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(futurize) plan(multisession) library(mgcv) ## Adopted from example("bam", package = "mgcv") dat <- gamSim(1, n = 25000, dist = "normal", scale = 20) bs <- "cr" k <- 12 b <- bam(y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) + s(x3, bs = bs), data = dat) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[mgcv]** functions such as `bam()`. The **[mgcv]** package is one of the "recommended" packages in R. It provides methods for fitting Generalized Additive Models (GAMs). The `bam()` function can be used to fit GAMs for massive datasets ("Big Additive Models") with many thousands of observations, making it an excellent candidate for parallelization. ## Example: Fitting a Big Additive Model The `bam()` function supports parallel processing by setting up a **parallel** cluster and passing it as argument `cluster`. This is abstracted away by **futurize**: ```r library(mgcv) ## Adopted from example("bam", package = "mgcv") dat <- gamSim(1, n = 25000, dist = "normal", scale = 20) bs <- "cr" k <- 12 b <- bam(y ~ s(x0, bs = bs) + s(x1, bs = bs) + s(x2, bs = bs, k = k) + s(x3, bs = bs), data = dat) |> futurize() ``` This will distribute the calculations across the available parallel workers, given that we have set up parallel workers, e.g. ```r plan(multisession) ``` The built-in `multisession` backend parallelizes on your local computer and 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) ``` # Supported Functions The following **mgcv** functions are supported by `futurize()`: * `bam()` * `predict.bam()` [mgcv]: https://cran.r-project.org/package=mgcv