Streamsync VS Shiny

Compare Streamsync VS Shiny and see what are their differences

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Streamsync

Streamsync is an open-source framework for creating data apps. Build user interfaces using a visual editor; write the backend code in Python.

Shiny

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

Landing page //
2023-05-04

It's fast.

Streamsync enables significantly lower response times, when compared to Streamlit.
It only runs the user script once.
It uses WebSockets to keep frontend and backend states in sync.

It's neat.

Streamsync uses state-driven, reactive user interfaces. A data app's user interface is strictly separated from its logic.
It uses a consistent yet customisable UI design system.
No caching needed; the script runs once and things remain in memory. You can use globals and module attributes to store app-wide data.
Predictable flow of execution. Event handlers are plain, easily testable Python functions. No re-runs, no strange decorators.

Check out a live demo of an app.

Landing page //
2023-06-30

Streamsync

Website: streamsync.cloud
$ Details: free
Platforms: Linux Mac OSX Windows
Release Date: 2023 May

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Shiny

Website: shiny.posit.co
$ Details
Platforms: -
Release Date: -

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Streamsync 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.

Analysis of Shiny

Overall verdict

Shiny is generally considered a strong and effective tool for building interactive data visualizations and applications, particularly within the R environment. Its ease of use, flexibility, and ability to integrate with various data sources and other technologies make it a valuable tool for data scientists and statisticians.

Why this product is good

Shiny is a popular framework used for building interactive web applications directly from R, making it accessible to R users who want to create interactive web content without having deep knowledge of web development. It is highly favored because it allows for rapid prototyping, leverages the vast ecosystem of R packages, and provides built-in support for reactive programming. The framework enables users to create dynamic visualizations and applications that can be shared easily. It also has strong community support and extensive documentation, making it easier for beginners to learn and implement complex functionalities.

Recommended for

Shiny is recommended for data scientists, statisticians, and R programmers who want to create interactive web applications for data analysis and visualization. It is particularly useful for those who already have experience with R and are looking to share their findings or analyses interactively with others. It is also beneficial for educators and researchers who need to create accessible, web-based applications to demonstrate data-driven insights.

Streamsync videos

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Shiny videos

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SHINY - PS4 REVIEW

Category Popularity

0-100% (relative to Streamsync and Shiny)

Streamsync

Shiny

Developer Tools

14 14%

Developer Tools

86% 86

Web Frameworks

0 0%

Web Frameworks

100% 100

Application And Data

100 100%

Application And Data

0% 0

Python Web Framework

8 8%

Python Web Framework

92% 92

User comments

Share your experience with using Streamsync 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 a lot more popular than Streamsync. While we know about 34 links to Shiny, we've tracked only 2 mentions of Streamsync. 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.

Streamsync mentions (2)

How do I get honest feedback? (developer tool, Python)
I'll link the framework here in case you're familiar with Python and can directly provide some feedback. Source: about 2 years ago
Starfyre - A Python Web Framework for creating frontend web applications
Streamlit is popular, so is Dash. They're serving a need that truly exists. They target data apps, using out-of-the-box components. They're not after general web development, replacing React is the last thing they want to do. I'm also working in this category with streamsync.cloud. Source: about 2 years ago

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 2 months 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 / 9 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