# Parallelize 'plyr' 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(plyr)
    library(futurize)
    plan(multisession)

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

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

# Introduction

This vignette demonstrates how to use this approach to parallelize plyr functions such as `llply()`, `maply()`, and `ddply()`.

The plyr `llply()` function is commonly used to apply a function to the elements of a list and return a list. For example,

    library(plyr)
    xs <- 1:1000
    ys <- llply(xs, slow_fcn)

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

    library(futurize)
    library(plyr)
    xs <- 1:1000
    ys <- xs |> llply(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)

Another example is:

    library(plyr)
    library(futurize)
    plan(future.mirai::mirai_multisession)

    ys <- llply(baseball, summary) |> futurize()

# Supported Functions

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

- `a_ply()`, `aaply()`, `adply()`, `alply()`
- `d_ply()`, `daply()`, `ddply()`, `dlply()`
- `l_ply()`, `laply()`, `ldply()`, `llply()`
- `m_ply()`, `maply()`, `mdply()`, `mlply()`