A01=Garrett Grolemund
analysis
analysis of variance
Author_Garrett Grolemund
Category=UFM
data
data analysis
data modelling
data visualization
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
ggplot2
graphics
informatics
r
r data ggplot2 statistics informatics graphics visualization analysis data analysis data visualization data modelling analysis of variance variance
statistics
variance
visualization
Product details
- ISBN 9781449359010
- Publication Date: 26 Aug 2014
- Publisher: O'Reilly Media
- Publication City/Country: US
- Product Form: Paperback
Secure
checkout
Fast Shipping
Easy returns
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, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's 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. You'll 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 computer's 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 R's package system and debugging tools Practice and apply R programming concepts as you learn them
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.
