| group_by {tidybulk} | R Documentation |
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.
group_by(.data, ..., .add = FALSE, .drop = group_by_drop_default(.data)) ungroup(x, ...)
.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 |
When '.drop = TRUE', empty groups are dropped. See [group_by_drop_default()] for what the default value is for this argument. |
x |
A [tbl()] |
A [grouped data frame][grouped_df()], unless the combination of '...' and 'add' yields a non empty set of grouping columns, a regular (ungrouped) data frame otherwise.
These function are **generic**s, 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:
Other grouping functions:
filter()
`%>%` = magrittr::`%>%`
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)
# when factors are involved, groups can be empty
tbl <- tibble(
x = 1:10,
y = factor(rep(c("a", "c"), each = 5), levels = c("a", "b", "c"))
)