How to unnest column-list?

问题:

I have a tibble like:

tibble(a = c('first', 'second'), 
       b = list(c('colA' = 1, 'colC' = 2), c('colA'= 3, 'colB'=2))) 

# A tibble: 2 x 2
  a      b        
  <chr>  <list>   
1 first  <dbl [2]>
2 second <dbl [2]>

Which a would like to turn into this form:

# A tibble: 2 x 4
  a       colA  colB  colC
  <chr>  <dbl> <dbl> <dbl>
1 first     1.   NA     2.
2 second    3.    2.   NA 

I tried to use unnest(), but I am having issues preserving the elements' names from the nested values.

回答1:

You can do this by coercing the elements in the list column to data frames arranged as you like, which will unnest nicely:

library(tidyverse)

tibble(a = c('first', 'second'), 
       b = list(c('colA' = 1, 'colC' = 2), c('colA'= 3, 'colB'=2))) %>% 
    mutate(b = invoke_map(tibble, b)) %>% 
    unnest()
#> # A tibble: 2 x 4
#>   a       colA  colC  colB
#>   <chr>  <dbl> <dbl> <dbl>
#> 1 first     1.    2.   NA 
#> 2 second    3.   NA     2.

Doing the coercion is a little tricky, though, as you don't want to end up with a 2x1 data frame. There are various ways around this, but a direct route is purrr::invoke_map, which calls a function with purrr::invoke (like do.call) on each element in a list.

回答2:

With tidyr 1.0.0, we can use unnest_wider to directly add new columns.

tidyr::unnest_wider(df,b)
# A tibble: 2 x 4
#  a       colA  colC  colB
#  <chr>  <dbl> <dbl> <dbl>
#1 first      1     2    NA
#2 second     3    NA     2

data

df <- tibble(a = c('first', 'second'), 
   b = list(c('colA' = 1, 'colC' = 2), c('colA'= 3, 'colB'=2)))