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77 changes: 77 additions & 0 deletions joss.00550/10.21105.joss.00550.crossref.xml
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<full_title>The Journal of Open Source Software</full_title>
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<title>The drake R package: a pipeline toolkit for reproducibility and high-performance computing</title>
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<contributors><person_name sequence="first" contributor_role="author"><given_name>William</given_name><surname>Michael Landau</surname><ORCID>http://orcid.org/0000-0003-1878-3253</ORCID></person_name></contributors>
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<citation_list><citation key="ref1"><doi>10.5281/zenodo.1160697</doi></citation><citation key="ref2"><unstructured_citation>Xie, Yihui, 2017, knitr: A General-Purpose Package for Dynamic Report Generation in R, R package version 1.17, https://CRAN.R-project.org/package=knitr</unstructured_citation></citation><citation key="ref3"><unstructured_citation>GNU Make, Version 3.77, Free Software Foundation, Stallman, Richard, 1882114809, 1998</unstructured_citation></citation><citation key="ref4"><unstructured_citation>memoise: Memoisation of Functions, Wickham, Hadley and Hester, Jim and Müller, Kirill and Cook, Daniel, 2017, R package version 1.1.0, https://CRAN.R-project.org/package=memoise</unstructured_citation></citation><citation key="ref5"><unstructured_citation>remake: Make-like build management, FitzJohn, Rich, R package version 0.3.0, 2017, \urlhttps://github.com/richfitz/remake, e29028b548950a3132ea2d045b7f67344ce22a6b</unstructured_citation></citation></citation_list>
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71 changes: 71 additions & 0 deletions joss.00550/10.21105.joss.00550.html
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body = <<-EOF
<meta name="citation_title" content="The drake R package: a pipeline toolkit for reproducibility and high-performance computing">
<meta name="citation_author" content="Michael Landau, William">
<meta name="citation_publication_date" content="2018//">
<meta name="citation_journal_title" content="The Journal of Open Source Software">
<meta name="citation_pdf_url" content="http://www.theoj.org/joss-papers/joss.00550/10.21105.joss.00550.pdf">
<meta name="citation_doi" content="10.21105/joss.00550">
<meta name="citation_issn" content="2475-9066">
<div class="accepted-paper">
<h1>The drake R package: a pipeline toolkit for reproducibility and high-performance computing</h1>
<div class="columns links">
<div class="column four-fifths" style="padding-bottom: 10px;">
<strong>Authors</strong>
<ul class="author-list">
<li><a href="http://orcid.org/0000-0003-1878-3253" target="_blank">William Michael Landau</a></li>
</ul>
</div>
<div class="one-third column">
<span class="repo">Repository:<br /><a href="https://github.com/ropensci/drake">Repository link &raquo;</a></span>
</div>
<div class="one-third column">
<span class="paper">Paper:<br /><a href="http://www.theoj.org/joss-papers/joss.00550/10.21105.joss.00550.pdf">PDF link &raquo;</a></span>
</div>
<div class="one-third column">
<span class="paper">Review:<br /><a href="https://github.com/openjournals/joss-reviews/issues/550">View review issue &raquo;</a></span>
</div>

<div class="one-third column" style="padding-top: 20px;">
<span class="repo">DOI:<br /><a href="https://doi.org/10.21105/joss.00550">https://doi.org/10.21105/joss.00550</a></span>
</div>
<div class="one-third column" style="padding-top: 20px;">
<span class="paper">Status badge:<br /><img src="http://joss.theoj.org/papers/10.21105/joss.00550/status.svg"></span>
</div>
<div class="one-third column" style="padding-top: 20px;">
<span class="paper">
Submitted: 26 January 2018 <br />
Published: 26 January 2018
</span>

</div>
<div class="two-thirds column" style="padding-top: 20px;">
<span class="paper">Citation:<br />
<small>Landau, (2018). The drake R package: a pipeline toolkit for reproducibility and high-performance computing. <em>Journal of Open Source Software</em>, 3(21), 550. https://doi.org/10.21105/joss.00550</small>
</div>
</div>
<div class="paper-body">
<h1 id="summary">Summary</h1>
<p>The <a href="https://github.com/ropensci/drake">drake</a> R package <span class="citation" data-cites="drake">(Landau 2018)</span> is a workflow manager and computational engine for data science projects. Its primary objective is to keep results up to date with the underlying code and data. When it runs a project, <a href="https://github.com/ropensci/drake">drake</a> detects any pre-existing output and refreshes the pieces that are outdated or missing. Not every runthrough starts from scratch, and the final answers are reproducible. With a user-friendly R-focused interface, <a href="https://ropensci.github.io/drake">comprehensive documentation</a>, and <a href="https://github.com/ropensci/drake/blob/master/vignettes/parallelism.Rmd">extensive implicit parallel computing support</a>, <a href="https://github.com/ropensci/drake">drake</a> surpasses the analogous functionality in similar tools such as <a href="www.gnu.org/software/make/">Make</a> <span class="citation" data-cites="Make">(Stallman 1998)</span>, <a href="https://github.com/richfitz/remake">remake</a> <span class="citation" data-cites="remake">(FitzJohn 2017)</span>, <a href="https://github.com/r-lib/memoise">memoise</a> <span class="citation" data-cites="memoise">(Wickham et al. 2017)</span>, and <a href="https://github.com/yihui/knitr">knitr</a> <span class="citation" data-cites="knitr">(Xie 2017)</span>.</p>
<p>In reproducible research, <a href="https://github.com/ropensci/drake">drake</a>&#8217;s role is to provide tangible evidence that a project&#8217;s results are re-creatable. <a href="https://github.com/ropensci/drake">Drake</a> quickly detects when the code, data, and output are synchronized. In other words, <a href="https://github.com/ropensci/drake">drake</a> helps determine if the starting materials would produce the expected output if the project were to start over and run from scratch. This approach decreases the time and effort it takes to evaluate research projects for reproducibility.</p>
<p>Regarding high-performance computing, <a href="https://github.com/ropensci/drake">drake</a> interfaces with a <a href="https://github.com/ropensci/drake/blob/master/vignettes/parallelism.Rmd#parallel-backends">wide variety of technologies</a> to deploy the steps of a data analysis project. Options range from local multicore computing to serious distributed computing on a cluster. In addition, the parallel computing is implicit. In other words, <a href="https://github.com/ropensci/drake">drake</a> constructs the directed acyclic network of the workflow and determines which steps can run simultaneously and which need to wait for dependencies. This automation eases the cognitive and computational burdens on the user, enhancing the readability of code and thus reproducibility.</p>
<h1 id="references" class="unnumbered">References</h1>
<div id="refs" class="references">
<div id="ref-remake">
<p>FitzJohn, Rich. 2017. &#8220;remake: Make-like build management.&#8221; <a href="https://github.com/richfitz/remake" class="uri">https://github.com/richfitz/remake</a>.</p>
</div>
<div id="ref-drake">
<p>Landau, William Michael. 2018. &#8220;drake: an R-focused pipeline toolkit for reproducibility and high-performance computing.&#8221; https://github.com/ropensci/drake. <a href="https://doi.org/10.5281/zenodo.1160697" class="uri">https://doi.org/10.5281/zenodo.1160697</a>.</p>
</div>
<div id="ref-Make">
<p>Stallman, Richard. 1998. <em>GNU Make, Version 3.77</em>. Free Software Foundation.</p>
</div>
<div id="ref-memoise">
<p>Wickham, Hadley, Jim Hester, Kirill M&#252;ller, and Daniel Cook. 2017. &#8220;memoise: Memoisation of Functions.&#8221; https://CRAN.R-project.org/package=memoise.</p>
</div>
<div id="ref-knitr">
<p>Xie, Yihui. 2017. &#8220;knitr: A General-Purpose Package for Dynamic Report Generation in R.&#8221; https://CRAN.R-project.org/package=knitr.</p>
</div>
</div>
</div>
</div>
EOF
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<?xml version="1.0" encoding="utf-8" ?>
<article>
<articleinfo>
<title>The drake R package: a pipeline toolkit for reproducibility and high-performance computing</title>
<authors>
<author>
<name>William Michael Landau</name>
<orcid>0000-0003-1878-3253</orcid>
<affiliation>
<orgname>
1
</orgname>
</affiliation>
</author>
</authors>
<tags>
<tag>R</tag>
<tag>reproducibility</tag>
<tag>high-performance computing</tag>
<tag>pipeline</tag>
<tag>workflow</tag>
<tag>Make</tag>
</tags>
<date>4 January 2018</date>
<paper_doi>10.21105/joss.00550</paper_doi>
<software_repository>https://github.com/ropensci/drake</software_repository>
<software_archive>http://dx.doi.org/10.5281/zenodo.1160697</software_archive>
<paper_url>http://www.theoj.org/joss-papers/joss.00550/10.21105.joss.00550.pdf</paper_url>
</articleinfo>
<body>
<h1 id="summary">Summary</h1>
<p>The <a href="https://github.com/ropensci/drake">drake</a> R package <span class="citation" data-cites="drake">(Landau 2018)</span> is a workflow manager and computational engine for data science projects. Its primary objective is to keep results up to date with the underlying code and data. When it runs a project, <a href="https://github.com/ropensci/drake">drake</a> detects any pre-existing output and refreshes the pieces that are outdated or missing. Not every runthrough starts from scratch, and the final answers are reproducible. With a user-friendly R-focused interface, <a href="https://ropensci.github.io/drake">comprehensive documentation</a>, and <a href="https://github.com/ropensci/drake/blob/master/vignettes/parallelism.Rmd">extensive implicit parallel computing support</a>, <a href="https://github.com/ropensci/drake">drake</a> surpasses the analogous functionality in similar tools such as <a href="www.gnu.org/software/make/">Make</a> <span class="citation" data-cites="Make">(Stallman 1998)</span>, <a href="https://github.com/richfitz/remake">remake</a> <span class="citation" data-cites="remake">(FitzJohn 2017)</span>, <a href="https://github.com/r-lib/memoise">memoise</a> <span class="citation" data-cites="memoise">(Wickham et al. 2017)</span>, and <a href="https://github.com/yihui/knitr">knitr</a> <span class="citation" data-cites="knitr">(Xie 2017)</span>.</p>
<p>In reproducible research, <a href="https://github.com/ropensci/drake">drake</a>’s role is to provide tangible evidence that a project’s results are re-creatable. <a href="https://github.com/ropensci/drake">Drake</a> quickly detects when the code, data, and output are synchronized. In other words, <a href="https://github.com/ropensci/drake">drake</a> helps determine if the starting materials would produce the expected output if the project were to start over and run from scratch. This approach decreases the time and effort it takes to evaluate research projects for reproducibility.</p>
<p>Regarding high-performance computing, <a href="https://github.com/ropensci/drake">drake</a> interfaces with a <a href="https://github.com/ropensci/drake/blob/master/vignettes/parallelism.Rmd#parallel-backends">wide variety of technologies</a> to deploy the steps of a data analysis project. Options range from local multicore computing to serious distributed computing on a cluster. In addition, the parallel computing is implicit. In other words, <a href="https://github.com/ropensci/drake">drake</a> constructs the directed acyclic network of the workflow and determines which steps can run simultaneously and which need to wait for dependencies. This automation eases the cognitive and computational burdens on the user, enhancing the readability of code and thus reproducibility.</p>
<h1 id="references" class="unnumbered">References</h1>
<div id="refs" class="references">
<div id="ref-remake">
<p>FitzJohn, Rich. 2017. “remake: Make-like build management.” <a href="https://github.com/richfitz/remake" class="uri">https://github.com/richfitz/remake</a>.</p>
</div>
<div id="ref-drake">
<p>Landau, William Michael. 2018. “drake: an R-focused pipeline toolkit for reproducibility and high-performance computing.” https://github.com/ropensci/drake. <a href="https://doi.org/10.5281/zenodo.1160697" class="uri">https://doi.org/10.5281/zenodo.1160697</a>.</p>
</div>
<div id="ref-Make">
<p>Stallman, Richard. 1998. <em>GNU Make, Version 3.77</em>. Free Software Foundation.</p>
</div>
<div id="ref-memoise">
<p>Wickham, Hadley, Jim Hester, Kirill Müller, and Daniel Cook. 2017. “memoise: Memoisation of Functions.” https://CRAN.R-project.org/package=memoise.</p>
</div>
<div id="ref-knitr">
<p>Xie, Yihui. 2017. “knitr: A General-Purpose Package for Dynamic Report Generation in R.” https://CRAN.R-project.org/package=knitr.</p>
</div>
</div>
</body>
</article>

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