The data source is the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. forecast x days following the last day of the time series and show prediction intervals.process the raw data: what kinds of cases (confirmed/deaths) to consider and what should be the last day of the time series,.get the data from a daily updated web interface (JSON API),.

pick a country (the available slugified country codes are explained in the source file),.Now install/load some packages (forecast, jsonlite, ggplot2, and plotly), source the functions.R file.

You can find all the R code associated with this post in this GitHub repository:ĭownload or clone the repository and open the 01-workflow directory. There are countless dashboards out there showing case counts in space and time. In our world today, COVID-19 data needs no introduction.

In this post, you will learn about different options. Dependency management is one of the most important aspects of app development with Docker. For anything that is a little bit more complex, you will have to manage dependencies. We have reviewed Docker basics and how to dockerize a very simple Shiny app. You can turn your code into an interactive web app with not much extra code once you have a workflow and an interesting question. What makes programming languages like R and Python great for making data applications is the wealth of contributed extension packages that supercharge app development. Learn about dependency management when working with R and Docker. This also means that you have to manage these dependencies. The wealth of contributed R packages can supercharge Shiny app development.

Shiny package r