Is Python or R language better? Is Python similar to the R programming language? What are the differences between the two? To answer the first question, we must compare these two programming languages.

Comparing different programming languages ​​is not a new topic. Each programming language has its own characteristics, strengths and weaknesses. Not every language is suitable for every job. A programmer and developer must find a programming language for the work they want to do. This choice is very important in the success or not of programmer and developer projects. Comparing two programming languages ​​becomes more important when both languages ​​are useful for doing a specific project, such as data analysis.

If the programmer is not a beginner and wants to learn a new language in addition to the programming languages ​​he knows and has worked with; again, he has to choose one of the languages. Programming languages ​​are not few. By the way, these languages ​​have a long list. All these reasons make it important to compare programming languages. This article is dedicated to the comparison of two very important and widely used programming languages ​​(Python vs R).

Comparison of Python and R (Python vs R)

Perhaps comparing Python and any other language is not that important. Python and R are two very fierce competitors because both programming languages ​​are used in data analysis. In fact, Python and R can be used in Data Science projects. Of course, Python is not a popular programming language just because of its power in data analysis. Python has also played a defining role in the history of artificial intelligence and machine learning.

The R programming language was created in the university and by university professors. The first version of R was written by two professors from the School of Statistics at the University of Auckland in New Zealand (Robert Gentleman and Ross Ihaka, also known as R & R). But from 1997 onwards, in addition to those two professors, a team with dozens of experts (R Core Team) from all over the world undertook the promotion of this language.

If I want to define the R programming language very briefly, I must say that R is an environment and a language written by statistical experts for statistical computing. The only job of R is not to collect and analyze statistics. R is also used to make statistical charts and display data (Statistical Graphics). So, the R programming language is a very specialized language for a very specialized and specific field.

Python programming language is a high-level, object-oriented, functional and interpretive language. Python is a dynamic language, with highly readable code as well as diverse applications. Python may even be the backend programming language of a site. Python was created by a programmer (Guido van Rossum) in 1989.

Differences between Python and R

Now that we know the definition of Python and R, it’s time to compare them. The principle of comparison is to tell the similarities and differences. But there is a problem in comparing the two programming languages ​​Python and R: these two languages ​​are actually not similar to each other except that both are used for data analysis and statistical calculations and both have the power to analyze a large amount of data. So, in comparing Python and R, the main discussion is about the differences between these two languages, especially for data analysis and statistical calculations.

  • I think the most fundamental difference is obvious. Python is truly a versatile programming language. It doesn’t matter if a programmer wants to write a machine learning algorithm or develop a website, Python does all these things with power, speed and high flexibility. But R is only suitable for statistical calculations, that too of the academic and very complex type.
  • Those who use Python are programmers and developers, while R is used by academics, scientists, and research and development (R&D) professionals.
  • The third difference, which can be said to be the result of the second difference, is that Python is the most popular language among programmers and developers, and R is in 11th place. There is another reason for this dramatic difference: R is a harder and more complex language to learn.
  • Now that the difference in the popularity of these two programming languages ​​has been mentioned, let me also mention the difference in the salary of Python and R programmers. Interestingly, the salary of R programmers is slightly higher than that of Python. The salary of a Python programmer is around 100,000 dollars (annually). While an R programmer earns about $118,000 a year.
  • There are more R programmers learning Python (actually migrating to Python) than Python programmers learning R.
  • The R programming language has its own IDE: RStudio. But for coding with Python, you can use different code editors.

Is Python or R better for data science projects?

Did you think the differences were over? No! We just got to the main differences between Python and R. As I mentioned, these two languages ​​are competing for very specialized projects, not site and application development. If the possibilities of these two languages ​​for implementing data analysis projects, statistics and visualization of analysis results are not compared, a decisive answer to the question of whether Python or R is better cannot be given.

Basically, the comparison between Python and R is done with the aim of determining the best programming language for data collection, data exploration, data modeling, and data visualization.

  • In data collection, the most important difference between R and Python is that the Python language has the power to take data from almost any source, even websites. But the R language is not compatible with data that is taken from the web. It is necessary to convert those data into formats (CSV, Excel, and text files) compatible with R.
  • R programming language has many packages (libraries), more than 12 thousand packages. This means that there is a library for almost any analysis and modeling that a programmer wants to do. R with this feature is suitable for performing highly specialized statistical analysis. Python’s main library for data analysis is Pandas, and its main libraries for data modeling are Numpy and SciPy.
  • It can be said that the most important difference between Python and R is in the facilities and tools they have for data visualization and analysis results. As mentioned, R was created by academics and statisticians. They knew very well that displaying statistics and analysis results is a part of analysis, and if it is not done well, it is as if no analysis was done. For this reason, they made R in such a way that it takes clear, comprehensive, beautiful and diverse outputs from the results of analyzes (data visualization). Of course, Python also has libraries for data visualization. The image below is one of hundreds of graphs that can be analyzed as output from the R language.


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