Most data operations are usefully 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, ...)
| .data | a tbl |
|---|---|
| ... | Variables to group by. All tbls accept variable names, some will also accept functions of variables. Duplicated groups will be silently dropped. |
| add | When |
| x | A |
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:
data.frame: grouped_df
data.table: dtplyr::grouped_dt
SQLite: src_sqlite()
PostgreSQL: src_postgres()
MySQL: src_mysql()
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.
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 × 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 × 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#> # A tibble: 3 × 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 × 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#> # A tibble: 2 × 2 #> vs n #> <dbl> <int> #> 1 0 18 #> 2 1 14#> # A tibble: 1 × 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 × 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#> [1] "vs" "am"#> [1] "cyl" "vs" "am"