Git and GitHub Overview

As data scientists, we deal with a vast amount of data, and managing and tracking our work can be daunting. A centralized platform to store, manage, and share work is essential for our data science toolbox. One such platform that has immense popularity among data scientists is GitHub. This lesson will explore why and how data scientists use GitHub to manage their R projects.

Git and GitHub are powerful and popular software development tools for version control and collaboration. Most software developers and data scientists are familiar with these two tools in one form or another. In fact, GitHub is nearly synonymous with Git, and they’re often considered the same, though Git is the fundamental tool on which GitHub is built.

In Git, version control is achieved through repositories or collections of files and folders that make up a project. When working with R, that collection of files and folders is usually contained in an R project. In addition to housing the actual project work, a repository tracks file changes over time. The repository also enables us to create commits, which is like saving snapshots of the codebase at a particular point in time. Furthermore, we can create branches to develop new ...