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Based on our record, Shiny seems to be a lot more popular than Streamsync. While we know about 32 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.
I'll link the framework here in case you're familiar with Python and can directly provide some feedback. Source: about 1 year ago
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 1 year ago
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 / 2 months ago
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 / 9 months ago
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 1 year ago
We work along side bio-statisticians and data analysts, from my experience in this world I recommend to build some plots/graphs in R based on some information you find appealing. After you have some work to show off to potential employers , learn Shiny and publish those graphs online as your portfolio. Source: about 1 year ago
One of the most difficult yet most fun projects I’ve done. Using Shiny to make an app, all coded in R! Source: about 1 year ago
Streamlit - Turn python scripts into beautiful ML tools
Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications
Dash by Plotly - Dash is a Python framework for building analytical web applications. No JavaScript required.
Django - The Web framework for perfectionists with deadlines
Anvil.works - Build seriously powerful web apps with all the flexibility of Python. No web development experience required.
Ruby on Rails - Ruby on Rails is an open source full-stack web application framework for the Ruby programming...