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R finished October 2026 in eighth place on the TIOBE Index, ahead of SQL. That equals the best position the R programming language has ever held. Articles in 2024 described R as a fading tool. Python had overtaken R among many data teams. The new numbers tell a more mixed story. They also raise a fair question about what a ranking really measures.
R was developed by Ross Ihaka and Robert Gentleman in the early 1990s as an open-source language of statistics that was based on the S language. R grew popular in research laboratories and universities. Researchers were able to create packages and contribute without the need for expensive licences.
There were three reasons behind this development. Thousands of user packages covered various disciplines from biology to finance. Ggplot2 package revolutionized chart creation process. Dplyr and tidyr packages made data manipulation easy to read.
R was implemented in many data science courses. Open science practices promoted an open and reproducible computing environment. R studio version 1, which was released in 2011, made writing in R easier.
Firms started to adopt R. Microsoft completed the acquisition of Revolution Analytics, which was the leading commercial distributor of R software, on 6 April 2015.
Python emerged in 1991 and became a data science standard only in the 2010s. Being a general-purpose language, it encompasses data analysis, web services, automation, and machine learning in a single codebase.
The latter provided a significant boost to Python's adoption. Libraries like scikit-learn, TensorFlow, and PyTorch were developed on the basis of Python. R provides its own machine learning libraries, among which there are tidymodels packages for preprocessing, training, tuning, and evaluating. Deep learning research was developed on the basis of Python. The user-friendly syntax was also a draw for non-data scientists developers.
Generative AI has extended the gap even further. There are many AI frameworks, model libraries, and deployment platforms based on Python. It is easy to integrate models into applications, APIs, and cloud services. R excels at statistical modeling. Python has more to offer for building and deploying AI applications.
The decline story from 2024 does not match the current data. The table below traces the R programming comeback on the TIOBE Index.
TIOBE's CEO Paul Jansen reads these figures as consolidation among statistical languages. Python and R emerge as the two winners. In May 2026, MATLAB was close to leaving the top 20.
SAS neared its first exit from the top 30. In December 2025, Jansen also noted that some traditional software engineers look down on R for its unusual syntax and limited scalability in large production systems.
The numbers need careful reading. TIOBE estimates popularity from search-engine results and other online indicators. PYPL tracks how often people search for tutorials in each language. Neither index measures production deployments, enterprise usage or hiring demand. Python also remains far ahead.
In the 2026 Stack Overflow Developer Survey, 58.0% of the 14,099 respondents to the languages question used Python. Only 4.5% used R. That audience leans toward general software developers. It probably undercounts R.
R generally publishes a new feature release each spring, with patch releases in between. R 4.6.0 arrived on 24 April 2026. R 4.6.1 followed on 24 June. R 4.6.2, a bug-fix release, came out on 29 October.
Package support looks broad. A July 2026 install test on a university computing cluster found that 99.8% of CRAN and Bioconductor packages installed without problems on R 4.6.1. The test covered 24,348 of 24,394 CRAN packages and 2,379 of 2,384 Bioconductor 3.23 software packages. The result shows that packages install on one system. It does not show that every package works in every environment.
Tooling has changed too. The company behind RStudio became Posit in 2022. Posit has added Positron, a free data science IDE for Python and R. The Positron IDE uses the same Code OSS base as Visual Studio Code. Posit says RStudio has not been discontinued and keeps receiving updates. Posit has also expanded its publishing tools through Quarto, which supports workflows across R and Python.
R will continue to play an important part in statistical computing, academic research, and scientific research. According to TIOBE, Python will dominate industry applications, while R will lead academic research. Genomics, biostatistics, and econometrics have been traditional strongholds, with initiatives like Bioconductor backing them up.
Bayesian modelling is a related area of interest. In May 2026, TIOBE said it expected the Stan probabilistic programming language to enter its index. Stan is a separate language that R workflows commonly call.
How both the languages work together will guide the way forward. There are tools such as Reticulate that will allow people using R language to execute Python scripts and transfer data between the two. This is because while R is used for data visualization, Python is better suited for machine learning applications. Therefore, it is not about choosing between the two, but how to use them together.
The next year will offer firmer evidence than any popularity index. Hiring data and the 2027 developer surveys will show whether renewed interest turns into steady use. Adoption of positron among teams that work in both languages is another signal to watch.