{"id":21318805,"url":"https://github.com/sydney-informatics-hub/module3r","last_synced_at":"2026-01-05T20:41:24.257Z","repository":{"id":182735441,"uuid":"656551081","full_name":"Sydney-Informatics-Hub/Module3R","owner":"Sydney-Informatics-Hub","description":"Learn Machine Learning in the browser or locally in your RStudio IDE with interactive tutorials","archived":false,"fork":false,"pushed_at":"2024-05-29T23:21:52.000Z","size":17372,"stargazers_count":2,"open_issues_count":5,"forks_count":1,"subscribers_count":7,"default_branch":"main","last_synced_at":"2025-01-30T15:06:52.838Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Sydney-Informatics-Hub.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-06-21T07:11:23.000Z","updated_at":"2024-05-29T23:21:56.000Z","dependencies_parsed_at":"2023-07-21T07:49:11.863Z","dependency_job_id":"9bd219c2-3985-414b-a597-b65bbe55640a","html_url":"https://github.com/Sydney-Informatics-Hub/Module3R","commit_stats":null,"previous_names":["sydney-informatics-hub/module3r"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sydney-Informatics-Hub%2FModule3R","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sydney-Informatics-Hub%2FModule3R/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sydney-Informatics-Hub%2FModule3R/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Sydney-Informatics-Hub%2FModule3R/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Sydney-Informatics-Hub","download_url":"https://codeload.github.com/Sydney-Informatics-Hub/Module3R/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245504236,"owners_count":20626325,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-21T19:23:16.278Z","updated_at":"2026-01-05T20:41:24.215Z","avatar_url":"https://github.com/Sydney-Informatics-Hub.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Module3R\n\nLearn Machine Learning in the browser or locally in your RStudio IDE with interactive tutorials!\n\n## Installation\n\nYou can install the development version from [GitHub](https://github.com/) with:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"Sydney-Informatics-Hub/Module3R\")\n```\n\n## How to run the tutorials\n\nYou can start any tutorial with:\n\n``` r\nlearnr::run_tutorial(\"tutorial-of-choice\", package = \"Module3R\")\n```\n\nFor example:\n\n``` r\nlearnr::run_tutorial(\"Part-1\", package = \"Module3R\")\n```\n\n## List of available tutorials\n\n| Tutorial | Description                                                               |\n|:------------------|:----------------------------------------------------|\n| `Part 1` | Ames housing dataset - Predict selling prices                             |\n| `Part 2` | Pima Indian Women's diabetes dataset - Predict diabetes status            |\n| `Part 3` | Unsupervised dimensionality reduction - Transforming groups of predictors |\n\n## How to use the tutorials\n\nThese tutorials consist of content along with interactive components for checking and reinforcing understanding. Throughout the tutorials you will find:\n\n-   Narrative, figures and illustrations;\n\n-   Code exercises that you can edit and execute directly;\n\n-   Quiz questions...\n\nEach 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.\n\nEach tutorial includes a Table of Contents and it reveals content one sub-section at a time:\n\n![](images/toc.png)\n\nExercises 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:\n\n![](images/sol.png)\n\n### For the trainer\n\nIntroduction slides for the tutorials are [here](slides/Module3R.pptx). \nInstructions for adding new tutorials are [here](https://education.rstudio.com/blog/2020/09/delivering-learnr-tutorials-in-a-package/).\n\n## Code of Conduct\n\nPlease 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.\n\n### References\n\n-   *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*;\n-   *Adapted from \"Linear Regression and tidymodels\", available [here](https://www.gmudatamining.com/lesson-10-r-tutorial.html)*;\n-   *Max Kuhn and Julia Silge, \"Tidy Modeling with R\", Version 1.0.0(2022-12-20)*;\n-   *Adapted from \"Decision Trees and Random Forests\", available [here](https://www.gmudatamining.com/lesson-13-r-tutorial.html)*;\n-   *Adapted from \"Machine Learning with tidymodels\" workshop, licensed CC Y-SA 4.0. Available [here](https://workshops.tidymodels.org/)*;\n-   *Adapted from the learntidymodels package, available [here](https://github.com/tidymodels/learntidymodels)*.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsydney-informatics-hub%2Fmodule3r","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsydney-informatics-hub%2Fmodule3r","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsydney-informatics-hub%2Fmodule3r/lists"}