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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()

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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