Customer Reviews

Introduction to Scientific Programming and Simulation Using R

5 star:		(10)
4 star:		(2)
3 star:		(1)
2 star:		(0)
1 star:		(0)

 

 

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.

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

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.

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.

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.

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.

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.

I will keep this very short. This is a great book for beginners! It helps a bit if you know some basic programming concepts, but then that's not necessary. The book is well written, and it has great exercise questions to hone what you have learned. I will definitely recommend it to anybody starting out in R.

This is a great introduction to R with helpful examples. I would like to use this for a course at work. The Kindle edition is quite convenient.

This book is just OK for learning to program in R :

Subject matter coverage as it pertains to R programming and understanding R is a bit light with a lot of things missing. For example, there's not much discussion on coersion, search paths, understanding objects, how objects work together, understanding environments, handling dates...etc.

The index is poor. That's probably due to light coverage of R subject matter and just poor quality.

The problems provided at the end of the chapters are pretty good. But there are no answers available if you get stuck.

The flow of the text is good. The author does a good job of not introducing terms without first explaining them.
Worked out examples are pretty good.