Day 71 of 180 Days of Data Viz #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.

Went on vacation and took a needed break.  Several months ago, I got a job in NYC in this area and started this cycle of learning.  Moved to NYC and started new job.  It’s been quite a trip, so I took some needed R&R and feel refreshed and ready to work it.

Again, priorities are a bit back to basics on Front-End Web Dev vis a vis D3.  I’ve been going all in on D3 only to realize that I need these other skills that I’ve never really developed to make it all work.

Code Learning:

Understanding the Force

Three Takeaways:

  • “What the Force layout is really good at: offering insights on the relationships between connections. It helps us answer questions like: which of my friends know each other, and how do they know each other? How do the successful in Hollywood work with each other, and how often do they work together? How are Youtube stars interconnected?”
  • Force-directed graph drawing is a class of graph layout algorithms that calculate the positions of each node by simulating an attractive force between each pair of linked nodes, as well as a repulsive force between the nodes.”
  • “D3 implements the force-directed algorithm a little differently to give the user more control over the layout (from my understanding). It implements three primary forces upon the nodes at each tick:

    • The sum of the forces acting on each node by all other nodes
    • The force pushing and pulling between two linked nodes
    • The force pulling each node to a focal point, usually the center of the user-defined space”

Treehouse

Reading and Learning Data Visualization Theoretically/Critically:

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

p. 177-189 Multiple Variables

Three Takeaways

  • Remember about correlation – really big these days on back to basics and full understanding: “As one variable increases, the other one usually does too… Visualization-wise, a correlative and casual relationship between two variables will look similar, if not the same, but the latter usually requires rigorous statistical analysis and context from subject experts.” p. 177-178
  • You can double up on encodings, eg use both size and color, to create “redundant visual cues” for those data points that might be challenging to see with just one view.  Very helpful for scatter plots. p 180
  • Positive correlation: lines run parallel.   Negative correlation: lines cross consistently.  Weak correlation: lines have no clear direction. p 185
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