Doc Add Chart
Table of Contents
When to use this skill
When designing or evaluating any data display — charts, dashboards, tables, reports, inline graphics. Use it before writing visualisation code and when reviewing existing displays for clarity or integrity.
How to use this skill
For new visualisations
- Clarify the data story — what comparisons matter? What is the key insight? Who is the audience?
- Select approach using Tufte principles:
- High comparison need → small multiples.
- Dense data → data tables, sparklines.
- Time-series → line charts with minimal grid.
- Part-to-whole → avoid pie charts; prefer bar/table.
- Design with data-ink in mind — start minimal, add only what is necessary. Every element must earn its ink. Default to greyscale; use colour purposefully.
- Apply the Tufte Test — see Tufte's Principles for Data Visualisation.
For critiquing visualisations
- Check graphical integrity — calculate lie factor if proportions seem off; verify baselines and scales; look for 3D distortion.
- Identify chartjunk — decorative elements, heavy grids, unnecessary 3D effects, moiré patterns.
- Evaluate data-ink ratio — what can be erased? What is redundant?
- Apply the six analytical design principles — see Analytical Design, Sparklines, and Layering.
- Suggest improvements with specific before/after recommendations.
Key principles reference
- Tufte's Principles for Data Visualisation — core principles from The Visual Display of Quantitative Information: lie factor, data-ink ratio, chartjunk, small multiples, graphical integrity, and the 7-question Tufte Test.
- Analytical Design, Sparklines, and Layering — extensions from Envisioning Information, Visual Explanations, and Beautiful Evidence: the six principles of analytical design, sparklines, layering and 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 labelling.[ ]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.