Analytics bigdata datascience dataviz python R

R or Python? Which to Choose for Data Analysis.

Talk of being caught between a rock and a hard place. In the battle of the “best” data analysis tools (or programming languages), deciding the winner between R and Python can be a daunting task. While R has been built with awesome data visualization capabilities to the amusement of a statistician,  Python is often lionized for its user friendly syntax.

We have prepared this little guide to help you decide when to use each of these powerful data analysis tools.

When to use R 

R is powered by many ‘packages’ which makes it suitable for almost any type of data analysis. It can handle complex and large data sets – what this means is that R can actually be part of your big-data solution.

R is also very flexible, can do it all. From small tasks like building interactive web applications and GIS maps, to complex statistical simulation and modelling.

Another remarkable feature of R is its huge user community that provides support through mailing lists, user contributed documentation and active stack overflow group. It also has CRAN,

a huge repository of plugin packages that you can use without having to write a line code or develop anything yourself from scratch.

How to get started with R

To get started, simply go straight and download and install R Studio IDE.

Once you are done, we recommend that you start working with this popular packages:

You might also want to try your hands on caret for machine learning.

When to use Python

Python being a high-level programming language (for general purpose programming), is a great tool for developing algorithms for production use.  Python is also widely taught in colleges and its emphasis on code readability means that its easily adoptable for small, large, online and offline projects.

Python is also powered by mind-blowing packages such as pandas (for data manipulation), NumPy/SciPy (for scientific computing) and  matplotlib (to make graphics).

You might also want to dive straight into scikit-learn for machine learning.

How to get started with python

To get started, choose between Spyder, IPython Notebook and Rodeo to see which one best fits your needs. Python has no designated IDE, so the choice is yours to make.

Then go to the above mentioned packages and try them out.

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Also feel free to drop comments or questions. We will be happy to assist you.


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