Using dplyr window functions to calculate percentiles

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)

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
#>    <dbl> <chr> <dbl>
#>  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.

This is a dplyr approach that uses the tidy() function of the broom package, unfortunately it still requires do(), but it is a lot simpler.

library(dplyr)
library(broom)

mtcars %>%
    group_by(cyl) %>%
    do( tidy(t(quantile(.$mpg))) )

which gives:

    cyl   X0.  X25.  X50.  X75. X100.
  (dbl) (dbl) (dbl) (dbl) (dbl) (dbl)
1     4  21.4 22.80  26.0 30.40  33.9
2     6  17.8 18.65  19.7 21.00  21.4
3     8  10.4 14.40  15.2 16.25  19.2

Note the use of t() since the broom package does not have a method for named numerics.

This is based on my earlier answer for summary() here.

Not sure how to avoid do() in dplyr, but you can do this with c() and as.list() with data.table in a pretty straightforward manner:

require(data.table) 
as.data.table(mtcars)[, c(as.list(quantile(mpg, probs=p)), 
                        avg=mean(mpg), n=.N), by=cyl]
#    cyl   25%  50%   75%      avg  n
# 1:   6 18.65 19.7 21.00 19.74286  7
# 2:   4 22.80 26.0 30.40 26.66364 11
# 3:   8 14.40 15.2 16.25 15.10000 14

Replace by with keyby if you want them ordered by cyl column.