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

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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>
This commit is contained in:
HaHafeng
2026-02-20 23:09:27 +08:00
parent 23b422f758
commit 428a22adf2
62 changed files with 15416 additions and 299 deletions
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r-statistics-service/tools/chi_square.R Normal file
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#' @tool_code ST_CHI_SQUARE
#' @name 卡方检验
#' @version 1.0.0
#' @description 两个分类变量的独立性检验
#' @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$params
var1 <- p$var1
var2 <- p$var2
# ===== 参数校验 =====
if (!(var1 %in% names(df))) {
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = var1))
}
if (!(var2 %in% names(df))) {
return(make_error(ERROR_CODES$E001_COLUMN_NOT_FOUND, col = var2))
}
# ===== 数据清洗 =====
original_rows <- nrow(df)
df <- df[!is.na(df[[var1]]) & trimws(as.character(df[[var1]])) != "", ]
df <- df[!is.na(df[[var2]]) & trimws(as.character(df[[var2]])) != "", ]
removed_rows <- original_rows - nrow(df)
if (removed_rows > 0) {
log_add(glue("数据清洗: 移除 {removed_rows} 行缺失值 (剩余 {nrow(df)} 行)"))
}
# ===== 护栏检查 =====
guardrail_results <- list()
warnings_list <- c()
# 样本量检查
sample_check <- check_sample_size(nrow(df), min_required = 10, action = ACTION_BLOCK)
guardrail_results <- c(guardrail_results, list(sample_check))
log_add(glue("样本量检查: N = {nrow(df)}, {sample_check$reason}"))
guardrail_status <- run_guardrail_chain(guardrail_results)
if (guardrail_status$status == "blocked") {
return(list(
status = "blocked",
message = guardrail_status$reason,
trace_log = logs
))
}
# ===== 构建列联表 =====
contingency_table <- table(df[[var1]], df[[var2]])
log_add(glue("列联表维度: {nrow(contingency_table)} x {ncol(contingency_table)}"))
# 检查列联表有效性
if (nrow(contingency_table) < 2 || ncol(contingency_table) < 2) {
return(make_error(ERROR_CODES$E003_INSUFFICIENT_GROUPS,
col = paste(var1, "或", var2),
expected = 2,
actual = min(nrow(contingency_table), ncol(contingency_table))))
}
# ===== 期望频数检查（决定使用卡方还是 Fisher） =====
expected <- chisq.test(contingency_table)$expected
low_expected_count <- sum(expected < 5)
total_cells <- length(expected)
low_expected_pct <- low_expected_count / total_cells
use_fisher <- FALSE
is_2x2 <- nrow(contingency_table) == 2 && ncol(contingency_table) == 2
if (low_expected_pct > 0.2) {
warnings_list <- c(warnings_list, glue("期望频数 < 5 的格子占 {round(low_expected_pct * 100, 1)}%"))
if (is_2x2) {
use_fisher <- TRUE
log_add("2x2 表且期望频数不足，自动切换为 Fisher 精确检验")
} else {
log_add("期望频数不足，但非 2x2 表，继续使用卡方检验（结果需谨慎解读）")
}
}
# ===== 核心计算 =====
if (use_fisher) {
log_add("执行 Fisher 精确检验")
result <- fisher.test(contingency_table)
method_used <- "Fisher's Exact Test"
output_results <- list(
method = method_used,
p_value = jsonlite::unbox(as.numeric(result$p.value)),
p_value_fmt = format_p_value(result$p.value),
odds_ratio = if (!is.null(result$estimate)) jsonlite::unbox(as.numeric(result$estimate)) else NULL,
conf_int = if (!is.null(result$conf.int)) as.numeric(result$conf.int) else NULL
)
} else {
log_add("执行 Pearson 卡方检验")
result <- chisq.test(contingency_table, correct = is_2x2) # 2x2表使用Yates连续性校正
method_used <- if (is_2x2) "Pearson's Chi-squared test with Yates' continuity correction" else "Pearson's Chi-squared test"
# 计算 Cramér's V
n <- sum(contingency_table)
k <- min(nrow(contingency_table), ncol(contingency_table))
cramers_v <- sqrt(result$statistic / (n * (k - 1)))
# 效应量解释
v_interpretation <- if (cramers_v < 0.1) "微小" else if (cramers_v < 0.3) "小" else if (cramers_v < 0.5) "中等" else "大"
log_add(glue("χ² = {round(result$statistic, 3)}, df = {result$parameter}, p = {round(result$p.value, 4)}, Cramér's V = {round(cramers_v, 3)}"))
output_results <- list(
method = method_used,
statistic = jsonlite::unbox(as.numeric(result$statistic)),
df = jsonlite::unbox(as.numeric(result$parameter)),
p_value = jsonlite::unbox(as.numeric(result$p.value)),
p_value_fmt = format_p_value(result$p.value),
effect_size = list(
cramers_v = jsonlite::unbox(round(as.numeric(cramers_v), 4)),
interpretation = v_interpretation
)
)
}
# 添加列联表信息（精简版，不含原始数据）
# 将 table 转为纯数值矩阵以便 JSON 序列化
observed_matrix <- matrix(
as.numeric(contingency_table),
nrow = nrow(contingency_table),
ncol = ncol(contingency_table),
dimnames = list(rownames(contingency_table), colnames(contingency_table))
)
output_results$contingency_table <- list(
row_var = var1,
col_var = var2,
row_levels = as.character(rownames(contingency_table)),
col_levels = as.character(colnames(contingency_table)),
observed = observed_matrix,
row_totals = as.numeric(rowSums(contingency_table)),
col_totals = as.numeric(colSums(contingency_table)),
grand_total = jsonlite::unbox(sum(contingency_table))
)
# ===== 生成图表 =====
log_add("生成马赛克图")
plot_base64 <- tryCatch({
generate_mosaic_plot(contingency_table, var1, var2)
}, error = function(e) {
log_add(paste("图表生成失败:", e$message))
NULL
})
# ===== 生成可复现代码 =====
original_filename <- if (!is.null(input$original_filename) && nchar(input$original_filename) > 0) {
input$original_filename
} else {
"data.csv"
}
reproducible_code <- glue('
# SSA-Pro 自动生成代码
# 工具: 卡方检验
# 时间: {Sys.time()}
# ================================
library(ggplot2)
# 数据准备
df <- read.csv("{original_filename}")
var1 <- "{var1}"
var2 <- "{var2}"
# 数据清洗
df <- df[!is.na(df[[var1]]) & !is.na(df[[var2]]), ]
# 构建列联表
contingency_table <- table(df[[var1]], df[[var2]])
print(contingency_table)
# 卡方检验
result <- chisq.test(contingency_table)
print(result)
# 计算 Cramer V (效应量)
n <- sum(contingency_table)
k <- min(nrow(contingency_table), ncol(contingency_table))
cramers_v <- sqrt(result$statistic / (n * (k - 1)))
cat("Cramer V =", round(cramers_v, 3), "\\n")
# 可视化（马赛克图）
mosaicplot(contingency_table, main = "Mosaic Plot", color = TRUE)
')
# ===== 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 = output_results,
plots = if (!is.null(plot_base64)) list(plot_base64) else list(),
trace_log = logs,
reproducible_code = as.character(reproducible_code)
))
}
# 辅助函数：生成马赛克图（使用 ggplot2 模拟）
generate_mosaic_plot <- function(contingency_table, var1, var2) {
# 转换为长格式数据
df_plot <- as.data.frame(contingency_table)
names(df_plot) <- c("Var1", "Var2", "Freq")
p <- ggplot(df_plot, aes(x = Var1, y = Freq, fill = Var2)) +
geom_bar(stat = "identity", position = "fill") +
scale_y_continuous(labels = scales::percent) +
theme_minimal() +
labs(
title = paste("Association between", var1, "and", var2),
x = var1,
y = "Proportion",
fill = var2
) +
scale_fill_brewer(palette = "Set2")
tmp_file <- tempfile(fileext = ".png")
ggsave(tmp_file, p, width = 7, height = 5, dpi = 100)
base64_str <- base64encode(tmp_file)
unlink(tmp_file)
return(paste0("data:image/png;base64,", base64_str))
}