dplyr: group_by – R documentation

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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, ...)

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. 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()`

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(): no methods found.
ungroup(): no methods found.

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))

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

# 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()


# 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()

dplyr

A Grammar of Data Manipulation

v1.0.6

MIT + file LICENSE

Authors

Hadley Wickham [aut, cre] (<https://orcid.org/0000-0003-4757-117X>), Romain François [aut] (<https://orcid.org/0000-0002-2444-4226>), Lionel Henry [aut], Kirill Müller [aut] (<https://orcid.org/0000-0002-1416-3412>), RStudio [cph, fnd]

Initial release