Use filter() (and other dplyr functions) inside nested data frames with map()

Question

I'm trying to use map() of purrr package to apply filter() function to the data stored in a nested data frame.

"Why wouldn't you filter first, and then nest? - you might ask. That will work (and I'll show my desired outcome using such process), but I'm looking for ways to do it with purrr. I want to have just one data frame, with two list-columns, both being nested data frames - one full and one filtered.

I can achieve it now by performing nest() twice: once on all data, and second on filtered data:

library(tidyverse)

df <- tibble(
  a = sample(x = rep(c('x','y'),5), size = 10),
  b = sample(c(1:10)),
  c = sample(c(91:100))
)

df_full_nested <- df %>% 
  group_by(a) %>% 
  nest(.key = 'full')

df_filter_nested <- df %>%
  filter(c >= 95) %>%  ##this is the key step
  group_by(a) %>% 
  nest(.key = 'filtered')

## Desired outcome - one data frame with 2 nested list-columns: one full and one filtered.
## How to achieve this without breaking it out into 2 separate data frames?
df_nested <- df_full_nested %>% 
  left_join(df_filter_nested, by = 'a')

The objects look like this:

> df
# A tibble: 10 x 3
       a     b     c
   <chr> <int> <int>
 1     y     8    93
 2     x     9    94
 3     y    10    99
 4     x     5    97
 5     y     2   100
 6     y     3    95
 7     x     7    96
 8     y     6    92
 9     x     4    91
10     x     1    98

> df_full_nested
# A tibble: 2 x 2
      a             full
  <chr>           <list>
1     y <tibble [5 x 2]>
2     x <tibble [5 x 2]>

> df_filter_nested
# A tibble: 2 x 2
      a         filtered
  <chr>           <list>
1     y <tibble [3 x 2]>
2     x <tibble [3 x 2]>

> df_nested
# A tibble: 2 x 3
      a             full         filtered
  <chr>           <list>           <list>
1     y <tibble [5 x 2]> <tibble [4 x 2]>
2     x <tibble [5 x 2]> <tibble [4 x 2]>

So, this works. But it is not clean. And in real life, I group by several columns, which means I also have to join on several columns... It gets hairy fast.

I'm wondering if there is a way to apply filter to the nested column. This way, I'd operate within the same object. Just cleaner and more understandable code.

I'm thinking it'd look like

df_full_nested %>% mutate(filtered = map(full, ...))

But I am not sure how to map filter() properly

Thanks!

Answer 1

You can use map(full, ~ filter(., c >= 95)), where . stands for individual nested tibble, to which you can apply the filter directly:

df_nested_2 <- df_full_nested %>% mutate(filtered = map(full, ~ filter(., c >= 95)))

identical(df_nested, df_nested_2)
# [1] TRUE