用dplyr总结多列? [英] Summarizing multiple columns with dplyr?

问题描述

我在 dplyr 语法上有点挣扎.我有一个包含不同变量和一个分组变量的数据框.现在我想使用 R 中的 dplyr 计算每组中每一列的平均值.

I'm struggling a bit with the dplyr-syntax. I have a data frame with different variables and one grouping variable. Now I want to calculate the mean for each column within each group, using dplyr in R.

df <- data.frame(
    a = sample(1:5, n, replace = TRUE), 
    b = sample(1:5, n, replace = TRUE), 
    c = sample(1:5, n, replace = TRUE), 
    d = sample(1:5, n, replace = TRUE), 
    grp = sample(1:3, n, replace = TRUE)
)
df %>% group_by(grp) %>% summarise(mean(a))

这给了我grp"表示的每个组的a"列的平均值.

This gives me the mean for column "a" for each group indicated by "grp".

我的问题是:是否有可能一次获得每个组中每一列的平均值?或者我必须为每一列重复 df %>% group_by(grp) %>% summarise(mean(a)) 吗?

My question is: is it possible to get the means for each column within each group at once? Or do I have to repeat df %>% group_by(grp) %>% summarise(mean(a)) for each column?

我想要的是类似的东西

df %>% group_by(grp) %>% summarise(mean(a:d)) # "mean(a:d)" does not work

推荐答案

在 dplyr (>=1.00) 你可以在 across(everything() 中使用 across(everything()>summarise 将函数应用于所有变量:

In dplyr (>=1.00) you may use across(everything() in summarise to apply a function to all variables:

library(dplyr)

df %>% group_by(grp) %>% summarise(across(everything(), list(mean)))
#> # A tibble: 3 x 5
#>     grp     a     b     c     d
#>   <int> <dbl> <dbl> <dbl> <dbl>
#> 1     1  3.08  2.98  2.98  2.91
#> 2     2  3.03  3.04  2.97  2.87
#> 3     3  2.85  2.95  2.95  3.06

或者，purrrlyr 包提供相同的功能:

Alternatively, the purrrlyr package provides the same functionality:

library(purrrlyr)
df %>% slice_rows("grp") %>% dmap(mean)
#> # A tibble: 3 x 5
#>     grp     a     b     c     d
#>   <int> <dbl> <dbl> <dbl> <dbl>
#> 1     1  3.08  2.98  2.98  2.91
#> 2     2  3.03  3.04  2.97  2.87
#> 3     3  2.85  2.95  2.95  3.06

也不要忘记 data.table(使用 keyby 对排序组进行排序):

Also don't forget about data.table (use keyby to sort sort groups):

library(data.table)
setDT(df)[, lapply(.SD, mean), keyby = grp]
#>    grp        a        b        c        d
#> 1:   1 3.079412 2.979412 2.979412 2.914706
#> 2:   2 3.029126 3.038835 2.967638 2.873786
#> 3:   3 2.854701 2.948718 2.951567 3.062678

让我们尝试比较性能.

library(dplyr)
library(purrrlyr)
library(data.table)
library(bench)
set.seed(123)
n <- 10000
df <- data.frame(
  a = sample(1:5, n, replace = TRUE), 
  b = sample(1:5, n, replace = TRUE), 
  c = sample(1:5, n, replace = TRUE), 
  d = sample(1:5, n, replace = TRUE), 
  grp = sample(1:3, n, replace = TRUE)
)
dt <- setDT(df)
mark(
  dplyr = df %>% group_by(grp) %>% summarise(across(everything(), list(mean))),
  purrrlyr = df %>% slice_rows("grp") %>% dmap(mean),
  data.table = dt[, lapply(.SD, mean), keyby = grp],
  check = FALSE
)
#> # A tibble: 3 x 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 dplyr        2.81ms   2.85ms      328.        NA     17.3
#> 2 purrrlyr     7.96ms   8.04ms      123.        NA     24.5
#> 3 data.table 596.33µs 707.91µs     1409.        NA     10.3

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