Abstract
Data science is increasingly important and challenging. It requires computational tools and programming environments that handle big data and difficult computations, while supporting creative, high-quality analysis. The R language and related software play a major role in computing for data science. R is featured in most programs for training in the field. R packages provide tools for a wide range of purposes and users. The description of a new technique, particularly from research in statistics, is frequently accompanied by an R package, greatly increasing the usefulness of the description. The history of R makes clear its connection to data science. R was consciously designed to replicate in open-source software the contents of the S software. S in turn was written by data analysis researchers at Bell Labs as part of the computing environment for research in data analysis and collaborations to apply that research, rather than as a separate project to create a programming language. The features of S and the design decisions made for it need to be understood in this broader context of supporting effective data analysis (which would now be called data science). These characteristics were all transferred to R and remain central to its effectiveness. Thus, R can be viewed as based historically on a domain-specific language for the domain of data science.
Highlights
R has become a widely used medium for the practice of technically advanced data science; most importantly, a medium in which new applications and new ideas in the practice of data science are very often shared throughout the worldwide community
The language, data structure and functional capabilities of R, as they were implemented in the late 1990s, were modelled on the S software from Bell Labs, supplemented by some new ideas, reflecting developments in programming language design during this period
The S software was distinguished from many programming language designs in being motivated by a relatively specific scientific goal; namely, to support research in data analysis at Bell Labs and applications to challenging problems
Summary
R has become a widely used medium for the practice of technically advanced data science; most importantly, a medium in which new applications and new ideas in the practice of data science are very often shared throughout the worldwide community. The language, data structure and functional capabilities of R, as they were implemented in the late 1990s, were modelled on the S software from Bell Labs, supplemented by some new ideas, reflecting developments in programming language design during this period. The S software was distinguished from many programming language designs in being motivated by a relatively specific scientific goal; namely, to support research in data analysis at Bell Labs and applications to challenging problems. Other advances were key, notably information theory, coding and digital techniques for communication It was noted at the time, and even more since that the productivity and originality of much Bell Labs work seemed to derive from an organization and research atmosphere not found elsewhere. Along with ideas leading to the transistor and communication theory, this research environment nurtured an approach to what can be called data science
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