Split data frame by groups
group_keys(.tbl, ...) group_split(.tbl, ..., keep = TRUE)
Arguments
| .tbl | A tbl |
|---|---|
| ... | Grouping specification, forwarded to |
| keep | Should the grouping columns be kept |
Value
group_split()returns a list of tibbles. Each tibble contains the rows of.tblfor the associated group and all the columns, including the grouping variables.group_keys()returns a tibble with one row per group, and one column per grouping variable
Details
group_split() works like base::split() but
it uses the grouping structure from
group_by()and therefore is subject to the data maskit does not name the elements of the list based on the grouping as this typically loses information and is confusing.
group_keys() explains the grouping structure, by returning a data frame that has one row
per group and one column per grouping variable.
Grouped data frames
The primary use case for group_split() is with already grouped data frames,
typically a result of group_by(). In this case group_split() only uses
the first argument, the grouped tibble, and warns when ... is used.
Because some of these groups may be empty, it is best paried with group_keys()
which identifies the representatives of each grouping variable for the group.
Ungrouped data frames
When used on ungrouped data frames, group_split() and group_keys() forwards the ... to
group_by() before the split, therefore the ... are subject to the data mask.
Using these functions on an ungrouped data frame only makes sense if you need only one or the other, because otherwise the grouping algorithm is performed each time.
Rowwise data frames
group_split() returns a list of one-row tibbles is returned, and the ... are ignored and warned against
See also
Other grouping functions: group_by_all,
group_by, group_indices,
group_map, group_nest,
group_rows, group_size,
group_trim, groups
Examples
#> [[1]] #> # A tibble: 50 x 5 #> 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 40 more rows #> #> [[2]] #> # A tibble: 50 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 7 3.2 4.7 1.4 versicolor #> 2 6.4 3.2 4.5 1.5 versicolor #> 3 6.9 3.1 4.9 1.5 versicolor #> 4 5.5 2.3 4 1.3 versicolor #> 5 6.5 2.8 4.6 1.5 versicolor #> 6 5.7 2.8 4.5 1.3 versicolor #> 7 6.3 3.3 4.7 1.6 versicolor #> 8 4.9 2.4 3.3 1 versicolor #> 9 6.6 2.9 4.6 1.3 versicolor #> 10 5.2 2.7 3.9 1.4 versicolor #> # … with 40 more rows #> #> [[3]] #> # A tibble: 50 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 6.3 3.3 6 2.5 virginica #> 2 5.8 2.7 5.1 1.9 virginica #> 3 7.1 3 5.9 2.1 virginica #> 4 6.3 2.9 5.6 1.8 virginica #> 5 6.5 3 5.8 2.2 virginica #> 6 7.6 3 6.6 2.1 virginica #> 7 4.9 2.5 4.5 1.7 virginica #> 8 7.3 2.9 6.3 1.8 virginica #> 9 6.7 2.5 5.8 1.8 virginica #> 10 7.2 3.6 6.1 2.5 virginica #> # … with 40 more rows #> #> attr(,"ptype") #> # A tibble: 0 x 5 #> # … with 5 variables: Sepal.Length <dbl>, Sepal.Width <dbl>, #> # Petal.Length <dbl>, Petal.Width <dbl>, Species <fct>group_keys(ir)#> # A tibble: 3 x 1 #> Species #> <fct> #> 1 setosa #> 2 versicolor #> 3 virginica# this can be useful if the grouped data has been altered before the split ir <- iris %>% group_by(Species) %>% filter(Sepal.Length > mean(Sepal.Length)) group_split(ir)#> [[1]] #> # A tibble: 22 x 5 #> 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 5.4 3.9 1.7 0.4 setosa #> 3 5.4 3.7 1.5 0.2 setosa #> 4 5.8 4 1.2 0.2 setosa #> 5 5.7 4.4 1.5 0.4 setosa #> 6 5.4 3.9 1.3 0.4 setosa #> 7 5.1 3.5 1.4 0.3 setosa #> 8 5.7 3.8 1.7 0.3 setosa #> 9 5.1 3.8 1.5 0.3 setosa #> 10 5.4 3.4 1.7 0.2 setosa #> # … with 12 more rows #> #> [[2]] #> # A tibble: 24 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 7 3.2 4.7 1.4 versicolor #> 2 6.4 3.2 4.5 1.5 versicolor #> 3 6.9 3.1 4.9 1.5 versicolor #> 4 6.5 2.8 4.6 1.5 versicolor #> 5 6.3 3.3 4.7 1.6 versicolor #> 6 6.6 2.9 4.6 1.3 versicolor #> 7 6 2.2 4 1 versicolor #> 8 6.1 2.9 4.7 1.4 versicolor #> 9 6.7 3.1 4.4 1.4 versicolor #> 10 6.2 2.2 4.5 1.5 versicolor #> # … with 14 more rows #> #> [[3]] #> # A tibble: 22 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 7.1 3 5.9 2.1 virginica #> 2 7.6 3 6.6 2.1 virginica #> 3 7.3 2.9 6.3 1.8 virginica #> 4 6.7 2.5 5.8 1.8 virginica #> 5 7.2 3.6 6.1 2.5 virginica #> 6 6.8 3 5.5 2.1 virginica #> 7 7.7 3.8 6.7 2.2 virginica #> 8 7.7 2.6 6.9 2.3 virginica #> 9 6.9 3.2 5.7 2.3 virginica #> 10 7.7 2.8 6.7 2 virginica #> # … with 12 more rows #> #> attr(,"ptype") #> # A tibble: 0 x 5 #> # … with 5 variables: Sepal.Length <dbl>, Sepal.Width <dbl>, #> # Petal.Length <dbl>, Petal.Width <dbl>, Species <fct>group_keys(ir)#> # A tibble: 3 x 1 #> Species #> <fct> #> 1 setosa #> 2 versicolor #> 3 virginica# ----- use case 2: using a group_by() grouping specification # both group_split() and group_keys() have to perform the grouping # so it only makes sense to do this if you only need one or the other iris %>% group_split(Species)#> [[1]] #> # A tibble: 50 x 5 #> 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 40 more rows #> #> [[2]] #> # A tibble: 50 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 7 3.2 4.7 1.4 versicolor #> 2 6.4 3.2 4.5 1.5 versicolor #> 3 6.9 3.1 4.9 1.5 versicolor #> 4 5.5 2.3 4 1.3 versicolor #> 5 6.5 2.8 4.6 1.5 versicolor #> 6 5.7 2.8 4.5 1.3 versicolor #> 7 6.3 3.3 4.7 1.6 versicolor #> 8 4.9 2.4 3.3 1 versicolor #> 9 6.6 2.9 4.6 1.3 versicolor #> 10 5.2 2.7 3.9 1.4 versicolor #> # … with 40 more rows #> #> [[3]] #> # A tibble: 50 x 5 #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 6.3 3.3 6 2.5 virginica #> 2 5.8 2.7 5.1 1.9 virginica #> 3 7.1 3 5.9 2.1 virginica #> 4 6.3 2.9 5.6 1.8 virginica #> 5 6.5 3 5.8 2.2 virginica #> 6 7.6 3 6.6 2.1 virginica #> 7 4.9 2.5 4.5 1.7 virginica #> 8 7.3 2.9 6.3 1.8 virginica #> 9 6.7 2.5 5.8 1.8 virginica #> 10 7.2 3.6 6.1 2.5 virginica #> # … with 40 more rows #> #> attr(,"ptype") #> # A tibble: 0 x 5 #> # … with 5 variables: Sepal.Length <dbl>, Sepal.Width <dbl>, #> # Petal.Length <dbl>, Petal.Width <dbl>, Species <fct>iris %>% group_keys(Species)#> # A tibble: 3 x 1 #> Species #> <fct> #> 1 setosa #> 2 versicolor #> 3 virginica