L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Développement · SKILL.md

add-rcpp-integration

add-rcpp-integration

Add Rcpp or RcppArmadillo integration to an R package for high-performance C++ code. Covers setup, writing C++ functions, RcppExports generation, testing compiled code, and debugging. Use when an R function is too slow and profiling confirms a bottleneck, when you need to interface with existing C/C++ libraries, or when implementing algorithms (loops, recur…

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: add-rcpp-integration locale: caveman source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-04-19" description: > Add Rcpp or RcppArmadillo integration to an R package for high-performance C++ code. Covers setup, writing C++ functions, RcppExports generation, testing compiled code, and debugging. Use when an R function is too slow and profiling confirms a bottleneck, when you need to interface with existing C/C++ libraries, or when implementing algorithms (loops, recursion, linear algebra) that benefit from compiled code. license: MIT allowed-tools: Read Write Edit Bash Grep Glob metadata: author: Philipp Thoss version: "1.0" domain: r-packages complexity: advanced language: R tags: r, rcpp, cpp, performance, compiled-code

Add Rcpp Integration

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

When Use

  • R function too slow and profiling confirms bottleneck
  • Need to interface with existing C/C++ libraries
  • Implementing algorithms 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)

Steps

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. R/packagename-package.R contains @useDynLib directive.

If fail: usethis::use_rcpp() fails? Manually create src/, add LinkingTo: Rcpp and Imports: Rcpp to DESCRIPTION. Add #' @useDynLib packagename, .registration = TRUE and #' @importFrom Rcpp sourceCpp to 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 file uses #include <Rcpp.h> (or <RcppArmadillo.h> for Armadillo). Export annotation on its own line directly above function signature. 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 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 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. Confirm C++ function produces identical results to R equivalent. Handles edge cases (empty vectors, NA values) correctly.

If fail: Tests fail on NA handling? Add explicit NA checks in C++ code using NumericVector::is_na(). Tests fail on empty input? Add guard clause for zero-length vectors at top of 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. cleanup script removes compiled objects. Both files exist at package root level.

If fail: Verify cleanup has execute permissions (chmod +x cleanup). Makevars uses tabs (not spaces) for indentation if adding Makefile-style rules.

Step 7: Update .Rbuildignore

Ensure compiled artifacts handled:

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

Got: .Rbuildignore patterns prevent compiled object files from being included in package tarball. Preserves 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, .dll files.

Checks

  • 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 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, Linux.
  • Missing @useDynLib: Package-level doc must include @useDynLib packagename, .registration = TRUE

See Also

  • 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
Skills enregistrées