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()

Got:

src/

directory created, DESCRIPTION updated with

Rcpp

in LinkingTo and Imports, and

R/packagename-package.R

contains

@useDynLib

directive.

If fail: If

usethis::use_rcpp()

fails, manually create

src/

, add

LinkingTo: Rcpp

and

Imports: Rcpp

to DESCRIPTION, and add

#' @useDynLib packagename, .registration = TRUE

and

#' @importFrom Rcpp sourceCpp

to the package-level documentation file.

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;
}

Got: C++ source file exists at

src/my_function.cpp

with valid

// [[Rcpp::export]]

annotation and roxygen-style

//'

documentation comments.

If fail: Verify the file uses

#include <Rcpp.h>

(or

<RcppArmadillo.h>

for Armadillo), that the export annotation is on its own line directly above the function signature, and that return types map to valid Rcpp types.

Step 3: Generate RcppExports

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

Got:

R/RcppExports.R

and

src/RcppExports.cpp

generated automatically.

If fail: Check C++ syntax errors. Ensure

// [[Rcpp::export]]

tag is present above each exported function.

Step 4: Verify Compilation

devtools::load_all()

Got: Package compiles and loads without errors.

If fail: 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_))
})

Got: Tests pass, confirming the C++ function produces identical results to the R equivalent and handles edge cases (empty vectors, NA values) correctly.

If fail: If tests fail on NA handling, add explicit NA checks in the C++ code using

NumericVector::is_na()

. If tests fail on empty input, add a guard clause for zero-length vectors at the top of the function.

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

Got:

src/Makevars

sets compiler flags and

cleanup

script removes compiled objects. Both files exist at the package root level.

If fail: Verify

cleanup

has execute permissions (

chmod +x cleanup

) and that

Makevars

uses tabs (not spaces) for indentation if adding Makefile-style rules.

Step 7: Update .Rbuildignore

Ensure compiled artifacts are handled:

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

Got:

.Rbuildignore

patterns prevent compiled object files from being included in the package tarball, while preserving source files and Makevars.

If fail: Run

devtools::check()

and look for NOTEs about unexpected files in

src/

. Adjust

.Rbuildignore

patterns to exclude only

.o

,

.so

, and

.dll

files.

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

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