Day 2 of 180 Days of Data Viz Learning

I’m learning data visualization for 180 days straight because I need to JFDI.  See previous post.

Visualization Worked On or Created:

As a fan of soccer who is continuing to celebrate the USWNT victory at the World Cup (woo hoo Title IX is the gift the keeps on giving!), I pulled roster information from the USWNT website by going into the table view and extracting it into Excel.  I did some basic data clean-up so I could have values I wanted to work with, just some text-to-columns and formatting values in Excel.

I have Tableau for the next 12 days or so (finished grad school, so that student trial is nearly over), so some of the visualizations and pure investigation and playing with color schemes, different displays, etc. will come from there while I get my D3.js chops.

I experimented with colors and different items I could create with it.  Tomorrow, I’ll put in observations and summaries and more graphics into a webpage form.  Basically, Abby Wambach is a beast.  A few visualizations below:

caps_goals club_summary goals_per_player homestate_treemap

Decomposition of a Visualization:

Time Labs: Find Out If Your State Is America’s Past or Future

  • What are the:
    • Variables:
      • Year on state (shape), Year on y-position in bar chart, Demo % on x-position on bar chart,
    • Data Types:
      • Population projection year on map, Present demographics of state on bar chart, Future US 2060 projected demographics on bar chart
    • Encodings:
      • Color hue, Color saturation, Shape
  • What works well here?
    • Attempt at what appears to be a duel-ordered color palette on map to show difference between past and future.  Restraint on not using a hover over and having the widget under the map instead.
  • What does not work well and what would I improve?
    • Bar chart x-values do not line up with color shades, using the same color palette as that map that represents something else entirely on the bar charts is confusing.
  • What is the data source?  Do I see any problems with how it’s cited/used?
  • Any other comments about what I learned?
    • Color choice impacts understandability and ease of use.

Code Learning:

Udacity 1b D3 Building Blocks – section in progress

Three Takeaways:

  • We use D3 to create, remove, and manipulate DOM nodes programmatically.
  • Most methods defined in D3 return whatever object they were called on.
  • Generally learning how to use the Console to access and manipulate DOM elements

Reading and Learning Data Visualization Theoretically/Critically:

Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. William S. Cleveland; Robert McGill (PDF) from 7 Classic Foundational Vis Papers

Three Takeaways:

    • Ordering the Elementary Perceptual Tasks by the Accuracy of Extraction
      • Position along a common scale
      • Positions along non-aligned scales
      • Length, direction, angle
      • Area
      • Volume, curvature
      • Shading, color saturation
  1. Bar charts, divided bar charts, pie charts, statistical maps with shading can often be replaced with dot charts, dot charts with grouping, and framed rectangle charts depending on application based on these learnings.
  2. Weber’s Law “The law states that the change in a stimulus that will be just noticeable is a constant ratio of the original stimulus. It has been shown not to hold for extremes of stimulation.”-> eg. a framed rectangle increases accuracy

Encoding Values in a Graph 

Three Takeaways:

  1. When points alone are superior to lines or bars: When scales on both axes are quantitative.  Scatter plots include two quantitative scales because they are specifically designed to display the correlation (or lack of one) between two sets of measures
  2. Points alone are never a good option for encoding a series of values (time-series data) – use a line.  Lines alone display the overall shape of data as it changes through time more clearly than any other encoding method.
  3. When you wish to help your audience focus on individual values and compare individual values to one another, bars are ideal. This works especially well for discrete values that are not intimately connected to one another. Bars can be used, however, to encode values along a series of intimately connected values, such as a time series, when you’re less concerned about showing the overall shape of the values (which a line would do better) and more concerned about helping people examine and compare individual values.
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One thought on “Day 2 of 180 Days of Data Viz Learning

  1. Pingback: Day 3 of 180 Days of Data Viz Learning | Thoughts on learning and work

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