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I've got rather expansive development experience, but I'm new to data science. I have been trying to familiarize myself with the main concepts and deciding between R and Python to put my time into.

I know you can write Python in R and vice versa, but since data visualization in a beautiful manner would be a very high priority for me in the future and both Python and R use additional packages and libraries for data representation, I'm not entirely sure whether it's possible to use the additional libraries when integrating code into one another.

I would really appreciate it if someone could give me some advice regarding this and also provide me with some general tips about the best approaches, practices and tools when it comes to data visualization.

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  • $\begingroup$ Could you be more specific about what kind of integration you are looking for? $\endgroup$
    – Stereo
    Mar 30, 2016 at 16:28
  • $\begingroup$ I am trying to decide which language would be preferable for me, considering beautiful data visualization is a priority. I know you can use both languages side by side when needed, but I'm not entirely sure whether it's possible for instance to choose Python and when it comes to data visualization, be able to integrate R vidualization packages into your code. $\endgroup$
    – R7Sh
    Mar 30, 2016 at 19:06

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R is a more compact, target oriented, package. Good if you want to focus on very specific tasks (generally scientific). Python, on the other hand, is a general purpose language.

That being said, and obviously this is a matter of opinion, if you are an experienced developer go for Python. You'll have far more choices in libraries and a far bigger potential to build big software.

Some examples of 2D scientific plotting libraries:

Some examples of 3D scientific plotting libraries

Some examples of libraries typically used in Data Science in Python:

Also check the list for other relevant Scikit packages.

As for starting software I would advise you to use any of the already prepared Python distributions that already come with a bunch of scientific libraries inside as well as software such as IDEs. Some examples are:

Personally I'm a user of WinPython due to being portable (former user of Python XY, both are great). In any case these distributions will greatly simplify the task of having your scientific Python environment (so to speak) prepared. You just need to code. One IDE known to be specially good for scientists is Spyder. Yet these ones also will work:

As for data visualization tips you'll see that the most common functions in the libraries mentioned above are also the most widely used. For instance a library like Pandas let's you call plots directly from the object so there is already an intuitive approach to data visualization. A library like scikit-learn (check the site) already shows examples followed by data visualization of the results. I wouldn't be too concerned about this point. You'll learn just by roaming a bit on the libraries documentation (example).

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  • $\begingroup$ Thank you very much, very informative. Just what I needed. $\endgroup$
    – R7Sh
    Apr 12, 2016 at 12:24

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