r - 有没有办法在一组预定义变量上运行 dplyr 函数？

Question

df <- mtcars

prop <- df %>%
  group_by(cyl, .drop = FALSE) %>%
  filter(rowMeans(is.na(across(c(disp,  drat, wt)))) <= 0.5) %>%
  summarise(N = n(), across(c(disp, drat, wt, qsec, vs), ~mean(. == 1, na.rm=TRUE))) %>%
  select(disp,  drat, wt, qsec, vs)

有没有办法在外部向量上运行 filter()、summarise() 和 select()，例如:

select1 <- df %>% select(disp,  drat, wt)
select2 <- df %>% select(disp,  drat, wt, qsec, vs)

而不是每次都定义各自的变量来减少对错误的敏感性？

例如 summarise(N = n(), across(all_of(select2)), ~mean(. == 1, na.rm=TRUE)) %>% 给我一个错误.

谢谢!

Answer 1

使用 !! 来自 {rlang}

library(rlang)
library(dplyr)

df <- mtcars

select1 <- df %>% select(disp, drat, wt) %>% names()
select2 <- df %>% select(disp, drat, wt, qsec, vs) %>% names()

df %>%
    group_by(cyl, .drop = FALSE) %>%
    filter(rowMeans(is.na(across(!!select1))) <= 0.5) %>%
    summarise(N = n(), across(!!select2, ~ mean(. == 1, na.rm = TRUE))) %>%
    select(!!select2)

#> # A tibble: 3 × 5
#>    disp  drat    wt  qsec    vs
#>   <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     0     0     0     0 0.909
#> 2     0     0     0     0 0.571
#> 3     0     0     0     0 0

^{由 reprex package 创建于 2022-07-12 (v2.0.1)}