community

The first thing to remember when comparing Python and R users is:

Only 50% of Python users overlap with R

That assumes that all R programmers will refer to him as “Scientific and Numeric.” We also determined that this distribution held true regardless of programmer level.

To further understand the Python “propaganda”, please read about Python publicity results: www.linkedin.com/pulse/pytho…

If we just look at science and digital communities, this brings us to the second category of communities, which community? There are subcommunities within all the scientific and digital communities. Although there may be some overlap, as you suspect they do interact differently with the larger R/Python community.

Some examples of subcommunities using Python/R:

  • Deep learning

  • Machine learning

  • Advanced analysis

  • Forecast analysis

  • statistical

  • Exploration and data analysis

  • Academic Pity study

  • The almost endless field of computing research

While each area seems to be dedicated to a specific community, you’ll find R more popular in areas like statistics and exploration. Not so long ago, you could use R to do build runs or very meaningful explorations in much less time than you could install Python or use it to do the same explorations.

All that has been changed by disruptive technologies, Jupyter Notebook and Anaconda.

Note: Jupyter Notebokks: Python/R code can be edited in the browser; Anaconda: Easy to install and package for Python and R

Now that you can get up and running in an environment that facilitates reporting and off-the-shelf analysis, you’ve removed a barrier between the people who want to accomplish these tasks and their favorite languages. Python can now be packaged in a platform-independent way, and it can be faster to provide fast, low-cost analysis ratios.

Another difference that influences language choice in the community is the idea of “open source”. Not only open source libraries, but also the impact of a collaborative community committed to open source. Ironically, open-source licensed software such as Tensorflow to the GNU Scientific Library (Apache and GPL respectively) seems to have Python and R bindings. Despite R’s public copyright, there are many more people who support the Python community purely. On the other hand, there seems to be more business support for R, especially those with a statistical history.

Finally, there is more support for Python on Github in terms of community and collaboration. If I were to look at the latest Python package trends, I would see over 35,000 focused projects like Tensorflow. Conversely, if I look at the latest trends in R packages, like Shiny, Stan… And so on. They all have less than 2,000 followers.