Apply a Function over a List or Vector via Futures

future_lapply() implements base::lapply() using futures with perfect replication of results, regardless of future backend used. Analogously, this is true for all the other future_nnn() functions.

manip, programming, iteration
future_eapply(env, FUN, ..., all.names = FALSE, USE.NAMES = TRUE)

future_lapply(X, FUN, ..., future.stdout = TRUE, future.conditions = c("message", "warning"), future.globals = TRUE, future.packages = NULL, future.lazy = FALSE, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL)

future_replicate(n, expr, simplify = "array", future.seed = TRUE, ...)

future_sapply(X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)

future_tapply(X, INDEX, FUN = NULL, ..., default = NA, simplify = TRUE)

future_vapply(X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE)


An R environment.


A function taking at least one argument.


If TRUE, the function will also be applied to variables that start with a period (.), otherwise not. See base::eapply() for details.


See base::sapply().


A vector-like object to iterate over.


If TRUE (default), then the standard output of the underlying futures is captured, and re-outputted as soon as possible. If FALSE, any output is silenced (by sinking it to the null device as it is outputted). If NA (not recommended), output is not intercepted.


A character string of conditions classes to be captured and relayed. The default is to relay messages and warnings. To not intercept conditions, use conditions = character(0L). Errors are always relayed.


A logical, a character vector, or a named list for controlling how globals are handled. For details, see below section.


(optional) a character vector specifying packages to be attached in the R environment evaluating the future.


Specifies whether the futures should be resolved lazily or eagerly (default).


A logical or an integer (of length one or seven), or a list of length(X) with pre-generated random seeds. For details, see below section.


Average number of futures ("chunks") per worker. If 0.0, then a single future is used to process all elements of X. If 1.0 or TRUE, then one future per worker is used. If 2.0, then each worker will process two futures (if there are enough elements in X). If Inf or FALSE, then one future per element of X is used. Only used if future.chunk.size is NULL.


The average number of elements per future ("chunk"). If Inf, then all elements are processed in a single future. If NULL, then argument future.scheduling is used.


The number of replicates.


An R expression to evaluate repeatedly.


See base::sapply() and base::tapply(), respectively.


A list of one or more factors, each of same length as X. The elements are coerced to factors by as.factor(). See also base::tapply().


See base::tapply().


A template for the required return value from each FUN(X[ii], ...). Types may be promoted to a higher type within the ordering logical < integer < double < complex, but not demoted. See base::vapply() for details.

(optional) Additional arguments passed to FUN(). For future_*apply() functions and replicate(), any future.* arguments part of are passed on to future_lapply() used internally.


A named (unless USE.NAMES = FALSE) list. See base::eapply() for details.

For future_lapply(), a list with same length and names as X. See base::lapply() for details.

future_replicate() is a wrapper around future_sapply() and return simplified object according to the simplify argument. See base::replicate() for details. Since future_replicate() usually involves random number generation (RNG), it uses future.seed = TRUE by default in order produce sound random numbers regardless of future backend and number of background workers used.

For future_sapply(), a vector with same length and names as X. See base::sapply() for details.

future_tapply() returns an array with mode "list", unless simplify = TRUE (default) and FUN returns a scalar, in which case the mode of the array is the same as the returned scalars. See base::tapply() for details.

For future_vapply(), a vector with same length and names as X. See base::vapply() for details.

Global variables

Argument future.globals may be used to control how globals should be handled similarly how the globals argument is used with future(). Since all function calls use the same set of globals, this function can do any gathering of globals upfront (once), which is more efficient than if it would be done for each future independently. If TRUE, NULL or not is specified (default), then globals are automatically identified and gathered. If a character vector of names is specified, then those globals are gathered. If a named list, then those globals are used as is. In all cases, FUN and any arguments are automatically passed as globals to each future created as they are always needed.

Reproducible random number generation (RNG)

Unless future.seed = FALSE, this function guarantees to generate the exact same sequence of random numbers given the same initial seed / RNG state - this regardless of type of futures, scheduling ("chunking") strategy, and number of workers.

RNG reproducibility is achieved by pregenerating the random seeds for all iterations (over X) by using L'Ecuyer-CMRG RNG streams. In each iteration, these seeds are set before calling FUN(X[[ii]], ...). Note, for large length(X) this may introduce a large overhead. As input (future.seed), a fixed seed (integer) may be given, either as a full L'Ecuyer-CMRG RNG seed (vector of 1+6 integers) or as a seed generating such a full L'Ecuyer-CMRG seed. If future.seed = TRUE, then .Random.seed is returned if it holds a L'Ecuyer-CMRG RNG seed, otherwise one is created randomly. If future.seed = NA, a L'Ecuyer-CMRG RNG seed is randomly created. If none of the function calls FUN(X[[ii]], ...) uses random number generation, then future.seed = FALSE may be used.

In addition to the above, it is possible to specify a pre-generated sequence of RNG seeds as a list such that length(future.seed) == length(X) and where each element is an integer seed vector that can be assigned to .Random.seed. One approach to generate a set of valid RNG seeds based on fixed initial seed (here 42L) is:

seeds <- future_lapply(seq_along(X), FUN = function(x) .Random.seed,
                       future.chunk.size = Inf, future.seed = 42L)

Note that as.list(seq_along(X)) is not a valid set of such .Random.seed values.

In all cases but future.seed = FALSE, the RNG state of the calling R processes after this function returns is guaranteed to be "forwarded one step" from the RNG state that was before the call and in the same way regardless of future.seed, future.scheduling and future strategy used. This is done in order to guarantee that an R script calling future_lapply() multiple times should be numerically reproducible given the same initial seed.

Control processing order of elements

Attribute ordering of future.chunk.size or future.scheduling can be used to control the ordering the elements are iterated over, which only affects the processing order and not the order values are returned. This attribute can take the following values:

  • index vector - an numeric vector of length length(X)

  • function - an function taking one argument which is called as ordering(length(X)) and which much return an index vector of length length(X), e.g. function(n) rev(seq_len(n)) for reverse ordering.

  • "random" - this will randomize the ordering via random index vector sample.int(length(X)). For example, future.scheduling = structure(TRUE, ordering = "random").

  • future_eapply
  • future_lapply
  • future_replicate
  • future_sapply
  • future_tapply
  • future_vapply
## ---------------------------------------------------------
## lapply(), sapply(), tapply()
## ---------------------------------------------------------
x <- list(a = 1:10, beta = exp(-3:3), logic = c(TRUE, FALSE, FALSE, TRUE))
y0 <- lapply(x, FUN = quantile, probs = 1:3/4)
y1 <- future_lapply(x, FUN = quantile, probs = 1:3/4)
stopifnot(all.equal(y1, y0))

y0 <- sapply(x, FUN = quantile)
y1 <- future_sapply(x, FUN = quantile)
stopifnot(all.equal(y1, y0))

y0 <- vapply(x, FUN = quantile, FUN.VALUE = double(5L))
y1 <- future_vapply(x, FUN = quantile, FUN.VALUE = double(5L))
stopifnot(all.equal(y1, y0))

## ---------------------------------------------------------
## Parallel Random Number Generation
## ---------------------------------------------------------
# }
## Regardless of the future plan, the number of workers, and
## where they are, the random numbers produced are identical

y1 <- future_lapply(1:5, FUN = rnorm, future.seed = 0xBEEF)

y2 <- future_lapply(1:5, FUN = rnorm, future.seed = 0xBEEF)

stopifnot(all.equal(y1, y2))
# }
Documentation reproduced from package future.apply, version 1.1.0, License: GPL (>= 2)

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