Shiny

Syntax	Description
shiny	Search all fields
title:shiny	Title only
date:>2025-01-01	After a date
(a OR b) AND c	Group with parens

Shiny in Production: Data Products at Scale Workshop - rstudio::conf(2019L)

What is the 2-day Shiny in Production Workshop? That’s a great question, I’m glad you asked. Shiny in Production | Data Products at Scale Workshop - 2 Days This two-day workshop will teach you the best practices that make production-quality Shiny application performance possible. Does 30,000 simultaneous users for a Shiny application sound challenging? Shiny is used all over the world to deliver interactive, visual data products from data science teams to internal and external audiences at scale. The course will not spend time on writing applications, but will focus on the open source and professional ecosystem around shiny that includes performance optimization, testing, and production deployment. This workshop relies on RStudio professional products for deployment. You should take this workshop if you are comfortable writing Shiny apps but are afraid for what will happen when people start looking at them. The workshop is taught by Sean Lopp, RStudio Solutions Engineer. Sean works with RStudio product teams and helps enterprise customers to realize the value of R and Shiny. Speakers: Sean Lopp and a who’s-who of RStudio Engineers, R-Admins, and Shiny Developers

Shiny Train-the-Trainer Workshop - rstudio::conf(2019L)

What is the 2-day Shiny Train-the-Trainer Workshop? That’s a great question, I’m glad you asked. Register at https://rstd.io/conf Learn more at https://rstd.io/conf-agenda Shiny Train-the-Trainer Certification Workshop - 2 Day Day 1 of the course will be co-taught by Mine Cetinkaya-Rundel and Garrett Grolemund, RStudio Data Scientists and Professional Educators. On Day 2, Mine will teach the Shiny track and Garrett will teach the Tidyverse track. This two-day workshop will equip you to teach R effectively. We will draw on RStudio’s experience teaching R to recommend tips for designing, teaching, and supporting short R courses. On Day 1 of the course, you will learn practical activities that you can use immediately to improve your presentation style, learning outcomes, and student engagement. You will leave the class with a cognitive model of learning that you can use to develop your own effective workshops or courses within your organization. The course will also cover how to use RStudio Cloud and its curriculum of tutorials to jump-start your own lessons. On Day 2 of the course, participants will have the option to choose one of two tracks: Teaching the Tidyverse or Teaching Shiny. Teaching Shiny: Classroom examples will focus on teaching Shiny at the beginner and intermediate levels. The course materials will build on RStudio’s Mastering Shiny workshop as well as the upcoming book from the author of the Shiny package, Joe Cheng, and they will cover the entire lifecycle of a Shiny app: build ️ improving ️ share. Participants will receive the course materials for teaching Mastering Shiny. You should take this workshop if you work as a training partner and want to qualify as an RStudio Certified Shiny Instructor or if you are an advocate for R in your organization. You should be proficient in Shiny already and be prepared to submit examples of your work. Prior teaching experience is helpful, but not required. Please bring a laptop and a device that has video recording capabilities (such as a laptop or cell phone). Instructors: Garrett Grolemund, Mine Çetinkaya-Rundel

Joe Cheng, Mine Çetinkaya-Rundel

Interacting with Databases by Bárbara Borges from Shiny from useR! Brussels 2017

Connecting to an external database from R can be challenging. This is made worse when you need to interact with a database from a live Shiny application. To demystify this process, I’ll do two things. First, I’ll talk about best practices when connecting to a database from Shiny. There are three important packages that help you with this and I’ll weave them into this part of the talk. The DBI package does a great job of standardizing how to establish a connection, execute safe queries using SQL (goodbye SQL injections!) and close the connection. The dplyr package builds on top of this to make even easier to connect to databases and extract data, since it allows users to query the database using regular dplyr syntax in R (no SQL knowledge necessary). Yet a third package, pool, exists to help you when using databases in Shiny applications, by taking care of connection management, and often resulting in better performance. Second, I’ll demo these concepts in practice by showing how we can connect to a database from Shiny to create a CRUD application. I will show the application running and point out specific parts of the code (which will be publicly available)