Day 1 of 180 Days of Data Viz Learning
I’m learning data visualization for 180 days straight because I need to JFDI. See previous post.
Visualization Worked On or Created:
- Got to Hello world using Jerome Cukiers Getting to “Hello world” with D3
- Three Takeaways:
- Basic file structure: HTML, CSS, JS, data files working together with D3
- Understanding how D3 binds to DOM
- Thinking about how I will need to play ahead with files for larger projects
Decomposition of Visualization:
I do more than one of these a day on a notebook, but I want to make sure I share one as well that forces me to make more thought out assessment than my scribbled notes. I do write my decompositions initially because writing helps me learn more actively.
- What are the:
- Variables: Food type in x-position, City on map, % of hashtagged photos on Instagram on map
- Data Types: Quantitative # of Instagram photos of a hashtag
- Encodings: Retinal variables (size), Opacity, Area
- What works well here? One use to color on the maps and consistent locations.
- What does not work well and what would I improve? Size overwhelms some of the smaller data points.
- What is the data source? Do I see any problems with how it’s cited/used?
- Any other comments about what I learned? Extra commentary on each food makes for a much better narrative and user experience.
Done Already before 180 Days:
- Started Treehouse D3.js
D3.js Treehouse Intro to JSON
- Primitives, Objects, and Arrays stored in JSON
- Operations on Array
Data Visualization and D3.js on Udacity
- Finished 1a Visualization Fundamentals up to 1b D3 Building Blocks
- Three Important Takeaways:
- Visual Encoding: Order of effectiveness: Position, Length, Angle, Slope, Area, Volume, Color, Density
- Where D3.js lies in spectrum of tools
Reading and Learning Data Visualization Theoretically/Critically:
- “Whenever you’re tempted to add color to a data display, ask yourself these questions: “What purpose will this color serve?” and “Will it serve this purpose effectively?” If the answer is “It serves no useful purpose” or “It serves a purpose, but something other than color or this particular color would do the job better,” avoid using it.”
- Use soft, natural colors to display most information and bright and/or dark colors to highlight information that requires greater attention.
- Diverging palettes, which I prefer to call dual-ordered palettes, encode a range of quantitative values that are above or below some logical breakpoint in the middle. For example, a company’s profi ts can be displayed using a heatmap with a dual-ordered palette of colors: one set for profi ts and another for losses, with a zero midpoint.
Done previously to starting 180 days:
- Reading Data Viz Theory (eg. Nathan Yau’s Flowing Data) and doing three Data Decomposition Exercises daily