Data Journalism with R
Preface
This book is built with Quarto, and its source files are hosted on this GitHub repository.
About This Book
This is a practical guide to data journalism using R, built from my teaching experience at The University of Hong Kong.
Data journalism is a skill. Like any skill, you learn it by doing. This book combines foundational R knowledge with real-world examples and case studies, so you start with journalism questions and learn the R tools needed to answer them.
In the age of AI, it is often argued that automated systems can analyze data more efficiently than human journalists. Efficiency, however, is not the same as understanding. Learning the fundamentals of data analysis remains essential. A solid grasp of core concepts allows you to interpret results correctly, evaluate AI-generated outputs critically, and ask better questions when using AI tools. This book aims to equip journalists and students with the analytical foundation needed to use AI not passively, but thoughtfully and responsibly.
What You’ll Learn
By working through this book, you’ll gain practical skills in:
- R Fundamentals: Variables, data types, functions—the building blocks of data analysis
- Data Import & Wrangling: Load messy real-world data and transform it for analysis using tidyverse
- Data Analysis: Calculate statistics, find trends, and identify newsworthy patterns
- Data Visualization: Create charts and graphics that tell compelling stories
- Hands-on Journalism: Analyze real datasets and produce journalism-ready insights
Structure of This Book
This book is organized in five progressive parts:
- Getting Started — Set up R/RStudio and learn basic syntax
- Data Import & Wrangling — Load data and prepare it for analysis (the real work of data journalism)
- Data Analysis — Calculate statistics and find patterns using tidyverse
- Data Visualization — Create publication-ready charts and interactive maps
- Case Studies — Complete real-world examples applying all skills together
Each chapter builds on previous material, but you can also jump to specific case studies if you’re learning the skills in your own projects.
Who Should Read This
This book is for: - Journalists wanting to add data analysis to your reporting toolkit - Journalism students learning how to find and tell data-driven stories - Data storytellers interested in using R for analysis and visualization - Anyone with a journalism question and a dataset who wants to find the answer
You do not need prior programming experience. We start from scratch and assume no background in coding.
This is not a computer science textbook. It’s focused on what’s useful for journalism, not theoretical completeness. We skip topics that don’t matter for your work.
Prerequisites
To use this book, you’ll need: - A computer (Mac, Windows, or Linux) - R and RStudio installed (Chapter 1 covers this) - Curiosity about stories hiding in data - Persistence when code doesn’t work on the first try (totally normal!)
That’s it. Everything else is in this book.
How to Use This Book
Read sequentially (recommended): Start with Chapter 1 and work through. Each chapter assumes knowledge from previous ones.
Focus on what you need: If you’re working on a specific journalism project, you can jump to relevant chapters and reference earlier material as needed.
Code along: The best way to learn is by running the code examples. Don’t just read—type them into RStudio and experiment.
Use the case studies: Chapters “Case Studies” contain complete, realistic examples you can adapt for your own datasets.
Refer back: Bookmark the Key Functions tables at the end of each chapter. You’ll use them often.
Acknowledgements
This book is inspired by the Reporting with Data in R book by two professors at the University of Texas at Austin, Christian McDonald and Josephine Lukito. I would like to thank them for their great work and for sharing their materials with the public.
This book is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. You are free to share and adapt the content for non-commercial purposes, as long as you give appropriate credit and distribute your contributions under the same license.