Most of the students have no idea what the difference is between R vs MATLAB. That is because both of these languages are quite similar to one another. These languages also share common features, such as math functions and statistics. In this article, we will give you the best comparison between R vs MATLAB. Let’s begin-

## What is R?

R is an open-source and free programming language and software environment. It is used for statistical computations. It supports a number of statistical graphics. R also plays a significant role in machine learning with its graphical and statistical capabilities.

Like other programming languages, R has no limitations. By using C, C++, Java, or R, you can write packages that extend the features of R. Currently, R is only available as a command-line tool. You can also access it as a graphical user interface. The R studio IDE is one of the most popular IDEs available.

The R programming language is one of the most popular for executing programs stored in script files. There are other scripting languages where you can access its functionality, including Ruby, Python, Perl, etc. Furthermore, you can edit R programs easily with the help of eclipse, Notepad++, etc.

## What is Matlab?

MATLAB is the most popular software for mathematical computation. Engineers and scientists primarily use it. MATLAB stands for matrix laboratory. MathWorks created the program in the late 1970s. With MATLAB, you can plot, manipulate matrices, develop algorithms, create user interfaces, and so on.

The great feature of Matlab is that you can connect to other Java, C, C++, and Python programs without any issues. Why is MATLAB better than other tools? Due to its toolbox. There are many uses of Matlab. MATLAB can perform a variety of functions with the help of these toolboxes.

With the toolbox, you can also implement specialized technology. In MATLAB, you can find an M-file, a set of functions with which you can solve specific problems. The MATLAB toolbox can implement signal processing, control systems, neural networks, simulations, and many other applications.

## R vs Matlab: Key Differences

Both are popular choices on the market; let’s examine some significant differences.

Classes and Objects:

The program is much simpler with object-oriented programming. When using Matlab, a class can be regarded as a folder that contains functions used to work with objects of that class. In R, classes are packaged into Packages.

Installation:

Matlab cannot run the same licensed version of the software on different operating systems unless installed on the same physical machine. R can run on any operating system, but Matlab cannot.

Usage:

Generally, R is used to solve statistics-related problems, while Matlab is used to do other mathematics-related things, including calculus, graphing, etc. R is preferred over Matlab in the analytics field since it has many prepackaged programs that can solve analytical problems.

Learning Curve:

Newcomers to the programming language may find R challenging because it uses natural syntax. Its open-source nature means R has a massive amount of open-source code available for users to get started. The availability of code on the internet is limited since Matlab requires a license. On the other hand, Matlab is easier to learn because it offers many toolboxes for most of its functions.

Preferred Language:

Almost all industries perform data analysis with R, while universities use Matlab more often.

Add-on Products:

Many Matlab add-on products perform different tasks. For example, Simulink. This graphical programming environment achieves modeling, simulation, and analysis of multi-domain systems. There are no add-on products for R, but it does come with many packages.

## R vs Matlab: Which One Is Popular

The graph below shows the search trends for the terms R vs Matlab on Google in the last 5 years. Blue graphs show Matlab searches, and red graphs show R searches. In the last 5 years, the popularity of R programming has increased.

## Features of R and Matlab

Following are the features of R vs Matlab.

1. Operating System (OS)

The R programming language runs on Linux, Mac, and Windows user operating systems and the server-side Solaris OS. Almost all platforms have the same impact on R, which is highly platform-independent. Testing all OSs listed above can make it more accessible for CRAN to ensure that R packages work on all OSs. Meanwhile, MATLAB can also run on Mac, Linux, and Windows systems. It is interesting to note that MATLAB licenses use the MAC address of a computer to identify the licensee. Due to the MAC address being a hardware value, it remains the same across different operating systems. Thus, MATLAB is activated the same way when installed on various operating systems on the same computer.

2. Machine Learning

R vs MATLAB has extensive machine learning capabilities. They both have statistics and machine learning toolboxes that allow interactive data discovery and selection of features, validation schemes, model training, and analysis of the results. A user’s decision is primarily determined by what they want to accomplish with machine learning. They should use MATLAB if their job involves processing images. For complex algorithms, R would be the right choice.

3. Support and Documentation

R is an open-source language, so its community of developers is large and supportive. MATLAB documentation produced by MathWorks is incomparable and remarkable. There are hundreds of code examples in the documentation, searching online or from the MATLAB desktop. In addition to its technical support staff of 200 professionals, MATLAB has a dynamic culture worldwide.

4. Easy to Learn

R is known for its steep learning curve. Statistical software has been developed by statisticians and therefore is fully programmable. Non-programmers cannot use its GUI. Using R requires a certain level of expertise. However, the new GUI versions of R-Commander and R-Studio reached out to the developer community. On the other hand, MATLAB is a more accessible language to learn and remember, as the terminology between products is consistent and clear.

5. Cost

In comparing these two programming languages, R leads due to its affordability. The R programming languages are open-source, so users do not have to pay for them. It is free for everyone. On the other hand, MATLAB is an approved product, so they must pay a small amount to use it. A Matlab license also differs from its type of software. Matlab is licensed under Mathworks.

6. Performance

Matlab is faster at performing technical tasks, statistics, and machine learning. On the other hand, R programmers will achieve the same results faster than Matlab. In this case, it might be assumed that the use of its users determines the rate of R vs Matlab.

7. Add-on Products

Matlab has licensed software, including several add-ons. Matlab uses these add-ons for various tasks. R, on the other hand, has no such features. There are many packages in R. Each package performs a different task.

## R vs Matlab: Pros and Cons

**R Programming **

Pros | Cons |

RStudio IDE | The messy syntax for string concatenation |

Hacky | Arrays indices start with 1 |

Preferred Medium | Messy character encoding |

Shiny interactive plots | The poor syntax for classes |

Shiny apps | The messy syntax for array/vector combination |

Automated data reports | Very messy syntax |

Easy installation of packages | Tables must fit in the RAM |

Interactive | No push command for vectors/lists |

**Matlab **

Pros | Cons |

S-Functions | Data analysis |

REPL | Graphics and data visualization |

Simple variable control | Free |

Solve invertible matrix | Great community |

Simulink | Flexible statistical analysis toolkit |

Functions, statements, plots, and directory navigation is easy | Access to powerful, cutting-edge analytics |

Model-based software development | Easy packages setup |

## R vs Matlab: What Are Their Alternatives?

Following are the alternatives for the R programming and Matlab programming.

**NumPy**

NumPy can also be used as an efficient multi-dimensional container for generic data besides its obvious scientific applications. It allows you to define your data types. It makes NumPy easy to integrate with a diverse range of databases.

**Octave**

This software, based on a high-level programming language, is designed specifically for numerical computations. Octave helps solve linear and nonlinear problems numerically and perform other numerical experiments using a language that is mostly compatible with MATLAB.

**Tableau**

Anyone can use Tableau to see and understand their data. It can connect to almost any database; visualizations can be created by dragging and dropping.

**Python**

Guido Van Rossum created the Python programming language. You should use Python to start your programming career because of its elegant syntax and readable code.

**Matplotlib**

It produces publication-grade figures that can be displayed in a variety of hardcopy formats, as well as in interactive environments across a variety of platforms. We can use it in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits.

## Conclusion

It is clear now that MATLAB and R are very different software or tools. They both have their advantages and disadvantages. It is difficult to find a better package for data analysis than MATLAB. When it comes to image processing and other graphics, MATLAB is the most suitable option.

On the other hand, R is popular in the data analytics field. What you choose now is up to you. You can find R programming assignment help and MATLAB Assignment Help here. Our Experts are available 24/7 to help those students who are facing difficulties in completing their assignments.

## Frequently Asked Questions

**Q1. Is R still popular?**

In data science, R is becoming more and more popular. R occupies the 13th position among the most popular programming languages according to the TIOBE Index 2021.

### Q2. **Which is better: R or Python?**

In terms of machine learning, Python is the most powerful tool. However, it is not ideal for business analytics. A good thing about R is that it was developed by scientists and academics. This makes it ideal for solving statistical problems, machine learning, and data mining.