This was the source I probably used most heavily. Hadley Wickham also wrote a book on R packages, aptly named R Packages - which is also available online for free here.I started with this great blog post by Hillary Parker that goes over the minimal number of steps necessary to create a very simple R package and post it to Github.Here are just a few you should check out. The Tutorials I UsedĪs I mentioned above, there are a ton of good tutorials out there for developing your own R package. I will also do my best to cover every step of the process in pictures. There are a ton of tutorials out there so I will focus on the things I had to interpret form the tutorials I used, including how to incorporate C++ and python code into a package. We will work to put a simple personal package up on Github and make it so you can download it on another computer and have it work. These are mostly restricted to Github in terms of distribution.ĭeveloping a personal package is a really good place to start, so that is what we will focus on in this tutorial. Personal Packages: There are some packages that just have a bunch of random functions somebody wrote that they find useful.Utility Packages: These packages might do lots of general stuff like igraph or MCMCpack which provide lots of utility functions for dealing with networks and MCMC inference respectively.These kinds of packages also often accompany publications about new statistical techniques and provide a way for other people to use the method. Packages that have some specific topical function: This would include packages like readXL or scrapeR that are designed with a number of functions to do one sort of thing (read in files and scrape files off the web respectively).In my experience, most packages fall into one of three categories: An R package is a nice tidy way to reuse and share functions you have created.
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