R: A Language and Environment for Statistical Computing¶
Why this mattered¶
R mattered because it made advanced statistical computing programmable, extensible, graphical, and freely available in a single environment. Building on the S language tradition, R turned statistical analysis from a set of fixed procedures into an interactive language in which users could inspect data, fit models, simulate, visualize, and write reusable functions in the same workflow. Its importance was not just that it provided statistical routines, but that it made statistical methods objects of computation: models, data frames, plots, and functions could be manipulated, extended, and shared by users rather than only delivered by software vendors.
The paradigm shift was the creation of a common open platform for statistical method development. After R, a new method in statistics, bioinformatics, econometrics, epidemiology, machine learning, or data visualization could be distributed as code that other researchers could immediately run, modify, and build on. This helped shorten the path from methodological paper to practical use: packages became a publication-adjacent medium for disseminating algorithms, diagnostics, graphics, and reproducible workflows.
Its later influence is visible in the infrastructure of modern data science. R helped normalize interactive analysis, package ecosystems, literate statistical programming, and publication-quality visualization as core parts of scientific work. Subsequent breakthroughs in genomics, Bayesian modeling, causal inference, tidy data workflows, and reproducible research repeatedly used R not merely as a calculator, but as a shared computational language in which new statistical paradigms could become everyday tools.
Abstract¶
Most R novices will start with Appendix A [A sample session], page 80.This should give some familiarity with the style of R sessions and more importantly some instant feedback on what actually happens.Many users will come to R mainly for its graphical facilities.