R is a free software environment for statistical computing and graphics, and a dialect of the S langage de programmation. It was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R is considered an alternative implementation of S, with semantics derived from Scheme, which introduced powerful features like lexical scoping not present in early S.
The R Programming Language
- Ross Ihaka
- Robert Gentleman
R’s lineage traces directly back to the S language, developed at Bell Labs by John Chambers and colleagues. While S was primarily a commercial product (S-PLUS), R was conceived as a free, open-source alternative. Ross Ihaka and Robert Gentleman, academics at the University of Auckland, began the project in 1992 to create a language for their teaching needs that was syntactically similar to S but with different underlying mécanique. They incorporated ideas from functional programming languages like Scheme, most notably lexical scoping. This design choice distinguishes R from the earlier S versions and has profound implications for how functions handle variables, making code more predictable and easier to reason about.
The name “R” was chosen partly as a play on the names of its authors (Ross and Robert) and partly as a nod to its predecessor, S. The project was announced to the public on the S-news mailing list in 1993, and the R Core Team was formed in 1997 to manage the language’s development after it gained significant traction. R’s core is written in C and Fortran, allowing it to interface with high-performance numerical libraries, while users interact with it through its own high-level interpreted language. This combination of statistical heritage, open-source accessibility, and modern programming features fueled its rise to become a lingua franca for statistics and data science.
Taper
Perturbation
Usage
Précurseurs
- The S programming language developed at Bell Labs
- The Scheme programming language and its concept of lexical scoping
- The AWK language which influenced S’s data handling
- The C programming language in which R’s interpreter is written
- The Fortran language used for many of R’s numerical libraries
Applications
- development of the RStudio IDE
- creation of the Tidyverse ecosystem
- widespread use in academic research for statistical analysis
- data science and machine learning applications in industry
- bioinformatics analysis through the Bioconductor project
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