future_map2: Map over multiple inputs simultaneously via futures
In furrr: Apply Mapping Functions in Parallel using Futures

Description Usage Arguments Value Examples

These functions work exactly the same as purrr::map2() functions, but allow you to run the map in parallel. Note that "parallel" as described in purrr is just saying that you are working with multiple inputs, and parallel in this case means that you can work on multiple inputs AND process them all in parallel as well.

future_map2(.x, .y, .f, ..., .progress = FALSE, .options = future_options())

future_map2_chr(.x, .y, .f, ..., .progress = FALSE,
  .options = future_options())

future_map2_dbl(.x, .y, .f, ..., .progress = FALSE,
  .options = future_options())

future_map2_int(.x, .y, .f, ..., .progress = FALSE,
  .options = future_options())

future_map2_lgl(.x, .y, .f, ..., .progress = FALSE,
  .options = future_options())

future_map2_dfr(.x, .y, .f, ..., .id = NULL, .progress = FALSE,
  .options = future_options())

future_map2_dfc(.x, .y, .f, ..., .progress = FALSE,
  .options = future_options())

future_pmap(.l, .f, ..., .progress = FALSE, .options = future_options())

future_pmap_chr(.l, .f, ..., .progress = FALSE, .options = future_options())

future_pmap_dbl(.l, .f, ..., .progress = FALSE, .options = future_options())

future_pmap_int(.l, .f, ..., .progress = FALSE, .options = future_options())

future_pmap_lgl(.l, .f, ..., .progress = FALSE, .options = future_options())

future_pmap_dfr(.l, .f, ..., .id = NULL, .progress = FALSE,
  .options = future_options())

future_pmap_dfc(.l, .f, ..., .progress = FALSE, .options = future_options())

`.x`	Vectors of the same length. A vector of length 1 will be recycled.
`.y`	Vectors of the same length. A vector of length 1 will be recycled.
`.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. 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. Within a list, wrap strings in `get-attr()` to extract named attributes. If a component is not present, the value of `.default` will be returned.
`...`	Additional arguments passed on to `.f`.
`.progress`	A logical, for whether or not to print a progress bar for multiprocess, multisession, and multicore plans.
`.options`	The `future` specific options to use with the workers. This must be the result from a call to `future_options()`.
`.id`	If not `NULL` a variable with this name will be created giving either the name or the index of the data frame.
`.l`	A list of lists. The length of `.l` determines the number of arguments that `.f` will be called with. List names will be used if present.

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.

library(furrr)

plan(multiprocess)


x <- list(1, 10, 100)
y <- list(1, 2, 3)
z <- list(5, 50, 500)

future_map2(x, y, ~ .x + .y)

# Split into pieces, fit model to each piece, then predict
by_cyl <- split(mtcars, mtcars$cyl)
mods <- future_map(by_cyl, ~ lm(mpg ~ wt, data = .))
future_map2(mods, by_cyl, predict)

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

# Matching arguments by position
future_pmap(list(x, y, z), function(a, b ,c) a / (b + c))

# Vectorizing a function over multiple arguments
df <- data.frame(
  x = c("apple", "banana", "cherry"),
  pattern = c("p", "n", "h"),
  replacement = c("x", "f", "q"),
  stringsAsFactors = FALSE
)
future_pmap(df, gsub)
future_pmap_chr(df, gsub)