Claus Thorn Ekstrom
March 1, 2017
by Chapman and Hall/CRC
Reference - 408 Pages
ISBN 9781138631977 - CAT# K32083
Series: Chapman & Hall/CRC The R Series
For Librarians Available on CRCnetBASE >>
Presents concise examples and solutions to common problems in R
Explains how to read and interpret output from statistical analyses
Covers importing data, data handling, and creating graphics
New edition adds chapters on R Studio and Reproducible Research
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:
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.
Preface. Importing data. Reading spreadsheets. Importing data from other statistical software programs. Exporting data.Manipulating data. Working with data frames. Factors. Transforming variables. Statistical analyses. Descriptive statistics. Linear models. Generalized linear models. Methods for analysis of repeated measurements. Specific methods. Model validation. Contingency tables. Agreement. Multivariate methods. Resampling statistics and bootstrapping. Robust statistics. Non-parametric methods. Survival analysis. Graphics. High-level plots. More advanced graphics. Working with graphics. Getting information. R packages. The R workspace. R Studio. Getting information. Using R Studio for reproducible research. Large datasets. Bibliography. Index.