Short answer: R is no longer “just a statistics language.” In 2026, it has become a serious, practical, production‑ready tool for AI and machine learning, especially for analysts, researchers, and solo developers who want fast results without heavy engineering overhead.

Below is the full breakdown.

🚀 1. R is built for data — the foundation of all AI

AI systems live or die based on data quality.

R gives you:

· tidyverse for clean, readable data pipelines

· dplyr for fast transformations

· data.table for high‑performance operations

· ggplot2 for world‑class visualizations

This makes R one of the best environments for:

· feature engineering

· exploratory data analysis

· dataset cleaning

· statistical validation

Before you train a model, you need clean data — and R is unmatched here.

🤖 2.…