- Series: Use R! (Book 64)
- Paperback: 220 pages
- Publisher: Springer; 2013 edition (June 4, 2013)
- Language: English
- ISBN-10: 1461468671
- ISBN-13: 978-1461468677
- Product Dimensions: 6.1 x 0.6 x 9.2 inches
- Shipping Weight: 1 pounds (View shipping rates and policies)
- Average Customer Review: 10 customer reviews
- Amazon Best Sellers Rank: #550,969 in Books (See Top 100 in Books)
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Seamless R and C++ Integration with Rcpp (Use R!) 2013th Edition
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From the Back Cover
Rcpp is the glue that binds the power and versatility of R with the speed and efficiency of C++. With Rcpp, the transfer of data between R and C++ is nearly seamless, and high-performance statistical computing is finally accessible to most R users. Rcpp should be part of every statistician's toolbox. -- Michael Braun, MIT Sloan School of Management
Seamless R and C++ Integration with Rcpp is simply a wonderful book. For anyone who uses C/C++ and R, it is an indispensable resource. The writing is outstanding. A huge bonus is the section on applications. This section covers the matrix packages Armadillo and Eigen and the GNU Scientific Library as well as RInside which enables you to use R inside C++. These applications are what most of us need to know to really do scientific programming with R and C++. I love this book. -- Robert McCulloch, University of Chicago Booth School of Business
Rcpp is now considered an essential package for anybody doing serious computational research using R. Dirk's book is an excellent companion and takes the reader from a gentle introduction to more advanced applications via numerous examples and efficiency enhancing gems. The book is packed with all you might have ever wanted to know about Rcpp, its cousins (RcppArmadillo, RcppEigen etc.), modules, package development and sugar. Overall, this book is a must-have on your shelf. -- Sanjog Misra, UCLA Anderson School of Management
The Rcpp package represents a major leap forward for scientific computations with R. With very few lines of C++ code, one has R's data structures readily at hand for further computations in C++. Hence, high-level numerical programming can be made in C++ almost as easily as in R, but often with a substantial speed gain. Dirk is a crucial person in these developments, and his book takes the reader from the first fragile steps on to using the full Rcpp machinery. A very recommended book! -- Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark
Seamless R and C ++ Integration with Rcpp provides the first comprehensive introduction to Rcpp, which has become the most widely-used language extension for R, and is deployed by over one-hundred different CRAN and BioConductor packages. Rcpp permits users to pass scalars, vectors, matrices, list or entire R objects back and forth between R and C++ with ease. This brings the depth of the R analysis framework together with the power, speed, and efficiency of C++.
About the Author
Dirk Eddelbuettel has been a contributor to CRAN for over a decade and maintains around twenty packages. He is the Debian/Ubuntu maintainer for R and other quantitative software, edits the CRAN Task Views for Finance and High-Performance Computing, is a co-founder of the annual R/Finance conference, and an editor of the Journal of Statistical Software. He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.
Author interviews, book reviews, editors picks, and more. Read it now
Top customer reviews
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If you want to use R/C++ integration you need to be familiar with R and C++ first. That might sound silly, but it's an important point. I wasn't familiar with C++ when I started. I spent about two weeks going through a book called "Accelerated C++ coding," which was a bare minimum pre-req.
In addition, he often leaves out "obvious" parts of code in his book, but that aren't always that obvious for commoners such as myself. The learning curve for R/C++ integration is pretty steep, and unforgiving to those not willing to put in the time and intellectual effort.
In addition to this book, you will need to make full use of online resources on this topic. The book is absolutely not sufficient, or anywhere close for that matter.
There is also a low level of example code in general. This is not Dirk's fault, it's a more 'niche' coding community, so you have to be prepared to at times blaze your own trail.
Ultimately, if you are looking into R/C++ integration, you are concerned with fairly complex optimization and computational issues, and as a result should hopefully have experience learning tougher things.
I would have given this book 4/5 stars if it had a longer 30-50 page introduction, which gave a much more robust and thorough explanation. As it is now the introduction feels as though it were added as an afterthought, as it quickly rushes through lots of the 'basics', which are then taught again in following chapters. It is more of an overview, than a proper detail of how to actually write code in R/C++ starting at step 1.
I would have given this book 5/5 stars if it came with 3-6 mini-lessons and projects, which came in the form of existing code downloadable from Dirks' website. This would help with examples and code structure to help the starting coder get a handle on the myriad issues that often arise when trying to get R/C++/Compilers in communication.
But if you are reading this review, chances are you are using R/C++ integration. In which case you absolutely need this book, even if it could be better.
The Rccp package has developed to one of the major resources for writing additional R packages in the means of additional extensions to the R Base functionality. So this book is generally very welcome.
The book is written like a little cookbook, and it shows a lot of small C++ program listings, which get seamless integrated in R. I have bought this book, as I am looking for a performant programming technique within R. So as an R User/ Rcpp Beginner I want to know two things, before I start learning Rcpp programming with the book:
1. What are the benefits of learning/ using Rcpp?
2. How time consuming will learning and programming in Rcpp be?
Usually the first chapter of programming book answers these essential questions. But the first chapter of this book does NOT. Instead it provides very short examples of C++ programming, which do not work as a full C++ repitition. I think you can skip the first chapter.
The second chapter about tools and setup is very helpful.
The third chapter is starting with Rccp data types then. You can call me limited, but I had to read the beginning of this chapter four times, till I understood, that the RObject Class creates Rccp data objects and not R data objects. And this 'not very to the point' style repeats. The first example in the chapter is called 'A first Example: Returning Perfect Numbers'. If the task is returning data from C++ back to R, why isn't this reflected in the headline? In Example 2 the author creates a function - as far I understand this - using an argument call by reference by a pointer without explaining that. Further he does not explain - as far I understand this - that IntegerVector is constructor for a Integer Vector object, which gets initialized with argument of the function.
To estimate how much time consuming programming in Rcpp would be, the user wants to know about the functions delivered with the Rcpp data types. But Rcpp API, containing the data type related functions, is not explained.
This is a helpful book, especially as there are no alternatives for Rcpp in the moment. However, for R-users like me the book should be designed as a reference, which first transports the theory and then illustrates with code examples. I still do not know, how much work rcpp programming would make for me, as the Rcpp data type functions are not illustrated. Maybe the book is appropriate for experienced C++ programmers, which are used to learn the Rcpp API from the package documentation.