如何用dplyr和gtsummary为多列变量生成交叉表？报错求助

报错原因 #

tbl_cross()的col参数仅支持传入单个分类变量，你代码中用select(ends_with("group"))一次性传入多个变量，违反了函数参数设计要求，导致报错。

解决方案 #

要生成你需要的「多变量+行总计」交叉表，推荐用gtsummary包的tbl_summary()函数（更贴合需求），或者将数据重塑为长格式后分组处理，以下是两种可行方案：

方案一：用tbl_summary()快速生成目标表 #

先处理数据生成分组变量，再生成带总计和统计检验的交叉表：

library(dplyr)
library(gtsummary)

# 生成示例数据
set.seed(123)
member <- sample(c("Yes", "No"), 100, replace = TRUE)
author <- sample(c("Yes", "No"), 100, replace = TRUE)
review <- sample(0:10, 100, replace = TRUE)
publish <- sample(0:10, 100, replace = TRUE)
pay <- sample(0:10, 100, replace = TRUE)
data <- data.frame(member, author, review, publish, pay)

# 处理分组变量并生成交叉表
data %>%
  mutate(across(c(review, publish, pay), 
                ~cut(., breaks = c(-Inf, 4.5, 5.5, Inf),
                     labels = c("No", "Maybe", "Yes"),
                     include.lowest = TRUE), 
                .names = "{.col}_group")) %>% 
  select(member, ends_with("group")) %>%
  tbl_summary(
    by = member,  # 行维度为member分组
    type = all_categorical() ~ "categorical",
    statistic = all_categorical() ~ "{n} ({p}%)",  # 显示频数+百分比
    digits = all_categorical() ~ c(0, 1),  # 频数取整，百分比保留1位
    missing = "no"  # 不展示缺失值
  ) %>%
  add_overall(col_label = "Total") %>%  # 添加行总计列
  add_p(test = all_categorical() ~ "fisher.test") %>%  # 添加Fisher检验p值
  modify_spanning_header(all_stat_cols() ~ "**会员状态**")  # 美化表头分组

方案二：重塑数据后用tbl_cross分组实现 #

如果坚持使用tbl_cross()，需先将多列变量转为长格式，再分层生成交叉表：

library(dplyr)
library(tidyr)
library(gtsummary)

# 数据处理同方案一，先生成分组变量
data_processed <- data %>%
  mutate(across(c(review, publish, pay), 
                ~cut(., breaks = c(-Inf, 4.5, 5.5, Inf),
                     labels = c("No", "Maybe", "Yes"),
                     include.lowest = TRUE), 
                .names = "{.col}_group")) %>% 
  select(member, ends_with("group"))

# 转为长格式后分层生成交叉表
data_processed %>%
  pivot_longer(cols = ends_with("group"), 
               names_to = "指标", 
               values_to = "分组") %>%
  tbl_strata(
    strata = 指标,
    .tbl_fun = ~.x %>%
      tbl_cross(
        row = member,
        col = 分组,
        margin = "row",
        missing = "no",
        statistic = list(all_categorical() ~ "{n} ({p}%)"),
        digits = list(all_categorical() ~ c(0, 1))
      ) %>%
      add_p(test = all_categorical() ~ "fisher.test")
  )

内容的提问来源于stack exchange，提问作者Cherson