我们可以编写一对嵌套的函数，将countMap到多个变量，并对结果进行行绑定，只需简单地求值：

library(dplyr)
library(purrr)

count_multi <- function(.data, ...) {
  count_var <- function(var, .data) {
    .data %>% 
      count(Value = factor({{ var }})) %>%  # coerce to factor to allow multiple
      mutate(                               # var types and preserve ordering
        Variable = as.character(ensym(var)),
        .before = everything()
      )
  }
  map_dfr(enquos(...), count_var, .data = .data)
}

mtcars2 <- mtcars %>% 
  mutate(
    vs = factor(vs, labels = c("V", "S")),
    am = factor(am, labels = c("manual", "automatic"))
  )

mtcars2 %>% 
  count_multi(vs, am, cyl)

• • 输出：**

Variable     Value  n
1       vs         V 18
2       vs         S 14
3       am    manual 19
4       am automatic 13
5      cyl         4 11
6      cyl         6  7
7      cyl         8 14

我相信您可以使用kableExtra::pack_rows()为markdown中的每个Variable创建子标题。

下面的输出格式略有不同。但是，它允许子集（使用OP要求不允许的variable列）。

library(data.table)

df <- setDT(copy(mtcars))

# select columns as grouping by continuous variables is not appropriate
x <- c('cyl', 'gear')

y <- lapply(x, \(i) df[, .N, i])

names(y) <- x

y <- rbindlist(y, idcol=T, use.names=F)

names(y) <- c('variable', 'class', 'count')

   variable class count
1:      cyl     6     7
2:      cyl     4    11
3:      cyl     8    14
4:     gear     4    12
5:     gear     3    15
6:     gear     5     5