Day 10 of 180 Days of Data Viz #jdfi

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.

A note on reviewing and repetition today, I’ve really discovered the joy of writing and how well it helps me absorb information.  I know this from writing coding exercises and learning languages.  But five years of working in Silicon Valley and two years of grad school later, I’d become hopelessly dependent on typing everything, which is great for most activities.  However, writing forces me to slow down to absorb concepts rather than just copy.  Some chemistry definitely happens.  Here’s what the Scientific American has to say about that.  So much return of analog lately.

Visualization Worked On or Created: 

N/A Today  – > Focusing on the completing tutorial/exploration work (finishing Scott Murray’s book, D3.js in Udacity in Treehouse)  that will be more mentally taxing when I’m working full-time.

Decomposition of a Visualization:

Cyberwar, Visualized

  • What are the:
    • Variables (Data points, where they are, and how they’re represented):
      • Type of attack: Color Hue,  Attack Origins on x-axis by Symbol, Attack Types on x-axis by color Hue and Shape, Attack Targets on x-aaxis by Smybol, Live Attacks on x-Axis with Colr Hue
    • Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
      • Qualitative, Categorical
    • Encodings (Shape, Color, Position): Position, Color Hue, Size Animation
  • What works well here?
    • Showing frequency and origin and target of attacks.  Really tells a story of how what is happening.
  • What does not work well and what would I improve?
    •  The “explosion rings” don’t seem to add value and are a bit visually confusing.  Hard to keep track of types of attacks just based on color – the key isn’t visible/clear on the actual viz itself.
  • What is the data source?  Do I see any problems with how it’s cited/used?
    • Norse Corp.,”a company dedicated to monitoring and providing intelligence on global cyber warfare, the Attack Map shows in real-time all the cyber attacks currently happening on the Norse network: where the attacks originate, where they’re going, what kind of attacks they are, and so on.” – Fast Company Article description.  Obviously at the mercy of their detection systems and methodology, so we’re just getting the tip of the iceberg of what’s happening.
  • Any other comments about what I learned?
    • I like this concept of presenting something that’s happening largely invisibly to something hammering in the idea that cyberattack are indeed attacks and can often have warlike inplications.

Code Learning:

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

Three Takeaways

  • Negative/White Space -> developed around between shapes and forms of objects
  • The Bloom Cholera map and others demonstrates the use of white/negative space in maps to designate meaning.
  • Careful use of colors if they have no meaning, they’re distracting.  Everything from Excel to ggplot2 have default colors that aren’t useful.

Treehouse Adding Axes to Visualization

Three Takeaways

  • Using the .call() method for elegance
  • Transform rotate attribute to rotate around relative origin
  • Give visualization a buffer and multiple minimums and maximums of domain values by constant to give a longer axis

Three Takeaways

Aligned Left

  • You see, when chaining methods together, anytime after you call data(), you can create an anonymous function that accepts d as input. The magical data() method ensures that dis set to the corresponding value in your original data set, given the current element at hand.
  • How to think about functions in D3.js “Think of d as a lonely little placeholder value that just needs a warm, containing hug from a kind, caring function’s parantheses. (Extending this metaphor further, yes, it is creepy that the hug is being given by an anonymousfunction — stranger danger! — but that only confuses matters.)”
  • Note that an element’s class is stored as an HTML attribute. The class, in turn, is used to reference a CSS style rule. This may cause some confusion because there is a difference between setting a class (from which styles are inferred) and applying a style directly to an element. You can do both with D3. Although you should use whatever approach makes the most sense to you, I recommend using classes for properties that are shared by multiple elements, and applying style rules directly only when deviating from the norm. (In fact, that’s what we’ll do in just a moment.)

Reading and Learning Data Visualization Theoretically/Critically:

When Should I Use Logarithmic Scales in My Charts and Graphs?

Three Takeaways 

  • Use logarithmic scales to respond to skewness towards large values in cases which one or a few data points are much larger than the bulk of that data or to show percent change or multiplicative factors
  • Dot plots > bar charts for log data since bar charts, because they are judged by the length of the bar, will have a distorted meaning.
  • Can show values of change over time better than absolute numbers.

Bullet Graph Design Specification

Three Takeaways

  • Specifically, bullet graphs support the comparison of the featured measure to one or more related measures (for example, a target or the same measure at some point in the past, such as a year ago) and relate the featured measure to defined quantitative ranges that declare its qualitative state (for example, good, satisfactory, and poor). Its linear design not only gives it a small footprint, but also supports more efficient reading than radial meters.
  • Screen Shot 2015-07-26 at 2.50.32 PM
    • Text label
      • • A quantitative scale along a single linear axis
      • • The featured measure
      • • One or two comparative measures (optional)
      • • From two to five ranges along the quantitative scale to declare the featured measure’s qualitative state (optional)
  • Add color hues and saturation to the richness of using this tool.
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