Day 63 of 180 Days of Data Viz Learning #jfdi

I’m doing some form of data visualization learning for 180 days because I need to #JFDI.  See post explaining how and why I’m doing this.

Decomposition of a Visualization:

Vox US GDP Comparison Time

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • A-axis time
      • Area graph slider for GDP change through time
      • Countries by globes with map symbol
      • Individual country GDP metadata in tooltip
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Qualitative, Continuous
    • Encodings (Shape, Color, Position, etc.):
      • Position, Shape, Symbol
  • What works well here?
    • Showing trends through time – > eg seeing effect of dip in US GDP during depression
  • What does not work well and what would I improve?
    • Hard to tell what countries are what other than tooltip
    • Would be more compelling if certain events in history were annotated, eg depression, wars
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Gapminder
  • Any other comments about what I learned?
    • Really clever way of using animation and symbols

Code Learning:

Tableau Javascript API Tutorial

I’ve actually watched all the videos in this series on the Tableau Training site, which are probably some of the most well-done training videos I have.  This interactive tutorial is a nice brush-up for me and is better organizing than their docs for implementing work – in my opinion.

Three Takeways:

  • There are three categories of functions to select/interact with views, filter functions, switching tabs, and selecting values.  The distinction is important for development and not that clear upfront (at least it wasn’t to me).
  • Methods generally different between different even handlers that take in single versus multiple values
  • Filter
    • REPLACE method to only show one filter value
    • ADD allows multiple filter criteria
    • REMOVE clears values from filter

Reading and Learning Data Visualization Theoretically/Critically:

Reading Nathan Yau’s book Data Points – Visualization that Means Something

p. 68-87 Entertainment, Data Art, The Everyday

Three Takeaways

  • Nathan Yau quoting Amanda Cox “All of the great chartmakers make me feel something: alarm, wonder, surprise, joy … something.  Even, I think you might argue in the case of something like dashboard design, calm.” p. 69
    • I want the BI dashboard work I do to make people feel calm
  • Great example of drawing out simpe visual with humorous expression: p. 71
  • Data art is a way to allow yourself to “immerse yourself in the data, which is both personal and easy to relate to” p. 81

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