# 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) {
      message("x = ", x)
      Sys.sleep(0.1)  # emulate work
      x^2
    }

    xs <- 1:10
    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(plyr)

    library(futurize)
    plan(multisession) ## parallelize on local machine

    xs <- 1:1000
    ys <- xs |> llply(slow_fcn) |> futurize()
    #> x = 1
    #> x = 2
    #> x = 3
    #> ...
    #> x = 10

Note how messages produced on parallel workers are relayed as-is back to the main R session as they complete. Not only messages, but also warnings and other types of conditions are relayed back as-is. Likewise, standard output produced by `cat()`, `print()`, `str()`, and so on is relayed in the same way. This is a unique feature of Futureverse - other parallel frameworks in R, such as parallel, foreach with doParallel, and BiocParallel, silently drop standard output, messages, and warnings produced on workers. With futurize, your code behaves the same whether it runs sequentially or in parallel: nothing is lost in translation.

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()`

# Progress Reporting via progressr

For progress reporting, please see the [progressr] package. It is specially designed to work with the Futureverse ecosystem and provide progress updates from parallelized computations in a near-live fashion. See the `vignette("futurize-11-apply", package = "futurize")` for more details and an example.