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.
Usage
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data))
ungroup(x, ...)Arguments
- .data
A data frame, data frame extension (e.g. a tibble), or a lazy data frame (e.g. from dbplyr or dtplyr). See Methods, below, for more details.
- ...
In
group_by(), variables or computations to group by. Computations are always done on the ungrouped data frame. To perform computations on the grouped data, you need to use a separatemutate()step before thegroup_by(). Computations are not allowed innest_by(). Inungroup(), variables to remove from the grouping.- .add
When
FALSE, the default,group_by()will override existing groups. To add to the existing groups, use.add = TRUE.This argument was previously called
add, but that prevented creating a new grouping variable calledadd, and conflicts with our naming conventions.- .drop
Drop groups formed by factor levels that don't appear in the data? The default is
TRUEexcept when.datahas been previously grouped with.drop = FALSE. Seegroup_by_drop_default()for details.- x
A
tbl()
Value
A grouped data frame with class grouped_df,
unless the combination of ... and add yields a empty set of
grouping columns, in which case a tibble will be returned.
Methods
These function are generics, which means that packages can provide implementations (methods) for other classes. See the documentation of individual methods for extra arguments and differences in behaviour.
Methods available in currently loaded packages:
group_by(): dbplyr (tbl_lazy), dplyr (data.frame) .ungroup(): dbplyr (tbl_lazy), dplyr (data.frame,grouped_df,rowwise_df) .
Ordering
Currently, group_by() internally orders the groups in ascending order. This
results in ordered output from functions that aggregate groups, such as
summarise().
When used as grouping columns, character vectors are ordered in the C locale
for performance and reproducibility across R sessions. If the resulting
ordering of your grouped operation matters and is dependent on the locale,
you should follow up the grouped operation with an explicit call to
arrange() and set the .locale argument. For example:
data %>%
group_by(chr) %>%
summarise(avg = mean(x)) %>%
arrange(chr, .locale = "en")This is often useful as a preliminary step before generating content intended for humans, such as an HTML table.
Legacy behavior
Prior to dplyr 1.1.0, character vector grouping columns were ordered in the
system locale. If you need to temporarily revert to this behavior, you can
set the global option dplyr.legacy_locale to TRUE, but this should be
used sparingly and you should expect this option to be removed in a future
version of dplyr. It is better to update existing code to explicitly call
arrange(.locale = ) instead. Note that setting dplyr.legacy_locale will
also force calls to arrange() to use the system locale.
See also
Other grouping functions:
group_map(),
group_nest(),
group_split(),
group_trim()
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 × 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 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
#> # ℹ 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. 82.6
#> 2 6 183. 122.
#> 3 8 353. 209.
by_cyl %>% filter(disp == max(disp))
#> # 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 6 258 110 3.08 3.22 19.4 1 0 3 1
#> 2 24.4 4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 10.4 8 472 205 2.93 5.25 18.0 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())
#> `summarise()` has grouped output by 'vs'. You can override using the
#> `.groups` argument.
by_vs
#> # A tibble: 4 × 3
#> # Groups: vs [2]
#> 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 × 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 × 1
#> n
#> <int>
#> 1 32
# 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"
# You can group by expressions: this is a short-hand
# for a mutate() followed by a 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 6 160 110 3.9 2.62 16.5 0 1 4 4 1
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 1
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 2
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 0
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 1
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 0
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 1
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 1
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 1
#> # ℹ 22 more rows
# The implicit mutate() step is always performed on the
# ungrouped data. Here we get 3 groups:
mtcars %>%
group_by(vs) %>%
group_by(hp_cut = cut(hp, 3))
#> # A tibble: 32 × 12
#> # Groups: hp_cut [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 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
#> # ℹ 22 more rows
#> # ℹ 1 more variable: hp_cut <fct>
# If you want it to be performed by groups,
# you have to use an explicit mutate() call.
# Here we get 3 groups per value of vs
mtcars %>%
group_by(vs) %>%
mutate(hp_cut = cut(hp, 3)) %>%
group_by(hp_cut)
#> # A tibble: 32 × 12
#> # Groups: hp_cut [6]
#> 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
#> # ℹ 22 more rows
#> # ℹ 1 more variable: hp_cut <fct>
# when factors are involved and .drop = FALSE, groups can be empty
tbl <- tibble(
x = 1:10,
y = factor(rep(c("a", "c"), each = 5), levels = c("a", "b", "c"))
)
tbl %>%
group_by(y, .drop = FALSE) %>%
group_rows()
#> <list_of<integer>[3]>
#> [[1]]
#> [1] 1 2 3 4 5
#>
#> [[2]]
#> integer(0)
#>
#> [[3]]
#> [1] 6 7 8 9 10
#>