R for Empirical Research

The course will introduce the R programming language, which is a free, open source software that is popular for statistics and data science. We will discuss R tools for data manipulation (reading in, cleaning, creating variables, merging data sets), programming (writing functions, scoping, passing arguments), data visualization (ggplot) as well as techniques for reproducible research, maps, web apps, and animations. LLMs are significantly changing the need for direct knowledge of coding so we will try to focus more on the principles of data analysis and project management. The course will be hands-on so students will be expected to actively code and turn in assignments. However, I do not assume any prior knowledge of R so beginners are welcome. Economists typically program in Stata but in my opinion Stata introduces some very poor coding, workflow, and data management practices. Stata is also expensive, which inhibits both reproducibility and open science. In addition to learning R for your own research, R is widely used in industry (unlike Stata) so it can be a good human capital investment for you. We will operate mainly within the tidyverse, which is a specific collection of libraries. However, R is ever evolving and it’s impossible to cover all aspects within a semester but I hope to capture your interest. Students are required to have a laptop. Please install R and RStudio *before* the first class as we will begin programming in the first class.