Day 7 of 180 Days of Data Viz Learning

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.

Definitely got into a good groove in starting earlier today and managing my energy right.  Getting a good pace in the first week of starting any new habit or process can be difficult, so I’m glad I’ve gotten there already.

Visualization Worked On or Created: 

USWNT Visualizations done yesterday.  Added a bit more narrative and styling to this.

-I started exploring pieces of an old project at I did for a Techniques of Data Visualization course I took in grad school.  This course was much less about code and more about critical visual theory, a healthy skepticism about data, and storytelling.  I put together a project on looking at how US demographics have changed through time.  I’m planning to beef this up into possible a portfolio type piece using visuals generated in Tableau and D3.js eventually.

Decomposition of a Visualization:

-Break – > code learning catch-up day

Code Learning:

Udacity Problem Set One Finished – On section 2a Design Principles

Three Takeaways:

  • Visual Encodings + Data Types + Relationship = Chart Types!
    eg. x coordinate and y coordinate with a shape of circle + two continuous variables + y value is dependent on x value = scatter plot
  • Position along an aligned scale is most accurate for human visual perception while color hue and color saturation are least accurate for quantified human visual perception.
  • Data visualization takes advantage of human perception because
    • Humans can process visual information in parallel (all at once) as opposed to taking in information serially, like when reading text word by word.
    • The human eye is excellent at identifying differences in placement and color.

Treehouse D3.js – Styling and Scaling with Data – Using Linear Scales

Three Takeways

  • In D3, scales are used to map a domain in your data to a visual range. It can be a pixel position, size, or color. Think of scales that have different methods as well as attributes.
  • return parseInt(element.TMAX) // returns a string, using d3 will check the position they appear in the array arther than value, parseInt checks for maximum numeric value. Using a + sign will also coerce into a string, but careful doesn’t work with strings
  • Scales can also take an inverted value takes in number in domain and spits back corresponding range value.Reading and Learning Data Visualization Theoretically/Critically:

Automating the Design of Graphical Presentations of Relational Information.Jock Mackinlay (PDF) from 7 Classic Foundational Vis Papers

Three Takeaways:

  • Thinking about how to make tables of relations, tuples, etc, dealing with functional dependencies, etc. and challenges how to encode and express those relationships in psuedo-wireframes.
  • How to formalize conventions by taking view that graphical presentations are actually sentences of graphical languages that have precise syntactic and semantic definitions.
  • Precise definitions of graphical conventions used to design and interpret information presentations make theorems possible for an engine.
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