Day 154 of 180 Days of Data Viz Learning #jfdi #doneisbetterthanperfect

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.

Code Learning:
Chapter 6 Networks
  • Repulsion: nodes pushing away from each other.  Defined using .charge()
  • Canvas Gravity: nodes pulled toward layout center to keep interplay of forces from pushing them out of sight.  Defined using .gravity()
  • Attraction: Nodes that are connected to each other are pulled toward each other.  Sometimes, this force is based on the strength of connection, so that more strongly connected nodes are closer.  Defined using .linkDistance() and .linkStrength() p 186

Reading and Learning Data Visualization Theoretically/Critically:

Edward Tufte Visual Display of Quantitative Information

Chapter 1

Three Takeaways

  • “Graphics reveal data.  Indeed graphics can be more precise and revealing than conventional statistical computations” p 13
  • Don’t underestimate the use of comparative choropleths with existing knowledge to solve issues – eg examples of cancer clusters around certain geographic industries p19
  • But of course look for flaws “regional clustering seen on the maps, as well as some of the hot spots, may reflect varying diagnostic customs and fads along with actual differences in cancer rates between areas” p 20.  Maps and other visuals can be so straightfoward people don’t question more complex underlying issues.

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