Shiny is recommended for data scientists, statisticians, and R programmers who want to create interactive web applications for data analysis and visualization. It is particularly useful for those who already have experience with R and are looking to share their findings or analyses interactively with others. It is also beneficial for educators and researchers who need to create accessible, web-based applications to demonstrate data-driven insights.
Based on our record, FastAPI should be more popular than Shiny. It has been mentiond 289 times since March 2021. 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.
FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.7+ based on standard Python type hints. We use it to build the web API for the image search. - Source: dev.to / 16 days ago
The backend of this accountability AI application is built using FastAPI, a high-performance framework for Python. The app allows users to communicate with the AI, which helps them stay accountable by generating responses based on their input. Below is a breakdown of the key components used in the code:. - Source: dev.to / 29 days ago
Until I realized Anki has their own API Interface, which made me realize that I can automate this task, efficiently, while also getting hand on experience using FastAPI and LLMs (but am I going to change the stack later?.. Lets see). - Source: dev.to / 26 days ago
By the end of this tutorial, you’ll see how I used FastAPI and Jinja2 to turn raw JSON into a dynamic timeline of weather and history that feels informative and fun. - Source: dev.to / about 1 month ago
The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by... - Source: dev.to / about 1 month ago
There is a lot of way and the most common is shiny (https://shiny.posit.co/) but with a biais towards data app. Not having a Django-like or others web stack python may have talks more about the users of R than the language per se. Its background was to replace S which was a proprietary statistics language not to enter competition with Perl used in CGI and early web. R is very powerful and is Lisp in disguise... - Source: Hacker News / 2 months ago
In R, you can build Single Page Applications with Shiny, created by Posit https://shiny.posit.co/ It is very useful, if you don't know HTML,JS,CSS and want to create an interactive dashboard, showcasing your analysis, models, visualizations, or even to create an internal tool for your organization. It seems that reactR provides functions for building react components directly from R that can be used in Shiny apps. - Source: Hacker News / 9 months 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 / over 1 year 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 / almost 2 years 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 2 years ago
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