The Shiny Developer Series continues our spotlight on the COVID-19 Test Spotter application created by renowned data scientist and entrepreneur Mike Thomas! In this episode we learn how Mike implemented robust database techniques and logic to manage user-supplied data, navigating the use of multiple mapping APIs, and achieving a practical development-to-production strategy for releasing the application to the public. Plus we have a great discussion on what production means in the space of Shiny development, and much more.
Resources mentioned in the episode

COVID-19 At-Home Test Spotter (App) - ketchbrookanalytics.shinyapps.io/covid_test_spotter
COVID-19 At-Home Test Spotter (Code) - github.com/ketchbrookanalytics/covid_test_spotter
App blog post - www.ketchbrookanalytics.com/post/ketchbrook-analytics-launches-website-to-help-connecticut-residents-find-covid-19-test-kits
{googleWay} Shiny vignette - symbolixau.github.io/googleway/articles/googleway-vignette.html#shiny
{mongolite} user manual - jeroen.github.io/mongolite
{reactable} Interactive data tables for R - glin.github.io/reactable

Episode Timestamps
00:00:00 Episode Introduction

00:00:46 CRUD workflow and flowchart, with MongoDB database updates

00:09:41 Diving into the Google Maps and Places API

00:15:55 Geographic fencing, and dealing with unexpected user behavior.

00:22:35 Deploying with a dev and production branch.

00:28:15 Future plans for the app

00:30:50 Advice for building Shiny apps in production

00:31:20 Production apps is all about reproducibility: OS, dependencies, and the code.

00:33:15 Pay attention to warning messages in your console.

00:34:22 Modularization and Golem

00:38:08 You can have too much reactivity, such as overextending uiOutput() / renderUI()

00:40:10 Episode wrapup
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Episode 31: The Connecticut COVID-19 Test Spotter App (Part 2)