Using dplyr window functions to calculate percentiles

Question

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 1

In dplyr 1.0, summarise can return multiple values, allowing the following:

library(tidyverse)

mtcars %>% 
  group_by(cyl) %>%  
  summarise(quantile = scales::percent(c(0.25, 0.5, 0.75)),
            mpg = quantile(mpg, c(0.25, 0.5, 0.75)))

Or, you can avoid a separate line to name the quantiles by going with enframe:

mtcars %>% 
  group_by(cyl) %>%  
  summarise(enframe(quantile(mpg, c(0.25, 0.5, 0.75)), "quantile", "mpg"))

    cyl quantile   mpg
  <dbl> <chr>    <dbl>
1     4 25%       22.8
2     4 50%       26  
3     4 75%       30.4
4     6 25%       18.6
5     6 50%       19.7
6     6 75%       21  
7     8 25%       14.4
8     8 50%       15.2
9     8 75%       16.2

---

Answer for previous versions of dplyr

library(tidyverse)

mtcars %>% 
  group_by(cyl) %>% 
  summarise(x=list(enframe(quantile(mpg, probs=c(0.25,0.5,0.75)), "quantiles", "mpg"))) %>% 
  unnest(x)

    cyl quantiles   mpg
1     4       25% 22.80
2     4       50% 26.00
3     4       75% 30.40
4     6       25% 18.65
5     6       50% 19.70
6     6       75% 21.00
7     8       25% 14.40
8     8       50% 15.20
9     8       75% 16.25

This can be turned into a more general function using tidyeval:

q_by_group = function(data, value.col, ..., probs=seq(0,1,0.25)) {

  groups=enquos(...)
  
  data %>% 
    group_by(!!!groups) %>% 
    summarise(x = list(enframe(quantile({{value.col}}, probs=probs), "quantiles", "mpg"))) %>% 
    unnest(x)
}

q_by_group(mtcars, mpg)
q_by_group(mtcars, mpg, cyl)
q_by_group(mtcars, mpg, cyl, vs, probs=c(0.5,0.75))
q_by_group(iris, Petal.Width, Species)