R for Empirical Economics Research

The course will introduce the R statistical language, which is a free, open source software that is popular in statistics and the data science community. We will discuss R tools for data manipulation (reading in, cleaning, creating variables, merging data sets), programming (writing functions, scoping, passing arguments), data visualization (using ggplot) as well as techniques for creating reproducible research, creating maps, web apps, and animations. The course will be hands-on so students will be expected to 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.