Reproducible Research: Principles, practices, and tools for generating reproducible statistical analyses and reports

Dr. Steven J Pierce, CSTAT

This seminar will introduce the audience to a set of principles, practices, and free, open-source software tools that enable scientists to generate reproducible statistical analyses and reports. We will cover why reproducibility is important, then offer a vision of how to enhance the reproducibility of your work, with concrete steps you can take to achieve that goal. We will discuss tailoring the degree of reproducibility you aim to achieve for a given project, which may vary due to project context or constraints. In terms of software, we will describe how R, RStudio, Quarto, and TinyTex comprise a powerful suite of tools that uses dynamic documents to automate producing fully-formatted reports, manuscripts, or slides complete with narrative text, analysis results, figures, tables, and references. Git and GitHub.com add further value through support for version control and collaboration on the source code for dynamic documents. The session will include conceptual content, examples of dynamic documents, and links to supporting resources the audience can use to accelerate learning how to make their work more reproducible.

Prerequisites: This seminar is for quantitative scientists. Knowledge in R is helpful, but not required.

*This seminar is available for RECR Credit, 1.0 Hours, Attendance will be verified and a survey must be completed afterwards with well thought out responses to receive RECR Credit.