Rcpp features two functions that enable code compilation inline and exportation directly into R: cppFunction() and evalCpp(). A third function called sourceCpp() exists to read in C++ code in a separate file though can be used akin to cppFunction().

Below is an example of compiling a C++ function within R. Note the use of "" to surround the source.

# Note - This is R code.
# cppFunction in Rcpp allows for rapid testing.
require(Rcpp)

# Creates a function that multiples each element in a vector
# Returns the modified vector.
cppFunction("
NumericVector exfun(NumericVector x, int i){
x = x*i;
return x;
}")

# Calling function in R
exfun(1:5, 3)

To quickly understand a C++ expression use:

# Use evalCpp to evaluate C++ expressions
evalCpp("std::numeric_limits<double>::max()")
## [1] 1.797693e+308

Rcpp Attributes makes the process of working with R and C++ straightforward. The form of attributes take:

// [[Rcpp::attribute]]

The use of attributes is typically associated with:

// [[Rcpp::export]]

that is placed directly above a declared function header when reading in a C++ file via sourceCpp().

Below is an example of an external C++ file that uses attributes.

// Add code below into C++ file Rcpp_example.cpp

#include <Rcpp.h>
using namespace Rcpp;

// Place the export tag right above function declaration.
// [[Rcpp::export]]
double muRcpp(NumericVector x){

    int n = x.size(); // Size of vector
    double sum = 0; // Sum value

    // For loop, note cpp index shift to 0
    for(int i = 0; i < n; i++){
        // Shorthand for sum = sum + x[i]
        sum += x[i];
    }

    return sum/n; // Obtain and return the Mean
}

// Place dependent functions above call or
// declare the function definition with:
double muRcpp(NumericVector x);

// [[Rcpp::export]]
double varRcpp(NumericVector x, bool bias = true){

    // Calculate the mean using C++ function
    double mean = muRcpp(x);
    double sum = 0;

    int n = x.size();

    for(int i = 0; i < n; i++){
        sum += pow(x[i] - mean, 2.0); // Square
    }

    return sum/(n-bias); // Return variance
}

To use this external C++ file within R, we do the following:

require(Rcpp)

# Compile File
sourceCpp("path/to/file/Rcpp_example.cpp")

# Make some sample data
x = 1:5

all.equal(muRcpp(x), mean(x))
## TRUE

all.equal(varRcpp(x), var(x))
## TRUE

Within C++, one can set different compilation flags using:

 // [[Rcpp::plugins(name)]]

List of the built-in plugins:

// built-in C++11 plugin
// [[Rcpp::plugins(cpp11)]]

// built-in C++11 plugin for older g++ compiler
// [[Rcpp::plugins(cpp0x)]]

// built-in C++14 plugin for C++14 standard
// [[Rcpp::plugins(cpp14)]]

// built-in C++1y plugin for C++14 and C++17 standard under development
// [[Rcpp::plugins(cpp1y)]]

// built-in OpenMP++11 plugin
// [[Rcpp::plugins(openmp)]]

To use additional packages within the Rcpp ecosystem, the correct header file may not be Rcpp.h but Rcpp<PACKAGE>.h (as e.g. for RcppArmadillo). It typically needs to be imported and then the dependency is stated within

// [[Rcpp::depends(Rcpp<PACKAGE>)]]

Examples:

// Use the RcppArmadillo package
// Requires different header file from Rcpp.h
#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]

// Use the RcppEigen package
// Requires different header file from Rcpp.h
#include <RcppEigen.h>
// [[Rcpp::depends(RcppEigen)]]