Hands-on Programming With R ─ Write Your Own Functions and Simulations

This guide is ideal if you’re a professional, manager, or student who wants practical knowledge of analyzing data, without having to get a PhD in statistics. It’s also good for people who have a PhD in statistics, but may not know how to write programs that apply statistical methods to real data.

Discover how to apply the R language to data analysis through active learning and hands-on demonstration. You’ll learn how to use R libraries that useful and reliable for data analysis, and how they can save you time and stress.

• Learn from a PhD-level statistician who develops and leads R courses
• Start analyzing data with R, rather than absorb academic statistics concepts
• Run more powerful analyses and make better-looking graphs
• Spend less time coding, with ggplot2, plyr, reshape2, and lubridate
• Learn how to make decisions during a data analysis

Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. He sees data analysis as a largely untapped fountain of value for both industry and science. Garrett received his Ph.D at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.

Garrett is passionate about helping people avoid the frustration and unnecessary learning he went through while mastering data analysis. Even before he finished his dissertation, he started teaching corporate training in R and data analysis for Revolutions Analytics. He's taught at Google, eBay, Axciom and many other companies, and is currently developing a training curriculum for RStudio that will make useful know-how even more accessible.

Outside of teaching, Garrett spends time doing clinical trials research, legal research, and financial analysis. He also develops R software, he's co-authored the lubridate R package--which provides methods to parse, manipulate, and do arithmetic with date-times--and wrote the ggsubplot package, which extends the ggplot2 package.