These are the materials for workshops on tidymodels offered at posit::conf(2023). The tidymodels framework is a collection of packages for modeling and machine learning using tidyverse principles. This website hosts the materials for both the Introduction to tidymodels and Advanced tidymodels courses.

Introduction to tidymodels will teach you core tidymodels packages and their uses: data splitting/resampling with rsample, model fitting with parsnip, measuring model performance with yardstick, and basic pre-processing with recipes. Time permitting, you’ll be introduced to model optimization using the tune package. You’ll learn tidymodels syntax as well as the process of predictive modeling for tabular data.

Advanced tidymodels will teach you about model optimization using the tune and finetune packages, including racing and iterative methods. You’ll be able to do more sophisticated feature engineering with recipes. Time permitting, model ensembles via stacking will be introduced. This course is focused on the analysis of tabular data and does not include deep learning methods.

Is this workshop for me?

Depending on your background, one of Introduction to tidymodels or Advanced tidymodels might serve you better than the other.

Introduction to tidymodels

This workshop is for you if you:

  • are comfortable using tidyverse packages to read data into R, transform and reshape data, and make a variety of graphs, and
  • have had some exposure to basic statistical concepts such as linear models, residuals, etc.

Intermediate or expert familiarity with modeling or machine learning is not required. Interested students who have intermediate or expert familiarity with modeling or machine learning may be interested in the Advanced tidymodels workshop.

Advanced tidymodels

This workshop is for you if you:

  • have the prerequisite skills listed for the Introduction to tidymodels workshops,
  • have used tidymodels packages like recipes, rsample, and parsnip, and
  • have some experience with evaluating statistical models using resampling techniques like v-fold cross-validation or the bootstrap.

Participants who are new to tidymodels or machine learning will benefit from taking the Introduction to tidymodels workshop before joining this one. Participants who have completed the “Introduction to tidymodels” workshop will be well-prepared for this course.


The process to set up your computer for either workshop will look the same. Please join the workshop with a computer that has the following installed (all available for free):

# Install the packages for the workshop
pkgs <- 
  c("bonsai", "doParallel", "embed", "finetune", "lightgbm", "lme4",
    "plumber", "probably", "ranger", "rpart", "rpart.plot", "rules",
    "splines2", "stacks", "text2vec", "textrecipes", "tidymodels", 
    "vetiver", "remotes")


If you’re a Windows user and encounter an error message during installation noting a missing Rtools installation, install Rtools using the installer linked here.


These slides are designed to use with live teaching and are published for workshop participants’ convenience. There are not meant as standalone learning materials. For that, we recommend tidymodels.org and Tidy Modeling with R.

Introduction to tidymodels

Advanced tidymodels

Extra content (time permitting)

There’s also a page for slide annotations; these are extra notes for selected slides.


Quarto files for working along are available on GitHub. (Don’t worry if you haven’t used Quarto before; it will feel familiar to R Markdown users.)

Past workshops


This website, including the slides, is made with Quarto. Please submit an issue on the GitHub repo for this workshop if you find something that could be fixed or improved.

Reuse and licensing

Unless otherwise noted (i.e. not an original creation and reused from another source), these educational materials are licensed under Creative Commons Attribution CC BY-SA 4.0.