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R (and Dialects) versus Python for Data Science

Norman Matloff

Australian and New Zealand Journal of Statistics2026https://doi.org/10.1111/anzs.70041article
ABDC A
Weight
0.37

Abstract

R and Python are the two dominant language tools for data science today. Which one is better, and in what senses? This paper explores such questions, in terms of aspects such as learning curve, clarity of expression, coding philosophy, high‐performance computing capability and so on. Also, the article treats base‐ R and the tidyverse as two separate ‘dialects’ of R , so that the above comparisons are in many cases tripartite in nature.

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https://doi.org/https://doi.org/10.1111/anzs.70041

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@article{norman2026,
  title        = {{R (and Dialects) versus Python for Data Science}},
  author       = {Norman Matloff},
  journal      = {Australian and New Zealand Journal of Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1111/anzs.70041},
}

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R (and Dialects) versus Python for Data Science

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.