`R/future_eapply.R`

, `R/future_lapply.R`

, `R/future_replicate.R`

, and 3 more
`future_lapply.Rd`

`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.

future_eapply( env, FUN, ..., all.names = FALSE, USE.NAMES = TRUE, future.label = "future_eapply-%d" ) future_lapply( X, FUN, ..., future.stdout = TRUE, future.conditions = "condition", future.globals = TRUE, future.packages = NULL, future.lazy = FALSE, future.seed = FALSE, future.scheduling = 1, future.chunk.size = NULL, future.label = "future_lapply-%d" ) future_replicate( n, expr, simplify = "array", future.seed = TRUE, ..., future.label = "future_replicate-%d" ) future_sapply( X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE, future.label = "future_sapply-%d" ) future_tapply( X, INDEX, FUN = NULL, ..., default = NA, simplify = TRUE, future.label = "future_tapply-%d" ) future_vapply( X, FUN, FUN.VALUE, ..., USE.NAMES = TRUE, future.label = "future_vapply-%d" )

env | An R environment. |
---|---|

FUN | A function taking at least one argument. |

all.names | If |

USE.NAMES | See |

future.label | If a character string, then each future is assigned
a label |

X | A vector-like object to iterate over. |

future.stdout | If |

future.conditions | A character string of conditions classes to be
captured and relayed. The default is the same as the |

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

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

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

future.seed | A logical or an integer (of length one or seven),
or a list of |

future.scheduling | Average number of futures ("chunks") per worker.
If |

future.chunk.size | The average number of elements per future ("chunk").
If |

n | The number of replicates. |

expr | An R expression to evaluate repeatedly. |

simplify | See |

INDEX | A list of one or more factors, each of same length as |

default | See |

FUN.VALUE | A template for the required return value from
each |

... | (optional) Additional arguments passed to |

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.

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 `\ldots`

arguments are automatically
passed as globals to each future created as they are always needed.

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.

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 must 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")`

.*Note*, when elements are processed out of order, then captured standard output and conditions are also relayed in that order, that is out of order.

The implementations of `future_replicate()`

, `future_sapply()`

, and
`future_tapply()`

are adopted from the source code of the corresponding
base R functions, which are licensed under GPL (>= 2) with
'The R Core Team' as the copyright holder.

## --------------------------------------------------------- ## 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) print(y1)#> $a #> 25% 50% 75% #> 3.25 5.50 7.75 #> #> $beta #> 25% 50% 75% #> 0.2516074 1.0000000 5.0536690 #> #> $logic #> 25% 50% 75% #> 0.0 0.5 1.0 #>stopifnot(all.equal(y1, y0)) y0 <- sapply(x, FUN = quantile) y1 <- future_sapply(x, FUN = quantile) print(y1)#> a beta logic #> 0% 1.00 0.04978707 0.0 #> 25% 3.25 0.25160736 0.0 #> 50% 5.50 1.00000000 0.5 #> 75% 7.75 5.05366896 1.0 #> 100% 10.00 20.08553692 1.0stopifnot(all.equal(y1, y0)) y0 <- vapply(x, FUN = quantile, FUN.VALUE = double(5L)) y1 <- future_vapply(x, FUN = quantile, FUN.VALUE = double(5L)) print(y1)#> a beta logic #> 0% 1.00 0.04978707 0.0 #> 25% 3.25 0.25160736 0.0 #> 50% 5.50 1.00000000 0.5 #> 75% 7.75 5.05366896 1.0 #> 100% 10.00 20.08553692 1.0stopifnot(all.equal(y1, y0)) ## --------------------------------------------------------- ## Parallel Random Number Generation ## --------------------------------------------------------- # \donttest{ ## Regardless of the future plan, the number of workers, and ## where they are, the random numbers produced are identical plan(multisession) y1 <- future_lapply(1:5, FUN = rnorm, future.seed = 0xBEEF) str(y1)#> List of 5 #> $ : num 0.682 #> $ : num [1:2] 1.19 -0.474 #> $ : num [1:3] -1.081 -2.412 -0.466 #> $ : num [1:4] 1.143 -1.278 0.438 0.819 #> $ : num [1:5] -1.2935 -0.0878 -0.3909 0.5652 0.2253#> List of 5 #> $ : num 0.682 #> $ : num [1:2] 1.19 -0.474 #> $ : num [1:3] -1.081 -2.412 -0.466 #> $ : num [1:4] 1.143 -1.278 0.438 0.819 #> $ : num [1:5] -1.2935 -0.0878 -0.3909 0.5652 0.2253stopifnot(all.equal(y1, y2)) # } ## --------------------------------------------------------- ## Process chunks of data.frame rows in parallel ## --------------------------------------------------------- iris <- datasets::iris chunks <- split(iris, seq(1, nrow(iris), length.out = 3L)) y0 <- lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length)) y0 <- do.call(sum, y0) y1 <- future_lapply(chunks, FUN = function(iris) sum(iris$Sepal.Length)) y1 <- do.call(sum, y1) print(y1)#> [1] 876.5