Data Science and R, Base R, and the tidyverse Ecosystem

Thomas W. MacFarland
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Thomas W. MacFarland: Nova Southeastern University, Office of Institutional Effectiveness and College of Computing and Engineering

Chapter Chapter 4 in Introduction to Data Science in Biostatistics, 2024, pp 175-219 from Springer

Abstract: Abstract The purpose of this chapter is to provide introductory guidance and examples on the issue that quality software is never static but is instead subject to continuous improvement, whether the software is proprietary or open source. R is a typical example of this observation and the realization that software must either evolve or die. S was developed in the mid-1970s at a world-renowned telecommunications research laboratory, largely to meet unmet needs due to the deficiencies of then existing software. S continued for a time, until a new business model at the telecommunications company contributed to limited support for S. Later, S was reimagined and evolved into what is now known as R. R has seen continuous evolution, with each new version of R and the many thousands of freely available R-based packages contributing to its evolution. A major evolutionary jump for the R community was in the mid-2000s when the first packages associated with the tidyverse ecosystem became available, offering functionality that finally encouraged many data scientists to embrace R as a first-choice language, using Base R as well as this new ecosystem of packages – the tidyverse.

Keywords: American Standard Code for Information Interchange (ASCII); Application Programming Interface (API); Base R; Beautiful Graphics; Cell; Column; Comprehensive R Archive Network (CRAN); Core tidyverse; Dataframe; Deaths of Despair; GNU Public License; Grammar of Graphics; Greenbar paper; Object; Pencil Trace; Pseudocode; R; Row; S; Scientific Notation; Tibble; Tidyverse Ecosystem; Variable; Workflow (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-46383-9_4

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