Map over multiple inputs simultaneously via futures

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.

Usage

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
)

Arguments

.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. ~ .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.

.options

The future specific options to use with the workers. This must be the result from a call to furrr_options().

.env_globals

The environment to look for globals required by .x and .... Globals required by .f are looked up in the function environment of .f.

.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 .progress argument will be deprecated and removed in a future version of furrr in favor of using the more robust progressr package.

.id

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.

.l

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.

Value

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.

Examples

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)
#> [[1]]
#> [1] 2
#> 
#> [[2]]
#> [1] 12
#> 
#> [[3]]
#> [1] 103
#> 

# 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)
#> $`4`
#>     Datsun 710      Merc 240D       Merc 230       Fiat 128    Honda Civic 
#>       26.47010       21.55719       21.78307       27.14774       30.45125 
#> Toyota Corolla  Toyota Corona      Fiat X1-9  Porsche 914-2   Lotus Europa 
#>       29.20890       25.65128       28.64420       27.48656       31.02725 
#>     Volvo 142E 
#>       23.87247 
#> 
#> $`6`
#>      Mazda RX4  Mazda RX4 Wag Hornet 4 Drive        Valiant       Merc 280 
#>       21.12497       20.41604       19.47080       18.78968       18.84528 
#>      Merc 280C   Ferrari Dino 
#>       18.84528       20.70795 
#> 
#> $`8`
#>   Hornet Sportabout          Duster 360          Merc 450SE          Merc 450SL 
#>            16.32604            16.04103            14.94481            15.69024 
#>         Merc 450SLC  Cadillac Fleetwood Lincoln Continental   Chrysler Imperial 
#>            15.58061            12.35773            11.97625            12.14945 
#>    Dodge Challenger         AMC Javelin          Camaro Z28    Pontiac Firebird 
#>            16.15065            16.33700            15.44907            15.43811 
#>      Ford Pantera L       Maserati Bora 
#>            16.91800            16.04103 
#> 

future_pmap(list(x, y, z), sum)
#> [[1]]
#> [1] 7
#> 
#> [[2]]
#> [1] 62
#> 
#> [[3]]
#> [1] 603
#> 

# Matching arguments by position
future_pmap(list(x, y, z), function(a, b ,c) a / (b + c))
#> [[1]]
#> [1] 0.1666667
#> 
#> [[2]]
#> [1] 0.1923077
#> 
#> [[3]]
#> [1] 0.1988072
#> 

# 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)
#> [[1]]
#> [1] "axxle"
#> 
#> [[2]]
#> [1] "bafafa"
#> 
#> [[3]]
#> [1] "cqerry"
#> 
future_pmap_chr(df, gsub)
#> [1] "axxle"  "bafafa" "cqerry"

# \dontshow{
# Close open connections for R CMD Check
if (!inherits(plan(), "sequential")) plan(sequential)
# }