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MatLab vs R: Comparison Of Features & Main Difference

A large amount of data generated every day throughout the world. According to Forbes research, 2.5 quintillion bytes of data produce daily. The majority of it is flying around on the internet or in the cloud. Users in the previous two years have created 90 percent of the world’s data. Scientific methodology, complicated algorithms, and data analytics are required to analyze. It is also important to draw useful insights from such massive amounts of data to assist commercial decision-making. This is to assist commercial decision-making. MatLab vs R are two of the most essential and capable data modeling tools used among data scientists and statisticians. Both of these tools can handle organized and unstructured data.

MatLab vs R: Definitions 

R programming language

R is a prominent and capable open-source statistical research and graphics language. It is also the programming language as well as a run-time environment. It includes a debugger, graphics, access to some system functions, and the ability to run script files. R supports a wide range of statistical methods. It is including linear and nonlinear modeling, time series analysis. And also machine learning algorithms, and traditional statistical testing.


MatLab is a language for researchers, engineers and technicians. It focuses on mathematics and technical computing. Computing mathematics such as data analytics, signal and image processing may all express in the desktop environment. 

MatLab has a function called ‘Toolboxes’ that allows you to create application-specific solutions. Toolboxes are collections of MatLab functions known as M-files that handle a certain series of issues. 

Toolboxes offered in a variety of fields. It includes simulations, digital signal processing, control systems. And also Deep Learning, neural networks, and many more.

MatLab vs R: Comparison & key difference

Let’s look at some of the similarities and differences of the two strong languages that data scientists utilize.

If you are new to both R and MatLab, both look the same initially. This is because they both provide access to statistics and math functions. And they both use by the same user community. Comparisons of a few basic factors, on the other hand, may lead you to an opposite conclusion.

It is simple to learn.

The learning curve of R is quite steep. R was created by statisticians. Therefore its full potential can only access via programming. There was no graphical user interface to assist non-programmers with the analysis. 

R’s working examples are difficult to understand and are not suitable for beginners. The updated GUI versions of R-Studio and R-commander, on the other hand, have benefited the developer community.


R is a free, open-source software application. In comparison, MatLab is not a free alternative. The cost of a license differs depending on what type of use. MatLab is a unique programming language developed by Numerical computing.


MatLab is quicker than R when it comes to scientific computational tasks, statistics, and machine learning. On the other hand, a skilled R developer can get quicker results and enhance performance.


R primarily uses for statistical data analytics and data handling. Whereas MatLab utilizes matrix manipulation, image processing. And also for signal processing and machine learning.

Support and documentation

R has a big developer community for assistance and documentation. The reason is that it is an open-source programming language. On the other hand, Mathworks unrival and outstanding in terms of MatLab documentation. 


Dozens of code examples include in the documentation. And it is completely searchable online and inside the MatLab desktop. Because MatLab is licensed software, it offers over 200 specialized technical support professionals. And a thriving community to help with difficulties.


Machine learning


R and MatLab are both powerful machine learning tools. R provides a large library set. Whereas MatLab has a Statistical and Machine Learning Toolbox with a classification application. That toolbox guides you through dynamically specifying validation schemes. And also exploring data, training models, selecting features, and evaluating results. 


The option you choose determines your goals in machine learning. If your project requires image processing, MatLab is the tool to use. On the other hand, R is the best choice if you wish to apply statistical approaches for simple algorithms.




MatLab vs R are both capable of visualizing data and displaying outcomes. Base graphics, Grid graphics, Lattice graphics, and Ggplot2. These are four distinct graphics solutions in R. The default graphics system in R is base graphics. And it is also the simplest of the four to understand.


MatLab also allows you to create apps with GUIs. Also, 2D and 3D charting functions include in the Graphics functions of MatLab. Simulink is a modeling and analyzing multi-domain dynamical system. And also it is a MatLab add-on product. Moreover, Simulink’s main interface consists of a graphical unit diagramming tool. And a collection of block libraries that can be customized.

Let’s wrap it up! 

Today, we discussed about MatLab vs R. 


MatLab is extensively used In research institutions and academic and industrial businesses. It also widely utilize in the aviation and aerospace industries. R is widely used in the fields of education, government and healthcare. 


Both (MatLab vs R) are strong in their own right, as we’ve seen above in the blog. R is an excellent starting point since, being a statistical programming language, it comes with many built-in statistical procedures.

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