Summary and Schedule
This is a new lesson built with The Carpentries Workbench.
R Shiny is a popular R package that allows you to transform complex analytics into powerful, user-friendly interactive visualisation tools.
This lesson is designed to provide an introduction to R Shiny and demonstrate how you can use it to create web apps and more interactive data visualisations. It is derived from the Carpentries Incubator R Shiny Geospatial lesson and rewritten for QCIF delivery.
Carpentry’s teaching is hands-on, so participants are encouraged to use their own computers to ensure the proper setup of tools for an efficient workflow. To most effectively use these materials, please make sure to download the data and install everything before working through this lesson.
R Skill Level
This lesson assumes you have prior knowledge of R and
experience with RStudio. If you’ve never used
R or RStudio before, or need a refresher,
start with R
for Reproducible Scientific Analysis.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction to Shiny |
What is a Shiny
App? :::::::::::::::::::::::::::::::::::::::::::::::: |
| Duration: 00h 35m | 2. The Basics of a User Interface |
How do we build a user interface in Shiny? How do we arrange inputs and outputs on a page? :::::::::::::::::::::::::::::::::::::::::::::::: |
| Duration: 01h 10m | 3. Inputs and Outputs |
How can users interact with a Shiny app? How do user inputs control what is displayed in the app? :::::::::::::::::::::::::::::::::::::::::::::::: |
| Duration: 03h 00m | 4. Organising Server Logic with Reactive Expressions |
Why does placing all logic inside rendering functions become
limiting? How can we reuse server logic across multiple outputs in a Shiny app? |
| Duration: 03h 40m | 5. Making Shiny Apps Public | How do I show the world my Shiny apps? |
| Duration: 04h 15m | 6. Bonus: Extending your Shiny App |
How can we extend a Shiny app with additional inputs and
outputs? How can we adapt an existing app to explore different data? |
| Duration: 04h 50m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Data Sets
The csv file used in this lesson lesson was generated from data from an Employment Projection Model: A set of employment forecasts which reflect the Brisbane City Council view of the likely SEQ Regional Employment patterns in the period between 2011 and 2041. They were prepared by the National Institute of Economic and Industrial Research.
We chose two of the datasets “Occupation employment by usual resident employment” and “Industry employment by usual resident employment” and retained only the variables describing region (SA4_Name), suburb (SA2_Name), occupation/industry (Name_2), and projected employment in 2041 (SC2_2041). To create a smaller dataset suitable for teaching, the top three occupations and industries within each region-suburb combination were selected and included in the final dataset.
Software Setup
This lesson assumes you have R and RStudio
installed on your computer.
- Download and install the latest version of R.
- Download and install RStudio.
- Install R Packages: