This is a 3 hour computing bootcamp for incoming PhD, MSS, and MSEM students to the Department of Statistical Science at Duke University.
The workshop will cover the following topics:
- Set up accounts on the department server
- Discuss how to responsibly use distributed computing resources.
- Recognize the problems that reproducible research helps address, featuring brief discussion of case studies case studies of (lack of) reproducibility gone wrong.
- Identify pain points in getting your analysis to be reproducible.
- The role of documentation, sharing, automation, and organization in making your research more reproducible. Introducing some tools to solve these problems, specifically R/RStudio/RMarkdown.
- Organize projects and folders to enable reproducibility and reusability
- Understand the structure of data files and the importance of documenting all changes made
- Using these practices, create a reproducible project workflow using R/RStudio/RMarkdown.
- Introduction to git/GitHub as a version control tool.
- Practice initiating a project directory, making / committing / pushing changes, and creating a pull request to someone else's remote repository.
- Discuss the role of version control in reproducibility of one's own project as well as in collaborative projects.
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Karl Broman - Wisc's Tools4RR
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Karl Broman - Reproducible Research
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Grolemund and Wickham - R for Data Science
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Case studies:
- Science retracts gay marriage paper without agreement of lead author, http://news.sciencemag.org/policy/2015/05/science-retracts-gay-marriage-paper-without-lead-author-s-consent
- Seizure study retracted after authors realize data got "terribly mixed", http://retractionwatch.com/2013/02/01/seizure-study-retracted-after-authors-realize-data-got-terribly-mixed/
- Bad spreadsheet merge kills depression paper, quick fix resurrects it, http://retractionwatch.com/2014/07/01/bad-spreadsheet-merge-kills-depression-paper-quick-fix-resurrects-it/