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A beginner's guide to R / Alain F Zuur, Elena N Ieno, Erik Meesters
New York : Springer, 2009
218 p.
Ký hiệu phân loại (DDC): 519.50285
Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R. "Its biggest advantage is that it aims only to teach R ... It organizes R commands very efficiently, with much teaching guidance included. I would describe this book as being handy--it's the kind of book that you want to keep in your jacket pocket or backpack all the time, ready for use, like a Swiss Army knife." (Loveday Conquest, University of Washington) "Whilst several books focus on learning statistics in R ..., the authors of this book fill a gap in the market by focusing on learning R whilst almost completely avoiding any statistical jargon ... The fact that the authors have very extensive experience of teaching R to absolute beginners shines throughout." (Mark Mainwaring, Lancaster University) "Exactly what is needed ... This is great, nice work. I love the ecological/biological examples; they will be an enormous help." (Andrew J. Tyne, University of Nebraska-Lincoln) Alain F. Zuur is senior statistician and director of Highland Statistics Ltd., a statistical consultancy company based in the UK. He has taught statistics to more than 5000 ecologists. He is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Elena N. Ieno is senior marine biologist and co-director at Highland Statistics Ltd. She has been involved in guiding PhD students on the design and analysis of ecological data. She is honorary research fellow in the School of Biological Sciences, Oceanlab, at the University of Aberdeen, UK. Erik H.W.G. Meesters is a researcher at the Dutch Institute for Marine Resources and Ecosystem Studies (IMARES). He specializes in coral reef ecology and applied statistics and conducts research on North Sea benthos and seal ecology.
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2
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Advanced R / Hadley Wickham
Boca Raton, FL : CRC Press, 2019
587 pages. : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 005.13
An Essential Reference for Intermediate and Advanced R Programmers Advanced R presents useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With more than ten years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R. The book develops the necessary skills to produce quality code that can be used in a variety of circumstances. You will learn: The fundamentals of R, including standard data types and functions Functional programming as a useful framework for solving wide classes of problems The positives and negatives of metaprogramming How to write fast, memory-efficient code This book not only helps current R users become R programmers but also shows existing programmers what's special about R. Intermediate R programmers can dive deeper into R and learn new strategies for solving diverse problems while programmers from other languages can learn the details of R and understand why R works the way it does.
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Bayesian essentials with R /Jean-Michel Marin, Christian P. Robert.
New York : Springer, 2014
xiv, 296 pages :illustrations (some color) ;
Ký hiệu phân loại (DDC): 519.542
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable.
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Computational genomics with R / Altuna Akalin
Boca Raton, FL : CRC Press, 2021
xxii, 440 pages. : illustrations (chiefly color) ; 26 cm.
Ký hiệu phân loại (DDC): 572.86
Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology
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Exploratory data analysis using R / Ronald K Pearson
Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018
547 pages. : illustrations ; 23 cm.
Ký hiệu phân loại (DDC): 006.312
"This textbook will introduce exploratory data analysis (EDA) and will cover the range of interesting features we can expect to find in data. The book will also explore the practical mechanics of using R to do EDA. Based on the author's course at the University of Connecticut, the book assumes no prior exposure to data analysis or programming, and is designed to be as non-mathematical as possible. Exercises are included throughout, and a Solutions Manual will be available. The author will also provide a supplemental R package through the Comprehensive R Archive Network that will include implementations of some of the features in this book, along with data examples, tools, and datasets"-
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