Python vs. R

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By Vijay Singh Khatri

If you’re into computer programming, you must have heard the terms “R” and “Python.” And, why won’t you? These are two of the biggest terms when it comes to open-source programming languages. People love to use these programming languages because of the continuous addition of fresh tools/libraries to their arsenals. Both these languages have an orientation towards data science. However, they are not similar. They have many dissimilarities, and thus you should know about them. In this article, we will give you a detailed comparison between Python vs. R so that you can decide which one is ideal for you.

What is Python?

Before we dive into the topic of Python vs. R, it’s important to discuss a couple of things about what they are. A python is a great tool for deploying and implementing machine learning at a massive scale. It can do various tasks like web harvesting, data wrangling, performing attribute selection, etc., with ease. It’s a known fact that Python codes are simpler to learn and are more vigorous. Earlier, Python didn’t have data analysis and machine learning library facilities. But more recently, Python is developing at a fast pace and providing top-notch API for machine learning or AI. The five Python libraries named SciPy, SciKit-Learn, Pandas, Seaborn, and NumPy, are enough to help you to perform data science jobs. Unlike R, Python makes it easier to replicate and access. Python should be your first pick if you want to utilize your analysis results in an app or site.

What is R?

Let us now focus on R. Academics and statisticians together are responsible for the development of R over 20 years. Today, R possesses one of the richest ecosystems for carrying out data analysis. CRAN (open-source storage) contains around 12000 packages. No matter what analysis you wish to execute, you will find a library for it. The massive range of libraries makes R perfect for statistical analysis purposes, especially if you wish to perform some specialized analytical work. The main feature that differentiates R from other similar products is the output. R possesses amazing tools for displaying the results. RStudio comes with the library knitr. Software engineer Xie Yihui is responsible for writing this package. He made the aspect of reporting effortless and sophisticated. Here, informing others about the findings with a document/presentation is simple.

Python vs. R – Which One is More Popular?

The IEEE Spectrum ranking is a metewand to measure how famous a computer programming language is. According to the IEEE Spectrum ranking, Python was ranked first in 2017, while R took sixth place.

What about job opportunities?

Now, you might wonder what the chances of landing a job related to data science by various computer programming languages are. SQL ranks very high in this list and gets followed by Python and Java. R comes in fifth place. If we take a look at the long-term trend, we will notice the edge Python has over R in being quoted in job descriptions. However, according to KDnuggets polls conducted in 2014, R beat Python as the former got 58% and the latter 42%. However, the KDnuggets polls 2016 revealed an interesting fact. In terms of loyalty, Python users (97%) became triumphant over R users (74%). Also, more R users (10%) switched to Python than Python users (5%) switched to R.

Python vs. R – Key Differences

  • Regarding their objectives, Python is used for deployment and production, while R is utilized for data analysis and statistics.
  • Python’s principal users are programmers and developers, while mainly R&D scholars make use of R.
  • Python is very easy to learn and smooth. On the contrary, R is pretty hard to learn at first.
  • Python is far more famous and had a popularity percentage change of 21.69% in 2018. R, however, ranks way lesser in terms of fame and had a popularity percentage change of only 4.23% in 2018.
  • Python gives the user the flexibility of matrix computation and optimization, that is, to build fresh models from scratch. Contrarily, R provides the flexibility of utilizing various libraries with ease.
  • Python is known for being well-integrated with the app, while R runs locally.
  • Some of the vital packages and libraries of Python are Caret, Pandas, Scikit-learn, TensorFlow, SciPy, etc. On the other hand, Tidyverse, zoo, ggplot2, Caret, etc.
  • Python has a good deployment algorithm. On the other hand, it is very easy to acquire major results.
  • Python’s Integrated Development Environment (IDE) contains Notebook, IPython, and Spyder. Contrarily, R’s IDE contains Rstudio.


As you can see from our Python vs. R comparison, both these programming languages have their features. Python possesses vast libraries regarding statistics, mathematics, and AI. When it comes to Machine Learning, Python could be considered a pure one. However, in terms of communication and econometrics, Python is not the best option. Thus, Python is perfect for Machine Learning integration and deployment purposes but not for business analytics. So, for that purpose, R is a better choice as it is developed by statisticians and academics. R can tackle machine learning, data science, and statistical problems with efficacy.

Now whether Python or R is right for you depends on your needs and preferences. It also depends on your experience. In case you’re a beginner and desire to learn about the workings of the algorithms and deploy the model, Python would be a great choice. Python not only includes vast libraries regarding mathematics, statistics, and Artificial Intelligence but also makes it easier for you to learn to construct a model on your own, and then you can take advantage of the functions from the machine learning libraries. But if you wish to concentrate on statistics, R would be a better choice. Similarly, if you want to go a bit further than statistics such as reproduction and deployment, you should give Python a try. However, R would be a superior choice for report writing and dashboard creating purposes.

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