R VS Python: What is the Difference


Both R and Python are widely used open-source programming languages. New libraries or tools are added to their respective catalogues on a regular basis. R is mostly used for statistical analysis, whereas Python offers a more comprehensive data science approach.

Python is a readable general-purpose programming language. R, on the other hand, was created by statisticians and includes their special language. This is just a brief idea, masters in computer science courses can give you a broad aspect of the same.


R was created over two decades by academics and statisticians. R now has one of the most diverse ecosystems for data analysis. CRAN contains around 12000 packages (open-source repository). You may locate a library for any type of analysis you want to conduct. R is the preferred choice for statistical analysis, especially for specialist analytical work, due to its extensive library.

The result is the most significant distinction between R and other statistical software. R provides excellent tools for communicating results. Knitr is a library included with Rstudio. This package was created by Xie Yihui. He made reporting easy and stylish. It is simple to communicate the findings through a presentation or a document.


Python can perform many of the same activities as R, including data manipulation, engineering, feature selection, web scraping, and app development. Python is a large-scale machine learning deployment and implementation tool. 

Python code is more maintainable and robust than R code. Python didn’t have many data analysis and machine learning libraries a few years ago. Python has recently caught up and now offers cutting-edge APIs for machine learning and artificial intelligence. Numpy, Pandas, Scipy, Scikit-learn, and Seaborn are five Python libraries that can be used to perform most data science tasks.

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In contrast to R, Python makes replication and accessibility easy. Python is the ideal choice if you need to use the results of your study in an application or website.

Key Differences

  • R is mostly used for statistical analysis, whereas Python offers a more comprehensive data science approach
  • R’s major goal is data analysis and statistics, whereas Python’s primary goal is deployment and production
  • R users are usually academics and researchers, whereas Python users are mostly programmers and developers
  • R allows you to use existing libraries, whereas Python allows you to build new models from the ground up
  • R is tough to understand at first, but Python is linear and easy to pick up.
  • Locally, R is integrated with Run, but Python is well-integrated with applications.
  • R and Python are both capable of handling large databases
  • Python can be used with the Spyder and Ipython Notebook IDEs, whereas R can be used with the R Studio IDE
  • Python has packages and libraries like pandas, scipy, scikit-learn, TensorFlow, and caret, whereas R has packages and libraries like tidyverse, ggplot2, caret, and zoo.


Not just what is mentioned in computer science engineering courses, but practically also, the decision between R and Python is based on:

  • Your mission’s objectives: statistical analysis or deployment
  • The amount of time you have available to invest
  • Most-used tool in your company/industry.