These scoped variants of group_by()
group a data frame by a
selection of variables. Like group_by()
, they have optional
mutate semantics.
group_by_all(.tbl, .funs = list(), ..., .add = FALSE, .drop = group_by_drop_default(.tbl)) group_by_at(.tbl, .vars, .funs = list(), ..., .add = FALSE, .drop = group_by_drop_default(.tbl)) group_by_if(.tbl, .predicate, .funs = list(), ..., .add = FALSE, .drop = group_by_drop_default(.tbl))
.tbl | A |
---|---|
.funs | A function |
... | Additional arguments for the function calls in
|
.add | See |
.drop | When |
.vars | A list of columns generated by |
.predicate | A predicate function to be applied to the columns
or a logical vector. The variables for which |
Existing grouping variables are maintained, even if not included in the selection.
Other grouping functions: group_by
,
group_indices
, group_keys
,
group_map
, group_nest
,
group_rows
, group_size
,
group_trim
, groups
# Group a data frame by all variables: group_by_all(mtcars)#> # A tibble: 32 x 11 #> # Groups: mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb [32] #> mpg cyl disp hp drat wt qsec vs am gear carb #> * <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 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows# Group by variables selected with a predicate: group_by_if(iris, is.factor)#> # A tibble: 150 x 5 #> # Groups: Species [3] #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # … with 140 more rows#> # A tibble: 32 x 11 #> # Groups: vs, am [4] #> mpg cyl disp hp drat wt qsec vs am gear carb #> * <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 #> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows# Like group_by(), the scoped variants have optional mutate # semantics. This provide a shortcut for group_by() + mutate(): d <- tibble(x=c(1,1,2,2), y=c(1,2,1,2)) group_by_all(d, as.factor)#> # A tibble: 4 x 2 #> # Groups: x, y [4] #> x y #> <fct> <fct> #> 1 1 1 #> 2 1 2 #> 3 2 1 #> 4 2 2group_by_if(iris, is.factor, as.character)#> # A tibble: 150 x 5 #> # Groups: Species [3] #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # … with 140 more rows