As I have mentioned in other posts, this semester I trying to maximize the number of intuitive visualizations for my students to master economic and mathematical concepts more in my teaching, as well as develop my own R skills. For my intermediate microeconomics course, I recently put together an interactive Shiny App with R that demonstrates the effect of a tax on consumers and producers.
Simply input your own (inverse) demand and supply functions and the size of the tax, and the graph and summary will update with the market equilibrium and the surpluses lost by the tax.
For my econometrics course this semester, I have been using R to help students visualize linear regression models. Running a regression in R is quite simple, as is intepretting the results, with a little bit of training. However, I emphasize that I want students to understand what is happening “inside the black box” of regression. I discourage blindly trusting R’s opaquely simple input and output, and get students to learn what R is doing under the hood, even if they will never have to manually estimate the model themselves.