springkmfk.blogg.se - Upload r package to github

This is also covered in Section 21.1.Ĭontinuous integration and deployment, a.k.a. We strongly recommend syncing your local Git repositories to a hosted service and, at this point in time, GitHub is the or, at least, “an” obvious choice. In Section 21.1, we explain why we think version control is so important. We say that based on Git’s general prevalence and, specifically, its popularity within the R package ecosystem. We strongly recommend the use of formal version control and, at this point in time, Git is the obvious choice. But even if it’s not RStudio, we strongly recommend working with an IDE that has specific support for R and R package development. That’s what we document, since it’s what we use and devtools is developed to work especially well with RStudio. In Section 5.2 we encouraged the use of the RStudio IDE for package development work. You will notice that we recommend using certain tools:Īn integrated development environment (IDE). In Chapter 22 we discuss how the nature of package maintenance varies over the lifecycle of a package.

Here we’ll discuss the use of version control and continuous integration.

Construct and execute basic programs in R using elementary programming techniques and tidyverse packages (e.g.In this last part of the book, we zoom back out to consider development practices that can make you more productive and raise the quality of your work.

Students will leave the course with basic computational skills implemented through many computational methods and approaches to data science while students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions, methods, and data. Major emphasis is placed on a pragmatic understanding of core principles of programming and packaged implementations of methods. The focus of the course is on generating reproducible research through the use of programming languages and version control software. This is an applied course for data scientists with little-to-no programming experience who wish to harness growing digital and computational resources.

Requirements: Internet connection and a computer.

INFO 5940 - Computing for Information Science