Add Rcpp Integration

Integrate C++ code into an R package using Rcpp for performance-critical operations.

When to Use

• •R function is too slow and profiling confirms a bottleneck
• •Need to interface with existing C/C++ libraries
• •Implementing algorithms that benefit from compiled code (loops, recursion)
• •Adding RcppArmadillo for linear algebra operations

Inputs

• •Required: Existing R package
• •Required: R function to replace or augment with C++
• •Optional: External C++ library to interface with
• •Optional: Whether to use RcppArmadillo (default: plain Rcpp)

Procedure

Step 1: Set Up Rcpp Infrastructure

usethis::use_rcpp()

This:

• •Creates src/ directory
• •Adds Rcpp to LinkingTo and Imports in DESCRIPTION
• •Creates R/packagename-package.R with @useDynLib and @importFrom Rcpp sourceCpp
• •Updates .gitignore for compiled files

For RcppArmadillo:

usethis::use_rcpp_armadillo()

Expected: src/ directory created, DESCRIPTION updated.

Step 2: Write C++ Function

Create src/my_function.cpp:

#include <Rcpp.h>
using namespace Rcpp;

//' Compute cumulative sum efficiently
//'
//' @param x A numeric vector
//' @return A numeric vector of cumulative sums
//' @export
// [[Rcpp::export]]
NumericVector cumsum_cpp(NumericVector x) {
  int n = x.size();
  NumericVector out(n);
  out[0] = x[0];
  for (int i = 1; i < n; i++) {
    out[i] = out[i - 1] + x[i];
  }
  return out;
}

For RcppArmadillo:

#include <RcppArmadillo.h>
// [[Rcpp::depends(RcppArmadillo)]]

//' Matrix multiplication using Armadillo
//'
//' @param A A numeric matrix
//' @param B A numeric matrix
//' @return The matrix product A * B
//' @export
// [[Rcpp::export]]
arma::mat mat_mult(const arma::mat& A, const arma::mat& B) {
  return A * B;
}

Step 3: Generate RcppExports

Rcpp::compileAttributes()
devtools::document()

Expected: R/RcppExports.R and src/RcppExports.cpp generated automatically.

On failure: Check C++ syntax errors. Ensure // [[Rcpp::export]] tag is present above each exported function.

Step 4: Verify Compilation

devtools::load_all()

Expected: Package compiles and loads without errors.

On failure: Check compiler output for errors. Common issues:

• •Missing system headers: Install development libraries
• •Syntax errors: C++ compiler messages point to the line
• •Missing Rcpp::depends attribute for RcppArmadillo

Step 5: Write Tests for Compiled Code

test_that("cumsum_cpp matches base R", {
  x <- c(1, 2, 3, 4, 5)
  expect_equal(cumsum_cpp(x), cumsum(x))
})

test_that("cumsum_cpp handles edge cases", {
  expect_equal(cumsum_cpp(numeric(0)), numeric(0))
  expect_equal(cumsum_cpp(c(NA_real_, 1)), c(NA_real_, NA_real_))
})

Step 6: Add Cleanup Script

Create src/Makevars:

PKG_CXXFLAGS = -O2

Create cleanup in package root (for CRAN):

#!/bin/sh
rm -f src/*.o src/*.so src/*.dll

Make executable: chmod +x cleanup

Step 7: Update .Rbuildignore

Ensure compiled artifacts are handled:

^src/.*\.o$
^src/.*\.so$
^src/.*\.dll$

Validation

• • devtools::load_all() compiles without warnings
• • Compiled function produces correct results
• • Tests pass for edge cases (NA, empty, large inputs)
• • R CMD check passes with no compilation warnings
• • RcppExports files are generated and committed
• • Performance improvement confirmed with benchmarks

Common Pitfalls

• •Forgetting compileAttributes(): Must regenerate RcppExports after changing C++ files
• •Integer overflow: Use double instead of int for large numeric values
• •Memory management: Rcpp handles memory automatically for Rcpp types; don't manually delete
• •NA handling: C++ doesn't know about R's NA. Check with Rcpp::NumericVector::is_na()
• •Platform portability: Avoid platform-specific C++ features. Test on Windows, macOS, and Linux.
• •Missing @useDynLib: The package-level doc must include @useDynLib packagename, .registration = TRUE

Related Skills

• •create-r-package - package setup before adding Rcpp
• •write-testthat-tests - testing compiled functions
• •setup-github-actions-ci - CI must have C++ toolchain
• •submit-to-cran - compiled packages need extra CRAN checks