This perspective on the Git landscape is presented in Basic Git Concepts and Daily Workflows. Happy Git aims to complement existing, general Git resources by highlighting the most rewarding usage patterns for data science. The use of Git/GitHub in data science has a slightly different vibe from that of pure software development, due to differences in the user’s context and objective. We also show the special synergy between R/R Markdown/RStudio and GitHub, which provides a powerful demonstration of why all this setup is worthwhile. In Early GitHub Wins, we rack up some early success with the basic workflows that are necessary to get your work onto GitHub. The first two parts, Installation and Connect Git, GitHub, RStudio, provide a “batteries included” quick start to verify your setup. The target reader is someone who uses R for data analysis or who works on R packages, although some of the content may be useful to those working in adjacent areas. Integrate Git and GitHub into your daily work with R and R Markdown.These videos will also help you learn good development practices that make collaboration within a team safe and easy. We’ll learn the key RStudio features in order to start programming in R on our own. We’ll install R, and RStudio RStudio, an extremely popular development environment for R. Develop a few key workflows that cover your most common tasks. RStudio offers 6 videos called the RStudio Essentials Series that help you learn how to program and manage R projects using RStudio’s tools including the RStudio Integrated Development Environment (IDE). Tutorial: Getting Started with R and RStudio In this tutorial we’ll learn how to begin programming with R using RStudio.
Install Git and get it working smoothly with GitHub, in the shell and in the RStudio IDE.
Happy Git provides opinionated instructions on how to:
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