An Empirical Analysis of the R Package Ecosystem

TL;DR

This paper provides a comprehensive empirical analysis of the R package ecosystem over two decades, revealing growth trends, distribution patterns, and licensing practices across CRAN, Bioconductor, and GitHub.

Contribution

It offers the first large-scale longitudinal empirical summary of the entire R package ecosystem, including detailed metrics and growth analysis.

Findings

Ecosystem growth has been robust with annual growth rates around 26-29%.

Distribution of packages and maintainers is highly right-skewed, with a few packages and maintainers supporting most dependencies.

Majority of packages are under copyleft licenses or lack licensing information.

Abstract

In this research, we present a comprehensive, longitudinal empirical summary of the R package ecosystem, including not just CRAN, but also Bioconductor and GitHub. We analyze more than 25,000 packages, 150,000 releases, and 15 million files across two decades, providing comprehensive counts and trends for common metrics across packages, releases, authors, licenses, and other important metadata. We find that the historical growth of the ecosystem has been robust under all measures, with a compound annual growth rate of 29% for active packages, 28% for new releases, and 26% for active maintainers. As with many similar social systems, we find a number of highly right-skewed distributions with practical implications, including the distribution of releases per package, packages and releases per author or maintainer, package and maintainer dependency in-degree, and size per package and release. For example, the top five packages are imported by nearly 25% of all packages, and the top ten maintainers support packages that are imported by over half of all packages. We also highlight the dynamic nature of the ecosystem, recording both dramatic acceleration and notable deceleration in the growth of R. From a licensing perspective, we find a notable majority of packages are distributed under copyleft licensing or omit licensing information entirely. The data, methods, and calculations herein provide an anchor for public discourse and industry decisions related to R and CRAN, serving as a foundation for future research on the R software ecosystem and "data science" more broadly.