287 pages, 51 b/w illustrations, 4 tables
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.
Rather than explore the many options available for every command as well as the ever-increasing number of packages, The R Primer focuses on the basics of data preparation and analysis and gives examples that can be used as a starting point. The numerous examples illustrate a specific situation, topic, or problem, including data importing, data management, classical statistical analyses, and high-quality graphics production. Each example is self-contained and includes R code that can be run exactly as shown, enabling results from The R Primer to be replicated. While base R is used throughout, other functions or packages are listed if they cover or extend the functionality. After working through the examples found in this text, new users of R will be able to better handle data analysis and graphics applications in R. Additional topics and R code are available from The R Primer's supporting website.
"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
- Reading spreadsheets
- Importing data from other statistical software programs
- Exporting data
- Working with data frames
- Transforming variables
- Descriptive statistics
- Linear models
- Generalized linear models
- Methods for analysis of repeated measurements
- Specific methods
- Model validation
- Contingency tables
- Multivariate methods
- Resampling statistics and bootstrapping
- Robust statistics
- Non-parametric methods
- Survival analysis
- High-level plots
- More advanced graphics
- Working with graphics
- Getting information
- R packages
- The R workspace
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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.