If you prefer to read physical books here's a collection that will help. Each of these books is focused on a facet of R programming, and include code samples. These are great resources to keep handy while you're programming as well.
If these look a little too advanced for you, take a look at look at the introductory statistics books.
The Art of R Programming by Norman Matloff - This book is fantastic. Matloff takes the reader from getting data into R all the way through to object-oriented programming. It's full of code samples, and all of his work is easy to follow. If you only by one book on this list, get this one. 400 pages.
R in a Nutshell by Joseph Adler - Another excellent resource to keep by your side while programming. Adler includes sections on the interface, working with R packages, statistical analysis, machine learning & more. One nice thing about this book is that he includes a section on improving the performance of R, which will be important to those working with large datasets. There are practical examples & code samples throughout. 636 pages.
Data Mining with Rattle & R by Graham Williams - This book goes beyond basic statistics and focuses specifically on data mining with R, through the use of Rattle. It includes sections on gathering & manipulating data, predictive analytics, cluster analysis & more. The writing style is easy to follow, and is a great first step towards meaningful work with R. 394 pages.
Doing Bayesian Data Analysis by John K. Kruschke - This book starts with Bayes' rule & probability then moves quickly into performing complex data analysis with R. This is advanced statistics, so it shouldn't be your first book. Still, if your goal is machine learning or predictive analytics you're going to want to know this material. 672 pages.