unnest_dt doesn't preserve list columns

[kendonB]

library(tidyverse)
make_df <- function(x){
  tibble(numbers = c(1, 2))
}
iris %>% group_by(Species) %>% 
  nest() %>% 
  mutate(df_col = data %>% map(make_df)) %>% 
  # Now there is a column of single data.frames as well as data
  # I'd like this to unnest like a numeric column.
  unnest(data)
#> # A tibble: 150 x 6
#> # Groups:   Species [3]
#>    Species Sepal.Length Sepal.Width Petal.Length Petal.Width df_col          
#>    <fct>          <dbl>       <dbl>        <dbl>       <dbl> <list>          
#>  1 setosa           5.1         3.5          1.4         0.2 <tibble [2 x 1]>
#>  2 setosa           4.9         3            1.4         0.2 <tibble [2 x 1]>
#>  3 setosa           4.7         3.2          1.3         0.2 <tibble [2 x 1]>
#>  4 setosa           4.6         3.1          1.5         0.2 <tibble [2 x 1]>
#>  5 setosa           5           3.6          1.4         0.2 <tibble [2 x 1]>
#>  6 setosa           5.4         3.9          1.7         0.4 <tibble [2 x 1]>
#>  7 setosa           4.6         3.4          1.4         0.3 <tibble [2 x 1]>
#>  8 setosa           5           3.4          1.5         0.2 <tibble [2 x 1]>
#>  9 setosa           4.4         2.9          1.4         0.2 <tibble [2 x 1]>
#> 10 setosa           4.9         3.1          1.5         0.1 <tibble [2 x 1]>
#> # ... with 140 more rows

iris %>% group_by(Species) %>% 
  nest() %>% 
  mutate(df_col = data %>% map(make_df)) %>% 
  # Now there is a column of single data.frames as well as data
  # I'd like this to unnest like a numeric column.
  tidyfast::dt_unnest(data)
#>        Species Sepal.Length Sepal.Width Petal.Length Petal.Width
#>   1:    setosa          5.1         3.5          1.4         0.2
#>   2:    setosa          4.9         3.0          1.4         0.2
#>   3:    setosa          4.7         3.2          1.3         0.2
#>   4:    setosa          4.6         3.1          1.5         0.2
#>   5:    setosa          5.0         3.6          1.4         0.2
#>  ---                                                            
#> 146: virginica          6.7         3.0          5.2         2.3
#> 147: virginica          6.3         2.5          5.0         1.9
#> 148: virginica          6.5         3.0          5.2         2.0
#> 149: virginica          6.2         3.4          5.4         2.3
#> 150: virginica          5.9         3.0          5.1         1.8

^{Created on 2020-08-07 by the reprex package (v0.3.0)}

[vincentarelbundock]

One approach would be to merge the original data back in after unnesting. There might also be ways to improve performance somewhat before doing this. For instance,

library(microbenchmark)
library(data.table)
library(tidyfast)
library(broom)
dat = data.table(group=sample(1:500, 1e4, replace=TRUE),
                 y=rnorm(1e4), 
                 x=rnorm(1e4))
fit = function(k) lm(y ~ x, k)
dat = dat[, .(data=.(.SD)), by=group][
          , model := lapply(data, fit)][
          , result := lapply(model, tidy)]

tf = function() dt_unnest(dat, result)
keep = function(x) dat[, result[[1L]], by=group][dat, on='group']
discard = function(x) dat[, result[[1L]], by=group]

all(tf() == discard())
#> [1] TRUE

microbenchmark(tf(), keep(), discard(),
               times=100)
#> Unit: milliseconds
#>       expr      min        lq      mean    median        uq       max neval
#>       tf() 9.661796 10.762439 14.220490 11.998833 16.118330 75.664467   100
#>     keep() 1.987287  2.142959  2.453322  2.238526  2.380653  6.861493   100
#>  discard() 1.040819  1.152247  1.350544  1.191882  1.265033  6.042629   100

^{Created on 2020-08-17 by the reprex package (v0.3.0)}