Summary

The drake package is an R-focused pipeline toolkit. It reproducibly brings results up to date and automatically arranges computations into successive parallelizable stages. It has a Tidyverse-friendly front-end, powerful interactive visuals, and a vast arsenal of multicore and distributed computing backends.

Package: drake
Title: Data Frames in R for Make
Version: 4.4.1.9000
Authors@R: c(
  person(
    family = "Landau",
    given = c("William", "Michael"),
    email = "will.landau@lilly.com",
    role = c("aut", "cre")),
  person(
    family = "Axthelm",
    given = "Alex",
    email = "aaxthelm@che.IN.gov",
    role = "ctb"),
  person(
    family = "Clarkberg",
    given = "Jasper",
    email = "jasper@clarkberg.org",
    role = "ctb"),
  person(
    family = "Eli Lilly and Company",
    role = "cph"))
Description: A solution for reproducible code and 
  high-performance computing.
License: GPL-3
Depends:
  R (>= 3.2.0)
Imports:
  codetools,
  crayon,
  eply,
  evaluate,
  digest,
  formatR,
  future,
  grDevices,
  igraph,
  knitr,
  lubridate,
  magrittr,
  parallel,
  plyr,
  R.utils,
  rprojroot,
  stats,
  storr (>= 1.1.0),
  stringi,
  stringr,
  testthat,
  utils,
  visNetwork,
  withr
Suggests: 
  abind,
  DBI,
  future.batchtools,
  MASS,
  methods,
  RSQLite,
  rmarkdown,
  tibble
VignetteBuilder: knitr
URL: https://github.com/wlandau-lilly/drake
BugReports: https://github.com/wlandau-lilly/drake/issues
RoxygenNote: 6.0.1

• URL: https://github.com/wlandau-lilly/drake

• Fit: drake falls easily within reproducibility and high-performance computing.

• Target audience: anyone who uses R for medium-to-long computations for which the results need to stay up to date with the dependencies.

Similar work

Remake

Drake overlaps with its direct predecessor, remake. In fact, drake owes its core ideas to remake and @richfitz, and explicit acknowledgements are in the documentation. However, 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 vast arsenal of parallel computing options, from local multicore computing to serious distributed computing. 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 Sun/Univa 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. In drake, this configuration is based on data frames that built-in wildcard templating functionality easily generates at scale.
3. Thorough documentation. Drake contains eight vignettes, 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 solved promptly.

Factual's drake

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

Other pipeline toolkits

There are many other successful pipeline toolkits, and the drake package distinguishes itself with its R-focused approach, Tidyverse-friendly interface, and parallel computing flexibility.

Requirements

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• does not violate the Terms of Service of any service it interacts with.
• has a CRAN and OSI accepted license.
• contains a README with instructions for installing the development version.
• includes documentation with examples for all functions.
• contains a vignette with examples of its essential functions and uses.
• has a test suite.
• has continuous integration, including reporting of test coverage, using services such as Travis CI, Coeveralls and/or CodeCov.
• I agree to abide by ROpenSci's Code of Conduct during the review process and in maintaining my package should it be accepted.

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