Python or R : Which one is best ?

Python is a versatile programming language that can do everything from data mining to plotting graphs. Its design philosophy is based on the importance of readability and simplicity.

• Beautiful is better than ugly.
• Explicit is better than implicit.
• Simple is better than complex.
• Complex is better than complicated.
• Flat is better than nested.
• Sparse is better than dense.
• Readability counts.

As you can imagine, algorithms in Python are designed to be easy to read and write. Blocks of Python code are separated by indentations. Within each block, you’ll discover a syntax that wouldn’t be out of place in a technical handbook.

Benefits of R

R is a programming environment specifically designed for data analysis that is very popular in the data science community. You’ll need to understand R if you want to make it far in your data science career. This interactive tutorial will help.

PYTHON VS R

Usage

Python, as we noted above, is often used by computer programmers since it is the Swiss knife of programming languages, versatile enough so that you can build websites and do data analysis at the same time.

R is primarily used by researchers and academics who don’t necessarily have a background or knowledge of computer science.

Syntax

Python has a nice clear “English-like” syntax that makes debugging and understanding code easier, while R has unconventional syntax that can be tricky to understand, especially if you have learned another programming language.

Learning curve

R is slightly harder to pick up, especially since it doesn’t follow the normal conventions other common programming languages have. Python is simple enough that it makes for a really good first programming language to learn.

Popularity

Python has always been among the top 5 most popular programming languages on Github, a common repository of code that often tracks usage habits across all programmers quite accurately, while R typically hovers below the top 10.

Python is versatile, simple, easier to learn, and powerful because of its usefulness in a variety of contexts, some of which have nothing to do with data science. R is a specialized environment that looks to optimize for data analysis, but which is harder to learn. You’ll get paid more if you stick it out with R rather than working with Python.