## 25.1 Introduction

Sometimes R code just isn’t fast enough. You’ve used profiling to figure out where your bottlenecks are, and you’ve done everything you can in R, but your code still isn’t fast enough. In this chapter you’ll learn how to improve performance by rewriting key functions in C++. This magic comes by way of the Rcpp package (Eddelbuettel and François 2011) (with key contributions by Doug Bates, John Chambers, and JJ Allaire).

Rcpp makes it very simple to connect C++ to R. While it is possible to write C or Fortran code for use in R, it will be painful by comparison. Rcpp provides a clean, approachable API that lets you write high-performance code, insulated from R’s complex C API.

Typical bottlenecks that C++ can address include:

• Loops that can’t be easily vectorised because subsequent iterations depend on previous ones.

• Recursive functions, or problems which involve calling functions millions of times. The overhead of calling a function in C++ is much lower than in R.

• Problems that require advanced data structures and algorithms that R doesn’t provide. Through the standard template library (STL), C++ has efficient implementations of many important data structures, from ordered maps to double-ended queues.

The aim of this chapter is to discuss only those aspects of C++ and Rcpp that are absolutely necessary to help you eliminate bottlenecks in your code. We won’t spend much time on advanced features like object-oriented programming or templates because the focus is on writing small, self-contained functions, not big programs. A working knowledge of C++ is helpful, but not essential. Many good tutorials and references are freely available, including http://www.learncpp.com/ and https://en.cppreference.com/w/cpp. For more advanced topics, the Effective C++ series by Scott Meyers is a popular choice.

### Outline

• Section 25.2 teaches you how to write C++ by converting simple R functions to their C++ equivalents. You’ll learn how C++ differs from R, and what the key scalar, vector, and matrix classes are called.

• Section 25.2.5 shows you how to use sourceCpp() to load a C++ file from disk in the same way you use source() to load a file of R code.

• Section 25.3 discusses how to modify attributes from Rcpp, and mentions some of the other important classes.

• Section 25.4 teaches you how to work with R’s missing values in C++.

• Section 25.5 shows you how to use some of the most important data structures and algorithms from the standard template library, or STL, built-in to C++.

• Section 25.6 shows two real case studies where Rcpp was used to get considerable performance improvements.

• Section 25.7 teaches you how to add C++ code to a package.

• Section 25.8 concludes the chapter with pointers to more resources to help you learn Rcpp and C++.

### Prerequisites

We’ll use Rcpp to call C++ from R:

library(Rcpp)

You’ll also need a working C++ compiler. To get it:

• On Windows, install Rtools.
• On Mac, install Xcode from the app store.
• On Linux, sudo apt-get install r-base-dev or similar.

### References

Eddelbuettel, Dirk, and Romain François. 2011. “Rcpp: Seamless R and C++ Integration.” Journal of Statistical Software 40 (8): 1–18. https://doi.org/10.18637/jss.v040.i08.