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.
What You Will Learn:
How to acquire and install R
Hot to import and export data and scripts
How to analyze data and generate graphics
How to program in R to write custom functions
Hot to use R for interactive statistical explorations
How to conduct bootstrapping and other advanced techniques
Table of Contents
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
