Want to know the difference between Matlab vs R? Don’t worry! In this blog, we will discuss the differences between Matlab vs R. So, you can easily choose the best language as per your requirement.

Nowadays many students are doing a programming courses in different languages because

- It can help them to build logical thinking & problem-solving skills
- It can improve creativity
- Can teach the practical application of maths
- Allow them to better understand the technologies around them
- And also it is the most demanding and students can make a good career in the future.

But there are a lot of languages to learn and students don’t know which one is the best language to learn. The same for Matlab and R they don’t know what are the differences between Matlab vs R.

If you are one of those who don’t know the differences between Matlab and R, then you don’t have to worry because, in this blog, you will learn all about both languages. With that information, you can choose the right language.

## What Is Matlab?

Table of Contents

Matlab is a programming platform developed by MathWorks, and it stands for matrix laboratory. It offers a multi-paradigm environment of programming to programmers.

The main purpose for using Matlab programming by engineers and scientists is for technical and mathematical computing.

Also, it is used for the implementation of algorithms, functional plotting, creation of user interfaces, and other programming interactions. In Matlab, you don’t need to code from scratch, in this, to perform almost every task, you go to build the toolbox that allows you to perform them.

You can also add more tools to its toolbox to extend the Matlab functionalities. And the toolbox contains some files that are the default ones known as M-files.

In Matlab, these files are crucial ones that are used for solving the specific set of problems in Matlab like control systems, signal systems, neural networks, etc. For image processing in the world, it is powerful software. It has excellent integration support with many tools like Stan, ggplot2, Octave, SketchUp, RapidMiner, Dynamo, etc.

## Feature Of Matlab

High-Level Language |

Interactive Environment |

Handling Graphics |

Mathematical Functions Library |

Application Program Interface (API) |

Toolboxes |

Accessing Data |

Interfacing with Other Languages |

Data Processing |

Machine Learning, Neural Networks, Beyond Statistics |

Text Analytics |

Multi-Platform Deployment |

**What Is R?**

R is an open-source programming language and it is from those languages which are popular for statistical computing and graphics.

In R programming by using the libraries and R packages you can increase the R capabilities.

There are hundreds of libraries in R programming that are pre-installed in it and used for statistical and graphical techniques.

It is used widely for clustering, machine learning algorithm, regression, time series analysis, etc. In R programming, you can embed packages that are user-generated.

You can create the packages by yourself in R programming or any other language like Java, C, C++, and Python.

R is available as a command-line tool, and also has graphical IDEs such as RStudio, and R tools for visual studio.

And with the help of scripting languages like Python, Perl, and Ruby, etc., you can access the functionalities of R.

Besides, R has integration support that is the best one with many tools and technologies like Stan, Jupyter, RapidMiner, Neptune, Apache Zeppelin, KNIME, cnvrg.io.

## Feature Of R

Open-source |

Strong Graphical Capabilities |

Highly Active Community |

A Wide Selection of Packages |

Comprehensive Environment |

Can Perform Complex Statistical Calculations |

Distributed Computing |

Running Code Without a Compiler |

Interfacing with Databases |

Data Variety |

Machine Learning |

Data Wrangling |

Cross-platform Support |

Data Handling and Storage |

Vector Arithmetic |

Generates Report in any Desired Format |

**Matlab Vs R: The Key Differences**

**Ease Of Learning**

If we talk about ease of learning, R is quite challenging to learn if you don’t have any programming knowledge. However, if you have prior knowledge of any other programming language such as Java, C++, C#, it will be easier to learn R programming.

R is not developed as a beginner-friendly language, but after the release of R studio and R commander, it became easier to code in R programming.

In contrast, Matlab is easier than R. It offers syntax which is simple to use. Also, the user who is not from a programming background can get familiar with Matlab. It offers a toolbox for performing tasks mostly.

**Performance**

As compared to Matlab, R is quite faster. But if you have a good command of R programming, you can only achieve the faster speed from it.

In contrast, for statistics, technical computation, and machine learning, Matlab offers you a faster speed than the normal speed of R programming.

**Cost**

R is free to use as it is an open-source product. So, anybody who wants can use it.

In contrast, Matlab is a licensed product. So before using it, you have to pay for it. And it is the product of Mathworks, and its cost depends on the type of usages.

**Support And Documentation**

R is an open-source programming language, so you may not get the official support and documentation. But there is a good part also: it has a large developers community for support and documentation. On the official website, you can also get the proper documentation.

In contrast, you can get the official Matlab after-sales support, and the customer support of Matlab is the best one globally. On the official site of Matlab, users can get the proper documentation. And also, for beginners to start with Matlab, it offers hundreds of prewritten codes.

**Functionalities**

If we talk about the functionalities, Matlab is used for many applications, such as signal processing, matrix manipulation, image processing, and machine learning.

In contrast, R is used for statistical analysis and data processing.

**Machine Learning**

When it comes to machine learning, both R and Matlab are good ones. For performing the tasks of machine learning, R has the library sets. In contrast, for performing the tasks of machine learning, Matlab has the statistics and machine learning toolbox. And these toolboxes are useful for exploring the data, selecting features, and many more.

**Platform Independent**

R is a platform-independent programming language, and on any platform like Windows, macOS, and Linux, you can run it. And also on the server-oriented OS.

If in R on macOS, you start the coding and want to edit on Windows, you can easily do it without any additional packages and code changes. On every operating system, the code works the same, and to ensure it, all the packages were tested on CRAN.

In contrast, Matlab is also a platform-independent language, and it can be run on Windows, Mac, and Linux. But there is one thing: you can’t run the same Matlab’s licensed version on more than specified numbers of computers. It’s because it uses the computers’ MAC address for identifying the Matlab license.

**Visualization**

Both R programming and Matlab provide users with best-in-class data visualization. R has the most advanced graphics implementations. Base graphics, Lattice graphics, Grid graphics, and Ggplot2 are the four graphic implementations.

The R programming language’s fundamental graphics can handle the majority of data visualization needs.

Matlab, on the other hand, has 2D and 3D graphing capabilities. Depending on your skillset, you may customize charts in Matlab either interactively or programmatically.

Simulink is used for data modeling, simulation, and analysis of multi-domains. Matlab also offers packages to augment its graphical capabilities.

## Matlab vs R- Google Search Trends

As you can see the graph below that are showing the search trends for the term Matlab vs R on google for the world in the past 5 years. The blue line you are seeing is for Matlab. And other one is for R. With this graph, you can assume R is the most popular than Matlab.

## Matlab vs R: In Tabular Form

Basis For Comparison | MATLAB | R |

Open Source | It is not open source. | It is open-source. |

Language Type | It is a high-performance language. | It is an interpreted language. |

Speed | It is faster than R. | It is slower than Matlab. |

Ease of Use | To program complex things in Matlab is easy as it has a lot of toolboxes. | R follows the programming language syntax, and for the newbies in the programming world, it can be difficult. |

Community Support | It has a closed community as it is licensed. | It has multiple community support as it is open-source. |

Functionalities | It is used to perform the different applications of engineering such as machine learning, image processing, signal processing, matrix manipulation, etc. | It is used for data processing as well as for statistical analysis. |

Availability of Libraries | Most of the functionalities that give the several functions are available in the form of the toolbox. | It has many packages with multiple functionalities. |

## Conclusion: Matlab vs R

In this blog, we have discussed the key differences of Matlab vs R. And after comparing the difference between Matlab vs R, we hope that you now get enough knowledge about it. But if you face any problem anywhere, then you can ask us for R programming assignment help. And don’t hesitate, feel free to contact us anytime. We are here to help you.

## FAQs

### Is MATLAB better than R?

When we talk about technical computing tasks, statistics, and machine learning, we can say Matlab is better as it is faster than R. However, the R developers who are high-skilled can get the results faster and can improve the performance as well.

### What can MATLAB do that R Cannot?

Matlab can interface with real-time hardware for signal processing, acquisition, and control, whereas R cannot.

### Is MATLAB faster than R?

Yes, Matlab is faster than R. It is used to perform several applications of engineering such as machine learning, image processing, signal processing, matrix manipulation, etc., whereas R is used for data processing and statistical analysis.