The Rcpp package has become one of the most important packages for R. Essentially, it allows us to integrate C++ code into our R scripts seamlessly. The main advantage of this, is that we can achieve major efficiency gains, especially if our code needs to use lots of loops. A second advantage, is that C++ has a library called the standard template library (STL) that has very efficient containers (for example, vectors and linked lists) that are extremely fast and well suited for many programming tasks. Depending on the case, you could expect to see improvements anywhere from 2x to 50x. This is why many of the most widely used packages have been rewritten to leverage Rcpp. In Rcpp, we can code in C++ using the typical C++ syntax, but we also have specific containers that are well suited to store R-specific elements. For example, we can use the Rcpp::NumericVector or the Rcpp::DataFrame, which are certainly not native C++ variable types.

The traditional way of using Rcpp involves writing the code in C++ and sourcing it in R. Since Rcpp 0.8.3, we can also use the Rcpp sugar style, which is a different way of coding in C++. Rcpp sugar brings some elements of the high-level R syntax into C++, allowing us to achieve the same things with a much more concise syntax.

We can use Rcpp in two ways, using the functions in an inline way, or coding them in a separate script and sourcing it via sourcecpp(). The latter approach is slightly preferable as it clearly separates the C++ and R code into different files.