group_map
Apply a function to each group
Description
![[Experimental]](../help/figures/lifecycle-experimental.svg)
group_map(), group_modify() and group_walk() are purrr-style functions that can
be used to iterate on grouped tibbles.
Usage
group_map(.data, .f, ..., .keep = FALSE) group_modify(.data, .f, ..., .keep = FALSE) group_walk(.data, .f, ...)
Arguments
.data |
A grouped tibble |
.f |
A function or formula to apply to each group. If a function, it is used as is. It should have at least 2 formal arguments. If a formula, e.g. In the formula, you can use
|
... |
Additional arguments passed on to |
.keep |
are the grouping variables kept in |
Details
Use group_modify() when summarize() is too limited, in terms of what you need
to do and return for each group. group_modify() is good for "data frame in, data frame out".
If that is too limited, you need to use a nested or split workflow.
group_modify() is an evolution of do(), if you have used that before.
Each conceptual group of the data frame is exposed to the function .f with two pieces of information:
-
The subset of the data for the group, exposed as
.x. -
The key, a tibble with exactly one row and columns for each grouping variable, exposed as
.y.
For completeness, group_modify(), group_map and group_walk() also work on
ungrouped data frames, in that case the function is applied to the
entire data frame (exposed as .x), and .y is a one row tibble with no
column, consistently with group_keys().
Value
-
group_modify()returns a grouped tibble. In that case.fmust return a data frame. -
group_map()returns a list of results from calling.fon each group. -
group_walk()calls.ffor side effects and returns the input.tbl, invisibly.
See Also
Other grouping functions:
group_by(),
group_nest(),
group_split(),
group_trim()
Examples
# return a list
mtcars %>%
group_by(cyl) %>%
group_map(~ head(.x, 2L))
# return a tibble grouped by `cyl` with 2 rows per group
# the grouping data is recalculated
mtcars %>%
group_by(cyl) %>%
group_modify(~ head(.x, 2L))
if (requireNamespace("broom", quietly = TRUE)) {
# a list of tibbles
iris %>%
group_by(Species) %>%
group_map(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))
# a restructured grouped tibble
iris %>%
group_by(Species) %>%
group_modify(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))
}
# a list of vectors
iris %>%
group_by(Species) %>%
group_map(~ quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)))
# to use group_modify() the lambda must return a data frame
iris %>%
group_by(Species) %>%
group_modify(~ {
quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)) %>%
tibble::enframe(name = "prob", value = "quantile")
})
iris %>%
group_by(Species) %>%
group_modify(~ {
.x %>%
purrr::map_dfc(fivenum) %>%
mutate(nms = c("min", "Q1", "median", "Q3", "max"))
})
# group_walk() is for side effects
dir.create(temp <- tempfile())
iris %>%
group_by(Species) %>%
group_walk(~ write.csv(.x, file = file.path(temp, paste0(.y$Species, ".csv"))))
list.files(temp, pattern = "csv$")
unlink(temp, recursive = TRUE)
# group_modify() and ungrouped data frames
mtcars %>%
group_modify(~ head(.x, 2L))