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R exists because statisticians needed a programming language designed by statisticians, for statisticians. The language treats data frames, vectors, and statistical functions as first-class citizens. Need a t-test? It's built in. Want a publication-ready scatter plot? Three lines of code. Need to fit a mixed-effects model? There's a package for that.
The language dominates academic statistics, biostatistics, epidemiology, and social science research. ggplot2 (R's plotting library) produces visualisations so consistently beautiful that you can spot an R-generated chart across a crowded slide deck. RMarkdown weaves code, output, and prose into reproducible reports. The tidyverse collection of packages makes data manipulation almost readable.
RStudio (now Posit) is the IDE that makes R usable — interactive console, variable browser, plot viewer, and package manager in one interface. VS Code with R extensions is an alternative. R scripts are plain text, editable anywhere. Python's pandas and matplotlib have absorbed some of R's traditional territory, but for serious statistical analysis, R remains the specialist tool that generalist languages can't quite match.