Data Viz

Considerations and Practical Tips

 

 

Dr. Kostelac and Branden DuPont

Medical College of Wisconsin

What is data visualization and how is it used?

  • "...anything that converts data sources into visual representation" (Duke University Libraries)
  • “Data visualization is a way to represent information graphically, highlighting patterns and trends in data and helping the reader to achieve quick insights.” Gartner Glossary
  • How is it used?

What do we mean by data visualization?

  • It is both the "what" and the "how"
  • What: Think charts, graphs, maps, tables
    • but there are lots of permutations of each of these
    • often have a spatial and/or temporal component
    • sometimes they work best in combination
  • How: Dashboards, infographics, reports, static web pages, presentations, etc.
  • Depends on the data, purpose, audience, questions you are trying to answer and other factors

Examples

What are some of the challenges and considerations?

  • You need to get to know the data
    • Cleaning, preparing, and understanding are the hardest parts
    • Documentation often is lacking or limited depending on the data source
       
  • Documentation and notes are important!
     
  • Considerations for the sensitivity of data
     
  • Lots of decisions to make along the way
     
  • Sometimes simple is better…

Tool options discussion and considerations

  • Cost
  • Frequency and process to update
  • Security
  • Sustainability
  • Ability to hire people with the skillset
  • Ease of use
  • Functionality and flexibility
  • Others

What Type of Data Viz Project Do I Have?

Which Type of Data Visualization Project Do I Have?

  • Exploratory
    • provide multiple analyses
    • filters for various perspectives
    • question is open ended
    • more interactivity
  • Explanatory
    • explore and understand an analysis
    • similar to a policy brief
    • question is discrete
    • less interactivity
    • annotation!

Susie Lu:

Explanatory vs Exploratory

 

How Do I Choose the Right Data Viz?

  • Good visualization is difficult, complex, and takes practice
  • Good place to start: FT Visual Vocabulary
  • Most of these can be made in standard viz tools
  • Collect examples you like (Washington Post, ProPublica, Urban Institute, Flowing Data)

Example: Line Chart

  • great for telling a story
  • making comparisons between groups over time

How to Improve Chart Design?

Elijah Meeks

 

  • Don't use the chart defaults, be intentional about design
  • Use annotations whenever possible -- even more than interactivity
  • Chart's meaning should be clear at a glance. Highlight or add narrative to key insights.
  • When appropriate add elements like
    • data source
    • contextual notes about the data
    • last chart update
    • who made the chart

Paul Krugman is Bad at Viz

 

 

https://twitter.com/paulkrugman/status/1305237645459628044?lang=en

How to Use Color Effectively?

Lisa Charlotte Ross

  • Color in data visualization is difficult
  • Best advice is to read several blog posts by Lisa Charlotte Ross
    • Explain what your colors encode
    • Grey/Black is the most important color
    • Use the same color for the same variables when appropriate
    • No more than 7 colors: 2 to 3 is ideal

Practical Examples?

Questions?

Tools

Copy of Build a Data Viz Project

By Branden DuPont

Copy of Build a Data Viz Project

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