Tutorial Overview


In this tutorial, you will learn how to use the future framework to turn sequential R code into parallel R code with minimal effort.

There are a few ways to parallelize R code. Some solutions come built-in with R (parallel package) and others are provided through R packages available on CRAN. The future framework, available on CRAN since 2015 and used by hundreds of R packages, is designed to unify and leverage common parallelization frameworks in R, to make new and existing R code faster with minimal effort by the developer.

The futureverse (https://futureverse.org) allows you, as the developer, to stay with your favorite programming style. For example, future.apply provides one-to-one alternatives to base R’s apply() and lapply() functions, furrr provides alternatives to purrr’s map() functions, and doFuture provides support for using foreach’s foreach() ...%dopar% syntax.

At the same time, the user can switch to a parallel backend of their choice – e.g., they can parallelize on their local machine, across multiple local or remote machines, towards the cloud, or on a job-scheduler on a high-performance computing (HPC) cluster. As a developer, you do not have to worry about which backend the user picks – your future-based code will remain the same regardless of the parallel backend.

PS. We will not cover asynchronous Shiny programming using futures and promises in this tutorial.

Acknowledgements: This tutorial and other work on futureverse is funded by Essential Open Source Software programme ran by the Chan Zuckerberg Initiative (CZI EOSS #4).


After completing this tutorial, my hope is that you:

  • find parallelization less magic

  • find parallelization less intimidating

  • feel comfortable parallelize your own R code

and understand how the future framework:

  • significantly lowers the bar to get started with parallelization

  • helps you avoid common mistakes and issues

  • takes care of many things you otherwise have to worry about

  • scales and is “future” proof

  • keeps getting improved

Preparing for this tutorial

  • R version: R (>= 4.0.0) is recommended, but all of the tutorial should work with R (>= 3.5.0). R 4.2.0 was released on April 22, 2022.

  • Operating system: Linux, macOS, or MS Windows

  • Terminal, RStudio, Rgui, R.app, RStudio Cloud, …: whichever you prefer

Ahead of time, before attending the tutorial, please install the following R packages:

install.packages("future")         # ~ 30 secs
install.packages("future.apply")   # ~ 15 secs
install.packages("furrr")          # ~ 60 secs
install.packages("foreach")        # ~ 10 secs
install.packages("doFuture")       # ~ 15 secs
install.packages("doRNG")          # ~ 15 secs
install.packages("plyr")           # ~ 60 secs
install.packages("future.callr")   # ~ 30 secs
install.packages("progressr")      # ~ 15 secs
install.packages("progress")       # ~ 15 secs

The time estimates are when install the package from source on a fresh Linux R setup with a 1 Gbit/s internet connection. It’s faster when installing from binaries on macOS and MS Windows.

If you already have some of these installed, please make sure to they are up-to-date before starting this tutorial, i.e.


If you have any issues, please reach out for help on https://github.com/HenrikBengtsson/future-tutorial-user2022/discussions/.