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.