Map over multiple input simultaneously (in "parallel")

These functions are variants of map() that iterate over multiple arguments simultaneously. They are parallel in the sense that each input is processed in parallel with the others, not in the sense of multicore computing, i.e. they share the same notion of "parallel" as base::pmax() and base::pmin().

Usage

pmap(.l, .f, ..., .progress = FALSE)

pmap_lgl(.l, .f, ..., .progress = FALSE)

pmap_int(.l, .f, ..., .progress = FALSE)

pmap_dbl(.l, .f, ..., .progress = FALSE)

pmap_chr(.l, .f, ..., .progress = FALSE)

pmap_vec(.l, .f, ..., .ptype = NULL, .progress = FALSE)

pwalk(.l, .f, ..., .progress = FALSE)

Arguments

.l

A list of vectors. The length of .l determines the number of arguments that .f will be called with. Arguments will be supply by position if unnamed, and by name if named.

Vectors of length 1 will be recycled to any length; all other elements must be have the same length.

A data frame is an important special case of .l. It will cause .f to be called once for each row.

.f

A function, specified in one of the following ways:

• A named function.

• An anonymous function, e.g. \(x, y, z) x + y / z or function(x, y, z) x + y / z

• A formula, e.g. ~ ..1 + ..2 / ..3. This syntax is not recommended as you can only refer to arguments by position.

...

Additional arguments passed on to the mapped function.

We now generally recommend against using ... to pass additional (constant) arguments to .f. Instead use a shorthand anonymous function:

# 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.

.progress

Whether to show a progress bar. Use TRUE to turn on a basic progress bar, use a string to give it a name, or see progress_bars for more details.

.ptype

If NULL, the default, the output type is the common type of the elements of the result. Otherwise, supply a "prototype" giving the desired type of output.

Value

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.

• _lgl(), _int(), _dbl(), _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 returns. .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 purrr_error_indexed.

See also

Other map variants: imap(), lmap(), map2(), map_depth(), map_if(), map(), modify()

Examples

x <- list(1, 1, 1)
y <- list(10, 20, 30)
z <- list(100, 200, 300)
pmap(list(x, y, z), sum)
#> [[1]]
#> [1] 111
#> 
#> [[2]]
#> [1] 221
#> 
#> [[3]]
#> [1] 331
#> 

# Matching arguments by position
pmap(list(x, y, z), function(first, second, third) (first + third) * second)
#> [[1]]
#> [1] 1010
#> 
#> [[2]]
#> [1] 4020
#> 
#> [[3]]
#> [1] 9030
#> 

# Matching arguments by name
l <- list(a = x, b = y, c = z)
pmap(l, function(c, b, a) (a + c) * b)
#> [[1]]
#> [1] 1010
#> 
#> [[2]]
#> [1] 4020
#> 
#> [[3]]
#> [1] 9030
#> 

# Vectorizing a function over multiple arguments
df <- data.frame(
  x = c("apple", "banana", "cherry"),
  pattern = c("p", "n", "h"),
  replacement = c("P", "N", "H"),
  stringsAsFactors = FALSE
  )
pmap(df, gsub)
#> [[1]]
#> [1] "aPPle"
#> 
#> [[2]]
#> [1] "baNaNa"
#> 
#> [[3]]
#> [1] "cHerry"
#> 
pmap_chr(df, gsub)
#> [1] "aPPle"  "baNaNa" "cHerry"

# Use `...` to absorb unused components of input list .l
df <- data.frame(
  x = 1:3,
  y = 10:12,
  z = letters[1:3]
)
plus <- function(x, y) x + y
if (FALSE) {
# this won't work
pmap(df, plus)
}
# but this will
plus2 <- function(x, y, ...) x + y
pmap_dbl(df, plus2)
#> [1] 11 13 15

# The "p" for "parallel" in pmap() is the same as in base::pmin()
# and base::pmax()
df <- data.frame(
  x = c(1, 2, 5),
  y = c(5, 4, 8)
)
# all produce the same result
pmin(df$x, df$y)
#> [1] 1 2 5
map2_dbl(df$x, df$y, min)
#> [1] 1 2 5
pmap_dbl(df, min)
#> [1] 1 2 5