R is best for traditional statistics, as opposed to AI, machine learning, predictive analytics, data science, data analysis or any other variant. If you're more concerned with Chi-squared tests than unit tests, or if you need to teach a mathematician or a biologist how to fit regression models and analyze residuals, goodness-of-fit statistics, p-values etc, then R is the best language for the job.

If you need to build a program (as opposed to just do a thing), or if you're more interested in accuracy than inference (as per most machine learning tasks), then Python with sklearn and pandas blows R out of the water.