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Know the difference between R and Python

Last updated on 30th Jan 2023, Artciles, Blog

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Parneet Singh (DevOps Engineer )

Parneet Singh is a DevOps senior engineer. She has expertise in trending domains like data science, artificial intelligence, machine learning, blockchain, etc. Her articles help the learners to get insights about the Domain.

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    • In this article you will learn:
    • 1.R.
    • 2.Python.
    • 3.Popularity index.
    • 4.Job Opportunity.
    • 5.Percentage of people switching.
    • 6.Difference between R and Python.
    • 7.R or Python Usage.
    • 8.Conclusion.


Over the past 20 years academics and statisticians have made R. R now has one of the most complete ecosystems for analysing data. In a CRAN you can choose from about 12,000 packages (open-source repository). The library can be found for any analysis that needs to be done. R is the best choice for statistical analysis especially for specialised analytical work because the library has so many options.The most innovative thing about R compared to other statistical programmes is that it has an output. R has great tools for sharing results. Rstudio has a library knitr built in. This was written by Xie Yihui. Made a report simple and stylish. It’s easy to share your findings with a presentation or a document.


Python can do most of the same things as R like wrangling data engineering, selecting features, scraping web pages making apps and so on. Python is a tool for putting machine learning into action on a large scale. Python codes are easier to keep up with and more stable than R codes. A long time ago Python didn’t have as many libraries for data analysis and machine learning. Recently Python has been catching up and now offers APIs that are at the cutting edge of machine learning or AI. Five Python libraries can do most of the work in data science: Numpy, Pandas, Scipy, Scikit-learn and Seaborn.Python on the other hand makes it easier to copy and share work than R. In fact, Python is the best choice if you want to use the results of the analysis in an app or website.

Popularity index:

The IEEE Spectrum ranking is a way to measure how well-known a programming language is. Python moved up to first place from third place the year before. R comes in sixth.

Job Opportunity:

  • By programming language the number of jobs in data science. SQL is way ahead of Python and Java which are far behind. R is fifth.
  • If you look at how Python and R have been used in job descriptions over time you can see that Python is used more often than R.
Applications of Python

Difference between the R and Python:

    Objective A Data analysis and statistics A Deployment and production
    Primary Users A Scholar and R&D A Programmers and developers
    Flexibility Simple to use available library Simple to construct a new models from scratch. I.e., matrix computation and optimization
    Learning curve Complex at a beginning Linear and a smooth
    Popularity of Programming Language. Percentage change 4.23% in 2018 21.69% in 2018
    Average Salary $99.000 $100.000
    Integration Run locally Well-integrated with the app
    Task Easy to get primary results Good to deploy algorithm
    Database size Handle big size Handle big size
    IDE Rstudio Spyder, Ipython Notebook
    Important Packages and library Tidyverse, ggplot2, caret, zoo Pandas, scipy, scikit-learn, TensorFlow, caret
    Disadvantages Slow,High Learning curve,Dependencies between the library. Not as more libraries as R.
    Advantages Graphs are made to talk. R made it beautiful,Large catalog for a data analysis,GitHub interface,RMarkdown, Shiny. Jupyter notebook: Notebooks help to share a data with colleagues,Mathematical computation,Deployment,Code Readability,Speed,Function in a Python.

R or Python Usage:

Guido van Rossum a computer guy came up with Python in the early 1990s. Python has a number of important libraries for math statistics and AI. can think of Python as a pure player in Machine Learning. But Python isn’t quite ready for econometrics and communication (yet). Python is the best tool for integrating and deploying Machine Learning but it is not the best tool for business analytics.The good news is that an academic and scientist made R. It is made to solve problems in statistics, machine learning and data science. Because it has powerful communication libraries R is the right tool for a data scientist. R also has a lot of packages that can be used for time series analysis, panel data and data mining. On top of that there are no tools that are better than R.

In opinion if beginner in a data science with necessary statistical foundation need to ask the following two questions:

R Programming
  • Do you want to find out how algorithms work?
  • Do you want to use a model?

If the answer to both questions is yes you might want to start with Python. On the one hand Python has great libraries that can be used to work with matrices or code algorithms. As a beginner it might be easier to learn how to build a model from scratch and then switch to using functions from a machine learning library. On the other hand if you already know the algorithm or want to start analysing data right away you can start with either R or Python. If you want to focus on a statistical method R is a good choice.Second Python is better if you need to do more than just statistics like deployment and reproducibility. If you need to write a report and make a dashboard for work R is a better choice.In short there is less of a statistical gap between R and Python. Both languages can do most of a job. Would be better to choose the one that fits your needs and that your coworkers are also using. When everyone speaks the same language things go better. When you know how to use one programming language, it’s easier to learn a second one.


In the end choosing between R and Python comes down to:

  • The goals of a mission are Analysis of statistics or deployment.
  • How much time can be put in.
  • The most-used tool in a company or industry.

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