DEEP Framework VS Shiny

Compare DEEP Framework VS Shiny and see what are their differences

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DEEP Framework

DEEP Framework is a Full-Stack JavaScript Framework for building cloud-native web applications.

Shiny

Shiny is an R package that makes it easy to build interactive web apps straight from R.

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2023-10-13

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2023-06-30

DEEP Framework

Website: github.com
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Shiny

Website: shiny.posit.co
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DEEP Framework features and specs

No features have been listed yet.

Shiny features and specs

Interactive Web Applications
Shiny allows for the creation of interactive web applications directly from R, facilitating dynamic data visualization and user engagement without requiring extensive web development knowledge.
Ease of Use
Shiny provides a high-level interface that allows users to create complex applications with minimal code, leveraging R's capabilities and intuitive declarative syntax.
Integration with R
As a product of Posit (formerly RStudio), Shiny seamlessly integrates with the R ecosystem, enabling users to incorporate statistical analysis and machine learning models into their web applications.
Customizable UI
Shiny offers a range of UI components and the ability to integrate custom HTML, CSS, and JavaScript, allowing for highly customized and polished web applications.
Reactive Programming
Shiny’s reactive programming model simplifies the process of building interactive applications by automatically updating output whenever input changes, reducing the need for manual event handling.
Community Support
Shiny has a large and active community, offering plentiful resources such as tutorials, examples, and forums for troubleshooting and learning.

Possible disadvantages of Shiny

Performance
Shiny applications may suffer from performance issues, especially with large datasets or complex operations, as R is single-threaded by nature and may not handle high concurrency well.
Scalability
Scaling Shiny applications to handle large numbers of users can be challenging and may require additional infrastructure, such as Docker containers or server clusters, and careful resource management.
Limited Language Support
Shiny primarily supports R, which may be a limitation for teams or projects that rely on other languages for data analysis or web development.
Learning Curve
Despite its user-friendly design, there is still a learning curve for users new to R or web development concepts, particularly when dealing with more advanced features or customizations.
Dependency Management
Managing dependencies and ensuring version compatibility can become complex, particularly as applications grow in size and sophistication.
Deployment Complexity
Deploying Shiny applications for production use can be complex, requiring knowledge of server environments, containerization, and continuous integration/continuous deployment (CI/CD) practices.

DEEP Framework videos

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Shiny videos

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SHINY - PS4 REVIEW

Category Popularity

0-100% (relative to DEEP Framework and Shiny)

DEEP Framework

Shiny

Web Frameworks

7 7%

Web Frameworks

93% 93

PHP MVC Framework

100 100%

PHP MVC Framework

0% 0

Developer Tools

5 5%

Developer Tools

95% 95

Python Web Framework

6 6%

Python Web Framework

94% 94

User comments

Share your experience with using DEEP Framework and Shiny. For example, how are they different and which one is better?

Social recommendations and mentions

Based on our record, Shiny seems to be more popular. It has been mentiond 34 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

DEEP Framework mentions (0)

We have not tracked any mentions of DEEP Framework yet. Tracking of DEEP Framework recommendations started around Mar 2021.

Shiny mentions (34)

Big Book of R
There is a lot of way and the most common is shiny (https://shiny.posit.co/) but with a biais towards data app. Not having a Django-like or others web stack python may have talks more about the users of R than the language per se. Its background was to replace S which was a proprietary statistics language not to enter competition with Perl used in CGI and early web. R is very powerful and is Lisp in disguise... - Source: Hacker News / about 1 month ago
React for R
In R, you can build Single Page Applications with Shiny, created by Posit https://shiny.posit.co/ It is very useful, if you don't know HTML,JS,CSS and want to create an interactive dashboard, showcasing your analysis, models, visualizations, or even to create an internal tool for your organization. It seems that reactR provides functions for building react components directly from R that can be used in Shiny apps. - Source: Hacker News / 8 months ago
R: Introduction to Data Science
A lighterweight alternative to renv is to use Posit Public Package Manage (https://packagemanager.posit.co/) with a pinned date. That doesn't help if you're installing packages from a mix of places, but if you're only using CRAN packages it lets you get everything as of a fixed date. And of course on the web side you have shiny (https://shiny.posit.co), which now also comes in a python flavour. - Source: Hacker News / about 1 year ago
Reflex – Web apps in pure Python
Sometimes the war is lost even before the battle begins. During grad school, I wrote a whole bunch of web apps entirely in R using Shiny. It was clunky as hell, but yeah, it worked. I went looking for what's up with Shiny these days and found this - https://shiny.posit.co/ So yeah, full on pivot into python. Pip install shiny. Alright! "No web development skills required. Develop web apps entirely in R I mean... - Source: Hacker News / almost 2 years ago
PSA: You don't need fancy stuff to do good work.
Python's pandas, NumPy, and SciPy libraries offer powerful functionality for data manipulation, while matplotlib, seaborn, and plotly provide versatile tools for creating visualizations. Similarly, in R, you can use dplyr, tidyverse, and data.table for data manipulation, and ggplot2, lattice, and shiny for visualization. These packages enable you to create insightful visualizations and perform statistical analyses... Source: about 2 years ago