Day 134 of 180 Days of Data Viz Learning #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.

*It’s often easier to solve many D3 problems going straight to documentation, at least for the use cases I’ve been working on, than Googling. Get in the habit of knowing if you need to look at Scale documentation for instance and getting more comfortable with that.

Coding Learning:

Introducing D3 Scale 

Three Takeaways

  • Remember conceptually “a scale is a function that takes an abstract value of data, such as the mass of a diamond in carats, and returns a visual value such as the horizontal position of a dot in pixels. D3’s quantitative scales are functions configured by two intervals. The input domain is an interval in the abstract dimension of data, often the extent of the observed values. The output range is an interval in the visual variable, such as the visible area defined by the chart size.”
  • There are scales for ordinal and categorical data. The band scale, for instance, simplifies the calculation of bar widths and positions, allowing configurable padding, alignment and rounding.
  • Scales can interpolate symbol sizes, font sizes, stroke widths, colors in various color spaces, geometric transforms, shapes and even deeply-nested objects.

    Parsing Raw Data from PGBovine

Three Takeaways

  • “The most important tip: assert the hell out of your parsing code. Write down every assumption you have about the data’s format in the form of assertions, then revise those assertions as you find out which parts of the data inevitably violate them.” Follow this later with a verifier program to check integrity of cleaned data.
  • Use sets or counter data structure to store the occurrences of categorical variables.
  • Have program print to see where parsing failed and to be able to pass a parameter to specify a starting point so you don’t have to restart at beginning.

Reading and Learning Data Visualization Theoretically/Critically:

The Functional Art: An Introduction to Information Graphics and Visualization by Alberto Cairo

p 114-123 – Visualizing for the Mind

Three Takeaways

  • Proximity in Gestalt Principles “notes that objects that are close to each other tend to be perceived as natural groups.”  “Identical objects as well will be perceived as belonging to a group”p. 114-115
  • “Continuity is better perceived in curves than in lines with sharp angles.” p 117
  • On a note when deploying perceptual tasks (in order of accuracy Position along common scale, position along nonaligned scales, “Length, Direction, Angle, Area, Volume, Curvature, Shading, Color Saturation” p 120): “The important criterion for a graph is not simply how fast we can see a result; rather it is whether through the use of the graph we can see something that would have been harder to see otherwise or that could not have been seen at all” p 123
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