Description
Book SynopsisR is rapidly becoming the standard software for statistical analyses, graphical presentation of data, and programming in the natural, physical, social, and engineering sciences. Getting Started with R is now the go-to introductory guide for biologists wanting to learn how to use R in their research. It teaches readers how to import, explore, graph, and analyse data, while keeping them focused on their ultimate goals: clearly communicating their data in oral presentations, posters, papers, and reports. It provides a consistent workflow for using R that is simple, efficient, reliable, and reproducible.This second edition has been updated and expanded while retaining the concise and engaging nature of its predecessor, offering an accessible and fun introduction to the packages dplyr and ggplot2 for data manipulation and graphing. It expands the set of basic statistics considered in the first edition to include new examples of a simple regression, a one-way and a two-way ANOVA. Finally, it introduces a new chapter on the generalised linear model.Getting Started with R is suitable for undergraduates, graduate students, professional researchers, and practitioners in the biological sciences.
Trade ReviewReview from previous edition The book would make the ideal text for a short course on data management and presentation - it truly packs an amazing amount of wisdom and wit between slim covers. * Trends in Ecology and Evolution *
I was engaged by the refreshing style of the authors, that while informal, gives the user clear step-by-step instructions for using the software. Apart from the clear biological leaning of the example data, this book is applicable to anyone learning R (even a statistician!). * Significance *
Table of ContentsPreface 1: Getting and getting acquainted with R 2: Getting your data into R 3: Data management, manipulation, and exploration with dplyr 4: Visualising your data 5: Introducing statistics in R 6: Advancing your statistics in R 7: Getting started with generalised linear models 8: Pimping your plots: scales and themes in ggplot2 9: Closing remarks Appendices
