The map functions transform their input by applying a function to each element of a list or atomic vector and returning an object of the same length as the input.
map() always returns a list. See the
modify() family for
versions that return an object of the same type as the input.
map_chr() return an
atomic vector of the indicated type (or die trying). For these functions,
.f must return a length-1 vector of the appropriate type.
map_vec() simplifies to the common type of the output. It works with
most types of simple vectors like Date, POSIXct, factors, etc.
.f for its side-effect and returns
map(.x, .f, ..., .progress = FALSE) map_lgl(.x, .f, ..., .progress = FALSE) map_int(.x, .f, ..., .progress = FALSE) map_dbl(.x, .f, ..., .progress = FALSE) map_chr(.x, .f, ..., .progress = FALSE) map_vec(.x, .f, ..., .ptype = NULL, .progress = FALSE) walk(.x, .f, ..., .progress = FALSE)
A list or atomic vector.
A function, specified in one of the following ways:
Additional arguments passed on to the mapped function.
We now generally recommend against using
# Instead of x |> map(f, 1, 2, collapse = ",") # do: x |> map(\(x) f(x, 1, 2, collapse = ","))
This makes it easier to understand which arguments belong to which function and will tend to yield better error messages.
Whether to show a progress bar. Use
The output length is determined by the length of the input. The output names are determined by the input names. The output type is determined by the suffix:
No suffix: a list;
.f() can return anything.
_chr() return a logical, integer, double,
or character vector respectively;
.f() must return a compatible atomic
vector of length 1.
_vec() return an atomic or S3 vector, the same type that
.f can return pretty much any type of vector, as long as its length 1.
walk() returns the input
.x (invisibly). This makes it easy to
use in a pipe. The return value of
.f() is ignored.
Any errors thrown by
.f will be wrapped in an error with class
map_if() for applying a function to only those elements
.x that meet a specified condition.
Other map variants:
# Compute normal distributions from an atomic vector 1:10 |> map(rnorm, n = 10) # You can also use an anonymous function 1:10 |> map(\(x) rnorm(10, x)) # Simplify output to a vector instead of a list by computing the mean of the distributions 1:10 |> map(rnorm, n = 10) |> # output a list map_dbl(mean) # output an atomic vector # Using set_names() with character vectors is handy to keep track # of the original inputs: set_names(c("foo", "bar")) |> map_chr(paste0, ":suffix") # Working with lists favorite_desserts <- list(Sophia = "banana bread", Eliott = "pancakes", Karina = "chocolate cake") favorite_desserts |> map_chr(\(food) paste(food, "rocks!")) # Extract by name or position # .default specifies value for elements that are missing or NULL l1 <- list(list(a = 1L), list(a = NULL, b = 2L), list(b = 3L)) l1 |> map("a", .default = "???") l1 |> map_int("b", .default = NA) l1 |> map_int(2, .default = NA) # Supply multiple values to index deeply into a list l2 <- list( list(num = 1:3, letters[1:3]), list(num = 101:103, letters[4:6]), list() ) l2 |> map(c(2, 2)) # Use a list to build an extractor that mixes numeric indices and names, # and .default to provide a default value if the element does not exist l2 |> map(list("num", 3)) l2 |> map_int(list("num", 3), .default = NA) # Working with data frames # Use map_lgl(), map_dbl(), etc to return a vector instead of a list: mtcars |> map_dbl(sum) # A more realistic example: split a data frame into pieces, fit a # model to each piece, summarise and extract R^2 mtcars |> split(mtcars$cyl) |> map(\(df) lm(mpg ~ wt, data = df)) |> map(summary) |> map_dbl("r.squared")
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