groupie

The groupie package provides groups_split() to create copies of a data frame with different combinations of grouping variables. This is useful for summarising data several ways in one go.

Caveat

This package is almost certainly overkill. You can easily implement the functions yourself.

The trick is to treat sets of grouping variables as ‘data’, map over them to create copies of your actual data grouped by each set of variables, and then map again with a summarise() function or whatever.

library(dplyr)
library(purrr)

grouping_vars <- c("vs", "am", "carb")
map(grouping_vars, group_by_at, .tbl = mtcars) %>%
  map(summarise,
      '6 cylinder' = sum(cyl == 6),
      'Large disp' = sum(disp >= 100),
      'low gears' = sum(gear <= 4))
#> [[1]]
#> # A tibble: 2 x 4
#>      vs `6 cylinder` `Large disp` `low gears`
#>   <dbl>        <int>        <int>       <int>
#> 1     0            3           18          14
#> 2     1            4            9          13
#> 
#> [[2]]
#> # A tibble: 2 x 4
#>      am `6 cylinder` `Large disp` `low gears`
#>   <dbl>        <int>        <int>       <int>
#> 1     0            4           19          19
#> 2     1            3            8           8
#> 
#> [[3]]
#> # A tibble: 6 x 4
#>    carb `6 cylinder` `Large disp` `low gears`
#>   <dbl>        <int>        <int>       <int>
#> 1     1            2            4           7
#> 2     2            0            8           8
#> 3     3            0            3           3
#> 4     4            4           10           9
#> 5     6            1            1           0
#> 6     8            0            1           0

This package also wraps the arrangements package to create sets of grouping variables. This again is very simple to do yourself.

Installation

You can install the development version of groupie from github with

# install.packages("remotes") # if not already installed
remotes::install_github("nacnudus/groupie")

Example

library(groupie)

library(dplyr)
library(purrr)

Summarise by

• cyl
• am
• mpg

mtcars %>%
  group_by(cyl, am, gear) %>%
  groups_split(individuals) %>%
  map(summarise, mpg = mean(mpg))
#> [[1]]
#> # A tibble: 3 x 2
#>     cyl   mpg
#>   <dbl> <dbl>
#> 1     4  26.7
#> 2     6  19.7
#> 3     8  15.1
#> 
#> [[2]]
#> # A tibble: 2 x 2
#>      am   mpg
#>   <dbl> <dbl>
#> 1     0  17.1
#> 2     1  24.4
#> 
#> [[3]]
#> # A tibble: 3 x 2
#>    gear   mpg
#>   <dbl> <dbl>
#> 1     3  16.1
#> 2     4  24.5
#> 3     5  21.4

• cyl
• cyl and am
• cyl, am and mpg

mtcars %>%
  group_by(cyl, am, gear) %>%
  groups_split(hierarchy) %>%
  map(summarise, mpg = mean(mpg))
#> [[1]]
#> # A tibble: 3 x 2
#>     cyl   mpg
#>   <dbl> <dbl>
#> 1     4  26.7
#> 2     6  19.7
#> 3     8  15.1
#> 
#> [[2]]
#> # A tibble: 6 x 3
#> # Groups:   cyl [3]
#>     cyl    am   mpg
#>   <dbl> <dbl> <dbl>
#> 1     4     0  22.9
#> 2     4     1  28.1
#> 3     6     0  19.1
#> 4     6     1  20.6
#> 5     8     0  15.0
#> 6     8     1  15.4
#> 
#> [[3]]
#> # A tibble: 10 x 4
#> # Groups:   cyl, am [6]
#>      cyl    am  gear   mpg
#>    <dbl> <dbl> <dbl> <dbl>
#>  1     4     0     3  21.5
#>  2     4     0     4  23.6
#>  3     4     1     4  28.0
#>  4     4     1     5  28.2
#>  5     6     0     3  19.8
#>  6     6     0     4  18.5
#>  7     6     1     4  21  
#>  8     6     1     5  19.7
#>  9     8     0     3  15.0
#> 10     8     1     5  15.4

Summarise by each one of cyl, am and mpg.

mtcars %>%
  group_by(cyl, am, gear) %>%
  groups_split(k_combinations, 1L) %>%
  map(summarise, mpg = mean(mpg))
#> [[1]]
#> # A tibble: 3 x 2
#>     cyl   mpg
#>   <dbl> <dbl>
#> 1     4  26.7
#> 2     6  19.7
#> 3     8  15.1
#> 
#> [[2]]
#> # A tibble: 2 x 2
#>      am   mpg
#>   <dbl> <dbl>
#> 1     0  17.1
#> 2     1  24.4
#> 
#> [[3]]
#> # A tibble: 3 x 2
#>    gear   mpg
#>   <dbl> <dbl>
#> 1     3  16.1
#> 2     4  24.5
#> 3     5  21.4

Summarise by all pairs of cyl, am and mpg.

mtcars %>%
  group_by(cyl, am, gear) %>%
  groups_split(k_combinations, 2L) %>%
  map(summarise, mpg = mean(mpg))
#> [[1]]
#> # A tibble: 6 x 3
#> # Groups:   cyl [3]
#>     cyl    am   mpg
#>   <dbl> <dbl> <dbl>
#> 1     4     0  22.9
#> 2     4     1  28.1
#> 3     6     0  19.1
#> 4     6     1  20.6
#> 5     8     0  15.0
#> 6     8     1  15.4
#> 
#> [[2]]
#> # A tibble: 8 x 3
#> # Groups:   cyl [3]
#>     cyl  gear   mpg
#>   <dbl> <dbl> <dbl>
#> 1     4     3  21.5
#> 2     4     4  26.9
#> 3     4     5  28.2
#> 4     6     3  19.8
#> 5     6     4  19.8
#> 6     6     5  19.7
#> 7     8     3  15.0
#> 8     8     5  15.4
#> 
#> [[3]]
#> # A tibble: 4 x 3
#> # Groups:   am [2]
#>      am  gear   mpg
#>   <dbl> <dbl> <dbl>
#> 1     0     3  16.1
#> 2     0     4  21.0
#> 3     1     4  26.3
#> 4     1     5  21.4

Contribute

Please note that the ‘groupie’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.