R is a powerful and flexible statistical and graphical environment that is freely distributed under the GNU Public Licencea for all major computing platforms (Windows, MacOSX and Linux). This open source licence along with a relatively simple scripting syntax has promoted diverse and rapid evolution and contribution. As the broader scientific community continues to gain greater instruction and exposure to the overall project, the popularity of R as a teaching and research tool continues to accelerate.
It is now widely acknowledged that R proficiency as a scientific skill set is becoming increasingly more desirable and useful throughout the scientific community. However, as with most open source developments, the emphasis of the R project remains on the expansive development of tools and features. Applied documentation still remains somewhat sparse and somewhat incomprehensible to the average biologist. Whilst there are a number of excellent texts on R emerging, the bulk of these texts are devoted to the R language itself. Any featured examples therein are used primarily for the purpose of illustrating the suite of commonly used R features and procedures, rather than to illustrate how R can be used to perform common biostatistical analyses.
R — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research.
Topics covered include:
• simple hypothesis testing, graphing
• exploratory data analysis and graphical summaries
• regression (linear, multi and non-linear)
• simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)
• frequency analysis and generalized linear models.
Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques.
The book is accompanied by a companion website www.wiley.com/go/logan/r with an extensive set of resources comprising all R scripts and data sets used in the book, additional worked examples, the biology package, and other instructional materials and links.