Welcome to Tidy Modeling with R! This book is a guide to using a collection of software in the R programming language for model building called tidymodels, and it has two main goals: First and foremost, this book provides a practical introduction to how to use these specific R packages to create models. We focus on a dialect of R called the tidyverse that is designed with a consistent, human-centered philosophy, and demonstrate how the tidyverse and the tidymodels packages can be used to produce high quality statistical and machine learning models. Second, this book will show you how to develop good methodology and statistical practices. Whenever possible, our software, documentation, and other materials attempt to prevent common pitfalls. In Chapter 1, we outline a taxonomy for models and highlight what good software for modeling is like.

This is the latest in my series of screencasts demonstrating how to use the tidymodels packages. If you are a tidymodels user, either just starting out or someone who has used the packages a lot, we are interested in your feedback on our priorities for 2022. The survey we fielded last year turned out to be very helpful in making decisions, so we would so appreciate your input again! Today's screencast is great for someone just starting out with workflowsets, the tidymodels package for handling multiple preprocessing/modeling combinations at once, with this week's #TidyTuesday dataset on giant pumpkins from competitons. Here is the code I used in the video, for those who prefer reading instead of or in addition to video.

This summer I've been competing in the SLICED machine learning competition, where contestants have two hours to open a new dataset, build a predictive model, and be scored as a Kaggle submission. Contestants are graded primarily on model performance, but also get points for visualization and storytelling, and from audience votes. Before SLICED I had almost no experience with competitive ML, so I learned a lot! As of today I'm 5th in the standings, short of the cutoff for the playoffs, so if you want to see me continue you can vote for me as an "Audience Choice" here! For four of the SLICED episodes (including the two weeks I was competing) I shared a screencast of my process.

Recently, I had the opportunity to showcase tidymodels in workshops and talks. Because of my vantage point as a user, I figured it would be valuable to share what I have learned so far. Let's begin by framing where tidymodels fits in our analysis projects. The diagram above is based on the R for Data Science book, by Wickham and Grolemund. The version in this article illustrates what step each package covers.