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Introduction to Scientific Programming and Simulation Using R (Chapman & Hall/CRC The R Series) Hardcover

ISBN-13: 978-1420068726 ISBN-10: 1420068725 Edition: 1st

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Introduction to Scientific Programming and Simulation Using R (Chapman & Hall/CRC The R Series) + The Art of R Programming: A Tour of Statistical Software Design + R Cookbook (O'Reilly Cookbooks)
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Product Details

  • Series: Chapman & Hall/CRC The R Series (Book 21)
  • 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 6.1 x 1.3 inches
  • Shipping Weight: 2.1 pounds (View shipping rates and policies)
  • Average Customer Review: 4.6 out of 5 stars  See all reviews (19 customer reviews)
  • Amazon Best Sellers Rank: #367,063 in Books (See Top 100 in Books)

Editorial Reviews

Review

This book is a good resource for someone who wants to learn R and use R for statistical computing and graphics. It will also serve well as a textbook or a reference book for students in a course related to computational statistics.
—Hon Keung Tony Ng, Technometrics, May 2011

… 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

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University of Melbourne, Parkville, Australia

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Customer Reviews

4.6 out of 5 stars
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The examples are clear and easy to follow.
M. J. O'CONNOR
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.
Concerned Student
This book explains what you need to know to program in R to model scientific mathematical simulation processes.
Geoffrey Hubona

Most Helpful Customer Reviews

19 of 19 people found the following review helpful By Peter Flom on April 9, 2010
Format: 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
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16 of 16 people found the following review helpful By Concerned Student on August 29, 2010
Format: Hardcover Verified Purchase
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.
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28 of 31 people found the following review helpful By Suvdmaa on May 31, 2009
Format: 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.
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7 of 7 people found the following review helpful By Roger S. Pinkham on June 4, 2010
Format: Hardcover Verified Purchase
To the best of my knowledge this is the only book its kind. The usual book reads like a stand-up comedians program of one-liners. This book gives an integrated discussion of how to write your own code to solve non-trivial scientific problems. I plan to use much of the material in a mathematics course I shall give in the fall.
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7 of 7 people found the following review helpful By Joseph Hilbe on March 24, 2010
Format: Hardcover
I am thoroughly impressed by this text. It is extremely well written, with a host of useful and interesting examples. The text is not for everyone, but if you use R, or wish to use R, for programming scientific and mathematical functions then this is a book you really should have. It also is excellent for programming scientific graphics.

If you are a scientist, or an applied mathematician, and wish to use R as your programming language, I can think of no better book to use as a resource. The style of writing is aimed at clarity. The authors don't simply write code and leave it for you to figure out, but rather they attempt to present the material in as clear a manner as possbile. I recommend it without reservations.
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5 of 5 people found the following review helpful By R. Martin on December 15, 2010
Format: Hardcover Verified Purchase
I have reviewed some very good books on R programming, but this one really stands above the others. If I could only keep one reference book for R, this would be the one.

Even though the authors clearly intended this as a university textbook, the examples are interesting and accessible to anyone who wants to learn R (I use the book for self-study). Part I introduces the basic features of the R language, data analysis, and graphics. Throughout the text, the authors use clear and interesting examples.

Part II presents some interesting applications of R, but there are probably other packages better suited to these types of numerical techniques. Part III, a concise overview of Probability, is primarily a reference--Don't expect to learn probability and statistics from this book. That said, I keep coming back to this book. It is a great resource.

Although textbooks can have pretty scary price tags, if you can find it used or at a discount, then it is worth adding to your collection.
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5 of 5 people found the following review helpful By M. J. O'CONNOR on September 1, 2010
Format: Hardcover Verified Purchase
I share the generally held view that R has a steep learning curve but the effort spent learning it is well rewarded. I study both statistics and econometrics and I find that R straddles both more easily than most statistical packages, because of its flexibility and the fact that someone, somewhere has thought about your problem and written a solution for it. However, once the fundamentals of R are grasped it is not long before one is left craving to know how to tap into the core strength of R, which is its programming capability. This is where "SP&SUR" comes into its own. It is easy to follow and builds up well. The examples are clear and easy to follow. This book is undoubtedly the key that unlocks R. Highly recommended.
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