Foundations of Data and Digital Journalism
This accessible, step-by-step guide is written for students and working professionals who want to better understand data journalism, web design, and the visualization of information.
Foundations of Data and Digital Journalism recognizes a growing need for general data knowledge in newsrooms across the globe, including an understanding of what’s possible for both data reporting and presentation and how it can be achieved. It serves as a roadmap for students and working journalists who seek to understand what data is and how to find it; how to harness it most effectively for news; how to think critically about analysis results, potential shortcomings in the data, and the inclusion of appropriate context; and how to present compelling, data-driven stories online. Interviews with a diverse range of current practitioners help the reader gain a deeper understanding of how these tools and techniques are used in digitally focused newsrooms today. Taking a holistic approach to data journalism, this book enables readers to:
- Assess a data set with a critical eye, understanding what it shows, how it was created, and for what purpose
- Master prominent and easily accessible software tools, including Google Sheets and R
- Translate findings and conclusions into plain English for a news audience without overstating what the data can show or being misleading
- Create impactful, attractive visualizations for an audience to explore
- Understand how the modern web works, including HTML5, CSS3, and responsive webpage frameworks, like Bootstrap
This is an ideal textbook for undergraduate and postgraduate journalism students and for working professionals looking to expand their skillset.
The book is supported with online student resources, including example datasets to support the material covered, available at Routledge.com.
List of figures
1. Introduction: Why data journalism?
2. Data, numeracy, and how to bulletproof information
3. Where data comes from—and how to get it
4. Starting with spreadsheets
5. Sort, filter, pivot: The building blocks of data analysis
6. Clean and repair: Techniques for more advanced analysis
7. Simple tools for everyday data visualization
8. Introduction to R and the tidyverse
9. Using R for data analysis
10. Making the modern web with HTML and CSS
11. More advanced CSS: Layouts, Bootstrap, and more
12. Where to learn more