Beginning R

1. Front Matter

   Pages i-xxiv

   PDF

2. Getting R and Getting Started

   • Larry Pace

   Pages 1-23

3. Programming in R

   • Larry Pace

   Pages 25-46

4. Writing Reusable Functions

   • Larry Pace

   Pages 47-63

5. Summary Statistics

   • Larry Pace

   Pages 65-76

6. Creating Tables and Graphs

   • Larry Pace

   Pages 77-92

7. Discrete Probability Distributions

   • Larry Pace

   Pages 93-101

8. Computing Normal Probabilities

   • Larry Pace

   Pages 103-111

9. Creating Confidence Intervals

   • Larry Pace

   Pages 113-123

10. Performing t Tests

    • Larry Pace

    Pages 125-138

11. One-Way Analysis of Variance

    • Larry Pace

    Pages 139-147

12. Advanced Analysis of Variance

    • Larry Pace

    Pages 149-164

13. Correlation and Regression

    • Larry Pace

    Pages 165-183

14. Multiple Regression

    • Larry Pace

    Pages 185-199

15. Logistic Regression

    • Larry Pace

    Pages 201-216

16. Chi-Square Tests

    • Larry Pace

    Pages 217-228

17. Nonparametric Tests

    • Larry Pace

    Pages 229-246

18. Using R for Simulation

    • Larry Pace

    Pages 247-256

19. The “New” Statistics: Resampling and Bootstrapping

    • Larry Pace

    Pages 257-268

20. Making an R Package

    • Larry Pace

    Pages 269-288

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

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.

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