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The DataScienceR repository is a curated collection of tutorials, sample code, and project templates for learning data science using the R programming language. It includes an assortment of exercises, sample datasets, and instructional code that cover the core steps of a data science project: data ingestion, cleaning, exploratory analysis, modeling, evaluation, and visualization. Many of the modules demonstrate best practices in R, such as using the tidyverse, R Markdown, modular scripting, and reproducible workflows. The repository also shows examples of linking R with external resources — APIs, databases, and file formats — and integrating into larger pipelines. It acts as a learning scaffold for students or beginners transitioning to more advanced data science work in R, offering a hands-on, example-driven approach. The structure encourages modularity, readability, and reproducible practices, making it a useful reference repository for learners and educators alike.
Features
- Tutorials and sample projects covering end-to-end data science in R
- Exercises and example datasets to practice analysis and modeling
- Use of tidyverse, R Markdown, and reproducible workflows
- Integration examples with APIs, databases, and external file formats
- Modular, readable code structure to encourage maintainability
- Educational and learner-focused scaffold for intermediate R users
Project Samples
