R Vs Python | Best Programming Language for Machine Learning

R Vs Python | Best Programming Language for Machine Learning

R Vs Python | Best Programming Language for Machine Learning

r vs python for machine learning

Machine learning is one of the hottest skills in the upcoming years. We have advanced it very rapidly in the strong age of AI.

A major number of developers are looking towards acquiring machine learning and deep learning skills. And if you are one of these people then you might get confused when it comes to choosing the right programming language for machine learning.

Coding languages are becoming more versatile and each one is unique by presenting specific features that you will not find anywhere else.

You only need to find the information, analyze it, and then sample anyone or another language to decide which is the best one or which one fulfills your needs?

In this article, we will be looking at the simple comparison between the two best programming languages that is Python and R which will guide you through your data science journey. Let’s see R vs Python for machine learning.

R Programming Language

It is an open-source, free, and powerful coding tool with high extensible features. The language was first initiated for the scientific data in the ’90s. R owns all the inclusive catalog of statistical and graphical methods.

Many well-known corporations like Facebook, Airbnb, and Uber are employing it. It is trustworthy for extensive research scientific data and broadly appropriate for any preferred option.

On top of that, it is having myriads of standard packages and ready-made solutions for better performance.

Before installing the IDE RStudio, it is recommended to use packages like dplyr, data.table, and plyr to simplify package manipulations.

What are the Pros of using R?

  • For the basic data analysis, you are required to use it without installing additional packages. A huge number of functions are also included in the language. It is easy to use as the testing statistical hypotheses can only take a few lines of code. But for bigger data sets, it is mandatory to have packages like data.table.
  • The necessary data processing packages are simplified up to an extreme level for installing IDE(RStudio).
  • The language is compatible with various platforms and operating systems. You can also import your data sheets using different tools like Microsoft Excel.

What are the cons of using R?

  • It is difficult to learn and bad for easy to code. Weak typing can be dangerous as the functions of R may have a nasty habit of returning some unexpected type of objects.
  • The language has specificity in comparison to other languages. For instance – vector indexation will always begin with 1 instead of 0.
  • The syntax for solving all your problems is not quite obvious and due to a large number of libraries, the documentation of less popular ones cannot be considered as complete.

Python Programming Language

The language was developed in the ’90s by Guido Van Rossum which is now deployed by huge organizations like YouTube, Google, NASA and much more.

In technical terms, it is an object-oriented and high-level tool which is integrated with the dynamic linguistics.

The language is made publicly usable by answering all the troubles with scientific data that are as simple as writing out your considerations about the solution.

There are plenty of powerful Python libraries available.

Python can quickly plunge into data science. For the coders and the beginners, the simple syntax makes it easy to write and debug the code rapidly.

Python comes in handy when you want to add the tasks of the data analysis to the work of web applications.

Before starting off with the Python, make sure to set up SciPy/NumPy for scientific procedures and pandas for the data manipulation.

Also, look at the matplotlib library for making the graphics by including the scikit-learn for machine learning.

What are the Pros of using Python?

  • Python is gaining more popularity as the developers are exploring more career options in it.
  • The data processing can be carried out not only for their search but also for processing the web application.
  • All the programming functions are presented.
  • Comes with short and clear syntaxes.
  • Provides a high speed of operation and a comfortable interface.
  • There are many Python tools and frameworks available for ML.

What are the Cons of using Python?

  • It has a lack of a common repository along with the deficiency of alternatives for many R libraries.
  • Due to dynamic typing, it becomes complicated to search for some functions and track faults by connecting with the incorrect assignment of different data to the same variables.

R vs Python for Machine Learning [Difference]

What are the Other Factors to be considered while choosing R vs Python for ML?

R is popular for data analytics whereas Python is designed as a general purpose language.

The former is preferred for ad-hoc analysis and exploring datasets while the latter is suitable for data manipulation and repeated tasks.


R is a low-level programming language that requires a longer code for simple procedures. This is the reason behind the low speed.

While Python is a high-level language that has become the choice for all in building critical yet faster applications.

Easy Learning

R comes with a steep learning curve. People with less to no experience find difficulty in the beginning.

Once you are good with the language, it becomes easy to understand.

On the other hand, Python is more emphasized on code readability and productivity by making it one of the simplest languages.


R comprises of easy to use complex formulas for carrying out the statistical tests and also contains readily available models for the same.

Python is more flexible when it comes to developing something from scratch and hence it is also used in mobile app development or websites.

Capacity to handle the Data

R is more efficient for the analysis due to a large number of packages along with the readily usable tests and advantage of using the formulas. The language can also be used for basic data analysis without installing any package.

To this, the Python packages for data analysis was an issue but it has improved with the recent versions. There are many Python libraries available for Data Science. Now, Pandas and Numpy are used in Python for data analysis which is also suitable for parallel computation.

Visualization and Graphics

It is understandable that the visualized data gets understood efficiently and more effectively than raw values.

R contains numerous packages that provide advanced graphical capabilities while the visualizations are crucial when it comes to choosing the data analysis software contained in Python.

It comes up with more number of libraries but they are complex and lay out a tidy output.

The Final Verdict

If you want to be a data scientist, you need special skills.

Before coming to any conclusion, it is mandatory to understand that a language is just a tool for the developers. It is substantial to manipulate it for generating a superior solution.

If you are a newbie then we recommend opting for Python. It is automatically accustomed to some structure and style of code design. Start learning Python.

But if you have a little technical knowledge then you can try your hands on R.

This is all about- R vs Python for machine learning. What’s your thought?

Keep Learning!

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