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Digital Health

Introduction to Digital Health for Global Public Health Master students with a practical focus on web technologies, programming, and data visualization.

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A. Computers, Networking, and the Web

Required Reading: https://ebookcentral.proquest.com/lib/th-deggendorf/detail.action?docID=7275452

1. Digging Deeper - Understanding your machine

• Interactive: The Status Quo and Future Outlook of Digital Health around the world

• Hands-on: Chapter 1 and 2

• Preparing a Simple Website with HTML and CSS

• Uploading your Site to Github

• More on the Git Protocol: https://twitter.com/alexxubyte/status/1708145139515109449

2. Networking - The web and its infrastructure

• Interactive: Servers, Clients, IPs and Protocols of the Internet
  • Tech Concepts Visualized: https://twitter.com/alexxubyte
  • URL/URI/URN: https://twitter.com/alexxubyte/status/1695825224019943714/photo/1
  • Request/Response on the Web: https://twitter.com/alexxubyte/status/1711406474654814575
  • HTTP Status Codes: https://twitter.com/alexxubyte/status/1712487706859905137/photo/1
  • HTTPS: https://twitter.com/bytebytego/status/1715973551235510532/photo/1
  • TCP/IP: https://twitter.com/bagder/status/1716693365234798909/photo/1
  • Advanced Network Protocols: https://twitter.com/alexxubyte/status/1708863540067696878
• Hands-on:
  • Extending the Webpage Chapter 3-6 - Additonal Elements, Lists, Links, Tables, Images
  • Serving your page using R Shiny

3. Presentation - Cascading Stylesheets (CSS)

Hands-on: Cascading Stylesheets - Chapters 7-10

4. Responsive and Interactive Websites

• Hands-on: Chapter 10-11
• Tutorial: https://developer.mozilla.org/en-US/docs/Web/CSS/CSS_grid_layout/Realizing_common_layouts_using_grids

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B. Data Analytics and Cleaning with R

Excercise:

• Applied EPI Tutorial

Reading:

• R Basics
• Tidyverse

1. Intro to RStudio & R Data Types (Module 1)

Prepare:

• R Basics:

Lecture:

• Data Types and Functions in R
• Vectors and Lists

2. Project Setup & Tidy Data (Module 2)

Prepare:

• Preparation Tutorial

Lecture:

• Import and Export

3. Cleaning Data with R (Module 3+4)

Prepare:

• Cleaning Tutorial

Lecture:

• Cleaning Data and Core Functions
• Working with Dates
• Characters and Strings
• Factors

4. Group Data (Module 5)

Lecture:

• Grouping Data

5. Joining and Pivoting Data (Module 7 - optional)

Lecture:

• Pivoting Data
• Joining Data
• De-Duplication

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C. Interactive Visualization with Shiny and ggplot

1. Visualizing Data with ggplot (Module 6)

Prepare:

• GGPlot Tutorial

Lecture:

• Tables for Presentation
• GGPlot Basics
• GGPlot Tips

Optional:

• RMarkdown
• Dashboard with RMarkdown

2. Creating Interactive Visualizations with Shiny

Lecture:

• Dashboards with Shiny

3. Theming Shiny Applications with HTML and CSS

• Let's give the last touch ups to our projects!

Exam

• Analyze: Take a dataset of your choice. Clean it, analyzeit and visualize it.
• Contextualize: Describe your key user persona and their aspired actions and targets
• Implement: Produce an interactive Data Visualization using Shiny

Submission:

• 2 pages reporting on the Context of your Visualizaton
• R Files necessary to build the visualization
• Data Files
• Link to you Github Repo
• Link to your Shiny Application
• renv.lock file with all your dependencies (See: https://rstudio.github.io/renv/articles/collaborating.html)

Limitations: You can upload a maximum of 20 files with 30M each during the final exam. If your dataset is larger than 30M please only upload a subset of it.

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Further Resources

• https://mastering-shiny.org/
• https://r4epis.netlify.app/