Reproducibility: Version Control, Git, and RStudio

Posted 9 Aug 2018

Sophia Lafferty-Hess (DVS Staff page), Jen Darragh (DVS Staff page), and I presented a Reproducibility workshop hosted by the Data and Visualization Services Department and cross-listed with the Responsible Conduct of Research (RCR) training efforts.

The workshops, officially entitled Reproducibility: RStudio and Git is a hands-on response to the importance of reproducibility, replication, and transparency in the research endeavor. General data management strategies are introduced to assist researchers in their efforts towards increased research reproducibility.

The hands-on exercises focuses on how Git and RStudio can work seamlessly together in support of reproducible research.

No previous experience with Git or R is required. However minimal experience with R is a plus due to the interoperable utility of the two specific tools (git and RStudio). Combining the two tools, which build on R’s widely extensible ecosystem, yields a powerful, reproducible-friendly and collaborative-friendly platform. For those who are not familiar with R, RStudio or Git, you may want to view the streaming video link from a previous post: Introduction to R.

Learning resources and workshop materials are available and shareable. The software (R, RStudio, Git) is open-source and free. Use the links below to learn at your own pace and at the comfort of your own workspace.

Rfun is a DVS learning series

The 'R We Having Fun Yet?' learning series is part of the broader Data & Visualization Services workshop series. DVS offers workshops on [R], Python, GIS and mapping, Research Data Management, and Visualization.

Rfun Blog

The blog features semester summaries of our workshop series and extra bits of information which may assist you in your practical data science journeys.