Textbook in PDF format

Introduces the R system, their capabilities required to perform basic numerical and graphical summaries of data.
Introduces using R for simulation including Monte Carlo experiments and Bayesian computation.
Features a new chapter on data frames, as well as new coverage on data mining, Rstudio, knitr, and dplyr.
Now in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data.
The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, it is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data, and this book is intended to be a useful resource for learning how to implement these procedures in R.
The new edition includes expanded coverage of ggplot2 graphics, as well as new chapters on importing data and multivariate data methods