dplyr：使用select_if（）进行条件列选择 [英] dplyr: conditional column selection using select_if()

问题描述

上一个问题的后续操作...

A follow-up from a previous question...

如何根据类型选择所有列，除了基于选择助手的一列功能？

How to select all columns based on a type, with the exception of one column based on a select helper function?

select_if(iris, is.numeric, vars(-contains("Width")))
Error: No tidyselect variables were registered

我将其存储在嵌套数据框中，并在purrr上运行它： map（）会使工作流程选项稍微复杂化：

I have it within a nested dataframe, and am running it over a purrr::map() which slightly complicates the workflow options:

iris %>% 
  group_by(Species) %>% 
  nest %>% 
  mutate(data = map(data, ~ .x %>% select_if(is.numeric) %>% mutate(count = sum(rowSums(.))))) %>%
  mutate(data = map(data, ~ .x %>% select_if(is.numeric) %>% 
                      mutate_all(funs((. / count) * 100 )))) %>%

  unnest 

推荐答案

最简单，最清晰的方法是将2个 select 函数整合在一起：

The simplest and clearest way to do this is to pipe together 2 select functions:

iris %>%
    select_if(is.numeric) %>%       # Select all numeric columns
    select(-contains('Width')) %>%  # Then drop 'Width' column(s)
    head

  Sepal.Length Petal.Length
1          5.1          1.4
2          4.9          1.4
3          4.7          1.3
4          4.6          1.5
5          5.0          1.4
6          5.4          1.7

即使在 map 函数：

iris %>% 
    group_by(Species) %>% 
    nest %>% 
    mutate(data = map(data, ~ .x %>%
                          select_if(is.numeric) %>%
                          select(-contains('Width')) %>%
                          mutate(count = sum(rowSums(.))))) %>%
    mutate(data = map(data, ~ .x %>%
                          select_if(is.numeric) %>%
                          select(-contains('Width')) %>% 
                          mutate_all(funs((. / count) * 100 )))) %>%

    unnest 

# A tibble: 150 x 4
   Species Sepal.Length Petal.Length count
   <fct>          <dbl>        <dbl> <dbl>
 1 setosa          1.58        0.433   100
 2 setosa          1.52        0.433   100
 3 setosa          1.45        0.402   100
 4 setosa          1.42        0.464   100
 5 setosa          1.55        0.433   100
 6 setosa          1.67        0.526   100
 7 setosa          1.42        0.433   100
 8 setosa          1.55        0.464   100
 9 setosa          1.36        0.433   100
10 setosa          1.52        0.464   100
# ... with 140 more rows

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