tufte-viz

# Tufte Visualization Ideation Apply Edward Tufte's principles to design clear, honest, high-density data visualizations. ## Workflow ### For new visualizations: 1. **Clarify the data story** - What comparisons matter? - What's the key insight to communicate? - Who's the audience? 2. **Select approach** using Tufte principles: - High comparison need → Small multiples - Dense data → Consider data tables, sparklines - Time-series → Line charts with minimal grid - Part-to-whole → Avoid pie charts; prefer bar/table 3. **Design with data-ink in mind** - Start minimal, add only what's necessary - Every element must earn its ink - Default to grayscale; use color purposefully 4. **Apply the eraser test before shipping** - For every element (label, tick, gridline, border, annotation): can it be erased without losing information that's not already conveyed elsewhere? - Watch for duplicate encodings: numeric labels next to a value already marked by a tick; legends duplicating direct labels; per-panel scale annotations duplicating a shared-scale caption. - If two elements compete for the same job, keep the visual one and drop the textual one (or vice versa) — not both. 5. **Apply the collision test before shipping** - For every text element in the plot (axis labels, point annotations, epoch labels, baseline labels, explanatory notes): mentally draw its bounding box. Does anything else — another text element, a data line, dense markers — live in or cross that box? - The eraser test catches *redundant* elements; the collision test catches *crowded* ones. Both must pass. - Standard fixes: move explanatory prose out of the plot into the figcaption; relocate band/epoch labels to a dedicated strip above the plot; push baseline/reference labels to the outside margin; give each in-plot annotation a leader line so the marker and the text occupy clearly separated space. - Watch especially: inverted axes (top of plot is now where extreme values cluster, where annotations also want to go); shared-scale small multiples (labels stacked near zero in every panel); dense scatter (text vanishes into the dot cloud unless explicitly cleared). 6. **Apply the Tufte test** (see references/tufte-principles.md) ### For critiquing visualizations: 1. **Check graphical integrity** - Calculate lie factor if proportions seem off - Verify baselines and scales - Look for 3D distortion 2. **Identify chartjunk** - Decorative elements - Heavy grids - Unnecessary 3D effects - Moiré patterns 3. **Evaluate data-ink ratio** - What can be erased? - What's redundant? 4. **Suggest improvements** with specific before/after recommendations ## Key Principles Reference - `references/tufte-principles.md` — core principles from *Visual Display of Quantitative Information*: lie factor, data-ink, chartjunk, small multiples, integrity. - `references/analytical-design.md` — extensions from *Envisioning Information*, *Visual Explanations*, and *Beautiful Evidence*: the 6 principles of analytical design, sparklines, layering & separation, micro/macro, range-frames, causality, confections. Load when designing dashboards, dense displays, sparklines, or explanatory graphics. **Quick checklist:** - [ ] Lie Factor ≈ 1.0 (no visual distortion) - [ ] Maximum data-ink ratio - [ ] Zero chartjunk - [ ] Clear labeling - [ ] Answers "compared to what?" - [ ] Shows causality or mechanism where relevant - [ ] Multivariate (not over-reduced) - [ ] Words, numbers, images integrated — not segregated - [ ] Reveals multiple levels of detail (micro + macro) - [ ] Layering: primary data dominates, secondary recedes - [ ] Appropriate data density