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.

GitUp is Git the way it should be

We here at the Bentley Historical Library have been using GitHub for quite some time now. (Really, it's only been since May 19th of this yea...

:floppy_disk: peer-to-peer sharing & live syncronization of files via command line - GitHub - datproject/dat: peer-to-peer sharing & live syncronization of files via command line

git-annex allows managing files with git, without checking the file contents into git. While that may seem paradoxical, it is useful when dealing with files larger than git can currently easily handle, whether due to limitations in memory, time, or disk space.

"Digital Object Storage and Versioning" file is a super-smart and useful discussion of design desiderata for preservation storage.