Transform a list column in a tibble to multiple columns after using enframe from {tibble}

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I have the following list

my_list <- list(`3920_0` = structure(c(623700, 96400),  .Dim = 1:2, .Dimnames = list("1", c("X", "Y"))), 
                `3864_0` = structure(c(665100, 167400), .Dim = 1:2, .Dimnames = list("1", c("X", "Y"))), 
                `1948_1` = structure(c(589800, 97900),  .Dim = 1:2, .Dimnames = list("1", c("X", "Y"))))

looking as follows

$`3920_0`
       X     Y
1 623700 96400

$`3864_0`
       X      Y
1 665100 167400

$`1948_1`
       X     Y
1 589800 97900

When I use enframe I get the following

# A tibble: 3 x 2
  name   value            
  <chr>  <list>           
1 3920_0 <dbl[,2] [1 x 2]>
2 3864_0 <dbl[,2] [1 x 2]>
3 1948_1 <dbl[,2] [1 x 2]>

That is almost what I want, however I would like to "split" the list in the "value" column into two columns "X" and "Y".

Alternatively, would there be a better solution to achieve the final table without enframe()?

tibble r
4 Answers

You can use unnest_wider after enframe :

my_list %>%
  tibble::enframe() %>%
  tidyr::unnest_wider(value, names_repair = ~c('name','x', 'y'))

#  name        x      y
#  <chr>   <dbl>  <dbl>
#1 3920_0 623700  96400
#2 3864_0 665100 167400
#3 1948_1 589800  97900

if you want to do this without enframe, a solution similar to @Sotos using purrr::map_df

purrr::map_df(my_list, as.data.frame,.id = 'name')
user_39629140

Having data frames in a list helps with output structure when you binding. So,

dplyr::bind_rows(lapply(my_list, data.frame), .id = 'id')

#          id      X      Y
#1...1 3920_0 623700  96400
#1...2 3864_0 665100 167400
#1...3 1948_1 589800  97900
user_56355800

Another option using rbindlist:

library(data.table)
rbindlist(lapply(my_list, data.frame), idcol = 'name')

     name      X      Y
1: 3920_0 623700  96400
2: 3864_0 665100 167400
3: 1948_1 589800  97900
user_92992590

We can also convert to tibble and bind them together

library(purrr)
library(tibble)
map_dfr(my_list, as_tibble, .id = 'name')
# A tibble: 3 x 3
#  name        X      Y
#  <chr>   <dbl>  <dbl>
#1 3920_0 623700  96400
#2 3864_0 665100 167400
#3 1948_1 589800  97900
user_37322710
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