f]", lower, upper)
+}
+
+# 格式化效应量
+format_effect_size <- function(value, type = "d") {
+  interpretation <- ""
+  if (type == "d") {  # Cohen's d
+    if (abs(value) < 0.2) interpretation <- "微小"
+    else if (abs(value) < 0.5) interpretation <- "小"
+    else if (abs(value) < 0.8) interpretation <- "中等"
+    else interpretation <- "大"
+  }
+  list(
+    value = round(value, 3),
+    interpretation = interpretation
+  )
+}