R vs Python | ¿R o Python?


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

Steps to preserve nature to protect this life-saving gas? But nature makes the world talk about oxygen using an invisible Covid19 virus by increasing the demand for medical oxygen around the world.. Therefore, it is our valuable responsibility to protect nature, how to plant saplings, etc., not only for the social cause but also for our good.

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As with life-saving oxygen, the assets that save the industry in the field of technology are data. The amount of data generated around the world increases with great differences day by day. And the technology industries that show a lot of interest in having and extracting valuable information from them for the growth of their business. As we already knew, the amount of data in the data sets was mostly in large quantities. Therefore, it is not possible to handle such a large amount of data manually to obtain valuable information as quickly as before generating the same amount of data. Therefore, industry experts need technical tools to handle this data. Among the hundreds of technical tools, there is always a war in the cloud between the two technical tools, namely, R y Python.

In this article, we are going to discuss the pros and cons of both programming languages ​​in handling 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 programming languages ​​of choice for data science students from beginners to professional level. Both programming languages ​​have considerable similarities in producing efficient results.

  • Both were created in the early 1990s. 1990.

  • Since they are open source programming languages, anyone can easily download and access them at no cost.

  • They have many libraries and special algorithmic functions to work with and solve data science and analysis problems..

  • As with other data analysis tools like SAS, SPSS, MATLAB, do not restrict users in terms of cost or complexity in troubleshooting.

  • Both provide a user-friendly work experience that is easily understandable and recognizable even by non-programmers..

  • A lot of new inventions and improvements that occur frequently in both tools to handle problems in the areas of data science, machine learning, deep learning, artificial intelligence and much more.

Therefore, it seems that neither is lower than the other and this is the reason for the R vs Python controversy. Just take a look, in summary, to understand this better.

What are Python and R?


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


If programmers and technical people want to excel in their passion for data science by addressing mathematical and statistical concepts, Python will be the best partner to support those situations. Therefore, this is the most preferred and favorite programming language for most data science students.

It has special libraries dedicated for Machine Learning and Deep Learning, which are also listed in the library package index called PyPI. And the documentation for those libraries is also available in the Python documentation format on their official site.


Ross Ihaka and Robert Gentleman were the initial creators of R. It was initially released in 1993 as 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 visualization.


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Has the richest environment to perform data analysis techniques. As with Python, has around 13000 library packages on the Comprehensive R Archive Network (CRAN) used especially for deep analysis.

It is more popular with academics and researchers. The most available number of projects carried out in R is almost under research criteria only. Commonly used in your own integrated development environment (HERE) called R Studio for a better and user-friendly experience.

How to choose a better one?

best R python

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The reasons for opting for a particular language are almost common in general for both Python and R. Therefore, you need to be wiser when choosing a programming language between these two. Consider your domain nature and flavor of preference when selecting one within R and Python.

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

Everything is based on your interests and the passion behind them. While Python codes are easy to understand and capable of doing more general data science tasks. Secondly, R codes are in basic academic language, easy to learn and the best effective tool for data analysis 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 statistics, researchers and non-coders.

Appeared for the first time:

Early decade 1990 Early decade 1990

Better for:

Deployment and production Analysis of data, statistics and research

Data set management:

  • Easy to handle large data sets
  • All dataset formats like .csv are accepted, .xlsx, etc.
  • Easy to handle large data sets
  • All dataset formats like .csv are accepted, .xlsx, etc.

Main Users:

Programmers and developers academics and researchers


Easy to understand Easy to learn


Notebook, Spyder, al R-Studio

Packages are available at:

Popular Libraries:

  • Pandas: to manipulate data
  • Numpy: for scientific informatics
  • Matplotlib: to make graphics
  • Scikit-learn: machine learning
  • dplyr: to manipulate data
  • chain: manipulate strings
  • ggplot2: to make graphics
  • caret: machine learning


  • A general-purpose, production-ready language
  • Best-in-class language for computing functions, code readability, speed and handling
  • Have the best functionalities and packages for deep learning and NLP
  • Collaborate with people of different origins
  • Working on a laptop is simple and easy to share with colleagues
  • The best language for producing graphics and visualization.
  • User-ready language with a large number of packages to handle the type of data analysis functionalities more efficiently
  • Have the best functionalities and packages for managing time series data.
  • It has a rich ecosystem with cutting-edge packages and an active community.
  • Complex statistical concepts can be solved using simple codes


  • Python doesn't have as many alternatives for packages as R provides
  • Python is poor in graphics display and output compared to R
  • Due to the packs shortage in number compared to R, it is quite difficult for non-algorithmic people to understand coding concepts in python as something other than R
  • R is comparatively slow in processing due to poor codes, but it has considerable packages to improve it.
  • It takes a long time to choose the correct package due to the large number of packages
  • Not the best like Python for learning deep learning and NLP

What to use?

Usage is purely based on user needs. When talking about Python, is the most efficient tool to meet machine learning needs, deep learning, data science and implementation. But still, has notable libraries for math, statistics, time series, etc., often not as efficient for business analysis, econometrics and type of research. It is the language ready for production because it has the ability to integrate our entire workflow as a single tool.

What to use?

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When talking about R, is the best tool to perform statistical analysis and research needs with greater precision. Most of the packages in this programming language were created by academics and researchers, is the added advantage. Therefore, has the ability to meet the needs of statisticians much faster than the needs of people with computer expertise. Although it has the best communication libraries for data science and machine learning. Without a doubt, is a step higher than python in exploratory data analysis and visualization.



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

  • What is the subject of your work?

  • What about the programming skills of your colleagues?

  • What is the time period of your job?

  • And finally your area of ​​interest?

Author's message:

Dear readers,

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

For further clarification and suggestions, connect with LinkedIn https://www.linkedin.com/in/shankar-dk-03470b1a2

I ask that you share your valuable thoughts on this article.. It will be more useful to me during my future jobs.

Thanks and regards

Shankar DK (data science student)

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