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
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