R Explained: The Language That Thinks in Data
Introduction Python does data science well, but R was born for it: created in 1993 by statisticians at the University of Auckland, it thinks in vectors, tables, and distributions the way other languages think in loops and objects. It powers academic research, clinical trials, and data journalism, and its superpower is that the things data people do all day — summarise, filter, plot — are one-liners, not library calls. If your work is data first and software second, R speaks your language natively. Vectors, data frames, and the tidyverse R's model rests on a few distinctive ideas: Vectors everywhere — the basic unit is a vector, not a single value, so operations apply to whole columns at once: add 1 to a vector and every element grows, no loop needed. Data frames — tables with named columns are a native type, not an add-on, so datasets feel like the language's home turf. Vectorised statistics — means, models, and distributions are built in, from mean() to linear regression i...