Data Analysis and Graphics using R

This is a nice book that blends modern statistical advice with practical R code that makes it easy to explore and understand the methods presented. What is nice about this book is the large number of examples that use real data provided in the various R packages. This gives the user a lot of examples where the statistical analysis reveals properties of the data that would suggest further transformations or analysis. This is an invaluable skill to learn for someone trying to learn how to use statistics to learn about your data. Examples of the the type of observations that would suggest modifications and further study include: recognition of outliers, recognition of data that might need transformations, recognition of heteroscedastic variance etc. Once you have recognized these properties there are steps one can take to improve the data modeling. This book talks about a great number of them.

Readers unfamiliar with this book can see what others have said here.

To learn this material as well as possible I worked through a great number of the book's statements and expanded (with further detail) upon anything that was unclear to me at the time. You can find the material I had time to write up by following the links on this page.

• Here
  

• Chapter 1 (a brief introduction to R)
  
• Chapter 2 (styles of data analysis)
  
• Chapter 3 (statistical models)
  
• Chapter 4 (a review of inference concepts)
  
• Chapter 5 (regression with a single predictor)
  
• Chapter 6 (multiple linear regression)
  
• Chapter 7 (exploiting the linear model framework)
  
• Chapter 11 (tree-based classification and regression)