Shankar D.K — Updated On May 18th, 2021
Beginner Programming Python R

This article was published as a part of the Data Science Blogathon.

Every human being needs oxygen to survive. Just think for a moment, How many of us took some steps to preserve nature to protect this life-saving gas? But nature makes the world talk about Oxygen using an unseen virus Covid19 by increasing the medical oxygen demand around the world. So it is our valuable responsibility to protect nature like planting saplings etc, not only for the social cause but for our sake also.

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As with the life-saving Oxygen, the industry-saving assets for the field of technology are Data. The amount of data generated worldwide is increasing with huge differences day by day. And the tech industries showing much interest in having and mining valuable insights from them for their business growth. As we knew already, the amount of data in the datasets were mostly in large numbers. So it is not possible to handle such a huge amount of data manually to fetch valuable insights as much quicker as before the same amount of data generated. So the industry experts need technical tools to handle these data. Among the hundreds of technical tools, a cloud war is always going between the two technical tools namely R and Python.

In this article, we are going to discuss the pros and cons of both programming languages in handling the data from a Data Science point of view.


R vs Python: Why this controversy?

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In general, both Python and R are the topmost preferred programming languages for Data Science learners right from the beginners to the professional level. Both the programming languages have considerable similarities in producing efficient results.

  • Both were created around the early 1990s

  • Since they are open source programming languages, anyone can easily download and access them free of cost.

  • They have a lot of libraries and special algorithmic functions to work and solve the data science and data analytics problems

  • As with other data analytics tools like SAS, SPSS, MATLAB they do not restrict the users in terms of cost as well as complexity in solving problems.

  • Both of them providing a user-friendly working experience that is easily understanding and recognizing even by the non-programmer

  • A lot of new inventions and improvements happening frequently in both the tools to handle the problems in the areas of Data Science, Machine Learning, Deep Learning, Artificial intelligence, and much more

Hence it looks like none is lower than the other and this is the reason for the controversy of R vs Python. Just have a look, in brief, to understand this better.

What are Python and R?


Python was first released in 1991 and designed initially by Guido van Rossum. Since it is an object-oriented programming language also called a general-purpose programming language that comes out with a philosophy that emphasizes code readability with efficiency.


If the programmers and the people from the technical environment want to excel in their data science passion by tackling the math and statistical concepts, python will be the best partner in supporting those situations. Hence this is the most preferable and favorite programming language for most Data Science learners.

It has dedicated special libraries for Machine Learning and Deep Learning as well are listed in its library packages index called PyPI. And the documentation for those libraries is also available in the Python Documentation format on its official site.


Ross Ihaka and Robert Gentleman were the initial creators of R. It was initially released in 1993 an implementation of the S programming language. The purpose behind the creation of this programming language is to produce effective results in Data Analysis, Statistical Methods, and Visualisation.


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It has the richest environment to perform data analysis techniques. As with python, it has around 13000 library packages in Comprehensive R Archive Network (CRAN) used especially for deep analytics.

It is most popular among scholars and researchers. The most available number of projects made in R almost comes under research criteria only. It is commonly used in its own integrated development environment (IDE) called R Studio for a better and user-friendly experience.


How to choose a better one?

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The reasons for opting for a particular language are almost common in general for both Python and R. So it is needed to be wiser while picking a programming language between these two. Consider your nature of the domain and your flavor of preference while selecting one within R and Python.

If the nature of your work deals with more codes in general and with less scope of research then prefer Python, if your purpose of work involves research and conceptual processes then choose R. Python is the programmer’s language where R is the language of academicians and researchers.

Everything is based on your interests and the passion behind them. While python codes are easy to understand and capable to do more data science tasks in general. On the other hand, R codes are in the basic academic language, easy to learn, and the best effective tool for Data Analytics tool in visualization.

Key difference


Key difference

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What it is?

It is a general-purpose language for data science  It is the best language for Statisticians,           researchers, and non-coders


                                      First Appeared:


Early 1990’s   Early 1990’s


                                           Best for:


Deployment and Production   Data analysis, Statistics, and Research

                                              Dataset handling:

  • Easy to handle huge datasets
  • All dataset formats like .csv, .xlsx, etc, are accepted
  • Easy to handle huge datasets
  • All dataset formats like .csv, .xlsx, etc, are accepted


                                               Primary Users:


Programmers and Developers   Academicians and Researchers




Easy to understand   Easy to learn




Notebook, Spyder, Colab   R-Studio

                             Packages are available at:


                    Popular libraries:

  • Pandas      : for manipulating data
  • Numpy       : for Scientific computing
  • Matplotlib  : to make graphics
  • Scikit-learn: Machine Learning
  • dplyr     : for manipulating data
  • string    : to manipulate strings
  • ggplot2 : to make graphics
  • caret      : Machine Learning




  • A production-ready and general-purpose language
  • Best in class language for computation, code readability, speed, and handling functions
  • Having the best functionalities and packages for deep learning and NLP
  • It collaborates people from different backgrounds
  • Working in a notebook is simple and easy to share with colleagues
  • Best language for producing graphs and visualization
  • User ready language with a huge number of packages for handling data analysis kind of functionalities with more efficiency
  • Having the best functionalities and packages for handling time-series data
  • It has a rich ecosystem with cutting edge packages and having an active community
  • Complex statistical concepts can solve using simple codes


  • Python does not have as many alternatives for the packages as R provides
  • Python is poor in visualization and producing graphs when compared to R
  • Due to shortage of packages in number when compared with R, it is quite difficult for non-algorithmic people to understand the coding concepts in python as not like R
  • R is comparatively slow in processing due to poor codes, but it has considerable packages to improve it.
  • It is time-consuming in choosing the right package because of the huge number of packages
  • It is not best as python in learning deep learning and NLP


What to Use?

Usage is purely based upon the user’s need. When speaking about python, it is the most efficient tool for doing Machine Learning, Deep Learning, Data Science, and Deployment needs. But still, it has notable libraries for maths, statistics, time series, etc, it often fails to show that much efficiency for business analysis, econometrics, research kind of needs. It is the production-ready language because it has the capability to integrate all our workflow as a single tool.

What to Use?

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When speaking about R, it is the best tool for doing statistical analysis and research needs with better accuracy. Most of the packages in this programming language were created by academicians and researchers is the added advantage. Hence it has the capability to fulfill the needs of statisticians much quicker than the needs of people from computer science backgrounds. Although it has the best communication libraries for data science as well as machine learning. Undoubtedly it is one step higher than python in Exploratory Data Analysis and visualization.



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Both the programming languages have similar pros and cons in general. Apart from all other things, the best one between Python and R is based on some of the following points in consideration only

  • What is the theme of your work?

  • What about your colleagues’ programming knowledge?

  • What is the time period of your work?

  • And finally your area of interest?


Message from the Author:

Dear Readers,

From this article, I hope you should gain at least some little knowledge on how to choose a better one between Python and R based on your needs.

For further clarifications and suggestions connect with on LinkedIn

I request you to share your valuable thoughts about this article. It will be more useful for me during my future works

Thanks and Regards

Shankar DK (Data Science Student)

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