separate output into multiple objects in R automatically

I'm not sure if this is possible, but is there a way in R to run a command and have it save the output into multiple objects based on group? For instance, I wrote a code that calculates what number of employees are in a supervisory role based on their department.

library(tidyverse)
sample <- tibble(department = c("Admin", "Admin", "Office of President", "Office of President"),
                 sup_status = c("Not Supervisor", "Supervisor", "Not Supervisor", "Supervisor"),
                 n = c(918, 152, 69, 192))

But, what I really want is a vector of the percentages of supervisors by department. I can get R to produce one long vector of all percentages:

library(tidyverse)

vector_of_all_percents <- sample %>%
  group_by(department) %>%
  mutate(sum_new = sum(n)) %>%
  rowwise() %>%
  mutate(percent = n/sum_new) %>%
  select(percent) %>%
  as_vector()

vector_of_all_percents
 percent1  percent2  percent3  percent4 
0.8579439 0.1420561 0.2643678 0.7356322

My actual data has many departments. Is there a way to adjust my above code to get R produce objects by department automatically, something like this:

vector_for_admin
 percent1  percent2
0.8579439 0.1420561 

vector_for_office
percent1  percent2 
0.2643678 0.7356322

I'm not sure if the slice() or split() commands are what I need, or if this is even possible. Any guidance would be very appreciated!

Solution

You can use split to create a list:

library(tidyverse)
sample <- tibble(department =c("Admin", "Admin", "Office of President", "Office of President"),
                 sup_status =c("Not Supervisor", "Supervisor", "Not Supervisor", "Supervisor"),
                 n = c(918, 152, 69, 192))

list_of_all_percents <- sample %>%
  group_by(department) %>%
  mutate(sum_new = sum(n)) %>%
  rowwise() %>%
  mutate(percent = n/sum_new) %>%
  split(.$department)

list_of_all_percents
#> $Admin
#> Source: local data frame [2 x 5]
#> Groups: <by row>
#> 
#> # A tibble: 2 x 5
#>   department sup_status         n sum_new percent
#>   <chr>      <chr>          <dbl>   <dbl>   <dbl>
#> 1 Admin      Not Supervisor   918    1070   0.858
#> 2 Admin      Supervisor       152    1070   0.142
#> 
#> $`Office of President`
#> Source: local data frame [2 x 5]
#> Groups: <by row>
#> 
#> # A tibble: 2 x 5
#>   department          sup_status         n sum_new percent
#>   <chr>               <chr>          <dbl>   <dbl>   <dbl>
#> 1 Office of President Not Supervisor    69     261   0.264
#> 2 Office of President Supervisor       192     261   0.736

So if you want to access percents for Admin, you just do

list_of_all_percents$Admin$percent
#> [1] 0.8579439 0.1420561

^{Created on 2020-02-20 by the reprex package (v0.3.0)}