正如@丹尼尔D. Sjoberg的宝贵评论（所有功劳都归功于他）：
1.用gtreg s show_header_names()检查头名称：

library(gtreg)
show_header_names()

  
show_header_names(tbl, tbl1)

output: 
  Column Name   Column Header      
------------  -------------------
  label         **Characteristic** 
  stat_0        **N = 5**

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现在在tbl中重命名相应的名称：

library(gtsummary)
library(gtreg)
library(dplyr)

tbl <- df |> 
  separate_rows(x, sep = ",") |> 
  select(x) |>
  count(x) |> 
  mutate(percent = round(n/nrow(df)*100)) |> 
  mutate(N_percent = paste0(n,"/", nrow(df), " (",percent,"%)"), .keep = "unused") |> 
  select(label = x, stat_0 = N_percent) |> 
  mutate(grouping_var = "x", .before = 1L) |> 
  # create a gtsummary class listing
  tbl_listing(df, group_by = grouping_var) |> 
  # style table with gtusmmary functions to make it look like `tbl_summary()`
  modify_header(
    label ~ "**Characteristic**",
    stat_0 ~ "**N = 5**"
  ) |> 
  modify_column_alignment(stat_0, align = "center") |> 
  modify_footnote(stat_0 = "n/N (%)")

tbl1 <- df1 |> 
  select(y) |> 
  tbl_summary(
    type = list(y ~ "categorical"))

tbl_stack(
  tbls = list(tbl, tbl1)
)

型
x1c 0d1x的数据

在我的answer的基础上，你可以使用tbl_custom_summary来实现你想要的结果。这里是使用我的自定义函数的一个通用版本，它也允许传递标识符列的名称，并通过添加distinct参数来处理这两种类型的变量。

library(dplyr, warn = FALSE)
library(tidyr)
library(gtsummary)

df <- df |>
  left_join(df1, by = "id")

my_summary <- function(variable, .id = "id", distinct = FALSE) {
  function(data, full_data, stat_display, ...) {
    if (distinct) {
      data <- distinct(data, .data[[.id]], .data[[variable]])
    }
    data |>
      summarise(
        n = n(),
        N = n_distinct(full_data[[.id]]),
        p = n / N
      )
  }
}

tbl <- df %>%
  separate_rows(x, sep = ",") |>
  tbl_custom_summary(
    include = -id,
    stat_fns = list(
      ~ my_summary("x", "id"),
      ~ my_summary("y", "id", distinct = TRUE)
    ),
    type = everything() ~ "categorical",
    statistic = list(
      x ~ "{n}/{N} ({p}%)",
      y ~ "{n} ({p}%)"
    )
  )

tbl |>
  modify_header(
    stat_0 ~ paste0("**N = ", nrow(df), "**")
  ) |> 
  modify_footnote(stat_0 = "n/N (%)")

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的数据