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(crossmap) xs <- list(1:5, 1:5) ys <- xmap(xs, ~ .y * .x) |> futurize() ``` # Introduction The **[crossmap]** package adds to the **[purrr]**-set of functions. For example, `xmap()` can apply a function to every combination of elements in a list, e.g. ```r library(crossmap) # Multiply the 15 combinations of values in 1:3 and -2:2 xs <- list(1:3, -2:2) ys <- xmap(xs, function(x, y) x * y) |> futurize() ``` Here `xmap()` evaluates sequentially over each combination of (.y, .x) elements. The **crossmap** package provides its own future-counterpart functions, e.g. there is a `future_xmap()` that mimics `xmap()`. The `futurize()` function transpiles `xmap()` into `future_xmap()`, meaning you can do: ```r library(futurize) # Multiply the 15 combinations of values in 1:3 and -2:2 xs <- list(1:3, -2:2) ys <- xmap(xs, function(x, y) x * y) |> futurize() ``` to process this `xmap()` call concurrently, which allows you to execute it on a set of 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) ``` # Supported Functions The `futurize()` function supports parallelization of the following **crossmap** functions: * `imap_vec()`, `map_vec()`, `map2_vec()`, `pmap_vec()`, `xmap_vec()` * `xmap()` * `xmap_chr()`, `xmap_dbl()`, `xmap_int()`, `xmap_lgl()` * `xmap_dfc()`, `xmap_dfr()` * `xmap_mat()`, `xmap_arr()` * `xwalk()` [crossmap]: https://cran.r-project.org/package=crossmap [other parallel backends]: https://www.futureverse.org/backends.html