Comparing MATLAB and R for prototyping and implementing analytics

A quick look at MATLAB® and R might suggest they’re fairly similar. They both offer access to math functions, a language, statistics, and a community of users. However, a closer look at the technical capabilities of each one and an assessment of other important factors, such as documentation and quality, leads to a different conclusion.


In contrast to R, MATLAB provides:

As a result, scientists, engineers, and their IT colleagues have often found that they are more productive using MATLAB than R for data science: to visualize and analyze data, prototype and validate models and analytics, and integrate analytics into their enterprise systems.

How MATLAB Outpaces R

High-Productivity Analysis and Modeling Tools

High-quality algorithms you can rely on: MATLAB algorithms are field-proven from use by millions of engineers and scientists. MathWorks has over 250 full-time quality and test engineers who validate product quality and accuracy, and ensure the software passes extensive test suites before being released.

Environment and apps tuned for engineering and scientific tasks: Unlike traditional programming languages, the MATLAB IDE is optimized for efficient data pre-processing and analytics. It includes apps that help you quickly accomplish common workflow tasks and iterate without writing code. Data exploration apps, for instance, enable data import, filtering, plotting, and fitting models to your data.


Easy-to-learn language: Compared to R, which was developed for statisticians, the MATLAB language is easy to learn and remember because the syntax is simple and consistent by design across products.

Core mathematical building blocks: MATLAB provides an extensive set of functions for linear algebra, matrix manipulation, and mathematics.


Extensive collection of application-specific toolboxes: Unlike community-developed libraries, MATLAB toolboxes include highly expressive function libraries and point-and-click apps that are all fully integrated in the MATLAB environment. Areas covered include statistics, machine learning, signal processing, image processing and computer vision, computer vision, optimization, symbolic computing, control systems, test and measurement, computational finance, and computational biology.

Point-and-click machine learning: The Statistics and Machine Learning Toolbox includes a classification app to guide you to interactively explore data, select features, specify validation schemes, train models, and assess results.

Train models to classify data using the Classification Learner app.

Faster Performance

Fast execution: Out of the box, MATLAB is faster than R for common technical computing tasks, statistics, and machine learning. The following table and chart illustrate results on the R benchmark 2.5 (also known as Urbanek), and on a benchmark of 15 popular classification, clustering, and regression algorithms. Built-in multithreading in MATLAB enables fast computation on multicore-enabled machines, particularly for linear algebra and clustering.

MATLAB compared with Microsoft R Open
Benchmark Category
General programming (Urbanek)
Linear algebra

Raw execution times for MATLAB and the CRAN and Microsoft R Open distributions of R. The upper area of the plot represents all the tests where MATLAB is faster than R.

Parallel computing: For computationally intensive tasks, you can run multiple MATLAB engines in parallel with Parallel Computing Toolbox. Many toolbox functions have built-in parallel computing support. You can also speed up execution with a parallel for-loop, by scaling up with compute clusters, or by using the Amazon EC2 Cloud with MATLAB Parallel Server.


Easy Deployment

Advanced programming tools: MATLAB has tools that developers or IT professionals need in order to integrate and deploy their analytics models. These include an interactive debugger, object-oriented programming, a code performance profiler, a unit testing framework, source control integration, and a graphical user interface builder.

Sharing and royalty-free integration: You can share your work by publishing your MATLAB code in HTML, PDF, XML, Microsoft Word, Microsoft PowerPoint, or LaTeX. You can share standalone MATLAB applications or create shared libraries to integrate into Java, Microsoft .NET, and Excel applications. Deployment with MATLAB Compiler is royalty-free so other users can access your application.


Faster Answers from Doc, Support, and Community

Complete documentation: An integral part of MathWorks products is the documentation. It’s fully searchable online and within the MATLAB desktop, and includes hundreds of code examples. User feedback like this is very common: “The documentation of MATLAB is really exceptional. Nothing I’ve seen compares to it.” Meanwhile, open source users commonly say “I need to Google stuff or look in online public forums, which is a complete time hog.”

Professional support: MathWorks has over 200 dedicated technical support experts worldwide ready to answer questions and troubleshoot problems. Most have advanced degrees and the team has a direct escalation path to development for advanced support.

Vibrant user community: MATLAB has an extensive community of engineers and scientists across many disciplines that spans industry, research, and academia. You can tap into the experience of thousands of MATLAB users and MathWorks staff with online resources such as the MATLAB File Exchange, MATLAB Answers, Cody, and blogs from the experts.

See also: machine learning with MATLAB, MATLAB plot gallery, data fitting with MATLAB, MATLAB vs. Python

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