Day 23 of 180 Days of Data Viz Learning #jfdi

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

I did miss out on a day this weekend, but I was looking and hopefully getting an apartment today in NYC, so I think that will have been worth it.  It’s been a week now so I’m cutting back on the getting settled in slack and back to the learning grind.

Visualization Worked On or Created: 

Dashboarding at work.  All I can say I think but spend a lot of time figuring out things and coming up with new questions.

Decomposition of a Visualization:

California Revenue Sources

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Revenue y-axis, Revenue type x-axis, Year x-axis, % Rev Difference Time Series, Revenue amount bar chart
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Continuous, Qualitative
    • Encodings (Shape, Color, Position, etc.):
      • Shape, Position, Color Hue
  • What works well here?
    • One solid total bar and cut off bars extremely effective at showing proportional comparison.
  • What does not work well and what would I improve?
    • Time series chart a little too cramped to interact well with tooltip
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Doesn’t cite it, although CA Rev sources are verifiable publicly
  • Any other comments about what I learned?
    • Using a running sum across table to use bar charts to express pieces of a whole

Code Learning:


Reading and Learning Data Visualization Theoretically/Critically:

A survey of powerful visualization techniques, from the obvious to the obscure

Three Takeways:

  • An index chart is an interactive line chart that shows percentage changes for a collection of time-series data based on a selected index point.
  • By stacking area charts on top of each other, we arrive at a visual summation of time-series values—a stacked graph. This type of graph (sometimes called a stream graph) depicts aggregate patterns and often supports drill-down into a subset of individual series.
  • Small multiples: showing each series in its own chart.

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