future_apply | R Documentation |

`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.envir = parent.frame(),
future.stdout = TRUE,
future.conditions = "condition",
future.globals = TRUE,
future.packages = NULL,
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.envir` |
An environment passed as argument |

`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.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)
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)
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
X <- matrix(c(1:4, 1, 6:8), nrow = 2L)
plan(multisession)
set.seed(0xBEEF)
Y1 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE)
print(Y1)
plan(sequential)
set.seed(0xBEEF)
Y2 <- future_apply(X, MARGIN = 1L, FUN = sample, future.seed = TRUE)
print(Y2)
stopifnot(all.equal(Y1, Y2))
```

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