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) slow_fcn <- function(x) { Sys.sleep(0.1) # emulate work x^2 } xs <- 1:1000 ys <- lapply(xs, slow_fcn) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize functions such as `lapply()`, `tapply()`, `apply()`, and `replicate()` in the **base** package, and `kernapply()` in the **stats** package. For example, consider the base R `lapply()` function, which is commonly used to apply a function to the elements of a vector or a list, as in: ```r xs <- 1:1000 ys <- lapply(xs, slow_fcn) ``` Here `lapply()` evaluates sequentially, but we can easily make it evaluate in parallel, by using: ```r library(futurize) ys <- lapply(xs, 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) ``` ## Kernel smoothing ```r library(futurize) plan(multisession) library(stats) xs <- datasets::EuStockMarkets k50 <- kernel("daniell", 50) xs_smooth <- kernapply(xs, k = k50) |> futurize() ``` # Supported Functions The `futurize()` function supports parallelization of the common base R functions. The following **base** package functions are supported: * `lapply()`, `vapply()`, `sapply()`, `tapply()` * `mapply()`, `.mapply()`, `Map()` * `eapply()` * `apply()` * `replicate()` with `seed = TRUE` as the default * `by()` * `Filter()` The `rapply()` function is not supported by `futurize()`. The following **stats** package function is also supported: * `kernapply()` [other parallel backends]: https://www.futureverse.org/backends.html