R/dplyr 函数：频率 table 包括分组变量的总计

Question

我正在处理来自家庭调查的数据，我想计算对各种问题的回答频率表（每个受访者可能有多个答案）。为了加快对调查数据的分析，我在尝试保持 tidyverse 逻辑的同时编写了一些本地函数。我已经成功编写了一个函数，允许我在使用分组变量时计算频率。

library(tibble)
library(dplyr)

my_df <- tibble(id = c(1, 1, 2, 2, 3, 4, 5, 7, 8, 8),
                country = c("A", "A", "A", "A", "A", "B", "B", "B", "B", "B"),
                region = c("ax", "ax", "ax", "ay", "ay", "bx", "bx", "by", "by", "by"),
                district = c("ax1", "ax1", "ax2", "ay1", "ay2", "bx1", "bx1", "by1", "by1", "by1"),
                question = c("answer1", "answer2", "answer1", "answer2", "answer1", "answer1", "answer1", "answer2", "answer1", "answer2"))

freq <- function(df, var, id_var, ...) {
  
  n <- df %>%
    group_by(...) %>% 
    summarise(n = NROW(unique({{id_var}})), .groups = "drop") %>% 
    left_join(distinct(df, {{var}}), by = character(), .)
  
  df %>%
    group_by(..., {{var}}) %>%
    summarise(cases = n(), .groups = "drop") %>%
    left_join(n) %>%
    mutate(freq_answer = cases/n*100) %>%
    ungroup()
}

my_df %>% 
  freq(question, id, country, region)

# A tibble: 7 x 6
  country region question cases     n freq_answer
  <chr>   <chr>  <chr>    <int> <int>       <dbl>
1 A       ax     answer1      2     2         100
2 A       ax     answer2      1     2          50
3 A       ay     answer1      1     2          50
4 A       ay     answer2      1     2          50
5 B       bx     answer1      2     2         100
6 B       by     answer1      1     2          50
7 B       by     answer2      2     2         100

出于报告目的，在某些情况下，我想计算每个分组级别的频率并将结果合并到一个数据框中。我找到了一个解决方案，可以让我对固定数量的分组变量执行此操作，并且能够获得所需的结果。显然，如果我使用或多或少的分组变量，我将不得不指定额外的函数。

freq_sum <- function(df, var, id_var, group1, group2) {
  
  df0 <- freq({{df}}, {{var}}, {{id_var}}) %>%
    add_column({{group1}} :="Total", .before = 1) %>% 
    add_column({{group2}} :="Total", .after = 1)
  
  df1 <- freq({{df}}, {{var}}, {{id_var}}, {{group1}}) %>%
    add_column({{group2}} :="Total", .after = 1)
  
  df2 <- freq({{df}}, {{var}}, {{id_var}}, {{group1}}, {{group2}})
  
  rbind(df2, df1, df0)
  
}

my_df %>% 
  freq_sum(question, id, country, region)

   country region question cases     n freq_answer
   <chr>   <chr>  <chr>    <int> <int>       <dbl>
 1 A       ax     answer1      2     2       100  
 2 A       ax     answer2      1     2        50  
 3 A       ay     answer1      1     2        50  
 4 A       ay     answer2      1     2        50  
 5 B       bx     answer1      2     2       100  
 6 B       by     answer1      1     2        50  
 7 B       by     answer2      2     2       100  
 8 A       Total  answer1      3     3       100  
 9 A       Total  answer2      2     3        66.7
10 B       Total  answer1      3     4        75  
11 B       Total  answer2      2     4        50  
12 Total   Total  answer1      6     7        85.7
13 Total   Total  answer2      4     7        57.1

我的问题：有没有人对如何使freq_sum功能更general/elegant有什么建议，不需要事先指定号码分组变量？

我对如何实现这一目标有一些初步想法，但我不确定如何实施它们或它们是否可行。

freq_sum <- function(df, var, id, ...) {
  
  df0 <- df %>% 
    freq({{var}}, {{id}}, ...)
  
  grouping_vars <- df0 %>% 
    select(1:{{var}}) %>%
    select(-last_col()) %>% 
    names()
  
  # From grouping_vars create a list with vectors that contain increasingy less grouping variables.
  [1] "country" "region" "district"
  [2] "country" "region"
  [3] "country"
  
  # Use the elements of the list as input in the freq() function.
  # Add the missing grouping variables to the resulting data frames.
  # Combine all dataframes in a single data frame.
  
}

Answer 1

如果有人有类似的问题：在下面两个问题的答案的帮助下，我找到了一个涉及for循环的解决方案，可以按预期工作，并且可以让我自由选择要汇总的分组变量的数量.

freq_sum <- function(df, var, id_var, ...) {
   
  var_names <- names(select(df, ...))
  df_total <- bind_rows(setNames(rep("Total", length(var_names)), var_names))
  df_final <- df %>% freq({{var}}, {{id_var}}, ...)
  
  for (i in 1:length(var_names)-1) {
    
    v <- var_names[1:i]
    
    df_final <- df %>%
      freq({{var}}, {{id_var}}, across(v)) %>% 
      add_column(!!!df_total[!names(df_total) %in% names(.)]) %>% 
      rbind(df_final,.) %>% 
      distinct()
  }
  
  df %>%
    freq({{var}}, {{id_var}}) %>%
    add_column(!!!df_total[!names(df_total) %in% names(.)]) %>%
    rbind(df_final, .)
}

my_df %>% 
  freq_sum(question, id, country, region)

# A tibble: 13 x 6
   country region question cases     n freq_answer
   <chr>   <chr>  <chr>    <int> <int>       <dbl>
 1 A       ax     answer1      2     2       100  
 2 A       ax     answer2      1     2        50  
 3 A       ay     answer1      1     2        50  
 4 A       ay     answer2      1     2        50  
 5 B       bx     answer1      2     2       100  
 6 B       by     answer1      1     2        50  
 7 B       by     answer2      2     2       100  
 8 A       Total  answer1      3     3       100  
 9 A       Total  answer2      2     3        66.7
10 B       Total  answer1      3     4        75  
11 B       Total  answer2      2     4        50  
12 Total   Total  answer1      6     7        85.7
13 Total   Total  answer2      4     7        57.1

R/dplyr 函数：频率 table 包括分组变量的总计

R/dplyr function: Frequency table including totals of grouping variables

r

function

dplyr

magrittr

tidyverse