Day 14 of 180 Days of Data Viz Learning #jfdi

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.

Moving to New York tomorrow but still got some reading in to stay in step.

Note: As I’ve become more and more immersed in the Data Viz world, the more I’m more easily able to understand different viz because I can see the intent behind them more and more, even the bad ones.

I’ll need to keep mindful of this to make sure my broader audience at work will not have the same eye and to make sure to keep a simple narrative encoded in the optimal way for my audiences, even if the viz gets fancy.

Visualization Worked On or Created: 

N/A till after the my move  – > 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.  Also, moving across the country this week.

Decomposition of a Visualization:

N/A moving tomorrow packed all day

Code Learning:

NN/A moving tomorrow packed all day

Reading and Learning Data Visualization Theoretically/Critically:

In praise of connected scatter plots

Three Takeways

  • Annotations when done clear and correctly add to the narrative and don’t clutter.
  • Connected scatter plots are boss for time series, animating them is even better.
  • “The doubt I have when seeing or designing graphics like these is if they are more effective than two stacked line charts, each encoding the change of a single variable across time. As a reader, I believe they are, as they don’t force my eyes to move back and forth between two different displays. But we could argue that, first, I am not an “average” reader of graphics and, second, that I don’t have any evidence to back up my hunch. Maybe this could be a good topic for a small research project.”

Introduction to Cycle Plots

Three Takeaways

  • A cycle plot (Cleveland, Dunn, and Terpenning, 1978) shows both the cycle or trend and the day-of-the-week or the month-of-the-year effect. Thus the cycle plot retains the strengths of both more common plots illustrated above without either of their weaknesses.
  • Very useful in comparing similar periods of seasonality.
  • Could be very useful in overlaying days of week, months, etc for digital marketing conversions.

 

Day 13 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.

Two days until my move!  Still reviewing a bit though to keep learning through repetition and discovery on the daily.

Visualization Worked On or Created: 

N/A till after the my move  – > 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.  Also, moving across the country this week.

Decomposition of a Visualization:

N/A till after my move

Code Learning:

Treehouse D3.js Adding Event Listeners to a D3.js Selection and Various Event Listeners Documentation

Three Takeaways

  • d3.behavior.zoom() – Behavior is used by containing SVG element -> event behavior is the then applied to the elements in the SVG. The containing SVG is actually where zoom events are going to register. Additionally, they store data to the d3 events global and apply whatever zoom functions we define.
  • .scaleExtent will limit how far we can zoom it and out and .on has many behaviors browser can register
  • When using geometric shapes to represent data, typical the data should correspond to the color of the shape and the area of the shape.

Reading and Learning Data Visualization Theoretically/Critically:

N/A till after my move