Real-World Use & Statistics

The R community embraces the future framework

Real-World Use

If we look at our two main R package repositories, CRAN and Bioconductor, we find that the future framework is used by R packages spanning a wide range of areas, e.g. statistics, modeling & prediction, time-series analysis & forecasting, life sciences, drug analysis, clinical trials, disease modeling, cancer research, computational biology, genomics, bioinformatics, biomarker discovery, epidemiology, ecology, economics & finance, spatial, geospatial & satellite analysis, and natural language processing. That is just a sample based on published R packages - we can only guess how futures are used by users at the R prompt, in users’ R scripts, non-published R packages, Shiny applications, and R pipelines running internally in the industry and academia.

There are two major use cases of the future framework: (i) performance improvement through parallelization, and (ii) non-blocking, asynchronous user experience (UX). Below are some prominent examples. More examples can be found on R-university, which lists ~1,600 packages that need the future package.

EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters

Screenshot of the COVID-19 website dashboard with a world map annotated with colors indicating the trend of COVID infections in different regions

EpiNow2 is an R package to estimate real-time case counts and time-varying epidemiological parameters, such as current trends of COVID-19 incidents in different regions around the globe.

EpiNow2 uses futures to speed up processing. The future framework is used to estimate incident rates in different regions concurrently as well as running Markov Chain Monte Carlo (MCMC) in parallel.

Seurat: Large-Scale Single-Cell Genomics

A two-dimensional, UMAP-space, scatter plot displaying individual cells grouped into 23 well-separated subclasses that are color and label annotated.

Seurat is an R package designed for QC, analysis, and exploration of single-cell RNA-seq data. Seurat aims to enable users to identify and interpret sources of heterogeneity from single-cell transcriptomic measurements, and to integrate diverse types of single-cell data. Azimuth is a Seurat-based web application, e.g. HuBMAP - NIH Human Biomolecular Atlas Project

Seurat uses futures to speed up processing. The future framework makes it possible to process large data sets and large number of samples in parallel on the local machine, distributed on multiple machines, or via large-scale high-performance compute (HPC) environments. Azimuth uses futures to provide a non-blocking web interface.

Shiny: Scalable, Asynchronous UX

Thumbnail of the Shiny ICGC Genome Browser webpage. There is a title banner on top above two panels. In the left-hand side panel, there is a circular plot showing the 24 human chromosomes laid out on the circumference. Interacting genes are connected with edges, creating a web of connections across the plane of the circle but also short loops back to the same chromosome. In the right-hand side panel, there is a table that appears to list the genes of interest with some kind of values.

Shiny is an R package that makes it easy to build interactive web applications and dashboards directly from R. Shiny apps can run locally, be embedded in an R Markdown document, and be hosted on a webpage - all with a few clicks or commands. The combination of being simple and powerful has made Shiny the most popular solution for web applications in the R community. See the Shiny Gallery for real-world examples, e.g. the Genome Browser by the International Cancer Genome Consortium (ICGC) team.

Shiny uses the future framework to provide a non-blocking user interface and to scale up computationally heavy requests. It combines future with promises to turn a blocking, synchronous web interface into a non-blocking, asynchronous, responsive user experience.

Mlr3: Next-Generation Machine Learning

A schematic outline of a ML pipeline. On top, there is a left-to-right pipeline with 'Training Data' as input, with steps 'Scaling', 'Factor Encoding', 'Median Imputation', and a final 'Learner' state. At the bottom, there is a similar pipeline but with 'New Data' as the input. In each of the corresponding steps, there is a arrow coming from the top pipeline indicating pre-learned parameters. After the 'New Data' has flowed through all steps, the output is a 'Prediction'.

The mlr3 ecosystem provides efficient, object-oriented building blocks for machine learning (ML) for tasks, learners, resamplings, and measures. It supports large-scale, out-of-memory data processing.

mlr3 uses futures to speed up processing. The future framework is used in different ML steps, e.g. resampling of learners can be performed much faster when run in parallel. The framework makes sure proper parallel random-number generation (RNG) is used and guarantees reproducible results.

Targets: Pipeline Toolkit for Reproducible Computation at Scale

A drake dependency graph with a file 'raw_data_x.xlsx' node to the left, that a 'raw_data' node depends on, which in turn two nodes 'fit' and 'hist' depends on. The following 'report' node depend on the latter two nodes, and the last is the file 'report.html' output node. There is a legend to the left explaining how the states of the nodes are represented as colors and shapes.

The targets package, and its predecessor drake, is a general-purpose computational engine for statistics and data science that brings together function-oriented programming in R with make-like declarative workflows. It has native support for parallel and distributed computing while preserving reproducibility.

Both targets and drake identify targets in the declared dependency graph that can be resolved concurrently, which then can be processed in parallel on the local computer or distributed in the cloud via the future framework.

CRAN Statistics

Since the first CRAN release of future in June 2015, its uptake among end-users and package developers has grown steadily. As of June 2026, future is among the top-0.6% most downloaded packages on CRAN (Figure 1) and there are ~500 packages (+35%/year) on CRAN that directly depend on it (Figure 2). For map-reduce parallelization packages future.apply (top-0.8% most downloaded) and furrr (top 1.3%), the corresponding number of packages are ~300 (+55%/year) and ~180 (+25%/year), respectively.

If we consider recursive dependencies too, that is, packages that needs (depends or imports) the future package either directly or indirectly via another package, then ~1,250 (5.2%) of all ~23,900 CRAN packages rely on the future framework for processing (June 2026). If we include soft (Suggests) dependencies as well, then ~90% of all CRAN packages depend on the future framework. Bioconductor, which has ~2,400 packages, show similar ratios.

A line graph with 'Date' on the horizontal axis and 'Download rates on CRAN (four-week averages)' on the vertical axis. The dates goes from mid 2015 to 2026 and the ranks for 0 to 20%. Lines for package 'doParallel', 'future', 'future.apply', and 'furrr' are displayed in different colors. The 'future' and 'future.apply' surpassed 'doParallel' early 2022, and 'furrr' in 2026.

Figure 1: The download percentile ranks for future, future.apply, furrr, and doParallel averaged every four weeks. future is among the top-0.6% most downloaded packages on CRAN. The data are based on the Posit CRAN mirror logs. There are approximately 200 million package downloads per month from the Posit CRAN mirror alone. Since none of the other CRAN mirrors provide statistics, it is impossible to know the total amount of package installations.

As a reference1, the popular doParallel, released in 2011, which is commonly used together with foreach, released in 2009, was among the top-1.2% most downloaded packages during the same period and it has 1,050 reverse package dependencies (+7%/year) on CRAN. The number of users that download future has grown rapidly whereas the same number has stabilized for the doParallel package (Figure 1). Similarly, the number of reverse package dependencies on future grows significantly faster than for doParallel (Figure 2).

A line chart showing the growth in the number of reverse dependencies on CRAN for three R packages, 'future', 'future.apply', and 'furrr', from 2015 to 2025. The y-axis ranges from 0 to 500 dependencies. The 'future' package (green) rises steeply after 2018 and 470 by end of 2025; 'future.apply' (blue) grows steadily to 250; and 'furrr' (purple) follows a similar but slightly slower trajectory, ending near 180.

Package counts on the linear scale.

A logarithmic-scale line chart comparing CRAN reverse-dependency growth for 'doParallel', 'future', 'future.apply', and 'furrr' from 2015 to 2026. The y-axis spans roughly 10 to 1,000 dependencies. 'doParallel' (olive) leads throughout, rising smoothly from 90 to over 1,000. 'future' (green) increases from a few dependencies in 2015 to 500 by early 2026. 'future.apply' (blue) and 'furrr' (purple) start around 2018 and reach 300 and 200, respectively.

Package counts on the logarithmic scale.

Figure 2: Number of CRAN2 packages over time that depend on future, future.apply, furrr, and doParallel since the first release of future in June 2015. Left: The package counts on the linear scale without doParallel. Right: The same data on the logarithmic scale to fit also doParallel.

Footnotes

  1. Importantly, the comparison toward doParallel is only done as a reference for the current demand for parallelization frameworks in R and to show the rapid uptake of the future framework since its release. It is not a competition because foreach, which doParallel is commonly used with, can per design be used in combination with the future framework via doFuture. The choice between foreach with doFuture, future.apply, and furrr is a matter of preference of coding style - they all rely on futures for parallelization. Since the introduction of futurize, user can parallelize foreach, base-R apply, purrr, and much more as-is.↩︎

  2. Because historical data for reverse dependencies on Bioconductor are hard to track down, Bioconductor packages are not included in these graphs.↩︎