Is Python Overtaking R? A Deep Dive into Language Popularity Trends

Python has become a hot programming language, initially a general‑purpose scripting language and now the most popular language in data science, while some claim R is dying out.

According to the TIOBE index, Python ranks third worldwide, behind Java and C, with a usage increase of over 3% (10% rating) from August 2018 to August 2019, making it the fastest‑growing language among the top 20.

In contrast, R fell from 8th place in January 2018 to 20th today, dropping from a 2.6% rating to 0.8%.

The TIOBE editors noted in July that Python’s rise has pushed other languages down, with R being one of the affected languages.

Surveys such as Dice Insights and Burtch Works show companies shifting from R (and SAS) to Python for its scalability and ease of use, though R still retains a niche in academia.

DataCamp co‑founder Martijn Theuwissen acknowledges R’s continued development but observes Python’s momentum is stronger.

Historically, R was created in the early 1990s for statistical computing, and its ecosystem now includes over 13,000 CRAN packages and more than two million global users.

Python, while also taught in universities, is more common in computer‑science curricula, leading graduates to favor it for data‑science roles.

Reddit discussions highlight R’s strong statistical capabilities but raise concerns about its scalability and web‑browser performance compared to Python’s extensive libraries like NumPy and its seamless integration with Jupyter notebooks.

Microsoft’s rebranding of its R Server to Machine Learning Server reflects a shift toward Python‑centric analytics.

Overall, Python appears to have a brighter outlook in the current data‑science landscape, yet R remains valuable for many statistical tasks and retains a solid academic presence.