# Parallelize 'foreach' functions

The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the `futurize()` function. Easy!

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# TL;DR

    library(futurize)
    plan(multisession)
    library(foreach)

    slow_fcn <- function(x) {
      Sys.sleep(0.1)  # emulate work
      x^2
    }

    xs <- 1:1000
    ys <- foreach(x = xs) %do% slow_fcn(x) |> futurize()

# Introduction

This vignette demonstrates how to use this approach to parallelize functions such as `foreach()` and `times()` of the foreach package. For example, consider:

    library(foreach)
    xs <- 1:1000
    ys <- foreach(x = xs) %do% slow_fcn(x)

This `foreach()` construct is resolved sequentially. We can use the futurize package to tell foreach to hand over the orchestration of parallel tasks to futureverse. All we need to do is to pass the expression to `futurize()` as in:

    library(futurize)
    library(foreach)
    xs <- 1:1000
    ys <- foreach(x = xs) %do% slow_fcn(x) |> futurize()

This will distribute the calculations across the available parallel workers, given that we have set parallel workers, e.g.

    plan(multisession)

The built-in `multisession` backend parallelizes on your local computer and it works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

    plan(future.mirai::mirai_multisession)

and

    plan(future.batchtools::batchtools_slurm)

Here is another example that parallelizes `times()` of the foreach package via the futureverse ecosystem:

    library(futurize)
    library(foreach)
    ys <- times(10) %do% rnorm(3) |> futurize()

# Supported Functions

The `futurize()` function supports parallelization of the following foreach functions:

- `foreach(...) %do% { ... }`
- `times(...) %do% { ... }` with `seed = TRUE` as the default