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Based on our record, Shiny seems to be a lot more popular than Dash by Plotly. While we know about 32 links to Shiny, we've tracked only 1 mention of Dash by Plotly. 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.
Of course there are valid use cases for splitting frontend and backend technologies. NiceGUI is for those who don’t want to leave the Python ecosystem and like to reap the benefits of having all code in one place. There are other options like Streamlit, Dash, Anvil, JustPy, and Pynecone. But we initially created NiceGUI to easily handle the state of external hardware like LEDs, motors, and cameras. Additionally,... 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 / 10 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
Streamsync - Streamsync is an open-source framework for creating data apps. Build user interfaces using a visual editor; write the backend code in Python.
Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications
Streamlit - Turn python scripts into beautiful ML tools
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...