# Parallelize base-R apply functions

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

# TL;DR

    library(futurize)
    plan(multisession)

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

    xs <- 1:1000
    ys <- lapply(xs, slow_fcn) |> futurize()

# Introduction

This vignette demonstrates how to use this approach to parallelize functions such as `lapply()`, `tapply()`, `apply()`, and `replicate()` in the base package, and `kernapply()` in the stats package. For example, consider the base R `lapply()` function, which is commonly used to apply a function to the elements of a vector or a list, as in:

    xs <- 1:1000
    ys <- lapply(xs, slow_fcn)

Here `lapply()` evaluates sequentially, but we can easily make it evaluate in parallel, by using:

    library(futurize)
    ys <- lapply(xs, slow_fcn) |> 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)

## Kernel smoothing

    library(futurize)
    plan(multisession)

    library(stats)

    xs <- datasets::EuStockMarkets
    k50 <- kernel("daniell", 50)
    xs_smooth <- kernapply(xs, k = k50) |> futurize()

# Supported Functions

The `futurize()` function supports parallelization of the common base R functions. The following base package functions are supported:

- `lapply()`, `vapply()`, `sapply()`, `tapply()`
- `mapply()`, `.mapply()`, `Map()`
- `eapply()`
- `apply()`
- `replicate()` with `seed = TRUE` as the default
- `by()`
- `Filter()`

The `rapply()` function is not supported by `futurize()`.

The following stats package function is also supported:

- `kernapply()`