将 rlang::exec 与使用 rlang::ensym 的函数一起使用

using `rlang::exec` with functions that use `rlang::ensym`

我正在尝试编写一个有点复杂的自定义函数,因此为了简单起见,我创建了玩具示例。

假设我想写一个函数-

  1. 自动决定合适的功能给运行:例如, t 检验或方差分析。
  2. 接受 "quoted"unquoted 参数

所以我写了一个函数来 运行 t 检验(按预期工作):

set.seed(123)
library(rlang)
library(tidyverse)

# t-test function
fun_t <- function(data, x, y) {
  # make sure both quoted and unquoted arguments work
  x <- rlang::ensym(x)
  y <- rlang::ensym(y)

  # t-test
  broom::tidy(stats::t.test(
    formula = rlang::new_formula({{ y }}, {{ x }}),
    data = data
  ))
}

# works fine
fun_t(mtcars, am, wt)
#> # A tibble: 1 x 10
#>   estimate estimate1 estimate2 statistic p.value parameter conf.low
#>      <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>
#> 1     1.36      3.77      2.41      5.49 6.27e-6      29.2    0.853
#> # ... with 3 more variables: conf.high <dbl>, method <chr>,
#> #   alternative <chr>

fun_t(mtcars, "am", "wt")
#> # A tibble: 1 x 10
#>   estimate estimate1 estimate2 statistic p.value parameter conf.low
#>      <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>
#> 1     1.36      3.77      2.41      5.49 6.27e-6      29.2    0.853
#> # ... with 3 more variables: conf.high <dbl>, method <chr>,
#> #   alternative <chr>

然后我写了一个函数到 运行 方差分析(按预期工作):

# anova function
fun_anova <- function(data, x, y) {
  # make sure both quoted and unquoted arguments work
  x <- rlang::ensym(x)
  y <- rlang::ensym(y)

  # t-test
  broom::tidy(stats::aov(
    formula = rlang::new_formula({{ y }}, {{ x }}),
    data = data
  ))
}

# works fine
fun_anova(mtcars, cyl, wt)
#> # A tibble: 2 x 6
#>   term         df sumsq meansq statistic      p.value
#>   <chr>     <dbl> <dbl>  <dbl>     <dbl>        <dbl>
#> 1 cyl           1  18.2 18.2        47.4  0.000000122
#> 2 Residuals    30  11.5  0.384      NA   NA

fun_anova(mtcars, "cyl", "wt")
#> # A tibble: 2 x 6
#>   term         df sumsq meansq statistic      p.value
#>   <chr>     <dbl> <dbl>  <dbl>     <dbl>        <dbl>
#> 1 cyl           1  18.2 18.2        47.4  0.000000122
#> 2 Residuals    30  11.5  0.384      NA   NA

然后我写一个元函数从上面选择合适的函数-

fun_meta <- function(data, x, y) {
  # make sure both quoted and unquoted arguments work
  x <- rlang::ensym(x)
  y <- rlang::ensym(y)

  # which test to run?
  if (nlevels(data %>% dplyr::pull({{ x }})) == 2L) {
    .f <- fun_t
  } else {
    .f <- fun_anova
  }

  # executing the appropriate function
  rlang::exec(
    .fn = .f,
    data = data,
    x = x,
    y = y
  )
}

# using the meta-function
fun_meta(mtcars, am, wt)
#> Only strings can be converted to symbols

fun_meta(mtcars, "am", "wt")
#> Only strings can be converted to symbols

但这似乎不起作用。关于我在这里做错了什么以及如何让它发挥作用有什么想法吗?

问题似乎源于通过元函数中的 rlang::exec() 将相当于 x = rlang::ensym(am) 的内容传递给您的各个函数。

ensym() 函数只接受字符串或符号,所以这样做会导致出现错误消息。鉴于此,将您的 xy 参数转换为字符串应该会有所帮助。

所以元函数可以是:

fun_meta <- function(data, x, y) {
     # make sure both quoted and unquoted arguments work
     x <- rlang::ensym(x)
     y <- rlang::ensym(y)

     # which test to run?
     if (dplyr::n_distinct(data %>% dplyr::pull({{ x }})) == 2L) {
          .f <- fun_t
     } else {
          .f <- fun_anova
     }

     # executing the appropriate function
     rlang::exec(
          .fn = .f,
          data = data,
          x = rlang::as_string(x),
          y = rlang::as_string(y)
     )
}

(我从 nlevels 切换到 n_distinct() 因为 amcyl 不是因素,所以我没有得到正确的结果来与你的比较原始结果。)

现在可以同时使用裸符号和字符串了:

fun_meta(mtcars, am, wt)
    # A tibble: 1 x 10
  estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
     <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl>
1     1.36      3.77      2.41      5.49 6.27e-6      29.2    0.853      1.86
# ... with 2 more variables: method <chr>, alternative <chr>
> fun_meta(mtcars, "am", "wt")

fun_meta(mtcars, "am", "wt")
# A tibble: 1 x 10
  estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
     <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl>
1     1.36      3.77      2.41      5.49 6.27e-6      29.2    0.853      1.86
# ... with 2 more variables: method <chr>, alternative <chr>