Introduction to Scientific Programming and Simulation Using R [Hardcover]

Owen Jones (Author), Robert Maillardet (Author), Andrew Robinson (Author)

Book Description

ISBN-10: 1420068725 | ISBN-13: 978-1420068726 | Publication Date: March 11, 2009 | Edition: 1

Known for its versatility, the free programming language R is widely used for statistical computing and graphics, but is also a fully functional programming language well suited to scientific programming.

An Introduction to Scientific Programming and Simulation Using R teaches the skills needed to perform scientific programming while also introducing stochastic modelling. Stochastic modelling in particular, and mathematical modelling in general, are intimately linked to scientific programming because the numerical techniques of scientific programming enable the practical application of mathematical models to real-world problems.

Following a natural progression that assumes no prior knowledge of programming or probability, the book is organised into four main sections:

• Programming In R starts with how to obtain and install R (for Windows, MacOS, and Unix platforms), then tackles basic calculations and program flow, before progressing to function based programming, data structures, graphics, and object-oriented code
• A Primer on Numerical Mathematics introduces concepts of numerical accuracy and program efficiency in the context of root-finding, integration, and optimization
• A Self-contained Introduction to Probability Theory takes readers as far as the Weak Law of Large Numbers and the Central Limit Theorem, equipping them for point and interval estimation
• Simulation teaches how to generate univariate random variables, do Monte-Carlo integration, and variance reduction techniques

In the last section, stochastic modelling is introduced using extensive case studies on epidemics, inventory management, and plant dispersal. A tried and tested pedagogic approach is employed throughout, with numerous examples, exercises, and a suite of practice projects. Unlike most guides to R, this volume is not about the application of statistical techniques, but rather shows how to turn algorithms into code. It is for those who want to make tools, not just use them.

Editorial Reviews

Review

… a very coherent and useful account of its chosen subject matter. … The programming section … is more comprehensive than Braun & Murdoch (2007), but more accessible than Venables & Ripley (2000). … The book deserves a place on university library shelves … One very useful feature of the book is that nearly every chapter has a set of exercises. There are also plenty of well-chosen examples throughout the book that are used to explain the material. I also appreciated the clear and attractive programming style of the R code presented in the book. I found very little in the way of typos or solecisms. … I can strongly recommend the book for its intended audience. If I ever again have to teach our stochastic modelling course, I will undoubtedly use some of the exercises and examples from Scientific Programming and Simulation Using R.
—David Scott, Australian & New Zealand Journal of Statistics, 2011

It is not often that I think that a statistics text is one that most scientifc statisticians should have in their personal libraries. Introduction to Scientific Programming and Simulation Using R is such a text. … This text provides scientific researchers with a working knowledge of R for both reviewing and for engaging in the statistical evaluation of scientific data. …It is particularly useful for understanding and developing modeling and simulation software. I highly recommend the text, finding it to be one of the most useful books I have read on the subject.
—Journal of Statistical Software, September 2010, Volume 36

The authors have written an excellent introduction to scientific programming with R. Their clear prose, logical structure, well-documented code and realistic examples made the book a pleasure to read. One particularly useful feature is the chapter of cases studies at the end, which not only demonstrates complete analyses but also acts as a pedagogical tool to review and integrate material introduced throughout the book. … I would strongly recommend this book for readers interested in using R for simulations, particularly for those new to scientific programming or R. It is also very student-friendly and would be suitable either as a course textbook or for self-study.
—Significance, September 2009

…I think that the techniques of scientific programming presented will soon enable the novice to apply statistical models to real-world problems. The writing style is easy to read and the book is suitable for private study. If you have never read a book on scientific programming and simulation, then I recommend that you start with this one.
—International Statistical Review, 2009

About the Author

University of Melbourne, Parkville, Australia

Product Details

• Hardcover: 472 pages
• Publisher: Chapman and Hall/CRC; 1 edition (March 11, 2009)
• Language: English
• ISBN-10: 1420068725
• ISBN-13: 978-1420068726
• Product Dimensions: 9.3 x 6.3 x 1.3 inches
• Shipping Weight: 2.1 pounds (View shipping rates and policies)
• Average Customer Review: 4.7 out of 5 stars  See all reviews (13 customer reviews)
• Amazon Best Sellers Rank: #299,633 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

26 of 28 people found the following review helpful:

5.0 out of 5 stars Introduction to Scientific Programming and Simulation Using R (Hardcover), May 31, 2009

By 

Suvdmaa "Tuul" (Student of Uni.Melb) - See all my reviews

This review is from: Introduction to Scientific Programming and Simulation Using R (Hardcover)

This brand new book is written by lecturers of University of Melbourne, to all who interested in modelling and simulation. Recommended highly.

Whom?
- Many people use R as a calculator and statistics. However, one can discover the full potential of R, in sense of programming language. This brand new book aims to all those who are engaged with R&D activities, especially statisticians, physicist, modellers, engineers and managers. Requires some background knowledge in mathematics and statistics.
What?
- If you want to learn R as a programming language, how to model real life problems, provide reliable estimation that's the right book for you. The authors balanced theory and practice and demonstrated fascinating simulations on inventory, forest fire and epidemics, so you would not get bored. The book provides some excellent examples for all those have passion in modelling and simulation. The book has four chapters such as programming, numerical techniques, probability and statistics and simulation.
How?
- The book is written based on active learning principles. The theory is explained and then example is given for its direct application. Then, you wish you may exercise and practice. The book provides some case studies.

16 of 16 people found the following review helpful:

5.0 out of 5 stars Best book on R programiming that I've seen, April 9, 2010

By 

Peter Flom - See all my reviews
(REAL NAME)   

This review is from: Introduction to Scientific Programming and Simulation Using R (Hardcover)

This book is wonderful. I've tried several books that are variations on this theme - introductions to R programming for statisticians - but with this one, I feel like I am actually learning how to program. The text is clear, and the exercises are hard enough to make me think without being so hard as to make me want to throw the book across the room.

I highly recommend this book for anyone who wants to learn R in order to do statistical work

10 of 10 people found the following review helpful:

5.0 out of 5 stars The missing link for grad students, August 29, 2010

By 

Concerned Student (Los Angeles, CA) - See all my reviews

Amazon Verified Purchase

This review is from: Introduction to Scientific Programming and Simulation Using R (Hardcover)

If you are a graduate student in statistics, biostatistics, or the like, and are struggling with R programming, then this book should do the trick. So many books are out there regarding how to to do DATA ANALYSIS with R. This book is not another one of those. This book is what is needed to learn to program R for statistical computing NOT data analysis, and there is a big difference. This book will prepare you for classes in statistical computing and after doing some of the exercises, you will be ready for books like Maria Rizzo's Statistical Computing with R and Givens and Hoeting's Computational Statisics. You will learn how to write loops (I know everybody is trying to avoid this) and functions and how to do simulations, numerical optimization, root finding, and there is even some goodies about graphics. The book obviously doesnt cover everything, no book could, but if you combine this book with one of the others mentioned above, then you should be well equipped for statistical research using R.