future_map2 | R Documentation |
These functions work exactly the same as purrr::map2()
and its variants,
but allow you to 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, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_map2_chr( .x, .y, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_map2_dbl( .x, .y, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_map2_int( .x, .y, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_map2_lgl( .x, .y, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_map2_raw( .x, .y, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_map2_dfr( .x, .y, .f, ..., .id = NULL, .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_map2_dfc( .x, .y, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap_chr( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap_dbl( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap_int( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap_lgl( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap_raw( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap_dfr( .l, .f, ..., .id = NULL, .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pmap_dfc( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_walk2( .x, .y, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE ) future_pwalk( .l, .f, ..., .options = furrr_options(), .env_globals = parent.frame(), .progress = FALSE )
.x, .y |
Vectors of the same length. A vector of length 1 will be recycled. |
.f |
A function, formula, or vector (not necessarily atomic). If a function, it is used as is. If a formula, e.g.
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 |
... |
Additional arguments passed on to the mapped function. |
.options |
The |
.env_globals |
The environment to look for globals required by |
.progress |
A single logical. Should a progress bar be displayed? Only works with multisession, multicore, and multiprocess futures. Note that if a multicore/multisession future falls back to sequential, then a progress bar will not be displayed. Warning: The |
.id |
Either a string or Only applies to |
.l |
A list of vectors, such as a data frame. The length of |
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.
plan(multisession, workers = 2) 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)
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