MATLAB vs. R: What's the Difference?

MATLAB and R are statistical analysis software packages, each with a unique programming language designed for their respective environments. Both programs contain massive libraries with similar statistical methods available in each program. The most significant difference between the two programs is that MATLAB is a paid software part of Simulink, an environment for systems simulation. In contrast, R is a free, open-source programming language made for statistical computing. 

Explore the differences between MATLAB and R, what each software does, how you can use them, their advantages and disadvantages, and what you should consider when deciding between these two pieces of software. 

MATLAB

MATLAB, the acronym for Matrix Laboratory, began as a matrix calculator in the 1970s and was released as commercial software in 1984. Over the years, the software met the dynamic needs of scientists and engineers until it became the data analysis, signal processing, robotics, and systems development program it is today. Now, MATLAB is an interactive environment with professionally built tools, automation, and the ability to scale up to a big data processing environment if needed.

What is MATLAB used for?

MATLAB is a multipurpose programming and computing platform. MATLAB's ability to process data for analysis, create visualizations, connect with other programming languages, connect to hardware, and deploy parallel computing in the cloud gives it over 20 applications in a wide range of industries. Some of these applications include:

• Artificial intelligence (AI)

• Data science

• Machine learning and deep learning

• Image processing and computer vision

• Internet of Things

• Robotics

• Signal processing

• Wireless communications

MATLAB has over five million users. They are researchers and academics, students at colleges and universities, and many work in science and engineering positions. 

Advantages of MATLAB

MATLAB's wide range of capabilities, applications, and industry uses gives it many advantages. Some of these advantages include:

• Its ability to connect with Simulink to produce simulations and models for complex systems 

• Tools and packages built, tested, and designed for professional use. 

• The ability to build machine learning models and optimize their hyperparameters without coding using the AutoML (automated machine learning) features that streamline the workflow of manual processes, so those with less machine learning experience can create accurate models.

• Tools and packages to help beginners produce applications while they learn programming languages 

Disadvantages of MATLAB

MATLAB has many functions that work in many industries, but it has some limitations and accessibility issues. Some of the disadvantages of MATLAB include:

• MATLAB’s high license cost is a barrier to entry for those who do not have institutional access. 

• MATLAB cannot do the same things as a general-purpose programming language like C or Fortran. 

• Since MATLAB is an interpreted language, it's much slower than compiled languages like C. 

• The commands in MATLAB are not equivalent to those in other programming languages and only work in MATLAB. 

R

R started as an alternative, open-source programming language to the S programming language that John Chambers and colleagues developed while at Bell Laboratories. R contains a range of statistical capabilities and can produce high-quality graphics and plots. Rather than just a programming language, R provides you with an entire environment to work in. It has many statistical packages and a constantly developing community, making it a popular choice when you need in-depth statistical analysis. 

What is R used for?

R has many uses in statistics and data science, including data mining, data analysis, data manipulation, and graphic visualization. It can create complex statistical models and time series analysis, train machine learning models, and have an ever-growing number of statistical analysis packages. Because of its open-source nature, R is often used to research new statistical methods. Using R requires RStudio, which is the integrated development environment (IDE) built for the R environment. 

Advantages of R

R has many advantages in statistics and data analysis. Some of these advantages include:

• R and its IDE, RStudio, are free and open-source.

• R is a functional programming language allowing user functions, loops, and conditionals. 

• For those intensive tasks, you can link C, C++, and Fortran code to overcome some computational limitations.

• As of November 2024, the Comprehensive R Archive Network (CRAN) has over 21,000 packages available for users who need to extend R beyond its basic capabilities. 

Disadvantages of R

While R is a free and open-source programming language, it does have some limitations. These disadvantages include:

• R has a steep learning curve for beginners.

• R requires prerequisite knowledge of statistics to know which methods to use to solve a problem, unlike commercial software like MATLAB, which makes recommendations. 

• R has trouble handling big data sets, as all data must come from the same place. 

• Compared to MATLAB, R compiles its code slowly, taking more time to give an output.

Things to consider between MATLAB vs. R

MATLAB and R excel in statistical analysis, data analysis, and training machine learning and deep learning models. One of the most significant factors to consider between R and MATLAB is that R is free and open-source, whereas an individual MATLAB license is $1,015 per year [1]. Your budget will greatly determine which software you will use unless you work for an institution with access to MATLAB. 

Another factor to consider is that R outperforms MATLAB in statistical analysis methods, but MATLAB is better at integrating systems design directly into its program. This makes MATLAB a better choice for engineers and scientists designing systems for practical applications because it doesn’t require the same programming skills as R for automated machine-learning workflows. However, R is better suited for researchers and statisticians who use models.

Getting started with data analysis on Coursera

MATLAB and R are both powerful tools for statistical analysis. However, some key differences may set them apart in terms of price, uses, and graphics capabilities. Learn more about using MATLAB for data analysis with Practical Data Science with MATLAB Specialization from MathWorks on Coursera. If you want to learn more about data analysis using R, try the Data Analysis with R Programming, a part of the Google Data Analytics Professional Certificate on Coursera.