This function allows the user to plan the future, more specifically, it specifies how future():s are resolved, e.g. sequentially or in parallel.

plan(
strategy = NULL,
...,
substitute = TRUE,
.skip = FALSE,
.call = TRUE,
.cleanup = TRUE,
.init = TRUE
)

## Arguments

strategy The evaluation function (or name of it) to use for resolving a future. If NULL, then the current strategy is returned. Additional arguments overriding the default arguments of the evaluation function. Which additional arguments are supported depends on what evaluation function is used, e.g. several support argument workers but not all. For details, see the individual functions of which some are linked to below. If TRUE, the strategy expression is substitute():d, otherwise not. (internal) If TRUE, then attempts to set a strategy that is the same as what is currently in use, will skipped. (internal) Used for recording the call to this function. (internal) Used to stop implicitly started clusters. (internal) Used to initiate workers.

## Value

If a new strategy is chosen, then the previous one is returned (invisible), otherwise the current one is returned (visibly).

## Details

The default strategy is sequential, but the default can be configured by option future.plan and, if that is not set, system environment variable R_FUTURE_PLAN. To reset the strategy back to the default, use plan("default").

## Implemented evaluation strategies

• sequential: Resolves futures sequentially in the current R process.

• transparent: Resolves futures sequentially in the current R process and assignments will be done to the calling environment. Early stopping is enabled by default.

• multisession: Resolves futures asynchronously (in parallel) in separate R sessions running in the background on the same machine.

• multicore: Resolves futures asynchronously (in parallel) in separate forked R processes running in the background on the same machine. Not supported on Windows.

• multiprocess:(DEPRECATED) If multicore evaluation is supported, that will be used, otherwise multisession evaluation will be used.

• cluster: Resolves futures asynchronously (in parallel) in separate R sessions running typically on one or more machines.

• remote: Resolves futures asynchronously in a separate R session running on a separate machine, typically on a different network.

Other package may provide additional evaluation strategies. Notably, the future.batchtools package implements a type of futures that will be resolved via job schedulers that are typically available on high-performance compute (HPC) clusters, e.g. LSF, Slurm, TORQUE/PBS, Sun Grid Engine, and OpenLava.

To "close" any background workers (e.g. multisession), change the plan to something different; plan(sequential) is recommended for this.

## For package developers

Please refrain from modifying the future strategy inside your packages / functions, i.e. do not call plan() in your code. Instead, leave the control on what backend to use to the end user. This idea is part of the core philosophy of the future framework - as a developer you can never know what future backends the user have access to. Moreover, by not making any assumptions about what backends are available, your code will also work automatically with any new backends developed after you wrote your code.

If you think it is necessary to modify the future strategy within a function, then make sure to undo the changes when exiting the function. This can be done using:

  oplan <- plan(new_set_of_strategies)
on.exit(plan(oplan), add = TRUE)
[...]


This is important because the end-user might have already set the future strategy elsewhere for other purposes and will most likely not known that calling your function will break their setup. Remember, your package and its functions might be used in a greater context where multiple packages and functions are involved and those might also rely on the future framework, so it is important to avoid stepping on others' toes.

## Using plan() in scripts and vignettes

When writing scripts or vignettes that uses futures, try to place any call to plan() as far up (as early on) in the code as possible. This will help users to quickly identify where the future plan is set up and allow them to modify it to their computational resources. Even better is to leave it to the user to set the plan() prior to source():ing the script or running the vignette. If a .future.R exists in the current directory and / or in the user's home directory, it is sourced when the future package is loaded. Because of this, the .future.R file provides a convenient place for users to set the plan(). This behavior can be controlled via an R option - see future options for more details.

## Examples

a <- b <- c <- NA_real_

# An sequential future
plan(sequential)
f <- future({
a <- 7
b <- 3
c <- 2
a * b * c
})
y <- value(f)
print(y)
#> [1] 42str(list(a = a, b = b, c = c)) ## All NAs
#> List of 3
#>  $a: num NA #>$ b: num NA
#>  $c: num NA # A sequential future with lazy evaluation plan(sequential) f <- future({ a <- 7 b <- 3 c <- 2 a * b * c }, lazy = TRUE) y <- value(f) print(y) #> [1] 42str(list(a = a, b = b, c = c)) ## All NAs #> List of 3 #>$ a: num NA
#>  $b: num NA #>$ c: num NA

# A multicore future (specified as a string)
plan("multicore")
f <- future({
a <- 7
b <- 3
c <- 2
a * b * c
})
y <- value(f)
print(y)
#> [1] 42str(list(a = a, b = b, c = c)) ## All NAs
#> List of 3
#>  $a: num NA #>$ b: num NA
#>  $c: num NA ## Multisession futures gives an error on R CMD check on ## Windows (but not Linux or macOS) for unknown reasons. ## The same code works in package tests. # \donttest{ # A multisession future (specified via a string variable) plan("future::multisession") f <- future({ a <- 7 b <- 3 c <- 2 a * b * c }) y <- value(f) print(y) #> [1] 42str(list(a = a, b = b, c = c)) ## All NAs #> List of 3 #>$ a: num NA
#>  $b: num NA #>$ c: num NA
# }

## Explicitly close multisession workers by switching plan
plan(sequential)