![[Experimental]](figures/lifecycle-experimental.svg)
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 only works well for a single character grouping variable. Instead, use
group_keys()to access a data frame that defines the groups.
group_split() is primarily designed to work with grouped data frames.
You can pass ... to group and split an ungrouped data frame, but this
is generally not very useful as you want have easy access to the group
metadata.
Arguments
- .tbl
A tbl.
- ...
If
.tblis an ungrouped data frame, a grouping specification, forwarded togroup_by().- .keep
Should the grouping columns be kept?
Value
A list of tibbles. Each tibble contains the rows of .tbl for the
associated group and all the columns, including the grouping variables.
Note that this returns a list_of which is slightly
stricter than a simple list but is useful for representing lists where
every element has the same type.
Lifecycle
group_split() is not stable because you can achieve very similar results by
manipulating the nested column returned from
tidyr::nest(.by =). That also retains the group keys all
within a single data structure. group_split() may be deprecated in the
future.
See also
Other grouping functions:
group_by(),
group_map(),
group_nest(),
group_trim()
Examples
ir <- iris %>% group_by(Species)
group_split(ir)
#> <list_of<
#> tbl_df<
#> Sepal.Length: double
#> Sepal.Width : double
#> Petal.Length: double
#> Petal.Width : double
#> Species : factor<fb977>
#> >
#> >[3]>
#> [[1]]
#> # A tibble: 50 × 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
#> # ℹ 40 more rows
#>
#> [[2]]
#> # A tibble: 50 × 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
#> # ℹ 40 more rows
#>
#> [[3]]
#> # A tibble: 50 × 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
#> # ℹ 40 more rows
#>
group_keys(ir)
#> # A tibble: 3 × 1
#> Species
#> <fct>
#> 1 setosa
#> 2 versicolor
#> 3 virginica