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The drake R package logo

Data analysis can be slow. A round of scientific computation can take several minutes, hours, or even days to complete. After it finishes, if you update your code or data, your hard-earned results may no longer be valid. How much of that valuable output can you keep, and how much do you need to update? How much runtime must you endure all over again?

For projects in R, the drake package can help. It analyzes your workflow, skips steps with up-to-date results, and orchestrates the rest with optional distributed computing. At the end, drake provides evidence that your results match the underlying code and data, which increases your ability to trust your research.

6-minute video

Visit the first page of the manual to watch a short introduction.


What gets done stays done.

Too many data science projects follow a Sisyphean loop:

  1. Launch the code.
  2. Wait while it runs.
  3. Discover an issue.
  4. Rerun from scratch.

Ordinarily, it is hard to avoid rerunning the code from scratch.


But with drake, you can automatically

  1. Launch the parts that changed since last time.
  2. Skip the rest.

How it works

To set up a project, load your packages,


load your custom functions,

create_plot <- function(data) {
  ggplot(data, aes(x = Petal.Width, fill = Species)) +

check any supporting files (optional),

# Get the files with drake_example("main").
#> [1] TRUE
#> [1] TRUE

and plan what you are going to do.

plan <- drake_plan(
  raw_data = readxl::read_excel(file_in("raw_data.xlsx")),
  data = raw_data %>%
    mutate(Species = forcats::fct_inorder(Species)),
  hist = create_plot(data),
  fit = lm(Sepal.Width ~ Petal.Width + Species, data),
  report = rmarkdown::render(
    output_file = file_out("report.html"),
    quiet = TRUE
#> # A tibble: 5 x 2
#>   target   command                                                         
#>   <chr>    <expr>                                                          
#> 1 raw_data readxl::read_excel(file_in("raw_data.xlsx"))                   …
#> 2 data     raw_data %>% mutate(Species = forcats::fct_inorder(Species))   …
#> 3 hist     create_plot(data)                                              …
#> 4 fit      lm(Sepal.Width ~ Petal.Width + Species, data)                  …
#> 5 report   rmarkdown::render(knitr_in("report.Rmd"), output_file = file_ou…

So far, we have just been setting the stage. Use make() to do the real work. Targets are built in the correct order regardless of the row order of plan.

#> target raw_data
#> target data
#> target fit
#> target hist
#> target report

Except for files like report.html, your output is stored in a hidden .drake/ folder. Reading it back is easy.

readd(data) # See also loadd().
#> # A tibble: 150 x 5
#>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
#>          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
#> 1          5.1         3.5          1.4         0.2 setosa 
#> 2          4.9         3            1.4         0.2 setosa 
#> 3          4.7         3.2          1.3         0.2 setosa 
#> 4          4.6         3.1          1.5         0.2 setosa 
#> 5          5           3.6          1.4         0.2 setosa 
#> # … with 145 more rows

You may look back on your work and see room for improvement, but it's all good! The whole point of drake is to help you go back and change things quickly and painlessly. For example, we forgot to give our histogram a bin width.

#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.


So let's fix the plotting function.

create_plot <- function(data) {
  ggplot(data, aes(x = Petal.Width, fill = Species)) +
    geom_histogram(binwidth = 0.25) +

drake knows which results are affected.

config <- drake_config(plan)
vis_drake_graph(config) # Interactive graph: zoom, drag, etc.


The next make() just builds hist and report.html. No point in wasting time on the data or model.

#> target hist
#> target report


Reproducibility with confidence

The R community emphasizes reproducibility. Traditional themes include scientific replicability, literate programming with knitr, and version control with git. But internal consistency is important too. Reproducibility carries the promise that your output matches the code and data you say you used. With the exception of non-default triggers and hasty mode, drake strives to keep this promise.


Suppose you are reviewing someone else's data analysis project for reproducibility. You scrutinize it carefully, checking that the datasets are available and the documentation is thorough. But could you re-create the results without the help of the original author? With drake, it is quick and easy to find out.

#> All targets are already up to date.

config <- drake_config(plan)
#> character(0)

With everything already up to date, you have tangible evidence of reproducibility. Even though you did not re-create the results, you know the results are re-creatable. They faithfully show what the code is producing. Given the right package environment and system configuration, you have everything you need to reproduce all the output by yourself.


When it comes time to actually rerun the entire project, you have much more confidence. Starting over from scratch is trivially easy.

clean()       # Remove the original author's results.
make(plan) # Independently re-create the results from the code and input data.
#> target raw_data
#> target data
#> target fit
#> target hist
#> target report

Independent replication

With even more evidence and confidence, you can invest the time to independently replicate the original code base if necessary. Up until this point, you relied on basic drake functions such as make(), so you may not have needed to peek at any substantive author-defined code in advance. In that case, you can stay usefully ignorant as you reimplement the original author's methodology. In other words, drake could potentially improve the integrity of independent replication.

Readability and transparency

Ideally, independent observers should be able to read your code and understand it. drake helps in several ways.

  • The workflow plan data frame explicitly outlines the steps of the analysis, and vis_drake_graph() visualizes how those steps depend on each other.
  • drake takes care of the parallel scheduling and high-performance computing (HPC) for you. That means the HPC code is no longer tangled up with the code that actually expresses your ideas.
  • You can generate large collections of targets without necessarily changing your code base of imported functions, another nice separation between the concepts and the execution of your workflow

Aggressively scale up.

Not every project can complete in a single R session on your laptop. Some projects need more speed or computing power. Some require a few local processor cores, and some need large high-performance computing systems. But parallel computing is hard. Your tables and figures depend on your analysis results, and your analyses depend on your datasets, so some tasks must finish before others even begin. drake knows what to do. Parallelism is implicit and automatic. See the high-performance computing guide for all the details.

# Use the spare cores on your local machine.
make(plan, jobs = 4)

# Or scale up to a supercomputer.
drake_batchtools_tmpl_file("slurm") #
future::plan(batchtools_slurm, template = "batchtools.slurm.tmpl", workers = 100)
make(plan, parallelism = "future_lapply")


You can choose among different versions of drake. The CRAN release often lags behind the online manual but may have fewer bugs.

# Install the latest stable release from CRAN.

# Alternatively, install the development version from GitHub.

A few technical details:

  • You must properly install drake using install.packages(), devtools::install_github(), or similar. It is not enough to use devtools::load_all(), particularly for the parallel computing functionality, in which multiple R sessions initialize and then try to require(drake).
  • For make(parallelism = "Makefile"), Windows users may need to download and install Rtools.
  • To use make(parallelism = "future") or make(parallelism = "future_lapply") to deploy your work to a computing cluster (see the high-performance computing guide), you will need the future.batchtools package.


The main resources to learn drake are the user manual and the reference website. Others are below.

Cheat sheet

Thanks to Kirill for preparing a drake cheat sheet for the workshop.

Frequently asked questions

The FAQ page is an index of links to appropriately-labeled issues on GitHub. To contribute, please submit a new issue and ask that it be labeled as a frequently asked question.

Function reference

The reference section lists all the available functions. Here are the most important ones.

  • drake_plan(): create a workflow data frame (like my_plan).
  • make(): build your project.
  • r_make(): launch a fresh callr::r() process to build your project. Called from an interactive R session, r_make() is more reproducible than make().
  • loadd(): load one or more built targets into your R session.
  • readd(): read and return a built target.
  • drake_config(): create a master configuration list for other user-side functions.
  • vis_drake_graph(): show an interactive visual network representation of your workflow.
  • outdated(): see which targets will be built in the next make().
  • deps(): check the dependencies of a command or function.
  • failed(): list the targets that failed to build in the last make().
  • diagnose(): return the full context of a build, including errors, warnings, and messages.


Thanks to Kirill for constructing two interactive learnr tutorials: one supporting drake itself, and a prerequisite walkthrough of the cooking package.


Here are some real-world applications of drake in the wild.

There are also multiple drake-powered example projects available here, ranging from beginner-friendly stubs to demonstrations of high-performance computing. You can generate the files for a project with drake_example() (e.g. drake_example("gsp")), and you can list the available projects with drake_examples(). You can contribute your own example project with a fork and pull request.


Author Venue Date Materials
Amanda Dobbyn R-Ladies NYC 2019-02-12 slides, source
Will Landau Harvard DataFest 2019-01-22 slides, source
Karthik Ram RStudio Conference 2019-01-18 video, slides, resources
Sina Rüeger Geneva R User Group 2018-10-04 slides, example code
Will Landau R in Pharma 2018-08-16 video, slides, source
Christine Stawitz R-Ladies Seattle 2018-06-25 materials
Kirill Müller Swiss Institute of Bioinformatics 2018-03-05 workshop, slides, source, exercises

Context and history

For context and history, check out this post on the rOpenSci blog and episode 22 of the R Podcast.

Help and troubleshooting

The following resources document many known issues and challenges.

If you are still having trouble, please submit a new issue with a bug report or feature request, along with a minimal reproducible example where appropriate.

The GitHub issue tracker is mainly intended for bug reports and feature requests. While questions about usage etc. are also highly encouraged, you may alternatively wish to post to Stack Overflow and use the drake-r-package tag.


Development is a community effort, and we encourage participation. Please read for details.

Similar work

GNU Make

The original idea of a time-saving reproducible build system extends back at least as far as GNU Make, which still aids the work of data scientists as well as the original user base of complied language programmers. In fact, the name "drake" stands for "Data Frames in R for Make". Make is used widely in reproducible research. Below are some examples from Karl Broman's website.

There are several reasons for R users to prefer drake instead.

  • drake already has a Make-powered parallel backend. Just run make(..., parallelism = "Makefile", jobs = 2) to enjoy most of the original benefits of Make itself.
  • Improved scalability. With Make, you must write a potentially large and cumbersome Makefile by hand. But with drake, you can use wildcard templating to automatically generate massive collections of targets with minimal code.
  • Lower overhead for light-weight tasks. For each Make target that uses R, a brand new R session must spawn. For projects with thousands of small targets, that means more time may be spent loading R sessions than doing the actual work. With make(..., parallelism = "mclapply, jobs = 4"), drake launches 4 persistent workers up front and efficiently processes the targets in R.
  • Convenient organization of output. With Make, the user must save each target as a file. drake saves all the results for you automatically in a storr cache so you do not have to micromanage the results.


drake overlaps with its direct predecessor, remake. In fact, drake owes its core ideas to remake and Rich FitzJohn. Remake's development repository lists several real-world applications. drake surpasses remake in several important ways, including but not limited to the following.

  1. High-performance computing. Remake has no native parallel computing support. drake, on the other hand, has a thorough selection of parallel computing technologies and scheduling algorithms. Thanks to future, future.batchtools, and batchtools, it is straightforward to configure a drake project for most popular job schedulers, such as SLURM, TORQUE, and the Grid Engine, as well as systems contained in Docker images.
  2. A friendly interface. In remake, the user must manually write a YAML configuration file to arrange the steps of a workflow, which leads to some of the same scalability problems as Make. drake's data-frame-based interface and wildcard templating functionality easily generate workflows at scale.
  3. Thorough documentation. drake contains thorough user manual, a reference website, a comprehensive README, examples in the help files of user-side functions, and accessible example code that users can write with drake::example_drake().
  4. Active maintenance. drake is actively developed and maintained, and issues are usually addressed promptly.
  5. Presence on CRAN. At the time of writing, drake is available on CRAN, but remake is not.


Memoization is the strategic caching of the return values of functions. Every time a memoized function is called with a new set of arguments, the return value is saved for future use. Later, whenever the same function is called with the same arguments, the previous return value is salvaged, and the function call is skipped to save time. The memoise package is an excellent implementation of memoization in R.

However, memoization does not go far enough. In reality, the return value of a function depends not only on the function body and the arguments, but also on any nested functions and global variables, the dependencies of those dependencies, and so on upstream. drake surpasses memoise because it uses the entire dependency network graph of a project to decide which pieces need to be rebuilt and which ones can be skipped.


Much of the R community uses knitr for reproducible research. The idea is to intersperse code chunks in an R Markdown or *.Rnw file and then generate a dynamic report that weaves together code, output, and prose. Knitr is not designed to be a serious pipeline toolkit, and it should not be the primary computational engine for medium to large data analysis projects.

  1. Knitr scales far worse than Make or remake. The whole point is to consolidate output and prose, so it deliberately lacks the essential modularity.
  2. There is no obvious high-performance computing support.
  3. While there is a way to skip chunks that are already up to date (with code chunk options cache and autodep), this functionality is not the focus of knitr. It is deactivated by default, and remake and drake are more dependable ways to skip work that is already up to date.

drake was designed to manage the entire workflow with knitr reports as targets. The strategy is analogous for knitr reports within remake projects.

Factual's Drake

Factual's Drake is similar in concept, but the development effort is completely unrelated to the drake R package.

Other pipeline toolkits

There are countless other successful pipeline toolkits. The drake package distinguishes itself with its R-focused approach, Tidyverse-friendly interface, and a thorough selection of parallel computing technologies and scheduling algorithms.


Special thanks to Jarad Niemi, my advisor from graduate school, for first introducing me to the idea of Makefiles for research. He originally set me down the path that led to drake.

Many thanks to Julia Lowndes, Ben Marwick, and Peter Slaughter for reviewing drake for rOpenSci, and to Maëlle Salmon for such active involvement as the editor. Thanks also to the following people for contributing early in development.

Credit for images is attributed here.