Students who are interested in research – especially junior- and senior-year students preparing for independent work – are often encouraged to master the use of a fully-featured statistical software like Stata or R in order to help with their statistical analysis. For example, in the Economics program at Princeton, Stata is often the software of choice for classes like ECO 202 (Statistics and Data Analysis for Economics) or ECO 302/312 (Econometrics). Similarly, other departments (for example, for the Undergraduate Certificate Program in Statistics and Machine Learning) offer SML 201 (Introduction to Data Science) or ORF 245 (Fundamentals of Engineering Statistics) to prepare students in the use of R. Usually, students end up developing a preference for one or the other even if they eventually grow proficient in both. While our coursework (rightly!) emphasizes the statistical methods, we, as students, are often left to navigate the intricacies of the statistical tools on our own. This post is a primer of some of the core packages in R that are used for advanced statistical analysis. As you begin to search for tools in R that can help you with your analysis, I hope you will find this information useful.
Continue reading Essential Packages for Advanced Statistical Analysis in R – A Primer