Module3R

Learn Machine Learning in the browser or locally in your RStudio IDE with interactive tutorials!

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Sydney-Informatics-Hub/Module3R")

How to run the tutorials

You can start any tutorial with:

learnr::run_tutorial("tutorial-of-choice", package = "Module3R")

For example:

learnr::run_tutorial("Part-1", package = "Module3R")

List of available tutorials

Tutorial	Description
Part 1	Ames housing dataset - Predict selling prices
Part 2	Pima Indian Women's diabetes dataset - Predict diabetes status
Part 3	Unsupervised dimensionality reduction - Transforming groups of predictors

How to use the tutorials

These tutorials consist of content along with interactive components for checking and reinforcing understanding. Throughout the tutorials you will find:

• Narrative, figures and illustrations;

• Code exercises that you can edit and execute directly;

• Quiz questions...

Each tutorial automatically preserve work done within them, so if you work on a few exercises or questions and then return to the tutorial later, you can pick up right where you have left off.

Each tutorial includes a Table of Contents and it reveals content one sub-section at a time:

Exercises are interactive R code chunks that allow you to directly execute R code and see its results. When a solution code chunk is provided, there will be a Solution button on the exercise that you can click if you are stuck:

For the trainer

Introduction slides for the tutorials are here. Instructions for adding new tutorials are here.

Code of Conduct

Please note that this package is released with a Code of Conduct. By contributing to this package, you agree to abide by its terms.

References

• Tierney, Nicholas J, and Dianne H Cook. 2018. "Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations." arXiv Preprint arXiv:1809.02264;
• Adapted from "Linear Regression and tidymodels", available here;
• Max Kuhn and Julia Silge, "Tidy Modeling with R", Version 1.0.0(2022-12-20);
• Adapted from "Decision Trees and Random Forests", available here;
• Adapted from "Machine Learning with tidymodels" workshop, licensed CC Y-SA 4.0. Available here;
• Adapted from the learntidymodels package, available here.