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Seamless R and C++ Integration

The Rcpp package provides R functions and a (header-only for client packages) C++ library greatly facilitating the integration of R and C++.

All underlying R types and objects, i.e., everything a SEXP represents internally in R, are matched to corresponding C++ objects. This covers anything from vectors, matrices or lists to environments, functions and more. Each SEXP variant is automatically mapped to a dedicated C++ class. For example, numeric vectors are represented as instances of the Rcpp::NumericVector class, environments are represented as instances of Rcpp::Environment, functions are represented as Rcpp::Function, etc … The Rcpp-introduction vignette (now published as a TAS paper; an earlier introduction was also published as a JSS paper) provides a good entry point to Rcpp as do the Rcpp website, the Rcpp page and the Rcpp Gallery. Full documentation is provided by the Rcpp book.

Other highlights:

Documentation

The package ships with nine pdf vignettes, including a recent introduction to Rcpp now published as a paper in TAS (and as a preprint in PeerJ). Also available is an earlier introduction which was published as a JSS paper)

Among the other vignettes are the Rcpp FAQ and the introduction to Rcpp Attributes. Additional documentation is available via the Rcpp book by Eddelbuettel (2013, Springer); see ‘citation(“Rcpp”)’ for details.

Examples

The Rcpp Gallery showcases over one hundred fully documented and working examples. The package RcppExamples contains a few basic examples covering the core data types.

A number of examples are included as are 1437 unit tests in 622 unit test functions provide additional usage examples.

An earlier version of Rcpp, containing what we now call the ‘classic Rcpp API’ was written during 2005 and 2006 by Dominick Samperi. This code has been factored out of Rcpp into the package RcppClassic, and it is still available for code relying on the older interface. New development should always use this Rcpp package instead.

Other usage examples are provided by packages using Rcpp. As of November 2018, there are 1490 CRAN packages using Rcpp, a further 150 BioConductor packages in its current release as well as an unknown number of GitHub, Bitbucket, R-Forge, … repositories using Rcpp. All these packages provide usage examples for Rcpp.

Installation

Released and tested versions of Rcpp are available via the CRAN network, and can be installed from within R via

install.packages("Rcpp")

To install from source, ensure you have a complete package development environment for R as discussed in the relevant documentation; also see questions 1.2 and 1.3 in the Rcpp-FAQ.

Support

The best place for questions is the Rcpp-devel mailing list hosted at R-forge. Note that in order to keep spam down, you must be a subscriber in order to post. One can also consult the list archives to see if your question has been asked before.

Another option is to use StackOverflow and its ‘rcpp’ tag. Search functionality (use rcpp in squared brackets as in [rcpp] my question terms to tag the query) is very valuable as many questions have indeed been asked, and answered, before.

The issue tickets at the GitHub repo are the primary bug reporting interface. As with the other web resources, previous issues can be searched as well.

Authors

Dirk Eddelbuettel, Romain Francois, JJ Allaire, Kevin Ushey, Qiang Kou, Nathan Russell, Doug Bates, and John Chambers

License

GPL (>= 2)