R vs Python

By | August 25, 2019

When it comes to Data Science and Data Analytical tool in Programming languages for that we have two programming languages fighting head to head ‘Python’ and ‘R’. Both are open source programming languages and capable to serve for the Data Science and Data Analytical models. R could be a new programming language term for many Computer Science students but they must be aware of Python, but let me tell you Python is not only player in AI, Machine Learning and Data Science fields there are many other programming languages you never heard of before.

Data Science is itself a reason for which developers create R programming languages, but in past few years with the astonishing libraries, Python spread its empire everywhere and try to cover other programming languages. The only reason why python is so famous because of its simple syntax and an insane number of libraries and every day more and more libraries are adding to its arsenal.

Here in this article, we have provided a Comparison between R Vs Python programming language, here data Science and analytic would be the focal point of comparison. Before comparing these two high demanding languages let’s have a brief introduction of each.

R Programming Language

In 1995 Ross Ihaka and Robert Gentleman create an open-source programming language and named it R, which is an implementation of S programming language. The Goal behind of creating R was to create a new programming language which would be ideal for statistics, data analytics and graphical models.

Initially, R used for academics and research purposes, with an increase in data amount, enterprises required a tool which could help them to handle that data and make it useful in an efficient manner. R contains a large number of packages which use in the statistical application you name it R has it.

Python

Python is an Object-Oriented programming language which is capable of doing almost everything it is not specific of any field but capable of performing any task. Like R, Python can perform Data Science operation with its numpy, scipy, libraries and it even contains libraries like matplotlib which is capable of visualizing graphs.

Python provides us with simple syntax and amazing libraries so we could perform complex data science algorithm with ease. Though python does not contain as many as statistics packages like R but with a huge community and continuous updates python is keeping up with R.

Unlike R python is not only rigid to Data science with python we can also perform other tasks, whether it a web application or native software or scripting python is everywhere.

R vs Python: Head to Head Comparison

R Python
Programming Type
R is a Multi-paradigm programming language. Python is a Multi-Paradigm: Object-Oriented Programming language
Objective
Especially for Data Science and Analytics General Purpose programming language, main objectives are software development and production

Users

Mostly Data Scientist and analysts Programmers and developers

Learning Curve

High Learning curve and though to learn Low learning curve and easy to learn

Libraries and packages

It contains a large number of libraries Libraries are the python assets.

Data Science Libraries

It contains more Data Science libraries as compared to Python Python also has many libraries for data analytics and statistics.

Popularity

As R is limited with Data Science and Analytics it is not that much popular Python is everywhere and covers many fields, which make it more popular than R.

Average Salary

99,000$ vary according to experience and skills 100,000$ depend upon Developer skills and experience

Storage handling

R is capable of handle a huge amount of Data Python can also handle a huge amount of data.

Performance

On Data Analyse R provide better performance than Python Python lack to R when it comes to performance.

Famous Data Science Libraries

  • Tydiverse
  • ggplot2
  • caret
  • zoo
  • Pandas
  • Scipy
  • scikit-learn
  • TensorFlow
  • caret

Advantages

  • More packages for Data Analysis and statistics.
  • Huge Community
  • Data experts first choice
  • Better Visualization of Graphs
  • Easy to learn
  • Easy to read because of clear and indented syntax
  • Fast performance
  • Easy to implement complex algorithms
  • Native Object-Oriented paradigm
  • Libraries like TensorFlow

Disadvantages

  • Hard to learn
  • Slow performance
  • Does not have that many libraries for Data Analysis and Statistics.

Conclusion

There are Data Science experts who use both programming languages but they are less in number many developers want to stick with one programming language that’s why they choose python because it provides more flexibility than R.

Developers with a keen interest in Data Analysis and statistics always suggest choosing R because of its packages. Apparently, it is an individual choice which programming language a programmer wants to choose for his projects.

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