There IS however a youtube tutorial that covers the steps of this guide, you can find it here: This blog post is not intended to be a complete guide to packages. (* This guide will never be finished, so watch this space, I’ll be coming back to make edits as they seem appropriate).īy the end of this tutorial you will have a highly functional and well documented package, like this, suitable for even more advanced operations like continuous integration and advanced testing. Since I have changed my mind and worked on this task frequently, here’s a conclusive* guide of the steps I follow. Of course, each approach has a series of advantages and drawbacks, so it is very much a subjective thing. However, R-3.2.0 and R-3.2.1 share the same location.įor example, to use ~/R/win-library/3.Creating an R package can be a daunting task, but there are several tools to facilitate this process immensely. For example, R-3.0.x and R-3.1.x have different library locations. R creates a different library for each dot-version of R itself. In R, a library is the location on disk where you install your packages. To change the library location, you use the function. libPaths() and specify the library location.

To do this, you simply call the function.

Fortunately it is easy to modify the path where R installs your packages.

R uses a single package library for each installed version of R on your machine. In this post I give some tips for keeping my libraries clean. In these cases I want to isolate the packages I use, and ensure I don't 'pollute' my library with the most recent package versions. Most importantly, for these projects I want to ensure I have reproducible results. However, once any work finds it way into production server (where it runs on a regular schedule), I want my environment to be stable. For much of my data science work, I want to have the very latest package from CRAN or github.

Create r package rstudio