Getting Started with R R is rapidly becoming the standard computational environment for analysis, graphical presentations and programming in the biological sciences. This book details how to start doing statistics in R or how to integrate the use of R with an existing research programme and how to achieve this efficiently and reliably. Full description
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Learning how to get answers from data is an integral part of modern training in the natural, physical, social, and engineering sciences. One of the most exciting changes in data management and analysis during the last decade has been the growth of open source software. The open source statistics and programming language R has emerged as a critical component of any researcher's toolbox. Indeed, R is rapidly becoming the standard software for analyses, graphical presentations, and programming in the biological sciences. This book provides a functional introduction for biologists new to R. While teaching how to import, explore, graph, and analyse data, it keeps readers focused on their ultimate goals - communicating their data in oral presentations, posters, papers, and reports. It also provides a consistent method (workflow) for using R that is simple, efficient, reliable, accurate, and reproducible. The material in the book reproduces the engaging and sometimes humorous nature of the three-day course on which it is based.
I've been using R for a few years now but have never come across an introductory guide as concise and accessible as this one. I shall be recommending it to our MSc students with glowing praise! (Dr Tim Fawcett, Centre for Research in Animal Behaviour (CRAB), University of Exeter)
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. (Graeme D. Ruxton, 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)
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