I’m learning data visualization for 180 days straight because I need to #JFDI. See post explaining how and why I’m doing this.
Body/mind definitely responded well to the break-like day. Part of succeeding in self-study has a lot to do with just the right amount of pushing, like working out. You want to struggle to improve performance but not the point of injury. Same with the mind, you want to learn as much as possible and challenge yourself to put in the work but not to the point of burnout. As I get older, none of this gets easier. Hours mean less and efficiency mean more. I have to keep reminding myself of that. It’s easy to get bogged down in focusing on hours, which I’ve definitely found doesn’t necessarily correlate to results.
Visualization Worked On or Created:
USWNT Visualizations done and online. Plan to add more commentary and do a decomposition of those visuals tomorrow. More than anything, this is a reminder of what one of my old bosses told me about working in the digital advertising business in tech at large “It’s the easy things things that are hard.” Sometimes, getting a client creative signed off or just doing nitty gritty formatting can be more painful and time-consuming than the task at hand itself. In fact, the task at hand can be totally awesome – I’m super forwarding to doing the data clean-up, analysis, and visualization – but I definitely have man up things like re-sizing images, roadmapping, nomenclature, and working with HTML + CSS.
Decomposition of a Visualization:
The Things The World Fears Most, Ranked
- What are the:
- Variables (Data points, where they are, and how they’re represented):
- Countries: y-axis, Fears: x-axis. Region: color hue, Fears: lines, shapes
- Data Types (Quantitative, Qualitative, Categorical, Continuous, etc.):
- Quantitative, Categorical
- Encodings (Shape, Color, Position): Position, Color Hue, Angles, Length/Size
- What works well here?
- Interactivity and showing the disparity and different fears region to region
- What does not work well and what would I improve?
- The way fears and countries are ordered are just alphabetical. I would actually want to filter by fear and see the countries restacked for example or grouped to make things more interesting. I think there are some aspects of the study here that could be meaningful if viewed regionally.
- What is the data source? Do I see any problems with how it’s cited/used?
- Any other comments about what I learned?
- It’s interesting now that I’ve been looking at several visualizations everyday how much some areas in the visual, eg. functionality, tend to be better than others, such as encodings. I imagine that this might just break down into the type of organizations and how teams are structured within those organizations.
Udacity 1b D3 Building Blocks – section finished
- A callback is a function that gets executed once another function completes.
- With selectAll, every element in the old selection becomes a group in the new selection; each group contains an old element’s matching descendant elements.
- You were probably told that selections are arrays of DOM elements. False. For one, selections are asubclass of array; this subclass provides methods to manipulate selected elements, such as setting attributes and styles. Another reason selections aren’t literally arrays of elements is that they are arrays of arrays of elements: a selection is an array of groups, and each group is an array of elements.
Treehouse D3.js – Data Binding with D3.js – Finished Section
- When you’re binding more complex data, it can be challenging to remember which objects are where, especially if some of your app is failing bc of undefined or null values.
- Using debugger to explore with debugger with functions broken up on multiple lines and breakpoint to have access to variables. Explore methods on Chrome’s autocomplete. Use debugger to explore bl. ocks .org
- Think about how to combine methods and using conditionals for styling.
Reading and Learning Data Visualization Theoretically/Critically:
High-Speed Visual Estimation Using Preattentive Processing. C. G. Healey, K. S. Booth and J. T. Enns (PDF)from 7 Classic Foundational Vis Papers
- Preattentive processing refers to an initial organization of the visual field based on cognitive operations believed to be rapid, automatic, and spatially parallel. Examples of visual features that can be detected in this way include hue, intensity, orientation, size, and motion.
- The results showed that rapid and accurate estimation was indeed possible using either hue or orientation.
- Tools that support the visualization of multiple data dimensions must deal with a potential interaction between some or all of the features being used to represent the dimensions. Rather than trying to avoid this, we can sometimes control the interaction and use it to our advantage.