data-viz-storytelling-healylisted

# Data Visualization & Storytelling (Healy + AntV) > "The tools you use can help you live up to the right standards. > But they cannot make you do the right thing." > — Kieran Healy, *Data Visualization*, Ch. 1 ## When to Use This Skill - Deciding **which chart type** fits the analytical question - Writing a report or slide deck where **numbers need a narrative** - Reviewing a figure for **honesty / misleading patterns** - Drafting an insight summary with **claim → evidence → caveat** structure - Choosing between infographic (visual design) and statistical figure (accuracy) **For actual plotting code** → use `matplotlib`, `seaborn`, or `scientific-visualization`. --- ## Section 1 — Honesty & Judgment (Healy Ch. 1) Before choosing colors or chart types, verify the figure does not mislead. ### 1.1 Pre-plot Honesty Checklist | Check | Why it matters | |-------|----------------| | Baseline / zero start | Bar charts starting above zero exaggerate differences. Line charts may omit zero legitimately if the focus is trend, not level. | | Dual axes | Two Y-axes on one plot invite false correlation. Prefer faceted panels or indexed series. | | Cherry-picked window | Short time windows can hide long-term patterns. Always show context. | | Aggregation level | Averages can hide distribution shape. Consider showing raw data, box plots, or density. | | Proportional vs absolute | Normalize when comparing groups of different size; keep raw counts available. | | Color encoding | Do no