将数据框的列传递给内部函数

问题描述

我想将列插入通过自定义内部函数执行purrr::imap_dfr的函数中。

我的目标是df %>% diffmean(df,group,col1,col2)将运行t.test(col1 ~ group,.data = df)和t.test(col2 ~ group,.data = df。

ttests <- function(df,...) {
  group <- rlang::ensym(group)
  vars <- rlang::ensyms(...)

  df %>%
    dplyr::select(c(!!!vars)) %>%
    purrr::imap_dfr(function(.x,.y) {
      broom::tidy(t.test(.x ~ !!group)) %>%
      dplyr::mutate(name = .y) %>%
      dplyr::select(name,dplyr::everything())
  })
}

如果我只是在要分组的!!group列中简单地硬编码，并且如果我想用!!!vars切换出要选择的变量，则以上代码将起作用。

我只想将此泛型用于将来使用。

例如，使用diamonds中的ggplot2数据集：

diamonds <- diamonds %>%
  dplyr::mutate(carat = carat > 0.25)

diamonds %>%
  dplyr::select(depth,table,price,x,y,z) %>%
  purrr::imap_dfr(.,function(.x,.y) {
    broom::tidy(t.test(.x ~ diamonds$carat)) %>%
      dplyr::mutate(name = .y) %>%
      dplyr::select(name,dplyr::everything())
  })

产生：

  name   estimate estimate1 estimate2 statistic    p.value parameter   conf.low conf.high method                  alternative
  <chr>     <dbl>     <dbl>     <dbl>     <dbl>      <dbl>     <dbl>      <dbl>     <dbl> <chr>                   <chr>      
1 depth    -0.247     61.5      61.8      -4.86 0.00000143      808.    -0.347     -0.147 Welch Two Sample t-test two.sided  
2 table     0.263     57.7      57.5       3.13 0.00183         805.     0.0977     0.427 Welch Two Sample t-test two.sided  
3 price -3477.       506.     3983.     -197.   0             51886. -3512.     -3443.    Welch Two Sample t-test two.sided  
4 x        -1.77       3.99      5.76   -299.   0              6451.    -1.78      -1.76  Welch Two Sample t-test two.sided  
5 y        -1.75       4.01      5.76   -290.   0              6529.    -1.76      -1.73  Welch Two Sample t-test two.sided  
6 z        -1.10       2.46      3.55   -294.   0              6502.    -1.10      -1.09  Welch Two Sample t-test two.sided 

解决方法

R t.test基本语法实际上并不是设计用于rlang样式语法的，因此您需要对公式进行一些摸索。这应该工作

ttests <- function(df,group,...) {
  group <- rlang::ensym(group)
  vars <- rlang::ensyms(...)
  
  df %>%
    dplyr::select(c(!!!vars)) %>%
    purrr::imap_dfr(function(.x,.y) {
      rlang::eval_tidy(rlang::quo(t.test(!!rlang::sym(.y) ~ !!group,df))) %>% 
        broom::tidy() %>%
        dplyr::mutate(name = .y) %>%
        dplyr::select(name,dplyr::everything())
    })
}

基本上，我们正在构建表达式t.test(val ~ group,df)，然后对其求值。

这适用于示例输入

ggplot2::diamonds %>%
  dplyr::mutate(carat = carat > 0.25) %>% 
  ttests(carat,depth,table,price,x,y,z)
#   name  estimate estimate1 estimate2 statistic p.value parameter conf.low
#   <chr>    <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>
# 1 depth -2.47e-1     61.5      61.8      -4.86 1.43e-6      808. -3.47e-1
# 2 table  2.63e-1     57.7      57.5       3.13 1.83e-3      805.  9.77e-2
# 3 price -3.48e+3    506.     3983.     -197.   0.         51886. -3.51e+3
# 4 x     -1.77e+0      3.99      5.76   -299.   0.          6451. -1.78e+0
# 5 y     -1.75e+0      4.01      5.76   -290.   0.          6529. -1.76e+0
# 6 z     -1.10e+0      2.46      3.55   -294.   0.          6502. -1.10e+0

,

还可以选择转换为'long'格式，然后在进行nest_by

后应用公式

library(dplyr)
library(tidyr)

ttests <- function(df,...) {
   grp <- rlang::as_name(ensym(group))
   df %>%
       dplyr::select(!!! enquos(...),grp) %>%
        pivot_longer(cols = -grp) %>%
        nest_by(name) %>%
        transmute(name,new = list(broom::tidy(t.test(reformulate(grp,response = 'value'),data)))) %>%
        unnest_wider(c(new))
  }
  

ttests(diamonds,carat,z)
# A tibble: 6 x 11
#  name   estimate estimate1 estimate2 statistic    p.value parameter   conf.low conf.high method                  alternative
#  <chr>     <dbl>     <dbl>     <dbl>     <dbl>      <dbl>     <dbl>      <dbl>     <dbl> <chr>                   <chr>      
#1 depth    -0.247     61.5      61.8      -4.86 0.00000143      808.    -0.347     -0.147 Welch Two Sample t-test two.sided  
#2 price -3477.       506.     3983.     -197.   0             51886. -3512.     -3443.    Welch Two Sample t-test two.sided  
#3 table     0.263     57.7      57.5       3.13 0.00183         805.     0.0977     0.427 Welch Two Sample t-test two.sided  
#4 x        -1.77       3.99      5.76   -299.   0              6451.    -1.78      -1.76  Welch Two Sample t-test two.sided  
#5 y        -1.75       4.01      5.76   -290.   0              6529.    -1.76      -1.73  Welch Two Sample t-test two.sided  
#6 z        -1.10       2.46      3.55   -294.   0              6502.    -1.10      -1.09  Welch Two Sample t-test two.sided