创建动态分组依据 [英] Creating a dynamic Group By

问题描述

df = data.frame(
  A = c(1, 4, 5, 13, 2),
  B = c("Group 1", "Group 3", "Group 2", "Group 1", "Group 2"),
  C = c("Group 3", "Group 2", "Group 1", "Group 2", "Group 3")
)

df %>%
  group_by(B) %>%
  summarise(val = mean(A))

df %>%
  group_by(C) %>%
  summarise(val = mean(A))

我不想为每个 group_by 的唯一集合编写新的代码块，而是创建一个循环遍历 df 数据框，然后将结果保存到列表或数据框中。

Instead of writing a new chunck of code for each unique set of group_by I would like to create a loop that would iterate through the df data frame and save the results into a list or a data frame.

我想看看特征 A 的平均值如何随特征 B 和 C ，而不必为数据集中的每个分类功能编写新的代码块。

I would like to see how the average value of feature A is spread acorss features B and C, without having to write a new chunck of code for each categorical feature in the data set.

我尝试了此操作：

List_Of_Groups <- map_df(df, function(i) {
  df %>% 
    group_by(!!!syms(names(df)[1:i])) %>% 
    summarize(newValue = mean(A))
})

推荐答案

使用 purrr 的 map ，您可以将指定的代码块应用于所有字符列。基本上，您将字符变量的名称映射到随后的函数

Using purrr's map, you can apply the chunk of code you specified to all the columns that are character. Basically you map the names of character variables to the function that follows

purrr::map(names(df %>% select(where(is.character))), function(i) {
  df %>% 
    group_by(!!sym(i)) %>% 
    summarize(newValue = mean(A))
})

输出

# [[1]]
# A tibble: 3 x 2
#   B       newValue
#   <chr>      <dbl>
# 1 Group 1      7  
# 2 Group 2      3.5
# 3 Group 3      4  
# 
# [[2]]
# A tibble: 3 x 2
#   C       newValue
#   <chr>      <dbl>
# 1 Group 1      5  
# 2 Group 2      8.5
# 3 Group 3      1.5

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