Ebook: The R Primer
Author: Ekstrøm Claus Thorn
- Tags: R (Computer program language), Mathematical statistics, Data processing, MATHEMATICS, Applied, MATHEMATICS, Probability & Statistics, General
- Series: Chapman & Hall / CRC the R series
- Year: 2016
- Publisher: Chapman and Hall/CRC
- Edition: 2
- Language: English
- pdf
Praise for the first edition:
"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
The R Primer provides a collection of concise examples with solutions and interpretations of R problems frequently encountered by new users of this statistical software. Maintaining all the material from the first edition and adding substantial new material, the 2nd edition of The R Primer contains numerous examples that illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphical production. Each example is self-contained and includes R code that can be run exactly as shown, enabling results from the book to be replicated.
New to the Second Edition:
- Completely revised and updated with suggestions for using new and improved R packages
- Expanded with over 100 more pages
- New solutions for covering areas from web scraping over data wrangling to waffle plots and hanging rootograms.
- Additional intermediate and advanced topics in statistical data analysis including non-parametric statistics, random forests, penalized regression and curve smoothing.
Claus Thorn Ekstrøm is Professor at the Section of Biostatistics, University of Copenhagen where he teaches courses on statistics and R for beginners and advanced users. Professor Ekstrøm’s primary research interests lie within statistical genetics, genetic epidemiology, and bioinformatics, in particular genetic association studies, image analysis of microarray scans, and integrated analysis of gene expression and metabolic profile data.