How to do rowwise matching in data.table between list-column and external vector?

I’m trying to do a rowwise matching for vector elements nested inside a data.table list-column.

library(tibble)
library(data.table)

my_dt <-
  tibble::tribble(
    ~col_x,   ~col_y,
    "a",      c(1, 2, 3),
    "b",      c(4, 5, 6),
    "c",      c(7, 8, 9)
  ) %>%
  as.data.table()

external_vec <- 1:9

I want to match each element from col_y against external_vec such that I will get a new column in my_dt that is equivalent to external_vec %in% col_y, such as:

# desired output
##    col_x col_y                                 new_col
## 1:     a 1,2,3  TRUE, TRUE, TRUE,FALSE,FALSE,FALSE,...
## 2:     b 4,5,6 FALSE,FALSE,FALSE, TRUE, TRUE, TRUE,...
## 3:     c 7,8,9 FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,...

EDIT

I know how to implement a solution based on tidyverse’s purrr, like the following. However, I’m looking for a data.table native functionality.

# this works but relies on purrr and dplyr, which I hope to avoid in this problem
my_dt %>%
  tibble::add_column(ext_vec_as_col = list(external_vec)) %>%
  mutate(new_col = purrr::map2(.x = ext_vec_as_col, .y = col_y, .f = ~.x %in% .y) )
#>    col_x col_y  ext_vec_as_col                                 new_col
#> 1:     a 1,2,3 1,2,3,4,5,6,...  TRUE, TRUE, TRUE,FALSE,FALSE,FALSE,...
#> 2:     b 4,5,6 1,2,3,4,5,6,... FALSE,FALSE,FALSE, TRUE, TRUE, TRUE,...
#> 3:     c 7,8,9 1,2,3,4,5,6,... FALSE,FALSE,FALSE,FALSE,FALSE,FALSE,...

>Solution :

I’ve got the tidyverse-solution:

library(tibble)
library(dplyr)
library(tidyr)
library(purrr)
library(magrittr)

my_dt <-
  tibble::tribble(
    ~col_x,   ~col_y,
    "a",      c(1, 2, 3),
    "b",      c(4, 5, 6),
    "c",      c(7, 8, 9)
  )
external_vec <- 1:9

my_dt %>% 
  mutate(new_col = col_y %>% 
           map(~external_vec %in% .x)) %>% 
  unnest_wider(col_y, names_sep = "_") %>%  # this is just to see the result..
  unnest_wider(new_col, names_sep = "_") # this is just to see the result..

Usually, library(tidyverse) includes a lot of these, but I just thought that it would be nicer to know exactly what you need to make this work.

data.table solution

I think this is the equivalent:

my_dt[, new_col := lapply(col_y, \(x) external_vec %in% x)]
my_dt

Performance

Since that is important, here is some performance benchmark

bench::mark(
  tidy = 
    my_dt %>% 
    mutate(new_col = col_y %>% 
             map(~external_vec %in% .x)),
  dt = my_dt[, new_col := lapply(col_y, \(x) external_vec %in% x)],
  op_tidy = my_dt %>%
    tibble::add_column(ext_vec_as_col = list(external_vec)) %>%
    mutate(new_col = purrr::map2(.x = ext_vec_as_col, .y = col_y, .f = ~.x %in% .y)),
  check = FALSE
) %>% 
  plot()