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The R Language: An Engine for Bioinformatics and Data Science.

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Summary
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This review chronicles the R programming language's rise to prominence in data science, statistics, and bioinformatics over three decades. It highlights R's extensive packages, tools like R Shiny, and its crucial role in scientific reproducibility.

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Area of Science:

  • Computer Science
  • Statistics
  • Bioinformatics
  • Data Science

Background:

  • The R programming language has evolved significantly over its nearly 30-year history.
  • It is now a globally recognized language, ranking in the top 10 most popular.
  • A vast ecosystem of tens of thousands of R packages supports diverse applications.

Purpose of the Study:

  • To provide a historical overview of R's development and current standing.
  • To detail R's features, capabilities, and major tools.
  • To emphasize R's role in scientific reproducibility and its impact on data science.

Main Methods:

  • Historical chronicle of R's evolution.
  • Description of current R features and capabilities.
  • Illustration of key R tools, including IDEs, R Shiny, and machine learning methods.

Main Results:

  • R has become a dominant language in statistics, bioinformatics, and data science.
  • Its extensive package library covers a wide range of applications, from machine learning to transcriptome analysis.
  • R is a critical tool for ensuring reproducibility in scientific research.

Conclusions:

  • R is a versatile and powerful programming language with a rich history and a significant impact on modern science.
  • The R ecosystem, including its tools and community support, continues to drive innovation in data analysis and scientific discovery.
  • R's ongoing development and widespread adoption underscore its importance as a cornerstone of data science and reproducible research.