How to combine filter(across(starts_with("foo"), ~ . logical-condition)) with mutate(bar = map2(...))?

I want to use dplyr's filter() in combination with selection helpers such as starts_with().

The current post is a follow-up on this answer, but in a bit more sophisticated data structure that involves list-columns and map2() from {purrr} package.

Consider the following my_mtcars data frame:

library(tibble)

my_mtcars <-
  mtcars %>%
  rownames_to_column("cars")

I want to filter any column that starts with/contains the string "cars", to keep only the following cars:

cars_to_keep <- c("Merc 240D", "Fiat X1-9", "Ferrari Dino")

So from this answer we learned how to use selection helpers with filter() such that:

library(dplyr)

filter(my_mtcars, across(contains("cars"), ~ . %in% cars_to_keep))

##           cars  mpg cyl  disp  hp drat    wt qsec vs am gear carb
## 1    Merc 240D 24.4   4 146.7  62 3.69 3.190 20.0  1  0    4    2
## 2    Fiat X1-9 27.3   4  79.0  66 4.08 1.935 18.9  1  1    4    1
## 3 Ferrari Dino 19.7   6 145.0 175 3.62 2.770 15.5  0  1    5    6

So far so good.

The problem arises with the following data structure:

higher_level_tibble <- 
  tibble(my_data         = list(my_mtcars),
         the_cars_i_want = list(cars_to_keep))

## # A tibble: 1 x 2
##   my_data        the_cars_i_want
##   <list>         <list>         
## 1 <df [32 x 12]> <chr [3]>  

Although the following works:

library(purrr)

higher_level_tibble %>%
  mutate(my_filtered_data = map2(.x = my_data, .y = the_cars_i_want, .f = ~filter(.x, cars %in% .y)))

## # A tibble: 1 x 3
##   my_data        the_cars_i_want my_filtered_data
##   <list>         <list>          <list>          
## 1 <df [32 x 12]> <chr [3]>       <df [3 x 12]>   

This doesn't:

higher_level_tibble %>%
  mutate(my_filtered_data = map2(.x = my_data, .y = the_cars_i_want, .f = ~ filter(.x, across(starts_with("cars"), ~ . %in% .y))))

Error: Problem with mutate() column my_filtered_data.
i my_filtered_data = map2(...).
x Problem with filter() input ..1.
i Input ..1 is across(starts_with("cars"), ~. %in% .y).
x the ... list contains fewer than 2 elements

How can I utilize tidyselect helpers in filter(), all within purrr::map2()?

EDIT

desired output

higher_level_tibble %>%
  mutate(my_filtered_data = map2(.x = my_data, 
                                 .y = the_cars_i_want, 
                                 .f = ~ .x %>% filter( from the col in .x whose header starts with "cars", return only values that appear in .y )))

## # A tibble: 1 x 3
##   my_data        the_cars_i_want my_filtered_data
##   <list>         <list>          <list>          
## 1 <df [32 x 12]> <chr [3]>       <df [3 x 12]>  

A possible solution, using purrr::pmap_dfr:

library(tidyverse)

my_mtcars <-
  mtcars %>%
  rownames_to_column("cars")

cars_to_keep <- c("Merc 240D", "Fiat X1-9", "Ferrari Dino")

higher_level_tibble <- 
  tibble(my_data         = list(my_mtcars),
         the_cars_i_want = list(cars_to_keep))

higher_level_tibble %>% 
  pmap_dfr(~ ..1 %>% filter(across(contains("cars"), \(x) x %in% ..2))) %>% 
  nest(my_filtered_data = everything()) %>% 
  bind_cols(higher_level_tibble, .)

#> # A tibble: 1 × 3
#>   my_data        the_cars_i_want my_filtered_data 
#>   <list>         <list>          <list>           
#> 1 <df [32 × 12]> <chr [3]>       <tibble [3 × 12]>