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(purrr) slow_fcn <- function(x) { message("x = ", x) Sys.sleep(0.1) # emulate work x^2 } xs <- 1:10 ys <- xs |> map(slow_fcn) |> futurize() ``` # Introduction This vignette demonstrates how to use this approach to parallelize **[purrr]** functions such as `map()`, `map_dbl()`, and `walk()`. The **purrr** `map()` function is commonly used to apply a function to the elements of a vector or a list. For example, ```r library(purrr) xs <- 1:1000 ys <- map(xs, slow_fcn) ``` or equivalently using pipe syntax ```r xs <- 1:1000 ys <- xs |> map(slow_fcn) ``` Here `map()` evaluates sequentially, but we can easily make it evaluate in parallel, by using: ```r library(purrr) library(futurize) plan(multisession) ## parallelize on local machine xs <- 1:1000 ys <- xs |> map(slow_fcn) |> futurize() #> x = 1 #> x = 2 #> x = 3 #> ... #> x = 10 ``` Note how messages produced on parallel workers are relayed as-is back to the main R session as they complete. Not only messages, but also warnings and other types of conditions are relayed back as-is. Likewise, standard output produced by `cat()`, `print()`, `str()`, and so on is relayed in the same way. This is a unique feature of Futureverse - other parallel frameworks in R, such as **parallel**, **foreach** with **doParallel**, and **BiocParallel**, silently drop standard output, messages, and warnings produced on workers. With **futurize**, your code behaves the same whether it runs sequentially or in parallel: nothing is lost in translation. 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(purrr) library(futurize) plan(future.mirai::mirai_multisession) ys <- 1:10 |> map(rnorm, n = 10) |> futurize(seed = TRUE) |> map_dbl(mean) |> futurize() ``` # Supported Functions The `futurize()` function supports parallelization of the following **purrr** functions: * `map()`, `map_chr()`, `map_dbl()`, `map_int()`, `map_lgl()`, `map_dfr()`, `map_dfc()`, `walk()` * `map2()`, `map2_chr()`, `map2_dbl()`, `map2_int()`, `map2_lgl()`, `map2_dfr()`, `map2_dfc()`, `walk2()` * `pmap()`, `pmap_chr()`, `pmap_dbl()`, `pmap_int()`, `pmap_lgl()`, `pmap_dfr()`, `pmap_dfc()`, `pwalk()` * `imap()`, `imap_chr()`, `imap_dbl()`, `imap_int()`, `imap_lgl()`, `imap_dfr()`, `imap_dfc()`, `iwalk()` * `modify()`, `modify_if()`, `modify_at()` * `map_if()`, `map_at()` # Progress Reporting via progressr For progress reporting, please see the **[progressr]** package. It is specially designed to work with the Futureverse ecosystem and provide progress updates from parallelized computations in a near-live fashion. See the `vignette("futurize-11-apply", package = "futurize")` for more details and an example. [purrr]: https://cran.r-project.org/package=purrr [other parallel backends]: https://www.futureverse.org/backends.html