Mastering Shiny

Chapter 4. Case Study: ER Injuries

Introduction

I’ve introduced you to a bunch of new concepts in the last three chapters. So to help them sink in, we’ll now walk through a richer Shiny app that explores a fun dataset and pulls together many of the ideas that you’ve seen so far. We’ll start by doing a little data analysis outside of Shiny, then turn it into an app, starting simply, then progressively layering on more detail.

In this chapter, we’ll supplement Shiny with vroom (for fast file reading) and the tidyverse (for general data analysis):

library(shiny)
library(vroom)
library(tidyverse)

The Data

We’re going to explore data from the National Electronic Injury Surveillance System (NEISS), collected by the Consumer Product Safety Commission. This is a long-term study that records all accidents seen in a representative sample of hospitals in the United States. It’s an interesting dataset to explore because everyone is already familiar with the domain, and each observation is accompanied by a short narrative that explains how the accident occurred. You can find out more about this dataset on GitHub.

In this chapter, I’m going to focus on just the data from 2017. This keeps the data small enough (~10 MB) that it’s easy to store in Git (along with the rest of the book), which means we don’t need to think about sophisticated strategies for importing the data quickly (we’ll come back to those later in the book). You can see the code I used to create the extract for this chapter ...