Tufte's Principles for Data Visualisation

• Show the data.
• Induce the viewer to think about substance, not methodology or design.
• Avoid distorting what the data have to say.
• Present many numbers in a small space.
• Make large datasets coherent.
• Encourage eye comparison of different pieces of data.
• Reveal data at several levels of detail (broad overview to fine structure).
• Serve a reasonably clear purpose.
• Integrate closely with statistical and verbal descriptions.

• Does the graphic show the data clearly?
• Does it encourage thinking about content over decoration?
• Can the viewer compare data elements easily?

Lie Factor = Size of effect shown in graphic / Size of effect in data

• Lie Factor = 1.0 → truthful.
• Lie Factor > 1.05 or < 0.95 → distortion.

1. Representation of numbers should be directly proportional to quantities represented.
2. Clear, detailed, thorough labeling defeats distortion.
3. Show data variation, not design variation.
4. In time-series displays, standardise money (deflate) and use consistent baselines.
5. Dimensions of graphics should not exceed dimensions of data.
6. Graphics must not quote data out of context.

• 3D effects on 2D data (adds fake dimension).
• Truncated axes that exaggerate change.
• Inconsistent intervals.
• Area/volume encoding of linear data.
• Missing context or baselines.

• Heavy gridlines.
• Unnecessary borders and boxes.
• Redundant labels.
• Decorative elements.
• Excessive tick marks.
• 3D effects that add no information.

The eraser test: if you can erase something without losing data information, erase it.

• Viewer notices the design before the data.
• Colours/patterns used for decoration, not encoding.
• 3D effects, shadows, gradients without purpose.
• Clip art, icons, or illustrations that don't carry data.

• Same design structure repeated.
• Changes in data, not design.
• Enables direct visual comparison.
• High information density.
• Eye moves across variations effortlessly.

• Comparing across categories, time periods, or conditions.
• Showing change or variation.
• Revealing patterns across groups.
• When animation would work but static display is needed.

• Identical scales across all panels.
• Consistent visual encoding.
• Minimal between-panel decoration.
• Clear labelling of what varies.
• Tight spacing (data should dominate).

Graphics can often be reduced significantly while maintaining readability and gaining impact. Consider:

• Sparklines (word-sized graphics).
• Condensed time-series.
• Small multiple arrays.

• What is the minimum size that remains readable?
• Can we show more data in the same space?
• Are we wasting white space?

• Data points that also serve as labels (scatter plots with text).
• Axis that is also a data series.
• Range frames (axis shows data range, not arbitrary extent).

• Simple design, complex data.
• Complexity should come from data, not decoration.

Data > Labels > Annotations > Grids > Borders. Prominent elements should carry the most information.

• Use sparingly and purposefully.
• Ensure accessibility (colorblind-safe palettes).
• Grey as default; colour for emphasis or encoding.
• Avoid "rainbow" colour maps for sequential data.

• Clear, readable fonts.
• Appropriate sizing hierarchy.
• Horizontal text when possible.
• Labels close to the data they describe.