The Art of R Programming A Tour of Statistical Software Design

R is the world's most popular programming language for statistical computing. Drug developers use it to evaluate clinical trials and determine which medications are safe and effective; archaeologists use it to sift through mounds of artifacts and track the spread of ancient civilizations; and actuaries use it to assess financial risks and keep economies running smoothly. In The Art of R Programming, veteran author Norman Matloff takes readers on a guided tour of this powerful language, from basic object types and data structures to graphing, parallel processing, and much more. Along the way, readers learn about topics including functional and object-oriented programming, low-level code optimization, and interfacing R with C++ and Python. Whether readers are doing academic research, designing aircraft, or forecasting the weather, R is the tool of choice for statistical application development, and The Art of R Programmingis the definitive guide to learning R.

Norman Matloff is a professor of computer science (and was formerly a professor of statistics) at the University of California, Davis. His research interests include parallel processing and statistical regression, and he is the author of a number of widely-used Web tutorials on software development. He has written articles for the New York Times, the Washington Post, Forbes Magazine, and the Los Angeles Times, and is the co-author of The Art of Debugging (No Starch Press).

Introduction
Chapter 1: Getting Started
Chapter 2: Vectors
Chapter 3: Matrices and Arrays
Chapter 4: Lists
Chapter 5: Data Frames
Chapter 6: Factors and Tables
Chapter 7: R Programming Structures
Chapter 8: Doing Math and Simulations in R
Chapter 9: Object-Oriented Programming
Chapter 10: Input/Output
Chapter 11: String Manipulation
Chapter 12: Graphics
Chapter 13: Debugging
Chapter 14: Performance Enhancement: Tradeoffs in Time and Space
Chapter 15: Interfacing R to Other Languages
Chapter 16: Parallel R
Appendix A: Installing R
Appendix B: Installing and Using Packages