Glad I found this article by @JennyBryan as I work on some github-related projects. Discusses how to use github for structured collab: “like track changes [...] but more rigorous, powerful and scaled up” https://t.co/wwVhOUFhUe
@nathangs20 @Randy_Au @vboykis Related: I also wrote a Git&GitHub Explainer / Pep Rally type of piece:
Still data science / R oriented but, IMHO, helps people figure out how learning Git is going to *feel* and why it might be better than what they currently do.
@hancush @ladbroke Also in the syllabus and notes! Other notable resources I point students to include "Git for Humans" by @alicebartlett (https://t.co/JUGCCKQeZJ) and "Excuse me, do you have a moment to talk about version control?" by @JennyBryan (https://t.co/QEo9djtQbj)
To all my fellow research scientist looking into version control and collaboration tools:
1. Learn #markdown, ideally write a dynamic document using @rstudio and #bookdown
2. Start using #git and @github
Here is a quick guide: https://t.co/QBJzrumrY9
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Data analysis, statistical research, and teaching statistics have at least one thing in common: these activities all produce many files! There are data files, source code, figures, tables, prepared reports, and much more. Most of these files evolve over the course of a project and often need to be shared with others, for reading or edits, as a project unfolds. Without explicit and structured management, project organization can easily descend into chaos, taking time away from the primary work and reducing the quality of the final product. This unhappy result can be avoided by repurposing tools and workflows from the software development world, namely, distributed version control. This article describes the use of the version control system Git and and the hosting site GitHub for statistical and data scientific workflows. Special attention is given to projects that use the statistical language R and, optionally, R Markdown documents. Supplementary materials include an annotated set of links to step-by-step tutorials, real world examples, and other useful learning resources.