R is widely used for data science and statistics because it was designed specifically for these purposes. Unlike general programming languages, R includes built‑in functions for statistical analysis, probability distributions, and hypothesis testing.
One key reason to use R is its strong package ecosystem. Packages such as dplyr simplify data manipulation, ggplot2 enables advanced visualization, and caret supports machine learning workflows.
R also supports exploratory data analysis, which is crucial in understanding datasets before building models. Analysts can quickly summarize data, identify patterns, and visualize relationships using concise code.
Another reason R is popular is reproducibility. Using scripts and markdown documents, researchers can document every step of their analysis, making results transparent and repeatable.