R Shiny vs Shiny for Python: What are the Key Differences | R-bloggers

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R Shiny vs Shiny for Python: What are the Key Differences
Posted on October 6, 2022 by Dario Radečić in R bloggers | 0 Comments
[This article was first published on Tag: r - Appsilon | Enterprise R Shiny Dashboards , and kindly contributed to R-bloggers ]. (You can report issue about the content on this page here )
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If you haven’t been living under a rock for the past couple of weeks, you’ve likely noticed some groundbreaking news in the Shiny department. Yes, it’s finally available for Python ! But how is the current Shiny for Python version? How does R Shiny compare vs Shiny for Python (PyShiny)? We have the answers, so continue reading.
Shiny for Python is still a work in progress, but you can already build some great PyShiny demos !
Today we’ll go over a detailed R Shiny vs. Shiny for Python comparison in 4 key areas – startup code, UI elements, server code/reactivity, and dashboard styling. Each key area will demonstrate code and end-result differences for both programming languages.
At the time of writing this post, Shiny for Python is still in alpha. The Shiny for Python ecosystem is quite limited at the moment, so if you’re looking for enterprise-grade Shiny apps, skip this article and go straight to R Shiny for your enterprise needs . But with that being said, there’s still quite a lot you can do with PyShiny – let’s find out below.
Table of contents:
Summary of R Shiny vs. Shiny for Python
Boilerplate Code Needed to Run the App
So, what is a boilerplate code? Put simply, it’s the code that every app/dashboard has in common. In Shiny, it usually boils down to library imports, UI and server declaration, and their connection in a Shiny app.
Python and R have different views on best programming practices. In R, you import a package and have all the methods available instantly. In Python, you usually import required modules of a library and then call the methods with
.
syntax.
Want a Shiny app fast? Try Appsilon’s Shiny templates and have an R Shiny dashboard in less than 10 minutes .
To be fair, you can make Python work like R by using the from
import *
syntax, but it’s not recommended way. Avoid this approach at all costs.
Let’s now take a look at the amount of code needed to write the most basic R Shiny app that renders one heading element:
library(shiny) ui