Parallelize 'plyr' functions

Henrik Bengtsson

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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()