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. First let’s make sure that we have actually installed Git. In Early GitHub Wins, we rack up some early success with the basic workflows that are necessary to get your work onto GitHub. In order to connect RStudio with GitHub we need to configure Git, which is the version control software that GitHub is built on. 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.Develop a few key workflows that cover your most common tasks.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: Alternatively, if we were to choose to create a new R project based on a GitHub repository, you would need to select Version Control, followed by Git and then.
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