https://dash-app-dx9g2r0la6-8000.cloud.kyso.io
It was also really easy to make it, maybe 250 lines of python in total
(guide to making this app is here: https://kyso.io/KyleOS/creating-an-interactive-application-u...)
By Dash for Python, you mean the one from Plotly? https://plot.ly/products/dash/
Thank you for sharing ShinyProxy!
ShinyProxy is amazing. It is pretty easy to setup, but does require quite some specialized knowledge compared to the RStudio solutions.
Awesome!
R has such a huge library of software now, that it has gone far and wide outside of statistics and analytics - any kind of application can be built in R now.
In fact I see no point in using Python for mathematics or number crunching any more as R has it all and performance critical parts can be rewritten in modern Fortran for very high speed and made available inside of R transparently.
As you gain experience building applications, at some point you'll learn that you were wrong. Or you won't, in which case I feel sorry for whoever inherits your reinvented-wheel codebase.
The various software I've written is small, fast, light, with minimal dependencies and it's easy to install because I deliver it as OS packages for the operating systems I support; my users tell me they are happy. The memory requirements are miniscule and the software lightning fast. Its size is measured in kilobytes, not megabytes or gigabytes, which means I must be doing something right. The manual pages often exceed the software in size and are brimming with examples. I pay extra attention to being backwards compatible when I implement changes and enhancements. Regressions are non-existent.
So I know I'm right and that using a bloated "framework" would have been one of the stupidest things I could have ever done.
As for re-inventing wheels, I use what comes with the OS and leverage what's already there; I've purposely not implemented any algorithm re-implementations of my own, although I easily could have. I'm neither dumb nor stupid to go re-inventing wheels, in fact that's one of the reasons why I hate webshits' frameworks. They don't call them webshit for no reason.
While previously Shiny was primarily deployed through RStudio's solutions, there are now open source initiatives such as ShinyProxy, introducing Kubernetes as an option for deploying Shiny applications. The latest iterations of Shiny related libraries are facilitating automated testing and deployment. These developments allow companies to use Shiny in production, but it has to be said that the R ecosystem is not as developed as Python's from a traditional software development perspective.