purrr (version 0.3.5)

map2: Map over multiple inputs simultaneously.


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. They share the same notion of "parallel" as base::pmax() and base::pmin(). map2() and walk2() are specialised for the two argument case; pmap() and pwalk() allow you to provide any number of arguments in a list. Note that a data frame is a very important special case, in which case pmap() and pwalk() apply the function .f to each row. map_dfr(), pmap_dfr() and map2_dfc(), pmap_dfc() return data frames created by row-binding and column-binding respectively. They require dplyr to be installed.


map2(.x, .y, .f, ...)

map2_lgl(.x, .y, .f, ...)

map2_int(.x, .y, .f, ...)

map2_dbl(.x, .y, .f, ...)

map2_chr(.x, .y, .f, ...)

map2_raw(.x, .y, .f, ...)

map2_dfr(.x, .y, .f, ..., .id = NULL)

map2_dfc(.x, .y, .f, ...)

walk2(.x, .y, .f, ...)

pmap(.l, .f, ...)

pmap_lgl(.l, .f, ...)

pmap_int(.l, .f, ...)

pmap_dbl(.l, .f, ...)

pmap_chr(.l, .f, ...)

pmap_raw(.l, .f, ...)

pmap_dfr(.l, .f, ..., .id = NULL)

pmap_dfc(.l, .f, ...)

pwalk(.l, .f, ...)


An atomic vector, list, or data frame, depending on the suffix. Atomic vectors and lists will be named if .x or the first element of .l is named.

If all input is length 0, the output will be length 0. If any input is length 1, it will be recycled to the length of the longest.



Vectors of the same length. A vector of length 1 will be recycled.


Vectors of the same length. A vector of length 1 will be recycled.


A function, formula, or vector (not necessarily atomic).

If a function, it is used as is.

If a formula, e.g. ~ .x + 2, it is converted to a function. There are three ways to refer to the arguments:

  • For a single argument function, use .

  • For a two argument function, use .x and .y

  • For more arguments, use ..1, ..2, ..3 etc

This syntax allows you to create very compact anonymous functions.

If character vector, numeric vector, or list, it is converted to an extractor function. Character vectors index by name and numeric vectors index by position; use a list to index by position and name at different levels. If a component is not present, the value of .default will be returned.


Additional arguments passed on to the mapped function.


Either a string or NULL. If a string, the output will contain a variable with that name, storing either the name (if .x is named) or the index (if .x is unnamed) of the input. If NULL, the default, no variable will be created.

Only applies to _dfr variant.


A list of vectors, such as a data frame. The length of .l determines the number of arguments that .f will be called with. List names will be used if present.


Note that arguments to be vectorised over come before .f, and arguments that are supplied to every call come after .f.

See Also

Other map variants: imap(), invoke(), lmap(), map_if(), map(), modify()


Run this code
x <- list(1, 1, 1)
y <- list(10, 20, 30)
z <- list(100, 200, 300)

map2(x, y, ~ .x + .y)
# Or just
map2(x, y, `+`)

pmap(list(x, y, z), sum)

# Matching arguments by position
pmap(list(x, y, z), function(first, second, third) (first + third) * second)

# Matching arguments by name
l <- list(a = x, b = y, c = z)
pmap(l, function(c, b, a) (a + c) * b)

# Split into pieces, fit model to each piece, then predict
by_cyl <- mtcars %>% split(.$cyl)
mods <- by_cyl %>% map(~ lm(mpg ~ wt, data = .))
map2(mods, by_cyl, predict)

# 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)
pmap_chr(df, gsub)

# 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)

# 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)
map2_dbl(df$x, df$y, min)
pmap_dbl(df, min)

# If you want to bind the results of your function rowwise, use:
# map2_dfr() or pmap_dfr()
ex_fun <- function(arg1, arg2){
col <- arg1 + arg2
x <- as.data.frame(col)
arg1 <- 1:4
arg2 <- 10:13
map2_dfr(arg1, arg2, ex_fun)
# If instead you want to bind by columns, use map2_dfc() or pmap_dfc()
map2_dfc(arg1, arg2, ex_fun)

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