Synopses & Reviews
Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, youll learn how to load data, assemble and disassemble data objects, navigate Rs environment system, write your own functions, and use all of Rs programming tools.
RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. Youll gain valuable programming skills and support your work as a data scientist at the same time.
- Work hands-on with three practical data analysis projects based on casino games
- Store, retrieve, and change data values in your computers memory
- Write programs and simulations that outperform those written by typical R users
- Use R programming tools such as if else statements, for loops, and S3 classes
- Learn how to write lightning-fast vectorized R code
- Take advantage of Rs package system and debugging tools
- Practice and apply R programming concepts as you learn them
This guide is ideal if youre a professional, manager, or student who wants practical knowledge of analyzing data, without having to get a PhD in statistics. Its 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. Youll 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
About the Author
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.