1 Introduction
Thinking clearly with data is a necessary skill if you want your research to have social impact. The R programming language is a great avenue for honing this skill. It isn’t the only one, but as an open source program it has a large community of users working actively to develop new tools in R and refine existing ones. For social science research in particular, R is a popular choice.
For this reason, I wanted to put together a set of companion notes to work with alongside reading the book Thinking Clearly with Data by Bueno de Mesquita and Fowler (2021). This book makes the ins and outs of quantitative reasoning accessible to a broad audience, opting for intuition over equations (though some math is covered). However, for all its accessibility, Thinking Clearly with Data doesn’t provide much in the way of applied examples or insturction. That’s where this R Companion comes in. These notes are meant to be read in tandem with Thinking Clearly with Data. Relevant cross-references are provided in-text.
Just as Bueno de Mesquita and Fowler (2021) do in their book, these notes are split into two main parts. One deals with correlation, and the other deals with causation. Each provides applied examples in R dealing with descriptive inferences, correlations, and causal research designs talked about in Thinking Clearly with Data.
These companion notes also include a set of prerequisite chapters that provide a crash course in working with R. For new R users, I recommend starting here before going on to the substantive chapters on correlation and causation.
Okay, it’s time to get started!