In this lab, you’ll practice building interactive webpages using RShiny with COVID-19 case data. Rather than generating an .md file with your lab answers, your code will generate an RShiny application within an R Markdown document.

Learning goals

Reviewing reshaping data
Creating interactive tables using RShiny
Creating interactive figures using RShiny

Getting started

Each member of the team should:

Go to the course GitHub organization (or your team page) and locate your lab repo, which should be named lab-07-shiny-YOUR_TEAM_NAME.
Grab the URL of the repo, and clone it in RStudio by creating a new project from Version Control with Git.
Open the R Markdown document lab-07.Rmd and Knit it. Make sure it compiles without errors.

Warm up

Before we introduce the data, let’s warm up with some simple exercises.

Update the YAML of your R Markdown file with your team information and knit the document.
Commit your changes with a meaningful commit message.
Push your changes to GitHub.
Go to your repo on GitHub and confirm that your changes are visible in your Rmd and md files. If anything is missing, commit and push again.

Packages

We’ll use the tidyverse package for much of the data wrangling and visualization, and the shiny package to create our interactive data summaries and visualizations, and the DT package to render interactive tables.

library(tidyverse) 
library(shiny)
library(DT)

Data

We will be using a data set on COVID-19 cases. Read in the data set using the following command.

covid_cases <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")

Exercises

Examine the data.

A description of the data set was not provided, other than that it is “COVID-19” data. Navigate to the Github page where the data were obtained. Use the description of this Github page and an examination of the data you loaded to write a few sentences describing this data set. What information do these data provide the user? What are the observational units and variables? What dimension is the data set?

Reshape the data.

Convert the data set from wide to long format.

Create an Interactive Table.

Allow users to select the date to display in the table. Note the DT::renderDataTable might be useful (this is the renderDataTable function in the DT package).

Create an Interactive Figure.

Read in a US level COVID-19 dataset and convert this to a long dataframe.

US_data <- read_csv("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv")