https://github.com/sydney-informatics-hub/module3r
Learn Machine Learning in the browser or locally in your RStudio IDE with interactive tutorials
https://github.com/sydney-informatics-hub/module3r
Last synced: 6 months ago
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Learn Machine Learning in the browser or locally in your RStudio IDE with interactive tutorials
- Host: GitHub
- URL: https://github.com/sydney-informatics-hub/module3r
- Owner: Sydney-Informatics-Hub
- License: mit
- Created: 2023-06-21T07:11:23.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-05-29T23:21:52.000Z (about 2 years ago)
- Last Synced: 2025-01-30T15:06:52.838Z (over 1 year ago)
- Language: R
- Homepage:
- Size: 16.6 MB
- Stars: 2
- Watchers: 7
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("Sydney-Informatics-Hub/Module3R")
```
## How to run the tutorials
You can start any tutorial with:
``` r
learnr::run_tutorial("tutorial-of-choice", package = "Module3R")
```
For example:
``` r
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](slides/Module3R.pptx).
Instructions for adding new tutorials are [here](https://education.rstudio.com/blog/2020/09/delivering-learnr-tutorials-in-a-package/).
## Code of Conduct
Please note that this package is released with a [Code of Conduct](https://pages.github.sydney.edu.au/informatics/sih_codeofconduct/). 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](https://www.gmudatamining.com/lesson-10-r-tutorial.html)*;
- *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](https://www.gmudatamining.com/lesson-13-r-tutorial.html)*;
- *Adapted from "Machine Learning with tidymodels" workshop, licensed CC Y-SA 4.0. Available [here](https://workshops.tidymodels.org/)*;
- *Adapted from the learntidymodels package, available [here](https://github.com/tidymodels/learntidymodels)*.