Welding R and C++: A Tale of Two Programming Languages

TL;DR

The paper addresses integrating high‑performance C++ linear algebra into R via cpp11armadillo and cpp11eigen and benchmarks their performance and usability. It analyzes ATT benchmarks and a Balassa index workflow to compare Armadillo and Eigen against base R, highlighting vendoring capabilities and data‑transfer considerations. Key findings show Armadillo often delivers superior raw speed, Eigen excels in FFT tasks, and R remains easier to use though sometimes slower, with data movement shaping outcomes. The work provides practical guidance for practitioners choosing between Armadillo and Eigen in R, illustrating how C++ can accelerate computation while accounting for learning curves and environment constraints.

Abstract

This article compares `cpp11armadillo` and `cpp11eigen`, new R packages that integrate the powerful Armadillo and Eigen C++ libraries for linear algebra into the R programming environment. This article provides a detailed comparison between Armadillo and Eigen speed and syntax. The goal of these packages is to simplify a part of the process of solving bottlenecks by using C++ within R, these offer additional ease of integration for users who require high-performance linear algebra operations in their R workflows. This document aims to discuss the tradeoff between computational efficiency and accessibility.