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Data Analysis and Graphics Using R

Data Analysis and Graphics Using R
An Example-based Approach

2nd Edition

$89.00

Part of Cambridge Series in Statistical and Probabilistic Mathematics

  • Date Published: December 2006
  • availability: Replaced by 9780521762939
  • format: Hardback
  • isbn: 9780521861168

$89.00
Hardback

Replaced by 9780521762939
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About the Authors
  • Join the revolution ignited by the ground-breaking R system! Starting with an introduction to R, covering standard regression methods, then presenting more advanced topics, this book guides users through the practical and powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display and interpretation of data. The many worked examples, taken from real-world research, are accompanied by commentary on what is done and why. A website provides computer code and data sets, allowing readers to reproduce all analyses. Updates and solutions to selected exercises are also available. Assuming only basic statistical knowledge, the book is ideal for research scientists, final-year undergraduate or graduate level students of applied statistics, and practising statisticians. It is both for learning and for reference. This revised edition reflects changes in R since 2003 and has new material on survival analysis, random coefficient models, and the handling of high-dimensional data.

    • Practical, hands-on, example-based approach deals with real-world issues
    • Extensive use of graphs for exploration of data and interpretation of analyses
    • R code, data sets, updates and exercise solutions, all provided on companion website
    Read more

    Reviews & endorsements

    From reviews of previous edition: "The strength of the book is in the extensive examples of practical data analysis with complete examples of the R code necessary to carry out the analyses. I would strongly recommend the book to scientists who have already had a regression or a linear models course and who wish to learn to use R. I give it a strong recommendation to the scientist or data analyst who wishes to an easy-to-read and an understandable reference on the use of R for practical data analysis."
    R News

    From reviews of previous edition: "The text includes a wealth of practical examples, drawn from a variety of practical applications which should be easily understood by the reader. The methods demonstrated are suitable for use in areas such as biology, social science, medicine and engineering. The core of the book is taken up with detailed discussion of regression methods which leads onto more advanced statistical concepts."
    ISI Short Book Reviews

    From reviews of previous edition: "This book does an excellent job of describing the basics of a variety of statistical tools, both classical and modern, through examples from a wide variety of disciplines; the book's writing style is very readable, with clear explanations and precise introductions of all topics and terminology; the book also provides a wealth of examples from various physical and social sciences, engineering, and medicine that have been effectively chosen to illustrate not only the basics of the statistical methods, but also some of the interesting subtleties of the analyses that may require careful interpretation and discussion. I believe that they have created a readable book that is rich with clear explanations and illustrative examples of the capability of a diverse set of tools. The packaging of the material with the R language is natural, and the extensive web page of resources complements the book's usefulness for a road audience of statisticians and practitioners."
    Biometrics

    From Previous Edition: "Provide considerable insight into very powerful procedures."
    A. Ralph Henderson, Clinical Chemistry

    "There are many books published in applied statistics that explain the R language. However, the book under review stands out due to its versatility and because it is easy to follow and understand the context."
    Ita Cirovic Donev, The Mathematical Association of America

    "...A gentle tour guide for new R users, aiming to help them navigate through many powerful tools that the open source R system offers."
    Zhaohui Steve Qin, Center for Statistical Genetics, BioInformatics

    "The style of the book is a commendable "learn by example" - each of the many statistical techniques is centered on real-world examples. The collective of topics is eclectic and the book also comes with extensive R code."
    Carl James Schwarz, Biometrics

    See more reviews

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    Product details

    • Edition: 2nd Edition
    • Date Published: December 2006
    • format: Hardback
    • isbn: 9780521861168
    • length: 528 pages
    • dimensions: 261 x 184 x 33 mm
    • weight: 1.08kg
    • contains: 12 colour illus. 50 tables 150 exercises
    • availability: Replaced by 9780521762939
  • Table of Contents

    Preface
    1. A brief introduction to R
    2. Styles of data analysis
    3. Statistical models
    4. An introduction to formal inference
    5. Regression with a single predictor
    6. Multiple linear regression
    7. Exploiting the linear model framework
    8. Generalized linear models and survival analysis
    9. Time series models
    10. Multi-level models and repeated measures
    11. Tree-based classification and regression
    12. Multivariate data exploration and discrimination
    13. Regression on principal component or discriminant scores
    14. The R system - additional topics
    Epilogue - models
    References
    Index of R symbols and functions
    Index of terms
    Index of names.

  • Resources for

    Data Analysis and Graphics Using R

    John Maindonald, John Braun

    General Resources

    Welcome to the resources site

    Here you will find a range of free-of-charge teaching and learning materials for students and instructors using this textbook. These resources form an integral part of the teaching package, and provided tailored materials that save instructors' time and enhance student learning.

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    These resources are provided free of charge by Cambridge University Press with permission of the author of the corresponding work, but are subject to copyright. You are permitted to view, print and download these resources for your own personal use only, provided any copyright lines on the resources are not removed or altered in any way. Any other use, including but not limited to distribution of the resources in modified form, or via electronic or other media, is strictly prohibited unless you have permission from the author of the corresponding work and provided you give appropriate acknowledgement of the source.

    If you are having problems accessing these resources please email cflack@cambridge.org

  • Authors

    John Maindonald, Australian National University, Canberra
    John Maindonald is Visiting Fellow at the Mathematical Sciences Institute, Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health, to population genetics, machine learning, economic history, and forensic linguistics.

    John Braun, University of Western Ontario
    John Braun is Associate Professor of Statistical and Actuarial Sciences, University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists and most recently has become involved with a network of forestry researchers.

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