# Beginning R: An Introduction to Statistical Programming

Apress, Oct 17, 2012 - Computers - 336 pages

Beginning R: An Introduction to Statistical Programming is a hands-on 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 open-source implementation of the statistical language S, which was developed by AT&T. R has eclipsed S and the commercially-available S-Plus language, and has become the de facto standard for doing, teaching, and learning computational statistics.

R is both an object-oriented 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 freely-available R language for statistics Shows the use of R in specific uses case such as simulations, discrete probability solutions, one-way ANOVA analysis, and more Takes a hands-on and example-based 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 easy-to-read 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.

1. Getting R and Getting Started
2. Programming in R
3. Writing Reusable Functions
4. Summary Statistics

Part II. Using R for Descriptive Statistics
5. Creating Tables and Graphs
6. Discrete Probability Distributions
7. Computing Standard Normal Probabilities

Part III. Using R for Inferential Statistics
8. Creating Confidence Intervals
9. Performing t Tests
10. Implementing One-Way ANOVA
12. Simple Correlation and Regression in R
13. Multiple Correlation and Regression in R
14. Logistic Regression
15. Performing Chi-Square Tests
16. Working in Nonparametric Statistics

Part IV. Taking R to the Next Level
17. Using R for Simulation
18. Resampling and Bootstrapping
19. Creating R Packages
20. Executing R Packages

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### Contents

 Chapter 1 Getting R and Getting Started 1 Chapter 2 Programming in R 25 Chapter 3 Writing Reusable Functions 47 Chapter 4 Summary Statistics 65 Chapter 5 Creating Tables and Graphs 77 Chapter 6 Discrete Probability Distributions 93 Chapter 7 Computing Normal Probabilities 103 Chapter 8 Creating Confidence Intervals 113
 Chapter 14 Logistic Regression 201 Chapter 15 ChiSquare Tests 217 Chapter 16 Nonparametric Tests 229 Chapter 17 Using R for Simulation 247 Chapter 18 The New Statistics Resampling and Bootstrapping 257 Chapter 19 Making an R Package 269 Chapter 20 The R Commander Package 289 Index 303

 Chapter 9 Performing t Tests 125 Chapter 10 OneWay Analysis of Variance 139 Chapter 11 Advanced Analysis of Variance 149 Chapter 12 Correlation and Regression 165 Chapter 13 Multiple Regression 185
 Contents vii About the Author xvii About the Technical Reviewer xviii Acknowledgments xix