Most data operations are done on groups defined by variables. group_by() takes an existing tbl and converts it into a grouped tbl where operations are performed "by group". ungroup() removes grouping.

group_by(.data, ..., add = FALSE)

ungroup(x, ...)

Arguments

.data

a tbl

...

Variables to group by. All tbls accept variable names. Some tbls will accept functions of variables. Duplicated groups will be silently dropped.

add

When add = FALSE, the default, group_by() will override existing groups. To add to the existing groups, use add = TRUE.

x

A tbl()

Tbl types

group_by() is an S3 generic with methods for the three built-in tbls. See the help for the corresponding classes and their manip methods for more details:

Scoped grouping

The three scoped variants (group_by_all(), group_by_if() and group_by_at()) make it easy to group a dataset by a selection of variables.

Examples

by_cyl <- mtcars %>% group_by(cyl) # grouping doesn't change how the data looks (apart from listing # how it's grouped): by_cyl
#> # A tibble: 32 x 11 #> # Groups: cyl [3] #> mpg cyl disp hp drat wt qsec vs am gear carb #> * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 #> # ... with 22 more rows
# It changes how it acts with the other dplyr verbs: by_cyl %>% summarise( disp = mean(disp), hp = mean(hp) )
#> # A tibble: 3 x 3 #> cyl disp hp #> <dbl> <dbl> <dbl> #> 1 4 105.1364 82.63636 #> 2 6 183.3143 122.28571 #> 3 8 353.1000 209.21429
by_cyl %>% filter(disp == max(disp))
#> # A tibble: 3 x 11 #> # Groups: cyl [3] #> mpg cyl disp hp drat wt qsec vs am gear carb #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 3 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
# Each call to summarise() removes a layer of grouping by_vs_am <- mtcars %>% group_by(vs, am) by_vs <- by_vs_am %>% summarise(n = n()) by_vs
#> # A tibble: 4 x 3 #> # Groups: vs [?] #> vs am n #> <dbl> <dbl> <int> #> 1 0 0 12 #> 2 0 1 6 #> 3 1 0 7 #> 4 1 1 7
by_vs %>% summarise(n = sum(n))
#> # A tibble: 2 x 2 #> vs n #> <dbl> <int> #> 1 0 18 #> 2 1 14
# To removing grouping, use ungroup by_vs %>% ungroup() %>% summarise(n = sum(n))
#> # A tibble: 1 x 1 #> n #> <int> #> 1 32
# You can group by expressions: this is just short-hand for # a mutate/rename followed by a simple group_by mtcars %>% group_by(vsam = vs + am)
#> # A tibble: 32 x 12 #> # Groups: vsam [3] #> mpg cyl disp hp drat wt qsec vs am gear carb vsam #> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 1 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 1 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 2 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 0 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 0 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 1 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 1 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 1 #> # ... with 22 more rows
# By default, group_by overrides existing grouping by_cyl %>% group_by(vs, am) %>% group_vars()
#> [1] "vs" "am"
# Use add = TRUE to instead append by_cyl %>% group_by(vs, am, add = TRUE) %>% group_vars()
#> [1] "cyl" "vs" "am"