- Mastering Data Analysis with R
- Table of Contents
- Mastering Data Analysis with R
- Credits
- About the Author
- About the Reviewers
- www.PacktPub.com
- Preface
- 1. Hello, Data!
- 2. Getting Data from the Web
- 3. Filtering and Summarizing Data
- 4. Restructuring Data
- 5. Building Models (authored by Renata Nemeth and Gergely Toth)
- 6. Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
- 7. Unstructured Data
- 8. Polishing Data
- 9. From Big to Small Data
- 10. Classification and Clustering
- 11. Social Network Analysis of the R Ecosystem
- 12. Analyzing Time-series
- 13. Data Around Us
- 14. Analyzing the R Community
- A. References
- General good readings on R
- Chapter 1 – Hello, Data!
- Chapter 2 – Getting Data from the Web
- Chapter 3 – Filtering and Summarizing Data
- Chapter 4 – Restructuring Data
- Chapter 5 – Building Models (authored by Renata Nemeth and Gergely Toth)
- Chapter 6 – Beyond the Linear Trend Line (authored by Renata Nemeth and Gergely Toth)
- Chapter 7 – Unstructured Data
- Chapter 8 – Polishing Data
- Chapter 9 – From Big to Smaller Data
- Chapter 10 – Classification and Clustering
- Chapter 11 – Social Network Analysis of the R Ecosystem
- Chapter 12 – Analyzing Time-series
- Chapter 13 – Data Around Us
- Chapter 14 – Analysing the R Community
- Index
Gergely Daróczi is a former assistant professor of statistics and an enthusiastic R user and package developer. He is the founder and CTO of an R-based reporting web application at http://rapporter.net and a PhD candidate in sociology. He is currently working as the lead R developer/research data scientist at https://www.card.com/ in Los Angeles.
Besides maintaining around half a dozen R packages, mainly dealing with reporting, Gergely has coauthored the books Introduction to R for Quantitative Finance and Mastering R for Quantitative Finance (both by Packt Publishing) by providing and reviewing the R source code. He has contributed to a number of scientific journal articles, mainly in social sciences but in medical sciences as well.
I am very grateful to my family, including my wife, son, and daughter, for their continuous support and understanding, and for missing me while I was working on this book—a lot more than originally planned.
I am also very thankful to Renata Nemeth and Gergely Toth for taking over the modeling chapters. Their professional and valuable help is highly appreciated. David Gyurko also contributed some interesting topics and preliminary suggestions to this book. And last but not least, I received some very useful feedback from the official reviewers and from Zoltan Varju, Michael Puhle, and Lajos Balint on a few chapters that are highly related to their field of expertise—thank you all!
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