Lecture 2
Visual encodings, visualization integrity, color theory, elements of a theme
You MUST care about your different audiences
Visualization for Analysis
- visualizations for you and your team
- team and audience knows context
- tool for understanding datasets
- iterate quickly to develop insights
- rough drafts
- can make changes later
Visualization for Presentation
- audience external to you and team
- content is likely new and audience has no context
- designed to communicate useful information
- takes significant more time
- publication ready
Explain the encodings
For example: what scale are you using? What does that color represent? Is this normal?
It’s better to err on the side of too much explanation than it is too little. At least with the former, people can gloss over the details if they’re already familiar. They can still read the chart. With the latter, people who are unfamiliar with the visual encodings will get stuck.
Provide context
When readers can decode the shapes, colors and geometries on your chart, you are more than half way there to producing an awesome chart.
However, readers also need to understand the context of the data.
Another context example
Improve readability
Charts should read like text. At the most basic level, it should be obvious what the chart is about and how to interpret it.
Develop aesthetics
Default setting in the tools are generic and designed in such a way that they would work with many datasets and visualization types
You can (and should) develop aesthetics to make your charts less ugly
Note
In this context, aesthetics means a visual style. Do not confuse this with the aes() call in ggplot2.
Pre-attentive processing: the ability of the low-level human visual system to effortlessly identify certain basic visual properties.
Example: let’s help our friend Homer find a donut that looks different!
What stands out? This is pre-attentive processing in action!
Tamara Munzner
Computer scientist, information visualization expert, and professor at University of British Columbia.
Tamara proposed a nested model analysis framework for visualization
Four levels, three questions:
Domain
- Characterize the problems and data of a particular domain
- Who are the target users?
Tamara proposed a nested model analysis framework for visualization
Four levels, three questions:
Domain
- Characterize the problems and data of a particular domain
- Who are the target users?
Abstraction
- Translate from the domain specifics to the visualization vocabulary
- What is shown? data abstraction
- Why is the user looking at it? task abstraction
Tamara proposed a nested model analysis framework for visualization
Four levels, three questions:
Domain
- Characterize the problems and data of a particular domain
- Who are the target users?
Abstraction
- Translate from the domain specifics to the visualization vocabulary
- What is shown? data abstraction
- Why is the user looking at it? {{< solid arrow-right >}} task abstraction
Idiom
- How is it shown?
- Visual encoding idiom how to draw
- Interaction idiom how to manipulate
Tamara proposed a nested model analysis framework for visualization
Four levels, three questions:
Domain
- Characterize the problems and data of a particular domain
- Who are the target users?
Abstraction
- Translate from the domain specifics to the visualization vocabulary
- What is shown? data abstraction
- Why is the user looking at it? task abstraction
Idiom
- How is it shown?
- Visual encoding idiom how to draw
- Interaction idiom how to manipulate
Algorithm
- Efficient computation
The What: Abstracting the Data
Types of datasets
temporal!
Tables
- Typically a flat Tidy Data table (by analysis unit)
- Observations are rows, one item per row
- Attributes are columns
- May or may not have an identifier
Types of attributes
Categorical
- No order
- Example: names, countries, types
- Must be represented with visual channels that don’t convey order
Ordered
Ordinal
- Has implicit order
- But, you can’t do arithmetic
- Can be numerical (but should be treated as categorical)
- Example: t-shirt sizes, grade in school, rankings
Quantitative
- Also ordered
- You can do arithmetic
- Can be divergent or sequential
- Example: age, temperature, earnings
Types of attributes
Sequential
- There is an infinite range with a clear minimum
- You can perform arithmetic
- Example: age, number of goals, price
- Must be represented with visual channels that do convey order
Diverging
- There is a middle point
- And two opposite directions
- Many times the middle point is not zero
- Example: temperature, earnings, political affiliation index
Cyclic
- There is a cycle in the values
- Starting point may or may not be obvious
- Can be represented with cyclical channels
- Example: days of the week, hours in the day
The Why? (more on that next week)
Reminder of model
The How?:
Reminder of model
Marks and Channels
Marks are geometric primitives
Channels (encodings) control the appearance of marks
Channel (encoding) Types
Points
- Zero-dimensional
- Convey position only
- Additionally, can be size and shape coded
Lines
- One-dimensional
- Convey position and length
- Can only be width coded
Areas
- Two dimensional
- Are fully constrained
Mackinlay ’86
Another encoding guide (Noah Illinsky)
Although encoding is often undertaken without much intention or deeper consideration, it has significant impact on the ability of the visualization to communicate knowledge accurately and efficiently.
Position (example 2)
Position allows you to compare values based on where they are placed with reference to a coordinate system.
Considerations
- Avoid overplotting since many points can occupy the same space and obscure one another
Solutions
- Use transparency so that overlapping points make darker areas
- jitter (add noise so points no longer are on top of each other)
- Use binning to show aggregate data per pixel
Length
Length is most commonly used in the context of bar charts. The longer a bar is, the greater the value. Don’t truncate bar charts, use length in its entirety!
Angle
Angles range from 0 to 360 degrees in a circle.
Considerations
- Angles are most associated with pie charts. Pie chart is made up of parts that make up a whole.
- Don’t use too many categories (bar chart is better)
- The sum of all percentages should equal 100%!
Don’t even think about this!
Slope
Slope is similar to angle. Line charts are the most common use of slope to encode data.
Considerations
- Slope magnitude: steeper = greater change, flatter = lesser change
- The aspect ratio
- Visual change should match the context of the change
Cleveland, McGIll & McGill (1988) suggested that the average slope in a line chart should be \(45^o\), in order to make neutral comparisons between lines
This is still a good rule of thumb
Area
Like length, area can be used to represent data with size, but with two dimensions instead of one.
Considerations
- While the encoding might not be as precise from a visual perception perspective, area can provide a more intuitive, less abstract view for some types of data
- Make sure you scale by area, not edge (remember, area gets squared per unit increase)
- This means you should encode the length of a side as \(\sqrt{x}\)
Volume
Volume can used in the same way as area but has one more dimension.
Considerations
- Make sure you scale by volume, not edge (remember, volume gets cubed per unit increase)
- This means you would encode the side of a “box” as \(x^{1/3}\)
For 3-D encodings, you need to take the volume as proportional to the data
Color theory 101
- Color Theory 101: A Complete Color Guide
- Color Theory: A Simple Exercise in Mathematics and Graphic Design
Color encoding
Color as a visual encoding can be split into two categories: hue and saturation. Hue is what most people refer to as color (red, green, blue, etc.) Saturation is the amount of hue in a color.
- Qualitative: every color represents a distinct attribute (category)
- Sequential: color represents a range (saturation) from low to high (or vice-versa)
- Diverging: multiple hues represent a point of inflection of the data
Other built-in color palettes are usually better than the defaults
Most of these palettes are available to both ggplot2 and matplotlib. For R, you may have to load packages like RColorBrewer or viridis.
Color can help provide context
However, working correctly with color can be hard!
Color is not sortable
The incredibly challenging task of sorting colours
Color can convey implicit meaning!
Positive and negative association (emotional and cultural)
| Color | Positive Keywords | Negative Keywords |
|---|---|---|
| Blue | Life, survival, calm, cleansing, protection, divinity | Sadness, death, mourning |
| Red | Excitement, love, high fashion, glamour, strength, power, luck, prosperity | Danger, warning, death, aggression, mourning, communism |
| White | Purity, simplicity, innocence, weddings, sacred, sacrifice, equality | Death, bad luck, cowardice, surrender, cycle of death and rebirth |
| Black | Elegance, luxury, masculinity, maturity, age | Bad luck, death |
| Green | Environmentally friendly, good luck, nature, national color | Infidelity, jealousy, illness |
| Orange | Safety, sacred, fertility, love, health, happiness, bravery, innovation | Mourning |
| Purple | Magic, mystery, royalty, religious faith, ambiguity | Death, mourning |
| Pink | Femininity, love, romance, birth, tenderness, mentally stimulating, trust, architecture | Foreign color |
Consider color blindness
Consider printing
Most of the time your visualization will be displayed in full color. However, you may need to print sometimes and not have a color printer. Printed color reproduction may not be faithful to screen, but it’s another issue when printing in greyscale.
