Question

当您group_by多个变量时，dplyr会有助于找到这些组的交集。

例如，

mtcars %>% 
  group_by(cyl, am) %>%
  summarise(mean(disp))

产量

Source: local data frame [6 x 3]
Groups: cyl [?]

    cyl    am `mean(disp)`
  <dbl> <dbl>        <dbl>
1     4     0     135.8667
2     4     1      93.6125
3     6     0     204.5500
4     6     1     155.0000
5     8     0     357.6167
6     8     1     326.0000

我的问题是，有没有办法提供多个变量，但总结略微？我希望输出就像你手工完成的那样，变量变量。

df_1 <- 
  mtcars %>% 
  group_by(cyl) %>%
  summarise(est = mean(disp)) %>%
  transmute(group = paste0("cyl_", cyl), est)

df_2 <- 
  mtcars %>% 
  group_by(am) %>%
  summarise(est = mean(disp)) %>%
  transmute(group = paste0("am_", am), est)

bind_rows(df_1, df_2)

以上代码产生

# A tibble: 5 × 2
  group      est
  <chr>    <dbl>
1 cyl_4 105.1364
2 cyl_6 183.3143
3 cyl_8 353.1000
4  am_0 290.3789
5  am_1 143.5308

理想情况下，语法类似于

mtcars %>%
group_by(cyl, am, intersection = FALSE) %>%
summarise(est = mean(disp))

tidyverse中是否存在类似的内容？

（ps，我知道上面表中的group变量并不是很整洁，因为它包含两个变量，但我保证我的目的是整洁，好吗？:)）< / p>

Answer 1

我猜你正在寻找的是tidyr包......

gather首先复制数据集，以便每个因子都有n行，通过这些行进行分组; mutate然后创建分组变量。

library(dplyr)
library(tidyr)

mtcars %>%
  gather(col, value, cyl, am) %>% 
  mutate(group = paste(col, value, sep = "_")) %>%
  group_by(group) %>% 
  summarise(est = mean(disp))

Answer 2

purrr替代方案：

library(tidyverse)

map(c('cyl', 'am'), 
    ~ mtcars %>% 
      group_by_(.x) %>%
      summarise(est = mean(disp)) %>%
      transmute_(group = lazyeval::interp(~paste0(.x, '_', y), y = as.name(.x)),
                 ~est)) %>% 
  bind_rows()

# A tibble: 5 × 2
  group      est
  <chr>    <dbl>
1 cyl_4 105.1364
2 cyl_6 183.3143
3 cyl_8 353.1000
4  am_0 290.3789
5  am_1 143.5308

dplyr :: group_by（）包含多个变量但不是交集

2 个答案: