R vs Python: What’s the Difference?

By | November 25, 2021
R vs Python

When it comes to the best programming languages for data science, we have two top contenders that are fighting head to head ‘Python’ and ‘R’. Both are open-source programming languages and serve the cause of data science and data analytical models.

While R could be a new programming language for many computer science students, Python is a widely-known programming language that is suitable for data science. However, let me tell you that Python is not the only programming language that works well with AI, machine learning, and data science.

Nonetheless, for data science, most professionals prefer working with Python and R languages. However, beginners often find it difficult to decide if they should learn Python or R to get started with their career in data science.

Well, in this article, we have drawn a detailed comparison between R and Python programming languages. Also, data science and data analytics would be the focal point for the R vs Python comparison. But before we get started with the comparison, let’s have a brief introduction to each programming language.

R Programming Language

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

Vamware

Initially, R was used for academics and research purposes. However, as enterprises required a tool that could help them to handle huge amounts of data, R emerged to be the best option. Also, R comes with a large number of packages that make it quite easy for data scientists to process the data efficiently.

Python

Python is a general-purpose and object-oriented programming language that is suitable to use in a variety of fields, including web development, AI development, and data science. Like R, Python can perform various data science operations using libraries like NumPy and SciPy. It even has libraries like matplotlib, which is capable of visualizing graphs.

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Python provides us with simple syntax and amazing libraries so we could perform complex data science algorithms with ease. Though Python does not contain as many statistics packages as R, each update for Python is intended to make it more powerful feature-rich.

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.
Suitable For
Data Science and analytics Software development and production, web development, data science, AI & ML development.

Users

Mostly data scientists and analysts. Programmers and developers.

Learning Curve

R has a steep learning curve, thus it is difficult to learn. Python has a gradual learning curve, thus it is 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 has many libraries for data analytics and statistics.

Popularity

As R is limited to data science and analytics, it is not that popular Python is useful in many fields, which makes it more popular than R.

Average Salary

99,000$; vary according to experience and skills 100,000$; depends upon developer skills and experience

Storage Handling

R is capable of handling huge amounts of data. Python can also handle huge amounts of data.

Performance

When it comes to data analysis, R provides better performance than Python Python lags behind R when it comes to performing data analysis quickly and efficiently.

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
  • Its clear and indented syntax makes it easy to read and understand the Python code.
  • It allows the implementation of complex algorithms.
  • Supports object-oriented programming

Disadvantages

  • Hard to learn
  • Slow performance
  • Limited libraries for data analysis and statistics compared to R.

Conclusion

There are data science experts who use both Python and R programming languages for data science. However, many developers stick with one programming language and that’s why most of them choose Python over R because it provides more flexibility. By learning Python, an individual will not only be able to work in the field of data science but also in other fields, such as software development and web development.

However, developers with a keen interest in data analysis and statistics always suggest choosing R because of its packages. Apparently, it is the choice of an individual whether to go with Python or R programming language.

We hope that this Python vs R article has helped you understand all the crucial differences between the two so that you can easily choose one that seems the best as per your requirements.

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Author: Vinay

I am a Full Stack Developer with a Bachelor's Degree in Computer Science, who also loves to write technical articles that can help fellow developers.

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