![[Experimental]](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, ..., .keep = FALSE)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.
~ head(.x), it is converted to a function.In the formula, you can use
.or.xto refer to the subset of rows of.tblfor the given group.yto refer to the key, a one row tibble with one column per grouping variable that identifies the group
- ...
Additional arguments passed on to
.f- .keep
are the grouping variables kept in
.x
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.
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().
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))
#> [[1]]
#> # A tibble: 2 × 10
#> mpg disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 22.8 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 24.4 147. 62 3.69 3.19 20 1 0 4 2
#>
#> [[2]]
#> # A tibble: 2 × 10
#> mpg disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 160 110 3.9 2.62 16.5 0 1 4 4
#> 2 21 160 110 3.9 2.88 17.0 0 1 4 4
#>
#> [[3]]
#> # A tibble: 2 × 10
#> mpg disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 18.7 360 175 3.15 3.44 17.0 0 0 3 2
#> 2 14.3 360 245 3.21 3.57 15.8 0 0 3 4
#>
# 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))
#> # A tibble: 6 × 11
#> # Groups: cyl [3]
#> cyl mpg disp hp drat wt qsec vs am gear carb
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 22.8 108 93 3.85 2.32 18.6 1 1 4 1
#> 2 4 24.4 147. 62 3.69 3.19 20 1 0 4 2
#> 3 6 21 160 110 3.9 2.62 16.5 0 1 4 4
#> 4 6 21 160 110 3.9 2.88 17.0 0 1 4 4
#> 5 8 18.7 360 175 3.15 3.44 17.0 0 0 3 2
#> 6 8 14.3 360 245 3.21 3.57 15.8 0 0 3 4
# a list of tibbles
iris %>%
group_by(Species) %>%
group_map(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))
#> [[1]]
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 0.803 0.344 2.34 0.0238
#> 2 Sepal.Length 0.132 0.0685 1.92 0.0607
#>
#> [[2]]
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 0.185 0.514 0.360 7.20e- 1
#> 2 Sepal.Length 0.686 0.0863 7.95 2.59e-10
#>
#> [[3]]
#> # A tibble: 2 × 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) 0.610 0.417 1.46 1.50e- 1
#> 2 Sepal.Length 0.750 0.0630 11.9 6.30e-16
#>
# a restructured grouped tibble
iris %>%
group_by(Species) %>%
group_modify(~ broom::tidy(lm(Petal.Length ~ Sepal.Length, data = .x)))
#> # A tibble: 6 × 6
#> # Groups: Species [3]
#> Species term estimate std.error statistic p.value
#> <fct> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 setosa (Intercept) 0.803 0.344 2.34 2.38e- 2
#> 2 setosa Sepal.Length 0.132 0.0685 1.92 6.07e- 2
#> 3 versicolor (Intercept) 0.185 0.514 0.360 7.20e- 1
#> 4 versicolor Sepal.Length 0.686 0.0863 7.95 2.59e-10
#> 5 virginica (Intercept) 0.610 0.417 1.46 1.50e- 1
#> 6 virginica Sepal.Length 0.750 0.0630 11.9 6.30e-16
# a list of vectors
iris %>%
group_by(Species) %>%
group_map(~ quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)))
#> [[1]]
#> 25% 50% 75%
#> 1.400 1.500 1.575
#>
#> [[2]]
#> 25% 50% 75%
#> 4.00 4.35 4.60
#>
#> [[3]]
#> 25% 50% 75%
#> 5.100 5.550 5.875
#>
# to use group_modify() the lambda must return a data frame
iris %>%
group_by(Species) %>%
group_modify(~ {
quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)) %>%
tibble::enframe(name = "prob", value = "quantile")
})
#> # A tibble: 9 × 3
#> # Groups: Species [3]
#> Species prob quantile
#> <fct> <chr> <dbl>
#> 1 setosa 25% 1.4
#> 2 setosa 50% 1.5
#> 3 setosa 75% 1.58
#> 4 versicolor 25% 4
#> 5 versicolor 50% 4.35
#> 6 versicolor 75% 4.6
#> 7 virginica 25% 5.1
#> 8 virginica 50% 5.55
#> 9 virginica 75% 5.88
iris %>%
group_by(Species) %>%
group_modify(~ {
.x %>%
purrr::map_dfc(fivenum) %>%
mutate(nms = c("min", "Q1", "median", "Q3", "max"))
})
#> # A tibble: 15 × 6
#> # Groups: Species [3]
#> Species Sepal.Length Sepal.Width Petal.Length Petal.Width nms
#> <fct> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 setosa 4.3 2.3 1 0.1 min
#> 2 setosa 4.8 3.2 1.4 0.2 Q1
#> 3 setosa 5 3.4 1.5 0.2 median
#> 4 setosa 5.2 3.7 1.6 0.3 Q3
#> 5 setosa 5.8 4.4 1.9 0.6 max
#> 6 versicolor 4.9 2 3 1 min
#> 7 versicolor 5.6 2.5 4 1.2 Q1
#> 8 versicolor 5.9 2.8 4.35 1.3 median
#> 9 versicolor 6.3 3 4.6 1.5 Q3
#> 10 versicolor 7 3.4 5.1 1.8 max
#> 11 virginica 4.9 2.2 4.5 1.4 min
#> 12 virginica 6.2 2.8 5.1 1.8 Q1
#> 13 virginica 6.5 3 5.55 2 median
#> 14 virginica 6.9 3.2 5.9 2.3 Q3
#> 15 virginica 7.9 3.8 6.9 2.5 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$Species, ".csv"))))
list.files(temp, pattern = "csv$")
#> [1] "setosa.csv" "versicolor.csv" "virginica.csv"
unlink(temp, recursive = TRUE)
# group_modify() and ungrouped data frames
mtcars %>%
group_modify(~ head(.x, 2L))
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4