通过分组应用在数据帧列表上

Posted 2023-01-27

我有一个包含两个数据帧的列表。

library(tidyverse)
dat <- list("seniors" = data.frame(NAME = c("Cletus", "Agnes", "Hank", "Sue", "Maude"),
                                   COOL = c(0, 1, 1, 0, 1),
                                   GENDER = c("Male", "Female", "Male", "Female", "Female"),
                                   RACE = c("B", "B", "W", "W", "B")), 
            "juniors" = data.frame(NAME = c("Chester", "Chuck", "Bruce", "Carmen", "Cleo"),
                                   COOL = c(1, 1, 1, 0, 1),
                                   GENDER = c("Male", "Male", "Male", "Female", "Female"),
                                   RACE = c("W", "W", "B", "W", "W")))

如果我想在两个数据帧之间获取特定分组变量的计数，例如gender，并根据个人是否为cool进行分组，则可以使用以下代码：

results <- lapply(names(dat), function(x) 
  dat[[x]] %>% 
    group_by(COOL, GENDER) %>% 
    summarise(TOTAL = n()) %>%
    mutate(COHORT = x) %>% 
    select(COHORT, everything())
)
do.call(rbind, results)

但是，我希望能够获得超过n个分组变量的计数，而无需重复执行代码n次，并将所有结果都放在一个表中。并不是说我always要按COOL分组时，第二个分组变量就是要更改的变量。

[我期望的输出如下（请注意，TOTAL图未反映示例数据，我主要只是试图显示期望的表结构）。此外，我认识到此表结构不符合整洁的原则，只是需要这种方式才能最终在Excel中使用vlookup。

COHORT    COOL    GROUP_VAR    GROUP_VAL    TOTAL
SENIORS    0      GENDER       MALE         3
SENIORS    1      GENDER       MALE         5
SENIORS    0      GENDER       FEMALE       7
SENIORS    1      GENDER       FEMALE       2
SENIORS    0      RACE         B            2
SENIORS    1      RACE         B            3
SENIORS    0      RACE         W            7
SENIORS    1      RACE         W            9
JUNIORS    0      GENDER       MALE         3
JUNIORS    1      GENDER       MALE         5
JUNIORS    0      GENDER       FEMALE       3
JUNIORS    1      GENDER       FEMALE       1
JUNIORS    0      RACE         B            2
JUNIORS    1      RACE         B            7
JUNIORS    0      RACE         W            3
JUNIORS    1      RACE         W            2

我尝试将结果列表包装在另一个带有列名列表的lapply包装器中（请参见下文），但这不起作用：

group_names <- list("GENDER", "RACE")
lapply(names(dat), function(x) 
  lapply(names(group_names), function (y) 
      dat[[x]] %>% 
    group_by(COOL, y) %>% 
    summarise(TOTAL = n()) %>%
    mutate(COHORT = x,
           GROUP = y) %>% 
    select(COHORT, everything())
  )
)

任何人都知道我如何以一种优雅而有效的方式做到这一点？

谢谢！

答案

您可以使用函数tibble::enframe()将数据框列表转换为单个数据框，您可以在其中应用分组过程。根据dplyr::count()中的变量名称，指定分组变量：

library(dplyr)
library(tidyr)
library(tibble)

dat %>% 
  enframe("COHORT", "data") %>% 
  unnest(data) %>% 
  count(COHORT, COOL, GENDER, name="TOTAL")


# A tibble: 7 x 4
  COHORT   COOL GENDER TOTAL
  <chr>   <dbl> <fct>  <int>
1 juniors     0 Female     1
2 juniors     1 Female     1
3 juniors     1 Male       3
4 seniors     0 Female     1
5 seniors     0 Male       1
6 seniors     1 Female     2
7 seniors     1 Male       1

这是否回答了您的问题？

=========================================>

基于@DJC的评论，我在这里提出一个更合适的解决方案：

dat %>% 
  enframe("COHORT", "data") %>% 
  unnest(data) %>% 
  gather(GROUP_VAR, GROUP_VAL, GENDER, RACE) %>%
  count(COHORT, COOL, GROUP_VAR, GROUP_VAL, name="TOTAL")

# A tibble: 14 x 5
   COHORT   COOL GROUP_VAR GROUP_VAL TOTAL
   <chr>   <dbl> <chr>     <chr>     <int>
 1 juniors     0 GENDER    Female        1
 2 juniors     0 RACE      W             1
 3 juniors     1 GENDER    Female        1
 4 juniors     1 GENDER    Male          3
 5 juniors     1 RACE      B             1
 6 juniors     1 RACE      W             3
 7 seniors     0 GENDER    Female        1
 8 seniors     0 GENDER    Male          1
 9 seniors     0 RACE      B             1
10 seniors     0 RACE      W             1
11 seniors     1 GENDER    Female        2
12 seniors     1 GENDER    Male          1
13 seniors     1 RACE      B             2
14 seniors     1 RACE      W             1

另一答案

以下是解决问题的两种方法：