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<meta name="author" content="M. Rolland">
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<title>Reproducible Research in R (and friends)</title>
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<nav id="TOC" role="doc-toc" class="toc-active">
<h2 id="toc-title">Table of contents</h2>
<ul>
<li><a href="#project-organization" id="toc-project-organization" class="nav-link active" data-scroll-target="#project-organization"><span class="header-section-number">1</span> Project Organization</a></li>
<li><a href="#version-control" id="toc-version-control" class="nav-link" data-scroll-target="#version-control"><span class="header-section-number">2</span> Version Control</a></li>
<li><a href="#data-management" id="toc-data-management" class="nav-link" data-scroll-target="#data-management"><span class="header-section-number">3</span> Data Management</a></li>
<li><a href="#documentation" id="toc-documentation" class="nav-link" data-scroll-target="#documentation"><span class="header-section-number">4</span> Documentation</a></li>
<li><a href="#environment-management" id="toc-environment-management" class="nav-link" data-scroll-target="#environment-management"><span class="header-section-number">5</span> Environment Management</a></li>
<li><a href="#workflow-automation" id="toc-workflow-automation" class="nav-link" data-scroll-target="#workflow-automation"><span class="header-section-number">6</span> Workflow Automation</a></li>
<li><a href="#analysis-scripts" id="toc-analysis-scripts" class="nav-link" data-scroll-target="#analysis-scripts"><span class="header-section-number">7</span> Analysis Scripts</a></li>
<li><a href="#computational-reproducibility" id="toc-computational-reproducibility" class="nav-link" data-scroll-target="#computational-reproducibility"><span class="header-section-number">8</span> Computational reproducibility</a></li>
<li><a href="#reporting" id="toc-reporting" class="nav-link" data-scroll-target="#reporting"><span class="header-section-number">9</span> Reporting</a></li>
<li><a href="#validation" id="toc-validation" class="nav-link" data-scroll-target="#validation"><span class="header-section-number">10</span> Validation</a></li>
<li><a href="#sharing-code-and-data" id="toc-sharing-code-and-data" class="nav-link" data-scroll-target="#sharing-code-and-data"><span class="header-section-number">11</span> Sharing Code And Data</a></li>
<li><a href="#advanced-analysis-practices" id="toc-advanced-analysis-practices" class="nav-link" data-scroll-target="#advanced-analysis-practices"><span class="header-section-number">12</span> Advanced Analysis Practices</a></li>
</ul>
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</div>
<main class="content" id="quarto-document-content">
<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<h1 class="title">Reproducible Research in R (and friends)</h1>
<p class="subtitle lead">Cheatsheet</p>
</div>
<div class="quarto-title-meta">
<div>
<div class="quarto-title-meta-heading">Author</div>
<div class="quarto-title-meta-contents">
<p>M. Rolland, MSc <a href="mailto:[email protected]" class="quarto-title-author-email"><i class="bi bi-envelope"></i></a> <a href="https://orcid.org/0000-0002-8076-5550" class="quarto-title-author-orcid"> <img src="data:image/png;base64,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"></a></p>
</div>
</div>
<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
<p class="date">June 8, 2024</p>
</div>
</div>
<div>
<div class="quarto-title-meta-heading">Modified</div>
<div class="quarto-title-meta-contents">
<p class="date-modified">November 21, 2024</p>
</div>
</div>
</div>
</header>
<p>This cheatsheet provides essential guidelines and best practices for conducting reproducible research using R and related tools. It covers project organization, version control, data management, documentation, environment management, workflow automation, and more. For any suggestions or feedback, please feel free to email me.</p>
<section id="project-organization" class="level2" data-number="1">
<h2 data-number="1" class="anchored" data-anchor-id="project-organization"><span class="header-section-number">1</span> Project Organization</h2>
<ul>
<li>Use a consistent folder structure:
<ul>
<li><code>data/</code> - Analysis data files</li>
<li><code>scripts/</code> - Analysis scripts</li>
<li><code>outputs/</code> - Results (figures, tables)</li>
<li><code>docs/</code> - Documentation and reports</li>
</ul></li>
<li>Use RStudio Projects to facilitate project management and environment isolation</li>
<li>Maintain a project log to document progress, changes, and key decisions throughout the analysis</li>
<li>Reference:
<ul>
<li><a href="https://peerj.com/preprints/3192/">The concept of research compendium</a></li>
<li><a href="https://support.posit.co/hc/en-us/articles/200526207-Using-RStudio-Projects">Using RStudio projects</a></li>
</ul></li>
</ul>
</section>
<section id="version-control" class="level2" data-number="2">
<h2 data-number="2" class="anchored" data-anchor-id="version-control"><span class="header-section-number">2</span> Version Control</h2>
<ul>
<li>Use Git to track changes in scripts and documents</li>
<li>Commit regularly with meaningful messages</li>
<li>One repository per analysis</li>
<li>Make sure your <code>data/</code> folder is in the <code>.gitignore</code> file</li>
<li>Make sure there is no sensitive information in your code</li>
<li>Reference:
<ul>
<li><a href="https://happygitwithr.com/">Happy Git with R</a></li>
</ul></li>
</ul>
</section>
<section id="data-management" class="level2" data-number="3">
<h2 data-number="3" class="anchored" data-anchor-id="data-management"><span class="header-section-number">3</span> Data Management</h2>
<ul>
<li>Store raw data in <code>data/raw/</code> and never modify it directly</li>
<li>Produce a README describing the source data</li>
<li>Use scripts to clean and process data, save the cleaned data in <code>data/processed/</code></li>
<li>Document each step of data cleaning</li>
<li>Keep data cleaning separate from analysis</li>
<li>Organize your data in a tidy format: each variable is a column, each observation is a row</li>
<li>Reference:
<ul>
<li><a href="https://www.jstatsoft.org/article/view/v059i10">Principles of tidy data</a></li>
</ul></li>
</ul>
</section>
<section id="documentation" class="level2" data-number="4">
<h2 data-number="4" class="anchored" data-anchor-id="documentation"><span class="header-section-number">4</span> Documentation</h2>
<ul>
<li>Comment code extensively to explain steps and logic</li>
<li>Create README files to explain project structure and instructions for running the analysis</li>
<li>Document all functions clearly, including input parameters, output, and purpose</li>
<li>Reference:
<ul>
<li><a href="https://gricad-gitlab.univ-grenoble-alpes.fr/iab-env-epi/rolland_effects_2022">Example README file</a></li>
<li><a href="https://cran.r-project.org/web/packages/roxygen2/vignettes/roxygen2.html">{roxygen2} for function documentation</a></li>
</ul></li>
</ul>
</section>
<section id="environment-management" class="level2" data-number="5">
<h2 data-number="5" class="anchored" data-anchor-id="environment-management"><span class="header-section-number">5</span> Environment Management</h2>
<ul>
<li>Use <code>sessionInfo()</code> or <code>devtools::session_info()</code> to capture the R session information</li>
<li>Use <code>{renv}</code> to manage package versions</li>
<li>Reference:
<ul>
<li><a href="https://rstudio.github.io/renv/articles/renv.html">Introduction to renv</a></li>
<li><a href="https://gricad-gitlab.univ-grenoble-alpes.fr/iab-env-epi/rolland_effects_2022">Example on how to show the session info (scroll to bottom)</a></li>
</ul></li>
</ul>
</section>
<section id="workflow-automation" class="level2" data-number="6">
<h2 data-number="6" class="anchored" data-anchor-id="workflow-automation"><span class="header-section-number">6</span> Workflow Automation</h2>
<ul>
<li>Organize your analysis into a series of numbered and ordered scripts to create a clear and reproducible workflow (e.g., 01-data-cleaning.R, 02-data-analysis.R, 03-visualization.R)</li>
<li>Create a master script (e.g., run_all.R) that sequentially runs each numbered script</li>
</ul>
<p>OR</p>
<ul>
<li><p>Use Makefile or <code>{targets}</code> package to automate and document the workflow</p></li>
<li><p>Reference:</p>
<ul>
<li><a href="https://books.ropensci.org/targets/">{targets} Package</a><br>
</li>
<li><a href="https://mjrolland.github.io/ed-neuro-hpa/">Example Project using {targets}</a></li>
</ul></li>
</ul>
</section>
<section id="analysis-scripts" class="level2" data-number="7">
<h2 data-number="7" class="anchored" data-anchor-id="analysis-scripts"><span class="header-section-number">7</span> Analysis Scripts</h2>
<ul>
<li>Break analysis into small, reusable functions</li>
<li>Use meaningful and consistent naming conventions such as provided by the <a href="https://style.tidyverse.org/syntax.html#object-names">Tidyverse Naming Conventions</a> for variables and functions and by <a href="https://datacarpentry.org/rr-organization1/01-file-naming/index.html">data carpentry</a> for folders and files</li>
<li>Style your code according to standardized recommendations from the <a href="https://style.tidyverse.org/">Tidyverse Style Guide</a></li>
<li>Reference:
<ul>
<li><a href="https://tidyverse.tidyverse.org/articles/manifesto.html#embrace-functional-programming">Embrace functional programming</a></li>
<li><a href="https://style.tidyverse.org/">Tidyverse Style Guide</a></li>
</ul></li>
</ul>
</section>
<section id="computational-reproducibility" class="level2" data-number="8">
<h2 data-number="8" class="anchored" data-anchor-id="computational-reproducibility"><span class="header-section-number">8</span> Computational reproducibility</h2>
<ul>
<li>Set seeds to ensure reproducibility when using randomness in your analysis</li>
<li>Document all warnings</li>
<li>Reference:
<ul>
<li><a href="https://makemeanalyst.com/r-programming/random-number-seed/">Random number seed in R</a></li>
</ul></li>
</ul>
</section>
<section id="reporting" class="level2" data-number="9">
<h2 data-number="9" class="anchored" data-anchor-id="reporting"><span class="header-section-number">9</span> Reporting</h2>
<ul>
<li>Use RMarkdown (.Rmd) or Quarto (.Qmd) files to combine code, results, and narrative for creating dynamic reports</li>
<li>Reference:
<ul>
<li><a href="https://quarto.org/">Quarto Documentation</a></li>
</ul></li>
</ul>
</section>
<section id="validation" class="level2" data-number="10">
<h2 data-number="10" class="anchored" data-anchor-id="validation"><span class="header-section-number">10</span> Validation</h2>
<ul>
<li>Get your code reviewed prior to publication</li>
<li>Reference:
<ul>
<li><a href="https://mtlynch.io/human-code-reviews-1/">Code Review Practices</a></li>
</ul></li>
</ul>
</section>
<section id="sharing-code-and-data" class="level2" data-number="11">
<h2 data-number="11" class="anchored" data-anchor-id="sharing-code-and-data"><span class="header-section-number">11</span> Sharing Code And Data</h2>
<ul>
<li>Use repositories like <a href="https://github.com/">GitHub</a> or <a href="https://gitlab.com/">GitLab</a> for sharing code</li>
<li>Use repositories like <a href="https://zenodo.org/">Zenodo</a> or <a href="https://www.data.gouv.fr/fr/">data.gouv.fr</a> if you have data sets to share</li>
</ul>
</section>
<section id="advanced-analysis-practices" class="level2" data-number="12">
<h2 data-number="12" class="anchored" data-anchor-id="advanced-analysis-practices"><span class="header-section-number">12</span> Advanced Analysis Practices</h2>
<ul>
<li>Use the “many models” approach to fit and compare models across many subsets of data (e.g. EWAS). Storing models as list-columns in tibbles simplifies storage, manipulation and visualization while promoting modularity and reusability.
<ul>
<li>Reference:
<ul>
<li><a href="https://tim-tiefenbach.de/post/2023-dplyr-many-models/">Tiefenbach’s Many Models Tutorial</a></li>
<li><a href="https://r4ds.had.co.nz/many-models.html">R for Data Science: Many Models Chapter</a></li>
</ul></li>
</ul></li>
</ul>
</section>
<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
<div id="ref-marwick_packaging_2018" class="csl-entry" role="listitem">
Marwick, Ben, Carl Boettiger, and Lincoln Mullen. 2018. <span>“Packaging Data Analytical Work Reproducibly Using <span>R</span> (and Friends).”</span> Preprint. PeerJ Preprints. <a href="https://doi.org/10.7287/peerj.preprints.3192v2">https://doi.org/10.7287/peerj.preprints.3192v2</a>.
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