Python Vs Rebol (R): Which Is Better?

Syed Huzaifa
Aug 9 · 4 min read

We all know that Data Science has become one of the hottest areas of Information Technology (IT). For those who don’t know what is Data Science, Let me tell them that,

“Data science is the field of applying advanced analytics techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning, and other uses.”

Data Science has a very close relationship with Artificial Intelligence and Machine Learning as data is the key to perform AI and ML.

Two famous programming languages are said to be very efficient for performing Data Science. They’re:

  • Python

Let’s see which language is the best for Data Science among the two. Let’s start our countdown.

1. Open Source

When we talk about open source projects, Python and R both are open source languages supported by large communities which are expanding day by day. Both have a large number of tools contributed by dozens of people which are increasing continuously. Both are free to download. So In this round, both the languages are equal.

Winner: No one

2. Libraries And Tools

Pythonistas knows that python supports a vast range of libraries for data analysis and data manipulation such as Numpy, Pandas, Scipy, Matplotlib, Seaborn, etc. But when it comes to R language, the game changes because R language is specifically developed for statistical computing. There are over 12000 packages available for R-lang to perform data analysis. Python is a general-purpose language but R-lang is dedicated to statistical analysis. So, if one has a concern with statistical analysis only, he would definitely choose R-lang for performing the task. So, In this round, R-lang wins.

Winner: R-lang

3. Syntactically Easy

All the problem because of which newbies escape from programming is the syntax. New programmers try to find a language that is powerful, stable, fast, flexible, and syntactically easy. Ranking top languages according to their syntax, We find python as the easiest of all. Python has crystal clear syntax which increases the readability of the code and ultimately new programmers found it easy to learn. Coming to R-lang, The syntax of R-lang and Python is pretty much similar. Just a little change in the code. Let me show you an example.

Let’s check the syntax of python first:

string = "Hello, World!"

This is a simple code for printing “Hello, World!”. You can see how easy it is.

Let’s see the syntax of R-lang now:

string <- "Hello, World!"
print (string)

Pretty same? But python has more clean syntax as you can see = is much easier to type than <- We can say that python is syntactically easier than R-lang. So, In this round Python wins.

Winner: Python

4. Dedicated Libraries

Suppose, you want to make a pie-chart. Which language would you choose? you can do this by both (Python and R-lang) but there is a little difference.

If you choose python you’ll need a library like Matplotlib or some other for making a pie-chart but if you choose R-lang, there is no need for any library as this task can be performed by just R-lang alone. The reason is that python was not developed specifically for data science but later it gains power from open source contributors and today it is the top language for Data Science. On the other hand, R-lang was specially designed for such uses and every library for R is related to data manipulation, data wrangling, data analysis, etc. Python has a dedicated library for each and every use. So, in this round, R-lang wins.

Winner: R-lang

5. Popularity

A more popular language is a more robust language. By 2021, Python made its place in the first among the most used and popular languages. It even crossed C/C++ which were the most used and popular languages. The time span between the two languages is not much as Python was developed in 1991 and R-lang in 1995 but you can see the popularity index of both languages the only reason is that Python is flexible and R-lang is dedicated. So, In this round Python wins.

Winner: Python

6. Flexibility

Talking about the flexibility of two languages, First, we come to Python. Python is a general-purpose language that was developed for production and deployment but later it became a general language with the ability to perform Data Analysis, Web Development, Artificial Intelligence, and so on.

But when we see R-lang. Its objective was Data Analysis, Statistics and Statistical Analysis and till today it is used for this purpose. It was a dedicated language that was developed for Data Scientists and Data Engineers. So, in this round Python wins.

Winner: Python


We can’t compare the power of both languages each of them is a legend in its own objective. But talking about Data Science, There is a long debate about the best. Since it is a blog post, I will give my personal opinion. I think for Data Science, R is more suitable as it is dedicated to such things but Python is simply a solution for those who don’t want to use R lang and they just love Python to moon and stars. If you want something powerful for Data Analysis then you should go for R-lang since it has more statistical power but if you want a more general-purpose language that can handle all your tasks and have the ability to perform everything then Python is the best choice.

Is R-lang more powerful than Python? Is R-lang the future of Data Science? Which one of them you’ve used?

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Till then, Good Bye!


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