purrr (version 0.2.1)

map: Apply a function to each element of a list.

Description

map() returns the transformed input; walk() calls .f for its side-effect and returns the original input. map() returns a list or a data frame; map_lgl(), map_int(), map_dbl() and map_chr() return vectors of the corresponding type (or die trying); map_df() returns a data frame by row-binding the individual elements.

Usage

map(.x, .f, ...)
map_lgl(.x, .f, ...)
map_chr(.x, .f, ...)
map_int(.x, .f, ...)
map_dbl(.x, .f, ...)
map_df(.x, .f, ..., .id = NULL)
walk(.x, .f, ...)

Arguments

.x

A list or atomic vector.

.f

A function, formula, or atomic vector.

If a function, it is used as is.

If a formula, e.g. ~ .x + 2, it is converted to a function with two arguments, .x or . and .y

...

Additional arguments passed on to .f.

.id

If not NULL a variable with this name will be created giving either the name or the index of the data frame.

Value

• map() always returns a list.

  map_lgl() returns a logical vector, map_int() an integer vector, map_dbl(), a double vector, map_chr(), a character vector. The output of .f will be automatically typed upwards, e.g. logical -> integer -> double -> character.

  walk() (invisibly) the input .x. It's called primarily for its side effects, but this makes it easier to combine in a pipe.

Details

Note that map() understands data frames, including grouped data frames. It can be much faster than mutate_each() when your data frame has many columns. However, map()ll be slower for the more common case of many groups with functions that dplyr knows how to translate to C++.

See Also

map2() and pmap() to map over multiple inputs simulatenously

Examples

1:10 %>%
  map(rnorm, n = 10) %>%
  map_dbl(mean)

# Or use an anonymous function
1:10 %>%
  map(function(x) rnorm(10, x))

# Or a formula
1:10 %>%
  map(~ rnorm(10, .x))

# A more realistic example: split a data frame into pieces, fit a
# model to each piece, summarise and extract R^2
mtcars %>%
  split(.$cyl) %>%
  map(~ lm(mpg ~ wt, data = .x)) %>%
  map(summary) %>%
  map_dbl("r.squared")

# Use map_lgl(), map_dbl(), etc to reduce to a vector.
# * list
mtcars %>% map(sum)
# * vector
mtcars %>% map_dbl(sum)

# If each element of the output is a data frame, use
# map_df to row-bind them together:
mtcars %>%
  split(.$cyl) %>%
  map(~ lm(mpg ~ wt, data = .x)) %>%
  map_df(~ as.data.frame(t(as.matrix(coef(.)))))
# (if you also want to preserve the variable names see
# the broom package)