MATLAB really does best for solving computational problems in math and engineering. Especially when you have to use a lot of functions in your solving process, or if you have a nonlinear equation that must be iteratively solved. [MATLAB] can also perform things like integration and derivation on your equations that you put into it.
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React is a JavaScript user interface construction library that works well for:
Developing web apps with dynamic and complicated user interfaces.
creating reusable UI elements that may be used in other applications.
creating single-page applications with dynamic content updates that don't require a page reload.
The Virtual DOM's effective updating mechanism allows it to handle large volumes of data updates.
React, on the other hand, might be less suitable for:
Websites that are simple, stagnant, and have no interaction. Other libraries or simple HTML, CSS, and JavaScript may be a better fit in such circumstances.
Web sockets may be a better choice for applications that need real-time updates, such as chat or gaming apps.
When creating mobile apps, React Native is a better option.
Server side rendering only, as React is designed to run on the client side.
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React is fantastic for building performant user interfaces. Our web app is snappy and great for our customers.
React has the philosophy of doing one thing and doing it well which is the view layer of the application. This makes it incredibly intuitive and flexible for developers to use.
React has lead the way in being able to write modular and structured code. It is a drastic improvement since the days of spaghetti jQuery code.
React has an unmatched community. The amount of tools and libraries available is fantastic, and there plenty of solutions available online for common problems.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Debugging React is challenging. Bugs in react code generate stack traces internal to React and it is often totally unclear how it relates to the code you actually wrote.
Relating your React elements to corresponding DOM elements is difficult. The intentional separation of virtual and actual DOM also makes it difficult to map the elements to the structures in the DOM. This is partially ameliorated by the use of the React dev tool, which provides a DOM-like view of the React elements, but the tool still does not provide a direct correspondence with the DOM that is often necessary to figure out why something isn't right.
Because JSX is React-specific and not a language feature, a special compilation process is necessary to convert JSX code to normal JS. Coming from a C++ background, compiling things doesn't bother me, but many JS developers are used to a less structured development.
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MATLAB is pretty easy to use. You can extend its capabilities using the programming interface. Very flexible capabilities when it comes to graphical presentation of your data (so many different kinds of options for your plotting needs). Anytime you are working with large data sets, or with matrices, MATLAB is likely to be very helpful.
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React is just a bit of a different animal. I was avoiding it for the longest time. I thought for sure I would land on Vue or something else with a more approachable and familiar appearance. But after taking an online course in React, I started realize what people were raving about (and complaining about) and decided to implement it at our office for one of our products.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
The built-in search engine is not as performing as I wish it would be. However, the YouTube channel has a vast library of informative video that can help understanding the software. Also, many other software have a nice bridge into MATLAB, which makes it very versatile. Overall, the support for MATLAB is good.
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Since it's open-source and very popular, the community support for React and related tools and libraries is excellent. There are a lot of people using the same tools, and so issues tend to get fixed quickly and "recipes" are easy to come by. And since it's backed by Facebook, they have a dedicated engineering team working on the progression of React.
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How MATLAB compares to its competition or similar open access tools like R (programming language) or SciLab is that it's simply more powerful and capable. It embraces a wider spectrum of possibilities for far more fields than any other environment. R, for example, is intended primarily for the area of statistical computing. SciLab, on the other hand, is a similar open access tool that falls very short in its computing capabilities. It's much slower when running larger scripts and isn't documented or supported nearly as well as MATLAB.
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While this is a widely contested debate with various blog posts and benchmarks all over the place, its really a personal choice to determine what works for the team. Coming from a Angular 1.x background, I decided to try a new framework when Angular 2.x was announced and at that time React is gaining popularity and Vue hasn't taken off yet. Compared to Angular 1.x and Vue (hybrid of React and Angular) that split the logic from the html templates, I loved the way React breaks code into components using the jsx syntax. In my mind, this allows for cleaner components and easier maintenance
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MATLAB helps us quickly sort through large sets of data because we keep the same script each time we run an analyzation, making it very efficient to run this whole process.
The software makes it super easy for us to create plots that we can then show to investors or clients to display our data.
We are also looking to create an app for our product, and we will not be able to do that on MATLAB, therefore creating a limiting issue and a new learning curve for a programming language.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
