R vs Python

Which Is Best For Data Science And Data Analysis?

R is considered to be the best programming language for any statistician, as it has a comprehensive collection of statistical and visual methods.

Python, on the other hand, can do almost the same job as R, but it is favored by data scientists and data analysts because of its simplicity and high efficiency.

R is a versatile scripting language and highly flexible with a vibrant community and resource bank, while Python is a commonly used, object-oriented language that is easy to learn and debug.

So let’s move ahead with the contrast between R and Python and take a look at the correlation factors.


R vs Python

Let’s look at the  factors we use for the  comparison on R vs Python:

Ease of Learning:-

R has a steep learning curve and it is difficult for people with less or no programming experience, to begin with. Once you get a grip on the language, it’s not that hard to understand.

Python emphasizes flexibility and readability of code, making it one of the simplest programming languages. It is preferred because of its ease of training and its ability to understand.


R is a low-level programming language that needs longer codes for simple procedures. This is one of the reasons for the reduced speed.

Python is a high-level programming language and has been chosen to create essential and fast applications.

Data Handling Capabilities:-

R is useful for analysis due to a large number of packages, easy-to-use tests and the benefit of using formulas. But it can also be used for basic data analysis without any software being installed.

The Python data analysis packages were an issue, but this has improved with recent versions. Numpy and Pandas are used for the analysis of data in Python. It is also suitable for parallel computing.

Graphics & Visualization:-

Visualized data is interpreted more efficiently and effectively than raw data. R consists of a number of packages that provide advanced visual capabilities.

Visualizations are important when choosing data analysis tools, and Python has some incredible visualization libraries. It has more collections, but they’re complex and gives a tidy production.


Complex equations in R are simple to use and statistical tests and models are readily available and easy to use.

Python is a versatile language when it comes to building something from scratch. It is also used for website scripting or other applications.


Now, if we look at the popularity of both languages, they began at the same point a decade ago, but Python experienced a massive increase in popularity.

Python users are more faithful to their language when compared to users of R, as the rate of switching from R to Python is twice as high as Python to R.


R has a statistical model that can be written with only a few lines and the same piece of functionality can be written in different ways.

Python has a nice syntax which enables easier coding and debugging within itself and any piece of functionality is always written in the same way.

R vs Python: Advantages & Limitations

Advantages of R:

  • R is great for statistical analysis.
  • It is considered as the best tool for data visualization. Visualized data can be better understood than raw numbers. 
  • R programming produces the best results of visualization which can be used in research papers. The results can be recorded when required and can be reproduced to create a different result structure.

Limitations of R:

  • For the users with no programming knowledge, the R language will be a little difficult as it has a steep learning curve.
  • R language is considered as slow if the code is written poorly. To counter this drawback, it is important to include libraries to achieve proper output.

Advantages of Python:

  • Simple functionality helps you think more clearly when developing applications and for others who need to enhance and maintain the system.
  • It has built-in list and dictionary data structures that can be used to create fast running time data structures.
  • The python programming language provides a large standard library that covers areas such as internet protocols, string operations, web services resources and operating system interfaces.
  • Python has a clean object-oriented layout, improved process control capabilities, and good integration and text processing capabilities.

Limitations of Python:

  • Python is slower than other programming languages because it is an interpreted language.
  • It requires rigorous testing as errors show up during runtime.
  • This programming language is still considered weak on mobile computing platforms, as there are few applications created with Python as the core language.


Business software usually offers paid customer support, but R and Python do not have customer service support, which means that you are on your own if you have any issues.

Now with this, we have come to the end of the R vs Python comparison. All languages are leading the fight in the field of data science and data analytics. Yet Python emerges as the winner of both because of his immense popularity and ease in writing codes.

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