R vs Python in Data Science

R ProgrammingPythonServer Side Programming

Overview of Data Science

When we work on set of data then we apply different statistical functions on that dataset. These functions we use for an extensive exploration of descriptive statistics, statistical tests, plotting functions etc. Data science is actually a multidisciplinary exploration of algorithmic development, data inference and technology specifically to solve analytically complex problems. At the core of Data Science, we are having data.

In Python, Pandas is one of the Data Analysis Libraries, this is used for importing data from Excel spreadsheet, CSV and also from other data sources.

Overview of R

R is an open source language. This language is very popular, because it helps to develop more user friendly environment and to deliver a better way to do data analysis, statistics and graphical models. When it was developed,then at that time this language was used only for academic and research fields. But nowadays, Enterprise world also uses it. Now R is one of the fastest growing statistical languages in the corporate world.

Specialities for data science:

R belongs from a huge community. This community provides supports through mailing lists, user-contributed documentations and a very active Stack Overflow group. CRAN is a huge repository of curated R packages to which users can easily contribute. It is a collection of R functions and data. It makes easy to develop latest techniques and functionalities without having any need to develop everything from scratch.


R has many inbuilt functions for data analysis. R language is mainly applicable for statistics and data analysis purposes. R has many tools by default, which are very much essential in data analysis related research and developments.

Key domains of application

For data analysis, data visualization is a very important part, As R provides many packages like ggplot2, ggvis, lattice, etc. which are very helpful to make easier of these implementations.

Availability of Packages:

R has many packages for implementing data science related applications. Availability of huge number of Packages has made R as most resourceful and also versatile.

When and how to use R

When the data analysis task requires standalone computing or analysis on individual servers, in those situations R is being used. This language is very useful for exploratory work and it can handle any type of data analysis and can achieve a big solution towards the problem.


R language is mostly applicable in a data science environment.


Overview of Python

Python is a very flexible language, it is great to do something novel and mainly focuses on readability and simplicity. Python has many packages to do work on different fields of data science related applications.

Specialities for data science

For finding outliers in a data set Python and R both are good but in case of web service for uploading datasets and finding outliers, Python is better.


Python is a general purpose programming language that's why most of the data analysis functionalities are available.

Key domains of application −

Python also provides packages like Lasagne, Caffe, Keras, Mxnet, OpenNN, Tensor flow, etc. This packages allow to develop deep neural networks which are far more simple in Python.

Availability of Packages

Python has few packages for data analysis, just like Pandas and Scikit. But it makes very easy to achieve the goal.

When and how to use Python

When our data analysis tasks need to be integrated with web apps or if statistics codes need to be incorporated into a production database then in those situations Python is used. It is a very popular tool to implement algorithms for production use.


Python is widely used in many fields, such as −

  • Perform Computer Vision (Facilities like face-detection and color-detection)
  • Develop a game
  • Do Machine Learning (Giving a computer the ability to learn)
  • Build a website
  • Enable Robotics
  • Perform Scripting
  • Automate a web browser
  • Perform Scientific Computing
  • Perform Data Analysis
  • Perform Web Scraping (Harvesting data from websites)
  • Build Artificial Intelligence
Published on 08-Nov-2018 09:32:08