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!