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Expansion and evolution of the R programming language.

Timothy L Staples¹

¹School of Biological Sciences, The University of Queensland, Building 60, St Lucia, Queensland 4072, Australia.

Royal Society Open Science

|April 17, 2023

Summary

Programming languages evolve like natural languages. Analysis of R code reveals significant changes in function usage over time, driven by community extensions like the tidyverse, potentially leading to distinct dialects.

Keywords:

GitHub repositories lexicon programming functions software linguistics

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Area of Science:

Computational Linguistics
Software Evolution
Scientific Computing

Background:

Programming languages, like natural languages, evolve over time due to communication optimization.
The R programming language is widely used in scientific computing and has a mature, open-source ecosystem.
Understanding language evolution can provide insights into the dynamics of programming language change.

Purpose of the Study:

To investigate detectable changes in the usage of R functions over time.
To analyze the diversity and composition of R functions used in scientific computing.
To compare the evolution of programming languages with natural language and genetic evolution processes.

Main Methods:

Extracted 143,409,288 R functions from 393,142 GitHub repositories (2014-2021).
Applied linguistic and ecological analyses to detect changes in function diversity and composition.
Examined the impact of community-driven extensions, such as the 'tidyverse', on R's evolution.

Main Results:

The number of R functions in use has increased significantly between 2014 and 2021.
Function usage patterns have undergone substantial change, largely influenced by the 'tidyverse' collection.
Evidence suggests selective pressures for increased analytic complexity and declining functions ('extinction debts').

Conclusions:

Users actively shape programming languages, mirroring processes observed in natural languages and genetic evolution.
The evolution of R towards the 'tidyverse' may indicate a divergence into distinct dialects.
This potential dialectal division could impact the readability, continuity, and future of scientific inquiries coded in R.