r - 在 R 函数中使用变量作为参数

Question

我想为非常重复的 Action 编写一个函数。数据看起来像这样

id<-c(100,104,999,225,350,450)
sex<-c('female','male','male','female','male','male')
race<-c('black','white','white','white','black','white')
class<-c('a','a','c','b','c','b')
adur<-c(3,3,15,3,3,59)
bdur<-c(2,59,26,59,2,14)
cdur<-c(1,59,59,59,59,1)
ae<-c(1,1,1,1,1,0)
be<-c(1,0,1,0,1,1)
ce<-c(1,0,0,0,1,1)

mydata<-data.frame(id,sex,race,class,adur,bdur,cdur,ae,be,ce)

   id    sex  race class adur bdur cdur ae be ce
1 100 female black     a    3    2    1  1  1  1
2 104   male white     a    3   59   59  1  0  0
3 999   male white     c   15   26   59  1  1  0
4 225 female white     b    3   59   59  1  0  0
5 350   male black     c    3    2   59  1  1  1
6 450   male white     b   59   14    1  0  1  1

我想按不同的变量(性别、种族、类(Class))分组并进行一些计算。这是我的尝试。

stp_f<-function(ivar,idur,ie){
  x<-mydata %>% group_by(ivar) %>% summarise(sumdur=sum(idur),
                                              sumev=sum(ie),
                                              failrate=sumev/sumdur) %>%
    rename(var=ivar)
}

stp_f(sex,adur,ae)
stp_f(sex,bdur,be)
stp_f(sex,cdur,ce)

它不起作用，因为我认为 R 不会以这种方式读取变量。有人建议我放弃 tidyverse 并改用 data.table，但由于我不熟悉 data.table 语法，我发现很难全神贯注。有人可以在 data.table 中详细解释这一点或对此函数使用 dplyr 语法吗？

Answer 1

使用 tidyverse:

stp_f<-function(ivar,idur,ie){
  x <- mydata %>%
    group_by(get(ivar)) %>%
    summarise(
      sumdur = sum(get(idur)),
      sumev = sum(get(ie)),
      failrate = sumev / sumdur
    ) %>%
    rename(var = `get(ivar)`)
  
  x
}

stp_f("sex","adur","ae")
stp_f("sex","bdur","be")
stp_f("sex","cdur","ce")

输出:

> stp_f("sex","adur","ae")
# A tibble: 2 x 4
  var    sumdur sumev failrate
  <chr>   <dbl> <dbl>    <dbl>
1 female      6     2   0.333 
2 male       80     3   0.0375


> stp_f("sex","bdur","be")
# A tibble: 2 x 4
  var    sumdur sumev failrate
  <chr>   <dbl> <dbl>    <dbl>
1 female     61     1   0.0164
2 male      101     3   0.0297


> stp_f("sex","cdur","ce")
# A tibble: 2 x 4
  var    sumdur sumev failrate
  <chr>   <dbl> <dbl>    <dbl>
1 female     60     1   0.0167
2 male      178     2   0.0112