(R) 将 map() 与列表列一起使用的更简洁方法 [英] (R) Cleaner way to use map() with list-columns

问题描述

我正在尝试从 rowwise() 中移除列表列，因为我听说 tidyverse 团队正在取消它.但是，我不习惯使用 purrr 函数，所以我觉得必须有更好的方法来执行以下操作:

I am trying to move away from rowwise() for list columns as I have heard that the tidyverse team is in the process of axing it. However, I am not used to using the purrr functions so I feel like there must be a better way of doing the following:

我为每个物种创建了一个包含一个小标题的列表列.然后我想进入tibble并取某些变量的平均值.第一种情况是使用地图，第二种情况是我个人觉得更干净的 rowwise 解决方案.

I create a list-column containing a tibble for each species. I then want to go into the tibble and take the mean of certain variables. The first case is using map and second is the rowwise solution that I personally feel is cleaner.

有谁知道在这种情况下使用地图的更好方法吗?

Does anyone know a better way to use map in this situation?

library(tidyverse)
iris %>% 
  group_by(Species) %>% 
  nest() %>% 
  mutate(mean_slength = map_dbl(data, ~mean(.$Sepal.Length, na.rm = TRUE)),
         mean_swidth = map_dbl(data, ~mean(.$Sepal.Width, na.rm = TRUE))
         )
#> # A tibble: 3 x 4
#>   Species    data              mean_slength mean_swidth
#>   <fct>      <list>                   <dbl>       <dbl>
#> 1 setosa     <tibble [50 x 4]>         5.01        3.43
#> 2 versicolor <tibble [50 x 4]>         5.94        2.77
#> 3 virginica  <tibble [50 x 4]>         6.59        2.97

iris %>% 
  group_by(Species) %>% 
  nest() %>% 
  rowwise() %>% 
  mutate(mean_slength = mean(data$Sepal.Length, na.rm = TRUE),
         mean_swidth = mean(data$Sepal.Width, na.rm = TRUE))
#> Source: local data frame [3 x 4]
#> Groups: <by row>
#> 
#> # A tibble: 3 x 4
#>   Species    data              mean_slength mean_swidth
#>   <fct>      <list>                   <dbl>       <dbl>
#> 1 setosa     <tibble [50 x 4]>         5.01        3.43
#> 2 versicolor <tibble [50 x 4]>         5.94        2.77
#> 3 virginica  <tibble [50 x 4]>         6.59        2.97

^{由 reprex 包 (v0.2.1) 于 2018 年 12 月 26 日创建}

^{Created on 2018-12-26 by the reprex package (v0.2.1)}

推荐答案

不要使用两个 map，而是使用一个 summarise_at

Instead of having two map, use a single one, with summarise_at

library(tidyverse)
iris %>% 
   group_by(Species) %>% 
   nest() %>% 
   mutate(out = map(data, ~ 
               .x  %>% 
                 summarise_at(vars(matches('Sepal')), 
              funs(mean_s = mean(., na.rm = TRUE))))) %>% 
   unnest(out)

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