Category Archives: TidyX Screen Cast

TidyX Episode 173: Predicting Hall of Fame MLB Pitchers with Bayesian Logistic Regression

Last week, Ellis Hughes and I did our “Teach me something in R in 20min or less” series on predicting hall of fame MLB pitchers using logistic regression. This week, we complete the same task but instead do it using Bayesian logistic regression with {rstanarm}. We don’t get into priors or anything like that (simply use the default, weakly informative, priors that are specified in the function) since it is a 20min or less episode. However, we do cover:

  • Specifying the syntax of the model
  • Plotting the model results
  • Making out of sample predictions
  • Plotting posterior distributions for individual players

To watch the screen cast, CLICK HERE.

To access the code, CLICK HERE.

TidyX Episode 171: Get started with Bayesian regression in 20min using {rstanarm}

The “learn X in 20min” series seems to be a popular amongst the viewers, so Ellis Hughes and I decided to throw together one. This week, we are walking through the basic code for a Bayesian regression model using the {rstanarm} package. Because we only keep it to 20min, we use the default priors and don’t get into any sort of complex stuff about Bayes. We do, however, discuss making predictions of point estimates and posterior distributions, and show how you can explore model uncertainty.

To watch the screen cast, CLICK HERE.

To access the code, CLICK HERE.

TidyX Episode 168: Interactive Base R Plots

In our final episode of Base R plotting, Ellis Hughes and I use the {Lahman} baseball package to explore Hall of Fame Baseball players. In this episode we show how to make your Base R plots more interactive, allowing the user to click on the points within the plot and obtain information specific to the player.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.