`future_apply()`

implements `base::apply()`

using future with perfect
replication of results, regardless of future backend used.
It returns a vector or array or list of values obtained by applying a
function to margins of an array or matrix.

future_apply( X, MARGIN, FUN, ..., simplify = TRUE, 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_apply-%d" )

X | an array, including a matrix. |
---|---|

MARGIN | A vector giving the subscripts which the function will be
applied over. For example, for a matrix |

FUN | A function taking at least one argument. |

simplify | a logical indicating whether results should be simplified if possible. |

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 |

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

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

Returns a vector or array or list of values obtained by applying a
function to margins of an array or matrix.
See `base::apply()`

for details.

The implementations of `future_apply()`

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

## --------------------------------------------------------- ## apply() ## --------------------------------------------------------- X <- matrix(c(1:4, 1, 6:8), nrow = 2L) Y0 <- apply(X, MARGIN = 1L, FUN = table) Y1 <- future_apply(X, MARGIN = 1L, FUN = table) print(Y1)#> [[1]] #> ...future.X_jj #> 1 3 7 #> 2 1 1 #> #> [[2]] #> ...future.X_jj #> 2 4 6 8 #> 1 1 1 1 #>stopifnot(all.equal(Y1, Y0, check.attributes = FALSE)) ## FIXME Y0 <- apply(X, MARGIN = 1L, FUN = stats::quantile) Y1 <- future_apply(X, MARGIN = 1L, FUN = stats::quantile) print(Y1)#> [,1] [,2] #> 0% 1 2.0 #> 25% 1 3.5 #> 50% 2 5.0 #> 75% 4 6.5 #> 100% 7 8.0stopifnot(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 X <- matrix(c(1:4, 1, 6:8), nrow = 2L) plan(multisession) Y1 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = 0xBEEF) print(Y1)#> [,1] [,2] #> [1,] 7 6 #> [2,] 1 8 #> [3,] 3 4 #> [4,] 1 2#> [,1] [,2] #> [1,] 7 6 #> [2,] 1 8 #> [3,] 3 4 #> [4,] 1 2