Day 25 of 180 Days of Data Visualization 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.

Visualization Worked On or Created: 

This weekend I need to get started on some side project work with either Tableau or D3.js in making my own visuals or both with data I like, such as sports or something to do with social justice.  I’ve spent the past 24 days reviewing theoretical papers and industry best practices and getting my chops in with Tableau and D3.js as well as working with servers that will inform my work.  Now it’s time to create or else I won’t up my skills quickly enough.

Decomposition of a Visualization:

Japanese Earthquakes

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Earthquakes in point on map
      • Magnitude on y-Axis, date x-axis, bar chart
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Quantitative, Categorical
    • Encodings (Shape, Color, Position, etc.):
      • Size, Color Hue, Position on Map
  • What works well here?
    • Using the color to encode depth of earthquake.
    • Bar chart representation of skew of earthquake magnitudes.
  • What does not work well and what would I improve?
    • Not using the tooltip or annotations to call out major earthquakes or events is a missed opportunity here.  Filtering by depth and magnitude is also not utilized.
    • Sizes are too similar to see much difference on visual.
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Not clear though earthquake data is likely publicly available and reliable
  • Any other comments about what I learned?
    • Need to deploy all feature sets and tools at disposal whenever possible

Reading and Learning Data Visualization Theoretically/Critically:

Udacity Design Principles 2a

Three Takeaways:

  • The Grammar of Graphics Tufte 1990:
    Separate content from aesthetic, data from visual presentation as a distinction. Separately think about visual elements and structure of data. You can transform data without changing visual representation.  Allow for collaboration across teams.
    Common Elements
  • When thinking about creating a chart or graphic, it is often helpful to visually decompose what you want to achieve. In previous videos you saw how to abstract a chart into more basic visual encodings. In the map example, you saw that a choropleth is a combination of geography and color while a cartogram is a combination of geography and size. When talking about composable elements, a few of the most common are:
    • Coordinate System (cartesian vs. radial/polar)
      Scales (linear, logarithmic, etc.)
      Text annotations
      Shape (lines, circles, etc.)
      Data Types (Categorical, Continuous, etc.)
  • The beauty of the Grammar of Graphics surfaces when you combine these common components. For example, you can create a bar chart by mapping a value in the data to the height of the bar in cartesian space, but you can also can also map these values in polar coordinates, in which the data value corresponds to the radial degree of a slice, to get a pie chart.
    • Categorical + Continuous x Cartesian = Bar Chart
      Categorical + Continuous x Polar = Pie Chart
      Continuous + Continuous x Cartesian = Scatter Chart

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