r - Using dplyr window functions to calculate percentiles

I have a working solution but am looking for a cleaner, more readable solution that perhaps takes advantage of some of the newer dplyr window functions.

Using the mtcars dataset, if I want to look at the 25th, 50th, 75th percentiles and the mean and count of miles per gallon ("mpg") by the number of cylinders ("cyl"), I use the following code:

library(dplyr)
library(tidyr)


# load data
data("mtcars")

# Percentiles used in calculation
p <- c(.25,.5,.75)

# old dplyr solution 
mtcars %>% group_by(cyl) %>% 
  do(data.frame(p=p, stats=quantile(.$mpg, probs=p), 

                n = length(.$mpg), avg = mean(.$mpg))) %>%
  spread(p, stats) %>%
  select(1, 4:6, 3, 2)

# note: the select and spread statements are just to get the data into
#       the format in which I'd like to see it, but are not critical

Is there a way I can do this more cleanly with dplyr using some of the summary functions (n_tiles, percent_rank, etc.)? By cleanly, I mean without the "do" statement.

Thank you

Answer

If you're up for using purrr::map, you can do it like this!

library(tidyverse)

mtcars %>%
  tbl_df() %>%
  nest(-cyl) %>%

  mutate(Quantiles = map(data, ~ quantile(.$mpg)),
         Quantiles = map(Quantiles, ~ bind_rows(.) %>% gather())) %>% 
  unnest(Quantiles)

#> # A tibble: 15 x 3
#>      cyl key   value
#>      
#>  1     6 0%     17.8
#>  2     6 25%    18.6
#>  3     6 50%    19.7

#>  4     6 75%    21  
#>  5     6 100%   21.4
#>  6     4 0%     21.4
#>  7     4 25%    22.8
#>  8     4 50%    26  
#>  9     4 75%    30.4
#> 10     4 100%   33.9
#> 11     8 0%     10.4
#> 12     8 25%    14.4
#> 13     8 50%    15.2

#> 14     8 75%    16.2
#> 15     8 100%   19.2

^{Created on 2018-11-10 by the reprex package (v0.2.1)}

One nice thing about this approach is the output is tidy, one observation per row.