dplyr (version 1.0.10)

group_by: Group by one or more variables

Description

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, ...)

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.

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 separate mutate() step before the group_by(). Computations are not allowed in nest_by(). In ungroup(), 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 called add, and conflicts with our naming conventions.

.drop

Drop groups formed by factor levels that don't appear in the data? The default is TRUE except when .data has been previously grouped with .drop = FALSE. See group_by_drop_default() for details.

x

A tbl()

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(): dplyr:::methods_rd("group_by").

• ungroup(): dplyr:::methods_rd("ungroup").

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

# It changes how it acts with the other dplyr verbs:
by_cyl %>% summarise(
  disp = mean(disp),
  hp = mean(hp)
)
by_cyl %>% filter(disp == max(disp))

# 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
by_vs %>% summarise(n = sum(n))

# To removing grouping, use ungroup
by_vs %>%
  ungroup() %>%
  summarise(n = sum(n))

# By default, group_by() overrides existing grouping
by_cyl %>%
  group_by(vs, am) %>%
  group_vars()

# Use add = TRUE to instead append
by_cyl %>%
  group_by(vs, am, .add = TRUE) %>%
  group_vars()

# You can group by expressions: this is a short-hand
# for a mutate() followed by a group_by()
mtcars %>%
  group_by(vsam = vs + am)

# 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))

# 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)

# 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()