group_map(), group_modify() and group_walk() are purrr-style functions that can
be used to iterate on grouped tibbles.
group_map(.tbl, .f, ..., keep = FALSE) group_modify(.tbl, .f, ..., keep = FALSE) group_walk(.tbl, .f, ...)
Arguments
| .tbl | A grouped tibble |
|---|---|
| .f | A function or formula to apply to each group. It must return a data frame. 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 |
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 groupgroup_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_all,
group_by, group_indices,
group_keys, group_nest,
group_rows, group_size,
group_trim, groups
Examples
#> [[1]] #> # A tibble: 2 x 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 x 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 x 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 x 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 4if (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 tibble: 6 x 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 x 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% 5.88iris %>% group_by(Species) %>% group_modify(~ { .x %>% purrr::map_dfc(fivenum) %>% mutate(nms = c("min", "Q1", "median", "Q3", "max")) })#> # A tibble: 15 x 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"))))#> # A tibble: 150 x 5 #> # Groups: Species [3] #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species #> <dbl> <dbl> <dbl> <dbl> <fct> #> 1 5.1 3.5 1.4 0.2 setosa #> 2 4.9 3 1.4 0.2 setosa #> 3 4.7 3.2 1.3 0.2 setosa #> 4 4.6 3.1 1.5 0.2 setosa #> 5 5 3.6 1.4 0.2 setosa #> 6 5.4 3.9 1.7 0.4 setosa #> 7 4.6 3.4 1.4 0.3 setosa #> 8 5 3.4 1.5 0.2 setosa #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> # … with 140 more rows#> [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