Romain Francois, Professional R Enthusiast

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Wednesday, December 1 2010

RcppGSL 0.1.0


We released the first version of our RcppGSL package. RcppGSL extends Rcpp to help programmers code with the GNU Scientific Library (GSL).

The package contains template classes in the RcppGSL namespace that act as smart pointers to the associated GSL data structure. For example, a RcppGSL::vector<:double> object acts a smart pointer to a gsl_vector*. Having the pointer shadowed by a smart pointer allows us to take advantage of C++ features such as operator overloading, etc ... which for example allows us to extract an element from the GSL vector simply using [] instead of GSL functions gsl_vector_get and gsl_vector_set

The package contains a 11 pages vignette that explains the features in details, with examples. The vignette also discusses how to actually use RcppGSL, either in another package (preferred) or directly from the R prompt through the inline package.

Sunday, November 28 2010

parser 0.0-13

I've pushed a new version of the parser package to CRAN.

This is the first release that depends on Rcpp, which allowed me to reduce the code size and increase its maintainability.

This also features a faster version of nlines, a function that retrieves the number of lines of a text file.

Rcpp 0.8.9

Rcpp 0.8.9 was pushed to CRAN recently. Apart from minor bug fixes, this release concentrates on modules, with lots of new features to expose C++ functions and classes through R reference classes.

Apollo 17 Command Module

The Rcpp-modules vignette has all the details

The major points are highlighted in the NEWS entry below:

0.8.9   2010-11-28 (or even -27)

    o   Many improvements were made to in 'Rcpp modules':

        - exposing multiple constructors

        - overloaded methods

        - self-documentation of classes, methods, constructors, fields and 

        - new R function "populate" to facilitate working with modules in 

        - formal argument specification of functions.

        - updated support for Rcpp.package.skeleton.

        - constructors can now take many more arguments.
    o   The 'Rcpp-modules' vignette was updated as well and describe many
        of the new features

    o   New template class Rcpp::SubMatrix and support syntax in Matrix
        to extract a submatrix: 
           NumericMatrix x = ... ;
           // extract the first three columns
           SubMatrix y = x( _ , Range(0,2) ) ; 
           // extract the first three rows
           SubMatrix y = x( Range(0,2), _ ) ; 
           // extract the top 3x3 sub matrix
           SubMatrix y = x( Range(0,2), Range(0,2) ) ; 

    o   Reference Classes no longer require a default constructor for
        subclasses of C++ classes    

    o   Consistently revert to using backticks rather than shell expansion
        to compute library file location when building packages against Rcpp
	on the default platforms; this has been applied to internal test
        packages as well as CRAN/BioC packages using Rcpp

Friday, November 12 2010

What would impressionnists do with R ?

I've been playing with images recently, probably inspired from my trip in San Francisco. There was an exhibit at the De Young museum of fine arts with pieces borrowed from the Musée d'Orsay. I did not go to the exhibit because it is easy enough for me to just go to Paris and the Musée d'Orsay, but I guess this somewhat inspired me, along with the golden gate bridge, to do some R based impressionnism

The starting point is this picture of the golden gate bridge


The png package makes it straightforward to import png pictures into R (There are other ways as well).

Then, I generate randomly spaced circles so that they don't overlap, and fill each circle with the average color (on the RGB space) of all te pixels that are inside the circle

circles-1.png circles-2.png
circles-3.png circles-4.png

Then I do this many times, with translucent circles, and after some iterations ,the golden gate bridge starts to reveal itself


The code for this is included below

Here are other examples



Saturday, October 23 2010

Google slides

Last stop on my World tour was Google headquarters in Mountain View, California, where Dirk and I presented Rcpp, RInside, RProtoBuf, etc ... for 90 minutes today. The talk was recorded, and will be broadcasted on youtube at some point. In the meantime, the slides are available here:

Saturday, July 10 2010

Rcpp 0.8.4

Dirk uploaded Rcpp 0.8.4 to CRAN yesterday. This release quickly follows the release of Rcpp 0.8.3, because there was some building problems (particularly on the ppc arch on OSX).

Rcpp sugar


Already available in Rcpp 0.8.3, the new sugar feature was extended in 0.8.4 to cover more functions, and we have now started to adapt sugar for matrices with functions such as outer, row, diag, etc ...

Here is an example of using the sugar version of outer

NumericVector xx(x) ;
NumericVector yy(y);
NumericMatrix m = outer( xx, yy, std::plus<double>() ) ;
return m ;

This mimics the R code

> outer( x, y, "+" )

Here is the relevant extract of the NEWS file:

0.8.4   2010-07-09

o   new sugar vector functions: rep, rep_len, rep_each, rev, head, tail, diag
o	sugar has been extended to matrices: The Matrix class now extends the Matrix_Base template that implements CRTP. Currently sugar functions for matrices are: outer, col, row, lower_tri, upper_tri, diag

o   The unit tests have been reorganised into fewer files with one call each to cxxfunction() (covering multiple tests) resulting in a significant speedup

o	The Date class now uses the same mktime() replacement that R uses (based on original code from the timezone library by Arthur Olson) permitting wide dates ranges on all operating systems

o   The FastLM/example has been updated, a new benchmark based on the historical Longley data set has been added

o   RcppStringVector now uses std::vector<std::string> internally

o    setting the .Data slot of S4 objects did not work properly

bibtex 0.2-1

I've uploaded version 0.2-1 of my bibtex package to CRAN.

This release anticipates changes in R 2.12.0, and structures bibtex entries in object of the new class bibentry. The release also fixes various parser and lexer bugs

Tuesday, June 8 2010

Rcpp 0.8.1

We released Rcpp 0.8.0 almost a month ago. It finalized our efforts in designing a better, faster and more natural API than any version of Rcpp ever before. The journey from Rcpp 0.7.0 to Rcpp 0.8.0 has mainly been a coding and testing effort for designing the API.

And now for something completely different

We have now started (with release 0.8.1 of Rcpp) a new development cycle towards the 0.9.0 version with two major goals in mind

  • We want to improve documentation. To that end Rcpp 0.8.1 includes 4 new vignettes. more on that later.
  • We want to cross the boundaries between R and C++. Rcpp 0.8.1 introduces Rcpp modules. Modules allows the programmer to expose C++ classes and functions at the R level, with great ease.

new vignettes

Rcpp-FAQ :Frequently Asked Questions about Rcpp collects some of the frequently asked questions from the mailing list and from private exchanges with many people.

Rcpp-extending: Extending Rcpp shows how to extend Rcpp converters Rcpp::wrap and Rcpp::as to user defined types (C++ classes defined in someone else's package and third party types (C++ classes defined in some third party library used by a package. The document is based on our experience developping the RcppArmadillo package

Rcpp-package : Writing a package that uses Rcpp highlights the steps involved in making a package that uses Rcpp. The document is based on the Rcpp.package.skeleton function

finally, Rcpp-modules : Exposing C++ functions and classes with Rcpp modules documents the current feature set of Rcpp modules

Rcpp modules

Rcpp modules are inspired from the Boost.Python C++ library. Rcpp modules let you expose C++ classes and functions to the R level with minimal involvment from the programmer

The feature is best described by an example (more examples on the vignette). Say we want to expose this simple class:

This would typically involve external pointers. With Rcpp modules, we can simply declare what we want to expose about this class, and Rcpp takes care of the how to expose it:

The R side consists of grabbing a reference to the module, and just use the World class

The Rcpp-modules vignette gives more details about modules, including how to use them in packages

More details about 0.8.1 release

Here is the complete extract from our NEWS file about this release

Wednesday, June 2 2010

inline 0.3.5

The inline package is an amazing, yet simple, package for R. It allows to dynamically (within the R session) define R functions and S4 methods with inlined C/C++/Fortran code.

Together with RUnit, inline powers the entire unit test suite of Rcpp.

As agreed with Oleg Sklyar, who maintains inline, we made a few additions to inline to accomodate the needs of the Rcpp family of packages.


The main addition is cxxfunction which is very similar to cfunction, except that it only focuses on C++ code using the .Call calling convention. cxxfunction uses a plugin system allowing other packages to control the code that is generated before compilation, environment variables, etc ... For example, the next version of Rcpp defines an inline plugin that takes care of all the details (find the Rcpp include path, link against the Rcpp user library, etc ...)

Here is an example, from the cxxfunction help page using the Rcpp plugin (this will only work with the next version of Rcpp, because the current version does not know about this)

Here is an example using the plugin from the next version of RcppArmadillo


Another addition to inline concerns the package.skeleton. We've made it S4 generic in inline and defined methods for the CFunc and CFuncList classes. In short, this allows to prototype some code using inline and quickly dump the code into a proper package

For example, here we make two functions using cxxfunction and then generate a package skeleton directly from them

Furthermore, the package.skeleton methods are aware of the plugin system, which allows plugin to have some control of additional steps involved in making the package skeleton, such as Makevars files, etc ...


getDynLib has been introduced in this version of inline to grab a reference to the dynamic library associated with a package, a function (CFunc object) generated by inline, or a set of functions (CFuncList object) generated by inline

Monday, May 31 2010

highlight 0.2-0

I've released version 0.2-0 of highlight to CRAN

This version brings some more additions to the sweave driver that uses highlight to produce nice looking vignettes with color coded R chunks

The driver gains new arguments boxes, bg and border to control the appearance of the code chunks. When boxes is set to TRUE, the R code chunks are surrounded in boxes, and the arguments bg and border control the background color and the color of the box

Also, when the other highlight is available, the driver will also color code example code in any language that highlight supports. To use this, just surorund the code with <<lang=foo>> for the language foo. For example:

int main(){
return 0 ;

will output the content of the code chunk as highlighted c++. The Rcpp-modules vignette in the next version of Rcpp uses both these new features. (see the vignette source in r-forge. The vignette is rendered into latex using :

driver <- HighlightWeaveLatex(boxes = TRUE)
Sweave( 'Rcpp-modules.Rnw', driver = driver )

Friday, May 21 2010

highlight 0.1-8

I've pushed version 0.1-8 of highlight to CRAN. highlight is a syntax highlighter for R that renders R source code into some markup language, the package ships html and latex renderers but is flexible enough to handle other formats. Syntax highlighting is based on information about the code gathered by a slightly modified version of the R parser, available in the separate parser package.

Internal code has been modified to take advantage of new features of Rcpp such as the DataFrame c++ class.

Since R 2.11.0, it is possible to install custom handlers to respond to http request (GET, POST, ...). highlight takes advantage of this and responds to urls with html syntax highlighted functions. So if the httpd port used by the dynamic help system is 9000 (hint: tools:::httpdPort) :

Wednesday, May 12 2010

Rcpp 0.8.0


Version 0.8.0 of the Rcpp package was released to CRAN today. This release marks another milestone in the ongoing redesign of the package, and underlying C++ library.


Rcpp is an R package and C++ library that facilitates integration of C++ code in R packages.

The package features a set of C++ classes (Rcpp::IntegerVector, Rcpp::Function, Rcpp::Environment, ...) that makes it easier to manipulate R objects of matching types (integer vectors, functions, environments, etc ...).

Rcpp takes advantage of C++ language features such as the explicit constructor/destructor lifecycle of objects to manage garbage collection automatically and transparently. We believe this is a major improvement over PROTECT/UNPROTECT. When an Rcpp object is created, it protects the underlying SEXP so that the garbage collector does not attempt to reclaim the memory. This protection is withdrawn when the object goes out of scope. Moreover, users generally do not need to manage memory directly (via calls to new / delete or malloc / free) as this is done by the Rcpp classes or the corresponding STL containers.


Rcpp provides two APIs: an older set of classes we refer to the classic API (see below for the section 'Backwards Compatibility) as well as second and newer set of classes.

Classes of the new Rcpp API belong to the Rcpp namespace. Each class is associated to a given SEXP type and exposes an interface that allows manipulation of the object that may feel more natural than the usual use of macros and functions provided by the R API.

SEXP type Rcpp class
INTSXP Rcpp::IntegerVector
REALSXP Rcpp::NumericVector
RAWSXP Rcpp::RawVector
LGLSXP Rcpp::LogicalVector
CPLXSXP Rcpp::ComplexVector
STRSXP Rcpp::CharacterVector
VECSXP Rcpp::List
EXPRSXP Rcpp::ExpressionVector
ENVSXP Rcpp::Environment
SYMSXP Rcpp::Symbol
BUILTINSXP Rcpp::Function
LANGSXP Rcpp::Language
LISTSXP Rcpp::Pairlist
S4SXP Rcpp::S4
PROMSXP Rcpp::Promise
WEAKREFSXP Rcpp::WeakReference
EXTPTRSXP template < typename T> Rcpp::XPtr    

Some SEXP types do not have dedicated Rcpp classes : NILSXP, DOTSXP, ANYSXP, BCODESXP and CHARSXP.

Still missing are a few convenience classes such as Rcpp::Date or Rcpp::Datetime which would map useful and frequently used R data types, but which do not have an underlying SEXP type.

Data Interchange

Data interchange between R and C++ is managed by extensible and powerful yet simple mechanisms.

Conversion of a C++ object is managed by the template function Rcpp::wrap. This function currently manages :

  • primitive types : int, double, bool, float, Rbyte, ...
  • std::string, const char*
  • STL containers such as std::vector<T> and STL maps such as std::mapr< std::string, Tr> provided that the template type T is wrappable
  • any class that can be implicitely converted to SEXP, through operator SEXP()

Conversion of an R object to a C++ object is managed by the Rcpp::as<T> template which can handle:

  • primitive types
  • std::string, const char*
  • STL containers such as std::vector<T>

Rcpp::wrap and Rcpp::as are often used implicitely. For example, when assigning objects to an environment:

  // grab the global environment
  Rcpp::Environment global = Rcpp::Environment::global_env() ;
  std::deque z( 3 ); z[0] = false; z[1] = true; z[3] = false ;

  global["x"] = 2 ;                    // implicit call of wrap
  global["y"] = "foo";                 // implicit call of wrap
  global["z"] = z ;                    // impl. call of wrap>

  int x = global["x"] ;                // implicit call of as
  std::string y = global["y"]          // implicit call of as
  std::vector z1 = global["z"] ; // impl. call of as>

Rcpp contains several examples that illustrate wrap and as. The mechanism was designed to be extensible. We have developped separate packages to illustrate how to extend Rcpp conversion mechanisms to third party types.

  • RcppArmadillo : conversion of types from the Armadillo C++ library.
  • RcppGSL : conversion of types from the GNU Scientific Library.

Rcpp is also used for data interchange by the RInside package which provides and easy way of embedding an R instance inside of C++ programs.

inline use

Rcpp depends on the inline package by Oleg Sklyar et al. Rcpp then uses the 'cfunction' provided by inline (with argument Rcpp=TRUE) to compile, link and load C++ function from the R session.

As of version 0.8.0 of Rcpp, we also define an R function cppfunction that acts as a facade function to the inline::cfuntion, with specialization for C++ use.

This allows quick prototyping of compiled code. All our unit tests are based on cppfunction and can serve as examples of how to use the mechanism. For example this function (from the runit.GenericVector.R unit test file) defines from R a C++ (simplified) version of lapply:

  ## create a compiled function cpp_lapply using cppfunction 
  cpp_lapply <- cppfunction(signature(x = "list", g = "function" ), 
  		'Function fun(g) ;
		 List input(x) ;
		 List output( input.size() ) ;
		 std::transform( input.begin(), input.end(), output.begin(), fun ) ;
		 output.names() = input.names() ;
		 return output ;
  ## call cpp_lapply on the iris data with the R function summary
  cpp_lapply( iris, summary )	

Using Rcpp in other packages

Rcpp is designed so that its classes are used from other packages. Using Rcpp requires :

  • using the header files provided by Rcpp. This is typically done by adding this line in the package DESRIPTION file:
    	LinkingTo: Rcpp
    and add the following line in the package code:
    	#include <Rcpp.h>
  • linking against the Rcpp dynamic or static library, which is achieved by adding this line to the src/Makevars of the package:
    	PKG_LIBS = $(shell $(R_HOME)/bin/Rscript -e "Rcpp:::LdFlags()" )
    and this line to the src/ file:
    	PKG_LIBS = $(shell Rscript.exe -e "Rcpp:::LdFlags()")

Rcpp contains a function Rcpp.package.skeleton, modelled after package.skeleton from the utils package in base r, that creates a skeleton of a package using Rcpp, including example code.

C++ exceptions

C++ exceptions are R contexts are both based on non local jumps (at least on the implementation of exceptions in gcc), so care must be ensure that one system does not void assumptions of the other. It is therefore very strongly recommended that each function using C++ catches C++ exceptions. Rcpp offers the function forward_exception_to_r to facilitate forwarding the exception to the "R side" as an R condition. For example :

  SEXP foo( ) {
    try {
      // user code here
    } catch( std::exception& __ex__){
      forward_exception_to_r( __ex__ ) ;
    // return something here

Alternatively, functions can enclose the user code with the macros BEGIN_RCPP and END_RCPP, which provides for a more compact way of programming. The function above could be written as follows using the macros:

  SEXP foo( ) {
    // user code here
    // return something here

The use of BEGIN_RCPP and END_RCPP is recommended to anticipate future changes of Rcpp. We might for example decide to install dedicated handlers for specific exceptions later.

Experimental code generation macros

Rcpp contains several macros that can generate repetitive 'boiler plate' code:


For example:

  RCPP_FUNCTION_2( int, foobar, int x, int y){
     return x + y ;

This will create a .Call compatible function "foobar" that calls a c++ function for which we provide the argument list (int x, int y) and the return type (int). The macro also encloses the call in BEGIN_RCPP/END_RCPP so that exceptions are properly forwarded to R.

Examples of the other macros are given in the NEWS file.

This feature is still experimental, but is being used in packages highlight and RProtoBuf

Quality Assurance

Rcpp uses the RUnit package by Matthias Burger et al and the aforementioned inline package by Oleg Sklyar et al to provide unit testing. Rcpp currently has over 500 unit tests (called from more than 230 unit test functions) with very good coverage of the critical parts of the package and library.

Source code for unit test functions are stored in the unitTests directory of the installed package and the results are collected in the "Rcpp-unitTests" vignette.

The unit tests can be both during the standard R package build and testing process, and also when the package is installed. The latter use is helpful to ensure that no system components have changed in a way that affect the Rcpp package since it has been installed. To run the tests, execute


where an output directory can be provided as an optional first argument.

Backwards Compatibility

We believe the new API is now more complete and useful than the previous set of classes, which we refer to as the "classic Rcpp API". We would therefore recommend to package authors using 'classic' Rcpp to move to the new API. However, the classic API is still maintained and will continue to be maintained to ensure backwards compatibility for code that uses it.

Packages uses the 'Classic API' can use features of the new API selectively and in incremental steps. This provides for a non-disruptive upgrade path.


The package contains a vignette which provides a short and succinct introduction to the Rcpp package along with several motivating examples. Also provided is a vignette containing the regression test summary from the time the package was built.



Questions about Rcpp should be directed to the Rcpp-devel mailing list

 -- Dirk Eddelbuettel and Romain Francois
    Chicago, IL, USA, and Montpellier, France
	May 2010

Saturday, April 3 2010

embed images in Rd documents

The new help system that was introduced in R 2.10.0 and documented in an article of the R journal is very promising.

One thing that is planned for future versions of R (maybe 2.12.0) is some way to include images into Rd documents using the fig option of the Sexpr macro

Another way is to use data uri and embed the image directly inside the html code, so this morning I played with this and wrapped up this little c library into an R package called base64 and hosted in the Rcpp project at r-forge.

The package allows encoding and decoding files using the Base64 format. It currently has three functions: encode, decode and img. encode and decode do what their name implies, and img produces the html code suitable for embedding the image into an html document.

The help page for img actually contains an image, here is it:

and here is how it is produced:

	The following graph is embedded in the document using the \code{img} function	
	library( base64 )
	library( grDevices )
	library( graphics )
	library( stats )
	pngfile <- tempfile()
	png( pngfile, width = 600, height = 400 )
	plot( 1:100, rnorm(100), pch = 21, bg = "red", cex = 2 )
	img( pngfile, Rd = TRUE )


Thursday, February 18 2010

raster images and RImageJ

The next version of R includes support for raster images in standard and grid graphics.

The RImageJ package uses ImageJ through rJava to read and manipulate images from various formats

Paul Murrell closed the gap and contributed code that allows using images from the RImageJ package as raster objects.

makes the graph :


This feature depends on R >= 2.11.0, so will only get available when this version becomes current, in the meantime, you can get the package from its rforge project page

Sunday, February 14 2010

Rcpp 0.7.7

A good 2 days after 0.7.6 was released, here comes Rcpp 0.7.7. The reason for this release is that a subtle bug installed itself and we did not catch it in time

The new version also includes two new class templates : unary_call and binary_call that help integration of calls (e.g. Rcpp::Language objects) with STL algorithms. For example here is how we might use unary_call

This emulates the code

> lapply( 1:10, function(n) seq(from=n, to = 0 ) )

As usual, more examples in the unit tests

Saturday, February 13 2010

highlight 0.1-5

I've pushed the version 0.1-5 of highlight to CRAN, it should be available in a couple of days.

This version fixes highlighting of code when one wants to display the prompt and the continue prompt. For example, this code :

	mean = 5)


gets highlighted like this:

using this code:

> highlight( "/tmp/test.R", renderer=renderer_html(document=T), showPrompts = TRUE, output = "test.html" )

Under the hood, highlight now depends on Rcpp and uses some of the C++ classes of the new Rcpp API. See the get_highlighted_text function in the code.

Rcpp 0.7.6

Rcpp 0.7.6 was released yesterday. This is mostly a maintenance update since the version 0.7.5 had some very minor issues on windows, but we still managed however to include some new things as well.

Vectors can now use name based indexing. This is typically useful for things like data frame, which really are named lists. Here is an example from our unit tests where we grab a column from a data frame and then compute the sum of its values:

The classes CharacterVector, GenericVector(aka List) and ExpressionVector now have iterators. Below is another example from our unit tests, where we use iterators to implement a C++ version of lapply using the std::transform algorithm from the STL.

Generic vectors (lists) gain some methods that make them look more like std::vector from the STL : push_back, push_front, insert and erase. Examples of using these methods are available in our unit tests:

> system.file( "unitTests", "runit.GenericVector.R", 
+ package = "Rcpp" )

Tuesday, February 9 2010

Rcpp 0.7.5

Dirk released Rcpp 0.7.5 yesterday

The main thing is the smarter wrap function that now uses techniques of type traits and template meta-programming to have a compile time guess at whether an object is wrappable, and how to do it. Currently wrappable types are :

  • primitive types : int, double, Rbyte, Rcomplex
  • std::string
  • STL containers such as std::vector<T> as long as T is wrappable. This is not strictly tied to the STL, actually any type that has a nested type called iterator and member functions begin() and end() will do
  • STL maps keyed by strings such as std::map<std::string,T> as long as T is wrappable
  • any class that can be implicitely converted to SEXP
  • any class for which the wrap template is partly or fully specialized. (The next version of RInside has an example of that)

Here comes an example (from our unit tests) :

        funx <- cfunction(signature(), 
        std::map< std::string,std::vector<int> > m ;
        std::vector<int> b ; b.push_back(1) ; b.push_back(2) ; m["b"] = b ;
        std::vector<int> a ; a.push_back(1) ; a.push_back(2) ; a.push_back(2) ; m["a"] = a ;
        std::vector<int> c ; c.push_back(1) ; c.push_back(2) ; c.push_back(2) ; c.push_back(2) ; m["c"] = c ;
        return wrap(m) ;
        Rcpp=TRUE, verbose=FALSE, includes = "using namespace Rcpp;" )
R> funx()
[1] 1 2 2

[1] 1 2

[1] 1 2 2 2

Apart from that, other things have changed, here is the relevant section of the NEWS for this release

    o 	wrap has been much improved. wrappable types now are :
    	- primitive types : int, double, Rbyte, Rcomplex, float, bool
    	- std::string
    	- STL containers which have iterators over wrappable types:
    	  (e.g. std::vector, std::deque, std::list, etc ...). 
    	- STL maps keyed by std::string, e.g std::map
    	- classes that have implicit conversion to SEXP
    	- classes for which the wrap template if fully or partly specialized
    	This allows composition, so for example this class is wrappable: 
    	std::vector< std::map > (if T is wrappable)
    o 	The range based version of wrap is now exposed at the Rcpp::
    	level with the following interface : 
    	Rcpp::wrap( InputIterator first, InputIterator last )
    	This is dispatched internally to the most appropriate implementation
    	using traits

    o	a new namespace Rcpp::traits has been added to host the various
    	type traits used by wrap

    o 	The doxygen documentation now shows the examples

    o 	A new file inst/THANKS acknowledges the kind help we got from others

    o	The RcppSexp has been removed from the library.
    o 	The methods RObject::asFoo are deprecated and will be removed
    	in the next version. The alternative is to use as.

    o	The method RObject::slot can now be used to get or set the 
    	associated slot. This is one more example of the proxy pattern
    o	Rcpp::VectorBase gains a names() method that allows getting/setting
    	the names of a vector. This is yet another example of the 
    	proxy pattern.
    o	Rcpp::DottedPair gains templated operator<< and operator>> that 
    	allow wrap and push_back or wrap and push_front of an object
    o	Rcpp::DottedPair, Rcpp::Language, Rcpp::Pairlist are less
    	dependent on C++0x features. They gain constructors with up
    	to 5 templated arguments. 5 was choosed arbitrarily and might 
    	be updated upon request.
    o	function calls by the Rcpp::Function class is less dependent
    	on C++0x. It is now possible to call a function with up to 
    	5 templated arguments (candidate for implicit wrap)
    o	added support for 64-bit Windows (thanks to Brian Ripley and Uwe Ligges)

Thursday, February 4 2010

RProtoBuf: protocol buffers for R

We (Dirk and I) released the initial version of our package RProtoBuf to CRAN this week. This packages brings google's protocol buffers to R

I invite you to check out the main page for protobuf to find the language definition for protocol buffers as well as tutorial for officially (i.e. by google) supported languages (python, c++ and java) as well as the third party support page that lists language bindings offered by others (including our RProtoBuf package.

Protocol buffers are a language agnostic data interchange format, based on a using a simple and well defined language. Here comes the classic example that google uses for C++, java and python tutorials.

First, the proto file defines the format of the message.

Then you need to teach this particular message to R, which is simply done by the readProtoFiles function.

> readProtoFiles( "addressbook.proto" )

Now we can start creating messages :

> person <- new( tutorial.Person, 
+     name = "John Doe", 
+     id = 1234,
+     email = "" )

And then access, modify fields of the message using a syntax extremely close to R lists

> person$email <- ""
> person$name <- "Romain Francois"

In R, protobuf messages are stored as simple S4 objects of class "Message" that contain an external pointer to the underlying C++ object. The Message class also defines methods that can be accessed using the dollar operator

> # write a debug version of message
> # this is not how it is serialized
> writeLines( person$toString() )
name: "Romain Francois"
id: 1234
email: ""

> # serialize the message to a file
> person$serialize( "somefile" )

The package already has tons of features, detailed in the vignette

> vignette( "RProtoBuf" )

.. and there is more to come

Wednesday, January 13 2010

Rcpp 0.7.2

Rcpp 0.7.2 is out, checkout Dirk's blog for details

selected highlights from this new version:

character vectors

if one wants to mimic this R code in C

> x <- c( "foo", "bar" )
one ends up with this :
SEXP x = PROTECT( allocVector( STRSXP, 2) ) ;
SET_STRING_ELT( x, 0, mkChar( "foo" ) ) ;
SET_STRING_ELT( x, 1, mkChar( "bar" ) ) ;
return x ;

Rcpp lets you express the same like this :

CharacterVector x(2) ;
x[0] = "foo" ; 
x[1] = "bar" ;

or like this if you have GCC 4.4 :

CharacterVector x = { "foo", "bar" } ;

environments, functions, ...

Now, we try to mimic this R code in C :
rnorm( 10L, sd = 100 )
You can do one of these two ways in Rcpp :
Environment stats("package:stats") ;
Function rnorm = stats.get( "rnorm" ) ;
return rnorm( 10, Named("sd", 100 ) ) ;

or :

Language call( "rnorm", 10, Named("sd", 100 ) ) ;
return eval( call, R_GlobalEnv ) ;

and it will get better with the next release, where you will be able to just call call.eval() and stats["rnorm"].

Using the regular R API, you'd write these liks this :

SEXP stats = PROTECT( R_FindNamespace( mkString("stats") ) ) ;
SEXP rnorm = PROTECT( findVarInFrame( stats, install("rnorm") ) ) ;
SEXP call  = PROTECT( LCONS( rnorm, CONS(ScalarInteger(10), CONS(ScalarReal(100.0), R_NilValue)))) ;
SET_TAG( CDDR(call), install("sd") ) ;
SEXP res = PROTECT( eval( call, R_GlobalEnv ) );
return res ;

or :

SEXP call  = PROTECT( LCONS( install("rnorm"), CONS(ScalarInteger(10), CONS(ScalarReal(100.0), R_NilValue)))) ;
SET_TAG( CDDR(call), install("sd") ) ;
SEXP res = PROTECT( eval( call, R_GlobalEnv ) );
return res ;

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