Beginning R: An Introduction to Statistical Programming / Edition 1

by Larry Pace

Overview

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.

Table of Contents

Part I. Learning the R Language

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

11. Implementing Advanced 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

Product Details

ISBN-13:	9781430245544
Publisher:	Apress
Publication date:	10/17/2012
Edition description:	1st ed.
Pages:	336
Product dimensions:	7.50(w) x 9.30(h) x 0.90(d)

About the Author

Dr. Larry Pace is a statistics author and educator, as well as a consultant. He lives in the upstate area of South Carolina in the town of Anderson. He is a professor of statistics, mathematics, psychology, management, and leadership. He has programmed in a variety of languages and scripting languages including R, Visual Basic, JavaScript, C##, PHP, APL, and in a long-ago world, Fortran IV. He writes books and tutorials on statistics, computers, and technology. He has also published many academic papers, and made dozens of presentations and lectures. He has consulted with Compaq Computers, AT&T, Xerox Corporation, the U.S. Navy, and International Paper. He has taught at Keiser University, Argosy University, Capella University, Ashford University, Anderson University (where he was the chair of the behavioral sciences department), Clemson University, Louisiana Tech University, LSU in Shreveport, the University of Tennessee, Cornell University, Rochester Institute of Technology, Rensselaer Polytechnic Institute, and the University of Georgia.

Table of Contents

Part I. Learning the R Language

1. Getting Started

2. Dealing with Dates, Strings, and Data Frames

3. Input and Output

4. Control Structures

Part II. Using R for Descriptive Statistics

5. Functional Programming

6. Probability Distributions

7. Working with Tables

Part III. Using R for Inferential Statistics

8. Descriptive Statistics and Exploratory Data Analysis

9. Working with Graphics

10. Traditional Statistical Methods

11. Modern Statistical Methods

12. Analysis of Variance

13. Correlation and Regression

14. Multiple Regression

15. Logistic Regression

16. Modern Statistical Methods II

Part IV. Taking R to the Next Level

17. Data Visualization Cookbook

18. High-performance Computing

19. Text Mining