dplyr::across 中的函数参数用于在多列上进行转换

Question

我想使用

dplyr::across

将一些 [0,1] 变量重新编码为级别为 ["no","yes"] 的因子。我成功了，但我想了解如何在两个语法选项中定义函数参数。across

我在

library(dplyr)
library(forcats)

# toy dataset
df = data.frame(
  var_a = c(0,1,0,1,0,1,0,1,0,1),
  var_b = c(0,1,0,1,0,1,0,1,0,1),
  number_a = 1:10,
  number_b = 21:30)

# selection of columns 
some_cols = c("var_a", "var_b" )


# ---- 1) class trarnsfomation
# this works 
df2_a <- df %>% 
  mutate(across(.cols = some_cols, .fns = as.factor, .names = "{.col}_f")) 
# this works too 
df2_b <- df %>% 
  mutate(across(some_cols, ~ as.factor(.x), .names = "{.col}_f")) 


# ---- 2) Change factor levels 
# this DOES NOT work !!!
df3_a <- df2_a %>% 
  mutate(across(.cols = ends_with("_f"), 
                .fns = fct_recode, c(yes = "1", no = "0" ))) 
# this works  
df3_b <- df2_b %>% 
  mutate(across(ends_with("_f"), ~ fct_recode(.x , yes = "1", no = "0" )))

做错了什么？

Answer 1

df3_a

的每个元素都必须是命名字符串而不是命名向量，即您正在做的是

fct_recode

本来应该如此

~ fct_recode(.x, c(yes = "1", no = "0" ))

因此，要让你的代码正常工作

~ fct_recode(.x, yes = "1", no = "0" )

但是，正如警告告诉我们的那样，

library(dplyr, warn=FALSE)
library(forcats)

df %>%
  mutate(
    across(
      all_of(some_cols),
      .fns = as.factor, .names = "{.col}_f"
    ),
    across(
      ends_with("_f"),
      .fns = fct_recode, yes = "1", no = "0"
    )
  )
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `across(ends_with("_f"), .fns = fct_recode, yes = "1", no =
#>   "0")`.
#> Caused by warning:
#> ! The `...` argument of `across()` is deprecated as of dplyr 1.1.0.
#> Supply arguments directly to `.fns` through an anonymous function instead.
#> 
#>   # Previously
#>   across(a:b, mean, na.rm = TRUE)
#> 
#>   # Now
#>   across(a:b, \(x) mean(x, na.rm = TRUE))
#>    var_a var_b number_a number_b var_a_f var_b_f
#> 1      0     0        1       21      no      no
#> 2      1     1        2       22     yes     yes
#> 3      0     0        3       23      no      no
#> 4      1     1        4       24     yes     yes
#> 5      0     0        5       25      no      no
#> 6      1     1        6       26     yes     yes
#> 7      0     0        7       27      no      no
#> 8      1     1        8       28     yes     yes
#> 9      0     0        9       29      no      no
#> 10     1     1       10       30     yes     yes

的

...

参数在

across

中已被弃用。相反，现在应该使用匿名函数，就像您在

dplyr 1.1.0

中所做的那样。