R vs Python: Which One is Better for Data Science?


R Programming

R is one of the oldest programming language developed by academics and statisticians. R comes into existence in the year 1995. Now R is providing the richest ecosystem for data analysis.

Python Programming

On the other hand Python can do the same tasks as R programming language does. The major features of python are data wrangling, engineering, web scraping and so on.

Difference R or Python

R is more functional, Python is more object-oriented

R is more functional, it provides variety of functions to the data scientist i.e Im, predict and so on. Most of the work done by

R has more statistical support in general.

R was created as a statistical language, and it shows statsmodels in Python and other packages provide decent coverage for statistical methods, but the R ecosystem is far more large.

R has more data analysis built-in, Python relies on packages.

R provides the build in data analysis for summary statistics, it is supported by summary built-in functions in R

Straightforward to do non-statistical tasks in Python

With well-placed libraries like beautiful soup and request, web scraping in Python is much easier than R. This applies to other tasks that we don’t see closely

Parallels between the data analysis workflow in both.

R and Python are the clearest points of inspiration between the two (pandas were inspired by the Dataframe R Dataframe, the rvest package was inspired by the Sundersaute), and the two ecosystems are getting stronger.

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