I’m doing some form of data visualization learning for 180 days because I need to #JFDI.
Guess what? It took longer than 180 days, but it’s been a pretty cool journey. Did my daily learning will post a debrief early next week. This has been quite the intellectual and emotional exercise for me. Learned so much about data viz + more.
- One key with generators eg. d3.svg.arc is that they have particular settings p 144
- “One of the core uses of a layout in D3 is to update the graphical chart. All we need to do is make changes to the data or layout and then rebind the data to the existing graphical elements” p 146
- If transitions are distorted because of default data-binding keys, you may need to change sorts, eg pieChart.sort(null); in conjunction with exit and update behavior p 147
- Layouts in D3 expect a default representation of data, usually a JSON object array where the child elements in a hierarchy are stored in child attribute that points to an array. p 149
- You can either data munge or get use to using accessor functions p 149
- Pack layouts have a built-in .padding() function to adjust spacing and a .value() function to size out spacing and influence size of parent nodes p 151
Reading and Learning Data Visualization Theoretically/Critically:
- “Our perception of space is primarily two dimension. We perceive differences in vertical position (up and down) and in horizontal position (left and right) clearly and accurately. We also perceive a third dimension, depth, but not nearly as well.” p 71
- “We perceive hues only as categorically different, not quantitatively different; one hue is not more or less than another, they’re just different. In contrast, we perceive color intensity quantitatively, from low to high” p 71
- Both size and color intensity are not the best way to code quantitative values. The key is not good at matching a number to a relative size or color intensity -> use length or position if possible instead p 73