I think it’s fair to say that most academics who learn about R do so in the process of training or applying quantitative research methods. As a consequence, knowledge of R among academics tends to be limited to core (base) R packages (R Core Team, 2018) and a small handful of speciality statistical packages, e.g., {lavaan}, {lme4}, {MASS}, {car}, etc. With this in mind, the goal of this post is to provide an overview of three things to know beyond base R.

Recently I was asked if I could add to {rtweet} some basic functions for converting Twitter data into network data objects. I thought this was a reasonable request and a good opportunity for me to learn more about network analysis. But the task of converting Twitter data into network-friendly objects is something that has, at least for me, been really slow and inefficient. So, for the past several weeks, I’ve been slowly working toward what I think believe a simple but efficient solution.

This post describes how to download and perform a basic local install of R and Rstudio. The instructions should work for both macOS and Windows users. Although not required, installation tends to work best when operating systems are up-to-date. At the time of writing, this means R/Rstudio work best with macOS High Sierra and Windows 10. R vs Rstudio R is a statistical computing language/environment. It is distinct from Rstudio, which is an integrated development environment (IDE) or high- powered graphical user interface (GUI) optimized for working with the R language.