Each of these functions first group the data using dplyr::group_by() and then:
mutate_groups(): Apply calculations using dplyr::mutate().
transmute_groups(): Apply calculations using dplyr::transmute().
summarise_groups(): Summarise the data by applying calculations using dplyr::summarise().
arrange_groups(): Order the data using dplyr::arrange() with .by_group = TRUE.
The respective output is ungrouped.
mutate_groups(.data, .groups, ...) summarise_groups(.data, .groups, ...) summarize_groups(.data, .groups, ...) transmute_groups(.data, .groups, ...) arrange_groups(.data, .groups, ...)
| .data | A |
|---|---|
| .groups |
|
| ... | Arguments to pass onto the respective function. |
A tbl_spark or a data.frame depending on the input, .data.
#> # A tibble: 32 x 12 #> mpg cyl disp hp drat wt qsec vs am gear carb avgMpg #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 20.6 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 20.6 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 28.1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 19.1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 15.0 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 19.1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 15.0 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 22.9 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 22.9 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 19.1 #> # … with 22 more rows#> # A tibble: 6 x 3 #> am cyl avgMpg #> <dbl> <dbl> <dbl> #> 1 0 4 22.9 #> 2 0 6 19.1 #> 3 0 8 15.0 #> 4 1 4 28.1 #> 5 1 6 20.6 #> 6 1 8 15.4# Additional arguments can still be passed to the dplyr functions mtcars %>% mutate_groups(.groups = "am", avgMpg = mean(mpg), .before = mpg)#> # A tibble: 32 x 12 #> avgMpg mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 24.4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 24.4 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 24.4 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 17.1 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 17.1 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 17.1 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 17.1 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 17.1 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 17.1 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 17.1 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows