Newcomers to R are often intimidated by the command-line interface, the vast number of functions and packages, or the processes of importing data and performing a simple statistical analysis. The R Primer provides a collection of concise examples and solutions to R problems frequently encountered by new users of this statistical software. This new edition adds coverage of R Studio and reproducible research.
This is an extremely well-written book that thoroughly covers the subject and offers a significant number of examples that are explained very clearly. It will certainly be a beneficial resource for all R users. This reviewer teaches an online course in statistics using R, and believes that this text will serve as a very effective reference.
~R. Bharath, Northern Michigan University
This book is intended for readerswho are familiarwith statistics and already have some basic knowledge in R. It provides a collection of more than 170 examples or problems and solutions in an R-context, derived from questions the author encountered in the statistical consultancy service of the University of Copenhagen. The problems concern data import and data management, statistical analysis and graphical presentation. Each problem or example is stated in one sentence. The solution is self-contained and provides an R code that can be run by the reader so that the results can be replicated; related problems can then be dealt with by adapting the R code for the needs at hand. Alternative solutions are also indicated at the end of most entries.
~Peter Hackl, Stat Papers
Claus Thorn Ekstrom 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.
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.