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.


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