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R Cookbookby Paul Teetor
Synopses & Reviews
What people are saying about R in a Nutshell
"I am excited about this book. R in a Nutshell is a great introduction to R, as well as a comprehensive reference for using R in data analytics and visualization. Adler provides 'real world' examples, practical advice, and scripts, making it accessible to anyone working with data, not just professional statisticians."
--Martin Schultz, Arthur K. Watson Professor of Computer Science, Yale University
"R in a Nutshell is an ideal book for getting started with R. Newcomers will find the fundamentals for performing statistical analysis and graphics, all illustrated with practical examples. This book is an invaluable reference for anyone who wants to learn what R is and what is can do, even for longtime R users looking for new tips and tricks."
--David M. Smith, Editor of the "Revolutions" blog at REvolution Computing
Why learn R? Because it's rapidly becoming the standard for developing statistical software. R in a Nutshell provides a quick and practical way to learn this increasingly popular open source language and environment. You'll not only learn how to program in R, but also how to find the right user-contributed R packages for statistical modeling, visualization, and bioinformatics.
The author introduces you to the R environment, including the R graphical user interface and console, and takes you through the fundamentals of the object-oriented R language. Then, through a variety of practical examples from medicine, business, and sports, you'll learn how you can use this remarkable tool to solve your own data analysis problems.
Book News Annotation:
R is a powerful open source language which allows those familiar with it to perform complex statistical calculations, data analysis, and create graphics. This book by Teetor (quantitative developer specializing in analytics and software engineering) contains over 200 simplified recipes for quick data analysis functions with R. Designed to be a quick-start guide for beginners and an easy reference for intermediate R users, R Cookbook provides to-the-point guidance on: creating vectors, input data, output data, data structures, probability, statistics, graphic displays, and much more. The author also provides web links and suggestions for further reading. Annotation ©2011 Book News, Inc., Portland, OR (booknews.com)
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 youre a beginner, R Cookbook will help get you started. If youre an experienced data programmer, it will jog your memory and expand your horizons. Youll get the job done faster and learn more about R in the process.
"Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time." —Jeffrey Ryan, software consultant and R package author
Perform data analysis with R quickly and efficiently with the task-oriented recipes in this cookbook. Although the R language and environment include everything you need to perform statistical work right out of the box, its structure can often be difficult to master. R Cookbook will help both beginners and experienced data programmers unlock and use the power of R.
This practical book provides a collection of concise recipes that will help you be productive with R immediately. Youll get the job done faster and learn more about R in the process.
Key topics include:
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
Preface; Why I Wrote This Book; When Should You Use R?; R License Terms; Examples; How This Book Is Organized; Conventions Used in This Book; Using Code Examples; How to Contact Us; Safari® Books Online; Acknowledgments; R Basics; Chapter 1: Getting and Installing R; 1.1 R Versions; 1.2 Getting and Installing Interactive R Binaries; Chapter 2: The R User Interface; 2.1 The R Graphical User Interface; 2.2 The R Console; 2.3 Batch Mode; 2.4 Using R Inside Microsoft Excel; 2.5 Other Ways to Run R; Chapter 3: A Short R Tutorial; 3.1 Basic Operations in R; 3.2 Functions; 3.3 Variables; 3.4 Introduction to Data Structures; 3.5 Objects and Classes; 3.6 Models and Formulas; 3.7 Charts and Graphics; 3.8 Getting Help; Chapter 4: R Packages; 4.1 An Overview of Packages; 4.2 Listing Packages in Local Libraries; 4.3 Loading Packages; 4.4 Exploring Package Repositories; 4.5 Custom Packages; The R Language; Chapter 5: An Overview of the R Language; 5.1 Expressions; 5.2 Objects; 5.3 Symbols; 5.4 Functions; 5.5 Objects Are Copied in Assignment Statements; 5.6 Everything in R Is an Object; 5.7 Special Values; 5.8 Coercion; 5.9 The R Interpreter; 5.10 Seeing How R Works; Chapter 6: R Syntax; 6.1 Constants; 6.2 Operators; 6.3 Expressions; 6.4 Control Structures; 6.5 Accessing Data Structures; 6.6 R Code Style Standards; Chapter 7: R Objects; 7.1 Primitive Object Types; 7.2 Vectors; 7.3 Lists; 7.4 Other Objects; 7.5 Attributes; Chapter 8: Symbols and Environments; 8.1 Symbols; 8.2 Working with Environments; 8.3 The Global Environment; 8.4 Environments and Functions; 8.5 Exceptions; Chapter 9: Functions; 9.1 The Function Keyword; 9.2 Arguments; 9.3 Return Values; 9.4 Functions As Arguments; 9.5 Argument Order and Named Arguments; 9.6 Side Effects; Chapter 10: Object-Oriented Programming; 10.1 Overview of Object-Oriented Programming in R; 10.2 Object-Oriented Programming in R: S4 Classes; 10.3 Old-School OOP in R: S3; Chapter 11: High-Performance R;11.1 Use Built-in Math Functions; 11.2 Use Environments for Lookup Tables; 11.3 Use a Database to Query Large Data Sets; 11.4 Preallocate Memory; 11.5 Monitor How Much Memory You Are Using; 11.6 Functions for Big Data Sets; 11.7 Parallel Computation with R; 11.8 High-Performance R Binaries; Working with Data; Chapter 12: Saving, Loading, and Editing Data; 12.1 Entering Data Within R; 12.2 Saving and Loading R Objects; 12.3 Importing Data from External Files; 12.4 Exporting Data; 12.5 Importing Data from Databases; Chapter 13: Preparing Data; 13.1 Combining Data Sets; 13.2 Transformations; 13.3 Binning Data; 13.4 Subsets; 13.5 Summarizing Functions; 13.6 Data Cleaning; 13.7 Finding and Removing Duplicates; 13.8 Sorting; Chapter 14: Graphics; 14.1 An Overview of R Graphics; 14.2 Graphics Devices; 14.3 Customizing Charts; Chapter 15: Lattice Graphics; 15.1 History; 15.2 An Overview of the Lattice Package; 15.3 High-Level Lattice Plotting Functions; 15.4 Customizing Lattice Graphics; 15.5 Low-Level Functions; Statistics with R; Chapter 16: Analyzing Data; 16.1 Summary Statistics; 16.2 Correlation and Covariance; 16.3 Principal Components Analysis; 16.4 Factor Analysis; 16.5 Bootstrap Resampling; Chapter 17: Probability Distributions; 17.1 Normal Distribution; 17.2 Common Distribution-Type Arguments; 17.3 Distribution Function Families; Chapter 18: Statistical Tests; 18.1 Continuous Data; 18.2 Discrete Data; Chapter 19: Power Tests; 19.1 Experimental Design Example; 19.2 t-Test Design; 19.3 Proportion Test Design; 19.4 ANOVA Test Design; Chapter 20: Regression Models; 20.1 Example: A Simple Linear Model; 20.2 Details About the lm Function; 20.3 Subset Selection and Shrinkage Methods; 20.4 Nonlinear Models; 20.5 Survival Models; 20.6 Smoothing; 20.7 Machine Learning Algorithms for Regression; Chapter 21: Classification Models; 21.1 Linear Classification Models; 21.2 Machine Learning Algorithms for Classification; Chapter 22: Machine Learning; 22.1 Market Basket Analysis; 22.2 Clustering; Chapter 23: Time Series Analysis; 23.1 Autocorrelation Functions; 23.2 Time Series Models; Chapter 24: Bioconductor; 24.1 An Example; 24.2 Key Bioconductor Packages; 24.3 Data Structures; 24.4 Where to Go Next; R Reference; base; boot; class; cluster; codetools; foreign; grDevices; graphics; grid; KernSmooth; lattice; MASS; methods; mgcv; nlme; nnet; rpart; spatial; splines; stats; stats4; survival; tcltk; tools; utils; Bibliography; Colophhhhhhon;\n
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