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 ```r 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, ```r 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: ```r 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. ```r 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. ```r plan(future.mirai::mirai_multisession) ``` and ```r plan(future.batchtools::batchtools_slurm) ``` Another example is: ```r 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()` [plyr]: https://cran.r-project.org/package=plyr