Book description
Beginning R: An Introduction to Statistical Programming is a handson book showing how to use the R language, write and save R scripts, build and import data files, and write your own custom statistical functions. R is a powerful opensource implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commerciallyavailable SPlus language, and has become the de facto standard for doing, teaching, and learning computational statistics.
R is both an objectoriented language and a functional language that is easy to learn, easy to use, and completely free. A large community of dedicated R users and programmers provides an excellent source of R code, functions, and data sets. R is also becoming adopted into commercial tools such as Oracle Database. Your investment in learning R is sure to pay off in the long term as R continues to grow into the go to language for statistical exploration and research.
Covers the freelyavailable R language for statistics
Shows the use of R in specific uses case such as simulations, discrete probability solutions, oneway ANOVA analysis, and more
Takes a handson and examplebased approach incorporating best practices with clear explanations of the statistics being done
What you'll learn
Acquire and install R
Import and export data and scripts
Generate basic statistics and graphics
Program in R to write custom functions
Use R for interactive statistical explorations
Implement simulations and other advanced techniques
Who this book is for
Beginning R: An Introduction to Statistical Programming is an easytoread book that serves as an instruction manual and reference for working professionals, professors, and students who want to learn and use R for basic statistics. It is the perfect book for anyone needing a free, capable, and powerful tool for exploring statistics and automating their use.
Table of contents
 Title
 Dedication
 Contents at a Glance
 Contents
 About the Author
 About the Technical Reviewer
 Acknowledgments
 Introduction
 Chapter 1: Getting R and Getting Started

Chapter 2: Programming in R
 What is Programming?
 Getting Ready to Program
 The Requirements for Learning to Program
 Flow Control
 Essentials of R Programming
 Understanding the R Environment
 Implementation of Program Flow in R
 A First R Program
 Another Example—Finding Pythagorean Triples
 Using R to Solve Quadratic Equations
 Why R is ObjectOriented
 Conclusion

Chapter 3: Writing Reusable Functions
 Examining an R Function from the Base R Code
 Creating a Function
 Calculating a Confidence Interval for a Mean
 Avoiding Loops with Vectorized Operations
 Vectorizing IfElse Statements Using ifelse()
 Making More Powerful Functions
 Any, All, and Which
 Making Functions More Useful
 Confidence Intervals Revisited
 Conclusion
 Chapter 4: Summary Statistics
 Chapter 5: Creating Tables and Graphs
 Chapter 6: Discrete Probability Distributions
 Chapter 7: Computing Normal Probabilities
 Chapter 8: Creating Confidence Intervals
 Chapter 9: Performing t Tests
 Chapter 10: OneWay Analysis of Variance
 Chapter 11: Advanced Analysis of Variance
 Chapter 12: Correlation and Regression
 Chapter 13: Multiple Regression
 Chapter 14: Logistic Regression
 Chapter 15: ChiSquare Tests
 Chapter 16: Nonparametric Tests
 Chapter 17: Using R for Simulation
 Chapter 18: The “New” Statistics: Resampling and Bootstrapping
 Chapter 19: Making an R Package
 Chapter 20: The R Commander Package
 Index
Product information
 Title: Beginning R: An Introduction to Statistical Programming
 Author(s):
 Release date: October 2012
 Publisher(s): Apress
 ISBN: 9781430245544
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