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R is a freely available, open-source statistical programming environment which provides powerful statistical analysis tools and graphics outputs. R is now used by a very wide range of people; biologists (the primary audience of this book), but also all other scientists and engineers, economists, market researchers and medical professionals. R users with expertise are constantly adding new associated packages, and the range already available is immense. This text works through a set of studies that collectively represent almost all the R operations that biology students need in order to analyse their own data. The material is designed to serve students from first year undergraduates through to those beginning post graduate levels. Chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping, and text parsing. Examples are based on real scientific studies, and each one covers the use of more R functions than those simply necessary to get a p-value or plot. The book walks the reader through the data analysis process, starting with very simple plots, and continuing through more complex analyses and programming. It shows how to deal with issues such as error messages that can be confronting for beginners, in order to set students up for a successful scientific career using R. Prof. Dr Donald Quicke has had more than 40 years' experience teaching undergraduate and postgraduate biology students, initially at Sheffield University, UK and then at Imperial College London; Buntika Butcher gained her PhD at Imperial College and is currently Associate Professor in the Biology Department at Chulalongkorn University, Bangkok, with 20 years of teaching experience; Dr Rachel Kruft Welton did her master's degree at Imperial College, London and a PhD at University of Birmingham, UK before qualifying as a teacher. She has been a professional biology and science tutor for nearly 20 years, including mentoring undergraduates as part of Birmingham University's alumni scheme. Collectively the authors have a vast amount of teaching experience which they apply here to make the passage into R programming as gentle and easy as possible, whilst guiding the reader to tackle quite complicated programming.

Practical R for Biologists