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
NN/A moving tomorrow packed all day
Reading and Learning Data Visualization Theoretically/Critically:
- 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.”
- 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.