如何在R中循环遍历变量批量调用分组统计函数？

问题背景 #

现有一份学校员工数据集，自定义了group_the_data函数用于按指定变量分组统计员工数量并添加分组标签，目前通过手动三次调用函数再合并结果，希望优化成批量处理方式，避免重复代码。

数据集定义 #

data <- data.frame(person_id = c(1, 2, 3, 4, 5, 6, 7, 8), 
                   disability_status = c("yes", "no", "yes", "no", "yes", "no", "yes", "no"),
                   age_group = c("20-30","30-40","20-30","30-40","20-30","30-40","20-30","30-40"), 
                   teacher = c("yes", "no", "no", "yes", "no","yes", "no", "yes" ))

自定义分组函数 #

group_the_data <- function(data, 
                           variable, 
                           group_tag) {
  
  grouped_output <- data %>%
                    mutate(flag = 1) %>%
                    group_by({{variable}}) %>%
                    summarise(number_staff = sum(flag, na.rm = T)) %>%
                    mutate(grouping_tag := {{group_tag}})
  
  return(grouped_output)
  
}

原手动调用方式 #

disability_grouped <- group_the_data(data = data,
                                     variable = disability_status,
                                     group_tag = "disability status")

age_group_grouped <- group_the_data(data = data,
                                    variable = age_group,
                                    group_tag = "age group")

role_grouped <- group_the_data(data = data,
                               variable = teacher,
                               group_tag = "role")

all_data_grouped <- bind_rows(disability_grouped, age_group_grouped, role_grouped)

优化方案 #

方法1：Base R 循环实现 #

先定义需要分组的变量名与对应标签的映射，通过for循环批量调用函数，最后合并结果：

# 定义变量名与标签的映射关系
group_vars <- list(
  disability_status = "disability status",
  age_group = "age group",
  teacher = "role"
)

# 初始化空列表存储分组结果
result_list <- list()

# 循环遍历每个变量
for (var_name in names(group_vars)) {
  result_list[[var_name]] <- group_the_data(
    data = data,
    variable = !!sym(var_name),  # 将字符串转为可识别的变量
    group_tag = group_vars[[var_name]]
  )
}

# 合并所有分组结果
all_data_grouped <- bind_rows(result_list)

方法2：使用purrr包的map2函数（tidyverse风格） #

purrr::map2适合处理成对的输入（变量名和对应标签），代码更简洁紧凑：

library(purrr)

# 准备变量名和标签向量
vars <- c("disability_status", "age_group", "teacher")
tags <- c("disability status", "age group", "role")

# 批量调用函数并直接合并结果
all_data_grouped <- map2_dfr(vars, tags, function(var, tag) {
  group_the_data(
    data = data,
    variable = !!sym(var),
    group_tag = tag
  )
})

额外优化：简化自定义函数 #

原函数中通过mutate(flag=1)再sum(flag)统计数量的方式，可直接用summarise(number_staff = n())替代，更高效简洁：

group_the_data <- function(data, 
                           variable, 
                           group_tag) {
  
  data %>%
    group_by({{variable}}) %>%
    summarise(number_staff = n(), .groups = "drop") %>%  # .groups="drop"清理分组状态
    mutate(grouping_tag = {{group_tag}})
  
}

内容的提问来源于stack exchange，提问作者fe108