An empirical exploration of the diversified R ecosystem.

Area of Science:

• Computational statistics and data science.
• Software ecosystem analysis.
• Bibliometrics and scientometrics.

Background:

• R is a widely adopted software environment for statistical computing and graphics.
• Its evolution is tied to advances in IT and data-intensive research.
• The R ecosystem comprises software and a vast package network.

Purpose of the Study:

• To empirically review the R ecosystem's trends from 2005-2024.
• To analyze R adoption, package structure, disciplinary applications, and developer collaboration.
• To understand the factors driving R's long-term relevance.

Main Methods:

• Integration of Comprehensive R Archive Network (CRAN) usage data with Scopus bibliometric records.
• Analysis of long-term download trends for R software and packages.
• Keyword co-occurrence analysis for ecosystem structure and disciplinary applications.
• Collaboration network analysis of R package developers.

Main Results:

• Sustained growth in R software and package downloads observed.
• R package usage demonstrates stable and continuous expansion.
• The R ecosystem is structured around a statistical core, linked to machine learning and diverse applications.
• Widespread adoption of R across agricultural, biological, environmental, medical, and social sciences.
• Multi-author packages exhibit higher reuse and download activity, indicating the value of collaborative development.

Conclusions:

• R is a resilient, community-driven platform.
• Interdisciplinary collaboration and open-source innovation are key drivers of R's evolution.
• The R ecosystem's vitality is sustained by collective development and broad scientific adoption.