group_map() and group_walk() are purrr-style functions that can be used to iterate on grouped tibbles.

group_map(.tbl, .f, ...)

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. ~ head(.x), it is converted to a function.

In the formula, you can use

  • . or .x to refer to the subset of rows of .tbl for the given group

  • .y to refer to the key, a one row tibble with one column per grouping variable that identifies the group

...

Additional arguments passed on to .f

Value

  • group_map() row binds the data frames returned by .f

  • group_walk() calls .f for side effects and returns the input .tbl, invisibly

Details

Experimental lifecycle

  • Use group_map() when summarize() is too limited, in terms of what you need to do and return for each group. group_map() is good for "data frame in, data frame out". If that is too limited, you need to use a nested or split workflow.

  • group_map() and group_walk() are 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.

.f must return a data frame that does not contain any of the grouping variables of .tbl.

For completeness, 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

Examples

mtcars %>% group_by(cyl) %>% group_map(~ 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 4
if (requireNamespace("broom", quietly = TRUE)) { iris %>% group_by(Species) %>% group_map(~ 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
iris %>% group_by(Species) %>% group_map(~ { quantile(.x$Petal.Length, probs = c(0.25, 0.5, 0.75)) %>% tibble::enframe(name = "prob", value = "quantile") })
#> # A tibble: 9 x 3 #> # Groups: Species [3] #> Species prob quantile #> * <fct> <chr> <dbl> #> 1 setosa 25% 1.4 #> 2 setosa 50% 1.5 #> 3 setosa 75% 1.58 #> 4 versicolor 25% 4 #> 5 versicolor 50% 4.35 #> 6 versicolor 75% 4.6 #> 7 virginica 25% 5.1 #> 8 virginica 50% 5.55 #> 9 virginica 75% 5.88
iris %>% group_by(Species) %>% group_map(~ { .x %>% purrr::map_dfc(fivenum) %>% mutate(nms = c("min", "Q1", "median", "Q3", "max")) })
#> # A tibble: 15 x 6 #> # Groups: Species [3] #> Species Sepal.Length Sepal.Width Petal.Length Petal.Width nms #> * <fct> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 setosa 4.3 2.3 1 0.1 min #> 2 setosa 4.8 3.2 1.4 0.2 Q1 #> 3 setosa 5 3.4 1.5 0.2 median #> 4 setosa 5.2 3.7 1.6 0.3 Q3 #> 5 setosa 5.8 4.4 1.9 0.6 max #> 6 versicolor 4.9 2 3 1 min #> 7 versicolor 5.6 2.5 4 1.2 Q1 #> 8 versicolor 5.9 2.8 4.35 1.3 median #> 9 versicolor 6.3 3 4.6 1.5 Q3 #> 10 versicolor 7 3.4 5.1 1.8 max #> 11 virginica 4.9 2.2 4.5 1.4 min #> 12 virginica 6.2 2.8 5.1 1.8 Q1 #> 13 virginica 6.5 3 5.55 2 median #> 14 virginica 6.9 3.2 5.9 2.3 Q3 #> 15 virginica 7.9 3.8 6.9 2.5 max
# group_walk() is for side effects dir.create(temp <- tempfile()) iris %>% group_by(Species) %>% group_walk(~ write.csv(.x, file = file.path(temp, paste0(.y$Species, ".csv")))) list.files(temp, pattern = "csv$")
#> [1] "setosa.csv" "versicolor.csv" "virginica.csv"
unlink(temp, recursive = TRUE) # group_map() and ungrouped data frames mtcars %>% group_map(~ 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