usage.Rmd

---
title: "Real-World Use & Statistics"
description: "The R community embraces the future framework"
preview: images/site_preview.png
---

# Real-World Use

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```

If we look at our two main R package repositories,
[CRAN](https://cran.r-project.org/) and
[Bioconductor](https://bioconductor.org), 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.



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

<div>
<figure style="width: 45%; float: right; text-align: right; margin: 0; margin-left: 2ex;">
<img src="images/EpiNow2-COVID-dashboard.png" alt="Screenshot of the COVID-19 website dashboard with a world map annotated with colors indicating the trend of COVID infections in different regions"/>
<figcaption style="font-size: 50%;">Image credit: EpiNow2 team</figcaption>
</figure>

[**EpiNow2**](https://epiforecasts.io/EpiNow2/) is an R package to
estimate real-time case counts and time-varying epidemiological
parameters, such as [current trends of COVID-19
incidents](https://epiforecasts.io/covid/) 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.
</div>


## Seurat: Large-Scale Single-Cell Genomics

<div>
<figure style="width: 45%; float: right; text-align: right; margin: 0; margin-left: 2ex;">
<img src="images/seurat-sc.data3-1.png" alt="A two-dimensional, UMAP-space, scatter plot displaying individual cells grouped into 23 well-separated subclasses that are color and label annotated."/>
<figcaption style="font-size: 50%;">Image credit: Seurat team</figcaption>
</figure>

[**Seurat**](https://satijalab.org/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**](https://satijalab.org/azimuth/) is a Seurat-based
web application, e.g. [HuBMAP - NIH Human Biomolecular Atlas
Project](https://azimuth.hubmapconsortium.org/)

**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.
</div>



## Shiny: Scalable, Asynchronous UX

<div>
<figure style="width: 45%; float: right; text-align: right; margin: 0; margin-left: 2ex;">
<img src="images/shiny-icgc-genome-browser.png" alt="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."/>
<figcaption style="font-size: 50%;">Image credit: International Cancer Genome Consortium (ICGC) team</figcaption>
</figure>

[**Shiny**](https://shiny.rstudio.com/) 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](https://shiny.rstudio.com/gallery/) for
real-world examples, e.g. the [Genome
Browser](https://shiny.rstudio.com/gallery/genome-browser.html) 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.
</div>


## Mlr3: Next-Generation Machine Learning

<div>
<figure style="width: 45%; float: right; text-align: right; margin: 0; margin-left: 2ex;">
<img src="images/mlr3-pipe_action.svg" alt="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 flown through all steps, the output is a 'Prediction'."/>
<figcaption style="font-size: 50%;">Image credit: ml3r team</figcaption>
</figure>

The [**mlr3**](https://mlr3.mlr-org.com/) 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.
</div>


## Targets: Pipeline Toolkit for Reproducible Computation at Scale

<div>
<figure style="width: 45%; float: right; text-align: right; margin: 0; margin-left: 2ex;">
<img src="images/drake-example-graph.png" alt="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."/>
<figcaption style="font-size: 50%;">Image credit: targets/drake team</figcaption>
</figure>

The [**targets**](https://docs.ropensci.org/targets/) package, and its
predecessor [**drake**](https://docs.ropensci.org/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.
</div>


# Statistics

Since the first CRAN release of **future** in June 2015, its uptake
among end-users and package developers have grown steadily.  During
November 2024, **future** was among the top-0.8% most downloaded
package on CRAN (Figure 1) and there are 408 packages on CRAN that
directly depend on it (Figure 2).  For map-reduce parallelization
packages **future.apply** (top-0.9% most downloaded) and and **furrr**
(top 1.5%), the corresponding number of packages are 177 and 146,
respectively.  If we consider recursive dependencies too, that is,
packages that use the **future** package either directly or indirectly
via another package, then 85% of all ~22,000 CRAN packages may rely on
the future framework for their processing.

<!--
pkgs <- revdepcheck:::cran_revdeps("future")
pkgs <- tools::package_dependencies("future", which="all", reverse=TRUE, recursive=TRUE)
-->

<div style="width: 100%; margin-bottom: 3ex;">
<center>
<img src="figures/downloads_over_time_on_CRAN.png" alt="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 2024 and the ranks for 0 to 20%. Lines for package 'foreach', 'future', 'future.apply', and 'furrr' are displayed in different colors. The foreach curve is the highest but decreases slowly, whereas the other three are rapidly increasing toward the level of foreach." style="width: 70%;"/>
</center>
<em>

Figure 1: The download percentile ranks for <strong>future</strong>,
<strong>future.apply</strong>, <strong>furrr</strong>, and
<strong>foreach</strong> averaged every four weeks.
<strong>future</strong> is among the top-0.8% most downloaded packages
on CRAN. The data are based on the Posit CRAN mirror logs.  There
are approximately 150 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.

<!-- [https://cranlogs.r-pkg.org/downloads/total/last-month] -->
</em>
</div>


As a reference, the popular **foreach**, released in 2009, was among
the top-0.8% most downloaded packages during the same period and it
has 1,221 reverse package dependencies on CRAN.  The number of users
that download **future** has grown rapidly whereas the the same number
has slowly decreased for the **foreach** package (Figure 1).
Similarly, the number of reverse package dependencies on **future**
appear to grow faster than for **foreach** (Figure 2).

<div style="width: 100%; margin-bottom: 3ex;">
<center>
<img src="figures/revdep_over_time_on_CRAN.png" alt="A line graph with 2015-2024 on the horizontal axis and 'Number of reverse dependencies' on the vertical axis. Rapidly growing curves for three packages, 'future', 'future.apply', and 'furrr', are shown with 'future' increasing the fastest." style="width:50%"/><img src="figures/revdep_over_time_on_CRAN-log.png" alt="A line graph with 2015-2024 on the horizontal axis and 'Number of reverse dependencies' on the vertical axis, which is on the logarithmic scale. Curves for four packages, 'foreach', 'future', 'future.apply', and 'furrr', are shown, where foreach has more dependencies but with a lower slope than the others during recent years." style="width:50%"/>
</center> 
<em>

Figure 2: Number of CRAN packages that depend on
<strong>future</strong>, <strong>future.apply</strong>,
<strong>furrr</strong>, and <strong>foreach</strong> over time since
the first release of <strong>future</strong> in June 2015.  Left: The
package counts on the linear scale without <strong>foreach</strong>.
Right: The same data on the logarithmic scale to fit also
<strong>foreach</strong>.  (Because historical data for reverse
dependencies on Bioconductor are hard to track down, they are
currently not reported in these graphs.)

</em>
</div>

Importantly, the comparison toward **foreach** 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** can per design
be used in companion 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.