CherryPy
Django
Flask
web2py
Bottle
Pyramid
Tornado
BlueBream
Shiny
Node.js
Django
Ruby on Rails
Laravel
ASP.NET
Meteor
ExpressJS
CherryPy ShinyBased on our record, Shiny seems to be a lot more popular than CherryPy. While we know about 32 links to Shiny, we've tracked only 2 mentions of CherryPy. 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.
Generally, what needs to be done to create an Django/Electron app is to package (I'm using pyInstaller)the Django app into an stand-alone executable and then bundle that into an Electron app. The question is which server should be used for this case to server Django before packaging it with pyInstaller? At the moment I'm using cherryPy as a WSGI web server to serve Django. Source: about 2 years ago
I know there are plenty of questions about Flask and CherryPy and static files but I still can't seem to get this working. Source: about 2 years 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 / about 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: 12 months 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
Django - The Web framework for perfectionists with deadlines
Node.js - Node.js is a platform built on Chrome's JavaScript runtime for easily building fast, scalable network applications
Flask - a microframework for Python based on Werkzeug, Jinja 2 and good intentions.
web2py - Web2py is an open source web application framework.
Ruby on Rails - Ruby on Rails is an open source full-stack web application framework for the Ruby programming...
Bottle - bottle.py is a fast and simple micro-framework for python web-applications.
