引用 dplyr 的 cross() 中的列名

Question

Is it possible to refer to column names in a lambda function inside across() ?

df <- tibble(age = c(12, 45), sex = c('f', 'f'))
allowed_values <- list(age = 18:100, sex = c("f", "m"))

df %>%
  mutate(across(c(age, sex),
                c(valid = ~ .x %in% allowed_values[[COLNAME]])))

I just came across this question where OP asks about validating columns in a dataframe based on a list of allowed values.

dplyr just gained across() and it seems like a natural choice, but we need columns names to look up the allowed values.

The best I could come up with was a call to imap_dfr , but it is more cumbersome to integrate into an anlysis pipeline, because the results need to be re-combined with the original dataframe.

Answer 1

The answer is yes, you can refer to column names in dplyr 's across . You need to use cur_column() . Your original answer was so close! Insert cur_column() into your solution where you want the column name:

library(tidyverse)

df <- tibble(age = c(12, 45), sex = c('f', 'f'))
allowed_values <- list(age = 18:100, sex = c("f", "m"))

df %>%
  mutate(across(c(age, sex),
                c(valid = ~ .x %in% allowed_values[[cur_column()]])
                )
         )

Reference: https://dplyr.tidyverse.org/articles/colwise.html#current-column

Answer 2

I think that you may be asking too much of across at this point (but this may spur additional development, so maybe someday it will work the way you suggest).

I think that the imap functions from the purrr package may give you what you want at this point:

> df <- tibble(age = c(12, 45), sex = c('f', 'f'))
> allowed_values <- list(age = 18:100, sex = c("f", "m"))
> 
> df %>% imap( ~ .x %in% allowed_values[[.y]])
$age
[1] FALSE  TRUE

$sex
[1] TRUE TRUE

> df %>% imap_dfc( ~ .x %in% allowed_values[[.y]])
# A tibble: 2 x 2
  age   sex  
  <lgl> <lgl>
1 FALSE TRUE 
2 TRUE  TRUE 

If you want a single column with the combined validity then you can pass the result through reduce :

> df %>% imap( ~ .x %in% allowed_values[[.y]]) %>%
+   reduce(`&`)
[1] FALSE  TRUE

This could then be added as a new column to the original data, or just used for subsetting the data. I am not expert enough with the tidyverse yet to know if this could be combined with mutate to add the columns directly.