Recently, I updated the ribiosMath package. The aim was to increase the efficiency of several commonly used computational procedures (Kappa’s statistic, cosine similarity, etc.) by implementing them in C++.

During the course, I discovered that it has become much easier now than one to two years ago to implement C++ routines in R, with the help of tools such as devtools and Rcpp packages in R, and RStudio, an IDE for R programming.

Basic steps

  • Call devtools::use_rcpp() to setup your package so that it uses Rcpp easily.
  • Create C++ files, which can be done by RStudio. It will create a template file with the following essential elements
#include <Rcpp.h>
using namespace Rcpp;

// [[Rcpp::export]]
void func() {}

Note that [[Rcpp::export]] must precede each function that needs to be called by R functions.

  • Modify the function. Hit Ctrl/Cmd + Shift + D in RStudio to update the NAMESPACE file
  • Build and reload, by Ctrl/Cmd + Shift + B. This is a magic command, because in the backstage it does a lot:
    • it sets up the R environment to compile the code,
    • it generates automatically the R code to make the C++ routine available in R, i.e., setting up.Ccalls with entry points. This is done by calling Rcpp::compileAttributes().
    • it builds a dynamically linked library, or DLL, and makes it available to R.

What also excites me is the possibility to directly document the R-level function in the C++ source code file: just replacing the #' prefix used by the roxygen package with //'. That will allow roxygen generating Rd files automatically.

Optionally, one can even use // [[Rcpp::interface(r, cpp)]] to allow C++ code callable from C++ code in other packages.

Over all, the process is much more efficient than the conventional way of writing C code, which requires a lot of boilerplate activities: make manual R function wrappers with .C, document the R function, register the routines, modify the NAMESPACE file, etc.. I would recommend the new procedure to everyone who seriously works with C/C++-level code in R programming.

Thanks to the new procedure, I updated within a very short time the ribiosMath package. Like any other ribios package, it is open-source and freely available on Github. Any suggestion and feedback is welcome!

Shortcuts of Rstudio

  • Ctrl/Cmd + Shift + B: Build and reload a package
  • Ctrl/Cmd + Shift + D: Document a package

Further reading

  1. Using compiled code in your R package by Hadley Wickham.
  2. Rcpp for everyone by Masaki E. Tsuda.
  3. High performance functions with Rcpp in Advanced R by Hadley Wickham.
  4. Rcpp website by Dirk Eddelbuettel. It links to the Rcpp-introduction vignette in PDF format, which is a good read for beginners.
  5. doxygen documentation. It surprises me that very few websites link to this resource, which proves very useful when one wants to find particular operations of a given class.


2018.01.23: A few resources were appended which proved useful for learning the latest Rcpp package.