将 dplyr mutate_at 与自定义函数一起使用

Question

我想从表中取出两个变量并将它们除以第三个变量并将这些计算添加为两个新列。 mutate_at让我非常接近，但在自定义函数f下面，我想访问数据集中的另一列。 任何建议或替代的整洁工具方法？

library(dplyr)
# this works fine but is NOT what I want
f <- function(fld){
  fld/5
}

# This IS what I want where wt is a field in the data
f <- function(fld){
  fld/wt
}

mutate_at(mtcars, .vars = vars(mpg, cyl), .funs = funs(xyz = f))

# This works but is pretty clumsy
f <- function(fld, dat) fld/dat$wt
mutate_at(mtcars, .vars = vars(mpg, cyl), .funs = funs(xyz = f(., mtcars)))

# This is closer but still it would be better if the function allowed the dataset to be submitted to the function without restating the name of the dataset

f <- function(fld, second){
  fld/second
}

mutate_at(mtcars, .vars = vars(mpg, cyl), .funs = funs(xyz = f(., wt)))

Answer 1

library(tidyverse)
f <- function(num, denom) num/denom

mtcars %>% 
  mutate_at(vars(mpg, cyl), f, denom = quote(wt))

尽管在此特定示例中，不需要自定义函数。

mtcars %>% 
  mutate_at(vars(mpg, cyl), `/`, quote(wt))

dplyr 1.0.6 的更新版本：

mtcars %>% 
  mutate(across(c(mpg, cyl), `/`, wt))

Answer 2

也许是这样的？

f <- function(fld,var){
    fld/var
}

mtcars %>%
    mutate_at(vars(mpg,cyl), .funs = funs(xyz = f(.,wt)))

编辑 (2020-08-24)：

由于2020年下半年，随着推出dplyr 1.0.0的， mutate_at已被取代结合mutate与across功能：

mtcars %>%
    mutate(across(c(mpg, cyl), ~ f(.,wt), .names = "{col}_xyz"))

Answer 3

为什么不简单

mutate(mtcars, mpg2 = mpg / wt, cyl2 = cyl / wt)

Answer 4

有一个cur_data()函数将有助于使mutate_at()调用更加紧凑，因为您不必为应用于每一列的函数指定第二个参数：

f <- function(fld){
  fld / cur_data()$wt
}
mutate_at(mtcars, .vars=vars(mpg, cyl), .funs=funs(xyz = f))

附加说明：

1. 如果您需要该函数来引用分组变量，请使用cur_data_all()
2. mutate_at现在被mutate(.data, across()) mutate_at mutate(.data, across())取代，所以最好这样做

mtcars %>% mutate(across(.cols=c(mpg, cyl), .fns=f, .names='{.col}_xyz'))