*Beginning R, Second Edition* is a hands-on book showing how to use the R language, write and save R scripts, read in data files, and write custom statistical functions as well as use built in functions. This book shows the use of R in specific cases such as one-way ANOVA analysis, linear and logistic regression, data visualization, parallel processing, bootstrapping, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. It has been completely re-written since the first edition to make use of the latest packages and features in R version 3.

R is a powerful open-source language and programming environment for statistics 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, with a constantly evolving ecosystem of packages providing new functionality for data analysis. R has also become popular in commercial use at companies such as Microsoft, Google, and Oracle. 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 data analysis and research.

- Cover
- Title
- Copyright
- Dedication
- Contents at a Glance
- Contents
- About the Author
- In Memoriam
- About the Technical Reviewer
- Acknowledgments
- Introduction
- Chapter 1 : Getting Star?ted
- Chapter 2 : Dealing with Dates, Strings, and Data Frames
- Chapter 3 : Input and Output
- Chapter 4 : Control Structures
- Chapter 5 : Functional Programming
- Chapter 6 : Probability Distributions
- Chapter 7 : Working with Tables
- Chapter 8 : Descriptive Statistics and Exploratory Data Analysis
- Chapter 9 : Working with Graphics
- Chapter 10 : Traditional Statistical Methods
- Chapter 11 : Modern Statistical Methods
- Chapter 12 : Analysis of Variance
- Chapter 13 : Correlation and Regression
- Chapter 14 : Multiple Regression
- Chapter 15 : Logistic Regression
- Chapter 16 : Modern Statistical Methods II
- Chapter 17 : Data Visualization Cookbook
- Chapter 18 : High-Performance Computing
- Chapter 19 : Text Mining
- Index