Download PDF The R Primer By Claus Thorn Ekstrom
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The R Primer By Claus Thorn Ekstrom
Download PDF The R Primer By Claus Thorn Ekstrom
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- Sales Rank: #1842745 in eBooks
- Published on: 2011-08-29
- Released on: 2011-08-29
- Format: Kindle eBook
Review
"A ‘primer’ is supposed to be a book that covers very elementary needs. This book does so in a quite special way: It provides 142 problems with solutions, called ‘rules’ … The book efficiently addresses the many impediments against simply ‘go do it’ for people who have already done statistical analyses with software other than R and want to quickly learn how to do the same things in R. … Do I consider this book worth reading/buying ? Yes I do! … [it provides] a collection of useful starting points on how to accomplish practically relevant tasks for applied statistics in R."
―Ulrike Grömping, Journal of Statistical Software, January 2013
… contains a number of interesting self-contained examples, each illustrating a specific situation. One of the salient features is that it covers importing data, handling data, and creating graphics. … Valuable for readers interested in solving statistical problems using R. Summing Up: Recommended.
―CHOICE Magazine, April 2012
"This book provides a good introduction to R, using a clear layout and detailed, reproducible examples. An ideal tool for any new R user. … A wide range of topics are covered, making the book suitable for a variety of readers, from undergraduate students to professionals new to R. … an extremely helpful introduction to a very useful statistical package."
―Claire Keeble, Journal of Applied Statistics, 2012
"… a nice starting point for learning R, and suitable for self-study provided the reader has some background in statistics."
―Olle Häggström, International Statistical Review, 2012
About the Author
Claus Thorn Ekstrøm is an associate professor of statistics in the Department of Basic Sciences and Environment and leader of the Center for Applied Bioinformatics at the University of Copenhagen. His research interests include genetic marker error detection, simulation-based inference, image analysis, and the analysis of microarray DNA chips, metabolic profiles, and quantitative traits for complex human families.
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