Data Computing

Information is what we want   …    but data are what we’ve got.

front-coverData Computing introduces wrangling and visualization, the techniques for turning data into information. Ideal for self-study or as a classroom text, Data Computing shows how to condense and combine data from multiple sources to present them in a way that informs discovery and decision making. An accessible introduction to technical computing for those whose primary interest is with data, Data Computing builds on breakthrough software developments that let even beginners exploit the power of professional-level tools. ISBN 978-0-9839658-4-8


Praise for Data Computing

No matter what you do, you can use data to do it better. Gaining that superpower requires you to learn some programming tools and some mental tools. This book will teach you both: you’ll get the mental building blocks to think about data analysis, and the computational tools to turn those thoughts into code. If you’re just learning to swim in the data ocean, Danny’s lucid writing and thoughtful approach makes this book a great place to start!    — Hadley Wickham, Chief Scientist, RStudio

The book covers in a systematic way not only the stuff I’ve picked up in a fragmentary, bit-at-a-time way, but more important for me, stuff I wanted to know, and stuff I needed to know without realizing it. Better yet, Kaplan’s book makes it all easily accessible. — George Cobb, Professor Emeritus of Mathematics and Statistics, Mt. Holyoke College

Table of Contents

  1. Tidy Data: excerpt
  2. Computing with R: excerpt
  3. R Command Patterns: excerpt
  4. Files and Documents: excerpt
  5. Introduction to Data Graphics: excerpt
  6. Frames, Glyphs, and other Components of Graphics: excerpt
  7. Wrangling and Data Verbs: excerpt
  8. Graphics and their Grammar
  9. More Data Verbs
  10. Joining Two Tables
  11. Wide versus Narrow Data Layouts
  12. Ranks and Ordering
  13. Networks
  14. Collective Properties of Cases
  15. Scraping and Cleaning Data
  16. Using Regular Expressions
  17. Machine Learning


February 2017 review from The American Statistician.

December 2016 review from the MAA Reviews link here. (Note: this review is of a preview edition from early 2015.)