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Data Science with R

Data Analysis and Prediction Algorithms with R, Third Edition

By Daniel Paredes Inilupu

📖 Read Online

https://dparedesi.github.io/Data-Science-with-R-book/

📚 What You'll Learn

Part	Topics
I. Fundamentals	R objects, functions, data frames, tidyverse
II. Visualization	ggplot2, gapminder case study
III. Statistics	Probability, distributions, inference
IV. Data Wrangling	Importing data, text mining
V. Machine Learning	tidymodels, classification, regression, clustering
VI. Applied Cases	Real estate analysis, Google Analytics
VII. Generative AI	LLM APIs, AI-assisted coding
Appendix	Ethics checklist

✨ Key Features

• Modern Tidyverse: Uses the native pipe (|>) and modern dplyr verbs
• Tidymodels: Full ML chapters using the tidymodels framework
• Real-World Cases: Includes new case studies with real datasets
• GenAI Integration: Covers using LLMs and APIs within R workflow
• Ethics: Dedicated sections on algorithmic bias and ethics checklist

🛠️ Technical Stack

• R: 4.5.2+
• Framework: bookdown
• ML: tidymodels
• Visualization: ggplot2

🚀 Quick Start

# Install dependencies
source("setup_dependencies.R")

# Build the book
bookdown::render_book("index.Rmd")

The output will be generated in the docs/ folder.

📂 Datasets

All datasets used in this book are hosted directly in this repository and served via GitHub Pages. you can browse them in the docs/data/ directory.

To access them in R, you can use the URL pattern: https://dparedesi.github.io/Data-Science-with-R-book/data/[filename]

Example:

library(readr)
url <- "https://dparedesi.github.io/Data-Science-with-R-book/data/student-grades.csv"
grades <- read_csv(url)

📁 Structure

├── 01.intro/          Introduction
├── 02.fundamentals/   Objects, Functions, Data Frames
├── 03.visualization/  ggplot2, Gapminder Case Study
├── 04.statistics/     Probability and Inference
├── 05.wrangling/      Data Import, Text Mining
├── 06.machine-learning/  Supervised & Unsupervised Learning
├── 07.real-cases/     Applied Case Studies
├── 08.genai/          Generative AI with R
├── 09.appendix/       Ethics Checklist

📝 Citation

If you use this book in your work, please cite:

@book{paredes2024datascience,
  author = {Paredes Inilupu, Daniel},
  title = {Data Science with R: Data Analysis and Prediction Algorithms},
  year = {2025},
  edition = {3rd},
  publisher = {Leanpub},
  url = {https://leanpub.com/data-science-with-r}
}

💰 Support This Work

You can support this effort by purchasing the PDF version on Leanpub. The purchase includes access to future updates.

🤝 Contributing

See CONTRIBUTING.md for guidelines on how to report errors, suggest improvements, or submit fixes.

📄 License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

📧 Contact

Questions or suggestions? Email: dparedesi@uni.pe