use nest() and map() with multiple levels of aggregation

[nguyen]

I often find myself writing code that analyses the same dataset at different levels of aggregation. Almost always I keep the results in separate dataframes like in the example below:

library(tidyr)
library(dplyr)
library(purrr)

car_reg <- function(data){
  lm(data = data, mpg ~ cyl + hp)
}

by_vs_gear <- mtcars %>%
  group_by(vs, gear) %>% 
  nest() %>%
  mutate(car_model = map(data, car_reg))
  
  
by_vs <- mtcars %>%
  group_by(vs) %>% 
  nest() %>%
  mutate(car_model = map(data, car_reg))

Are there any tidyr or purrr idioms that would let me 'peel off' a level of aggregation and keep the original aggregated results all in one dataframe? Or maybe this is just a bad idea?

The ultimate idea is to have a dataframe that looks like this

library(tidyr)
library(dplyr)
library(purrr)

car_reg <- function(data){
  lm(data = data, mpg ~ cyl + hp)
}

by_vs_gear <- mtcars %>%
  group_by(vs, gear) %>% 
  nest() %>%
  mutate(car_model = map(data, car_reg))


by_vs <- mtcars %>%
  group_by(vs) %>% 
  nest() %>%
  mutate(car_model = map(data, car_reg), gear = NA)

aggregated <- by_vs_gear %>% bind_rows(by_vs)

without any of the extra work of keeping two sub-dataframes around