Darker cells mean a higher score for this topic on that metric.
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Quick answers
When should I pick Rough.js annotation?
When the story is: explainer highlights, ad concept frames, teaching callouts.
When should I avoid Rough.js annotation?
Skip it in cases like: dense quantitative comparison that needs precise lengths.
Who invented or popularised it?
Rough.js brought controllable sketch rendering to the open web
What does it encode?
imperfect strokes and fills as attention cues
Is it OK in ads?
Yes if one claim is visible at a glance in Meta, Google and retail print; otherwise link to a full figure.
Will a teacher accept it?
Yes for lab reports and poster boards if axes, units and a source line are present.
Rough.js annotation encodes values as imperfect strokes and fills as attention cues. Rough.js brought controllable sketch rendering to the open web.
In reports, ads and assignments it fits explainer highlights, ad concept frames, teaching callouts. Skip it when dense quantitative comparison that needs precise lengths.
Mistakes
- Failure modes 1 — dense quantitative comparison that needs precise lengths
- Failure modes 2 — 3-D pies and perspective bars
- Failure modes 3 — Dual axes that fake a correlation
- Failure modes 4 — Truncated axes that shout a small delta
- Failure modes 5 — Legend only, no direct labels
- Failure modes 6 — Missing source, unit or period
Fixes
- Fixes 1 — Return to explainer highlights, ad concept frames, teaching callouts.
- Fixes 2 — Flat, one axis, direct labels.
- Fixes 3 — Color-blind-safe palette plus pattern.
- Fixes 4 — Rewrite the title as a claim and add a source line.
Rough.js annotation: honour imperfect strokes and fills as attention cues, and switch when dense quantitative comparison that needs precise lengths.