With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression.
Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If youâÂÂre a beginner, R Cookbook will help get you started. If youâÂÂre an experienced data programmer, it will jog your memory and expand your horizons. YouâÂÂll get the job done faster and learn more about R in the process.
- Create vectors, handle variables, and perform other basic functions
- Input and output data
- Tackle data structures such as matrices, lists, factors, and data frames
- Work with probability, probability distributions, and random variables
- Calculate statistics and confidence intervals, and perform statistical tests
- Create a variety of graphic displays
- Build statistical models with linear regressions and analysis of variance (ANOVA)
- Explore advanced statistical techniques, such as finding clusters in your data
"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R languageâÂÂone practical example at a time."
âÂÂJeffrey Ryan, software consultant and R package author
About the Author
Paul Teetor is a quantitative developer with Masters degrees in statistics and computer science. He specializes in analytics and software engineering for investment management, securities trading, and risk management. He works with hedge funds, market makers, and portfolio managers in the greater Chicago area.
Table of Contents
Chapter 1 Getting Started and Getting Help
Chapter 2 Some Basics
Chapter 3 Navigating the Software
Chapter 4 Input and Output
Chapter 5 Data Structures
Chapter 6 Data Transformations
Chapter 7 Strings and Dates
Chapter 8 Probability
Chapter 9 General Statistics
Chapter 10 Graphics
Chapter 11 Linear Regression and ANOVA
Chapter 12 Useful Tricks
Chapter 13 Beyond Basic Numerics and Statistics
Chapter 14 Time Series Analysis
