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(foreach) slow_fcn <- function(x) { Sys.sleep(0.1) # emulate work x^2 } xs <- 1:1000 ys <- foreach(x = xs) %do% slow_fcn(x) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize functions such as `foreach()` and `times()` of the **[foreach]** package. For example, consider: ```r library(foreach) xs <- 1:1000 ys <- foreach(x = xs) %do% slow_fcn(x) ``` This `foreach()` construct is resolved sequentially. We can use the **futurize** package to tell **foreach** to hand over the orchestration of parallel tasks to futureverse. All we need to do is to pass the expression to `futurize()` as in: ```r library(futurize) library(foreach) xs <- 1:1000 ys <- foreach(x = xs) %do% slow_fcn(x) |> 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) ``` Here is another example that parallelizes `times()` of the **foreach** package via the futureverse ecosystem: ```r library(futurize) library(foreach) ys <- times(10) %do% rnorm(3) |> futurize() ``` # Supported Functions The `futurize()` function supports parallelization of the following **foreach** functions: * `foreach(...) %do% { ... }` * `times(...) %do% { ... }` with `seed = TRUE` as the default [foreach]: https://cran.r-project.org/package=foreach [other parallel backends]: https://www.futureverse.org/backends.html