fabioc-aloha chart-vocabulary
Pick the right chart for the story you want to tell, evaluate whether an AI-suggested chart is correct, and know when to override.
This skill is the selection and evaluation reference. It sits upstream of the plugin's other chart skills:
| Step in the flow | Skill |
|---|---|
| Frame the Big Idea before authoring | chart-big-idea |
| Pick the chart family + type | this skill |
| Author the Flint spec + render | flint-chart |
| Structural / hand-authored SVG | figure-generator + print-svg-style-guide |
| AI-generated illustration / hero image | replicate-imagery |
| Terminal, log, PR comment, or context window | ascii-chart |
| Verify after render | render-verify |
| Deliver / browse the gallery | docs-shell |
flint-chart §0.2 (Question → family → chart) is a compact router pointing into this catalog; when a decision needs more than the router's ~7 rows, read this file. When picking a chart to hand-author rather than render via Flint, this catalog is still the reference — the delivery skill differs, the taxonomy does not.
When to Use
- Choosing chart types for a dashboard, report, book figure, or data story
- Evaluating an AI-generated visualization (Copilot / ChatGPT / any tool that auto-selects chart types)
- Reviewing another agent's or teammate's chart choices for story-intent alignment
- Sanity-checking a dashboard's density (5-visual rule)
- Teaching a heir the chart taxonomy without them reading FT's catalog end to end
Module 1: Chart Catalog by Communication Goal
Seven communication goals. Pick the goal first, then the chart. Never pick the chart first.
Comparison
Show differences between items or groups.
| Chart | Best when | Avoid when |
|---|---|---|
| Horizontal bar | Ranking items; long category labels | More than 15 items (paginate or filter) |
| Grouped bar | Comparing 2-3 series across categories | More than 3 series (use small multiples) |
| Dot plot | Precise value comparison; tight ranges | Audience expects bars |
| Slope chart | Before/after comparison of ranked items | More than 10 items (too many crossing lines) |
| Radar | Multi-dimensional profile comparison | More than 7 axes; general audiences |
| Bullet chart | Actual vs. target with qualitative ranges | No clear target or benchmark |
Change Over Time
Show trends, seasonality, or evolution.
| Chart | Best when | Avoid when |
|---|---|---|
| Line | Continuous data; up to 5 series | Categorical time (use bar) |
| Area | Emphasizing volume or magnitude of change | Multiple overlapping series (use stacked) |
| Stacked area | Part-to-whole composition changing over time | Need to compare individual series precisely |
| Sparkline | Inline trend context (KPI cards, tables) | Trend shape matters less than precise values |
| Step line | Discrete changes (pricing, policy, thresholds) | Continuous gradual change |
| Small multiples | Comparing trends across 6-20 categories | Fewer than 4 categories (use single line chart) |
Proportion
Show part-to-whole relationships.
| Chart | Best when | Avoid when |
|---|---|---|
| Donut | 2-5 segments; one hero segment to highlight | More than 6 segments; comparing across groups |
| Stacked bar | Comparing composition across categories | More than 5 segments per bar |
| Waffle | Communicating percentages to general audiences | Precision matters (use table) |
| Treemap | Hierarchical part-to-whole with many items | Need to show change over time |
| Sunburst | Multi-level hierarchy exploration | Print or static context (needs interaction) |
| Waterfall | Showing additive/subtractive contributions | Non-sequential contributions |
Distribution
Show spread, shape, or outliers.
| Chart | Best when | Avoid when |
|---|---|---|
| Histogram | Single variable distribution shape | Comparing distributions (use violin or ridgeline) |
| Box plot | Comparing distributions across groups | General audiences (unfamiliar format) |
| Violin | Distribution shape comparison across groups | Fewer than 3 groups (use histogram) |
| Ridgeline | Many distributions stacked for pattern scanning | Precision on individual values |
| Scatter | Two-variable distribution and outlier detection | Categorical data |
| Beeswarm | Small dataset; every point matters | More than 500 points (use density) |
Relationship
Show correlation, causation, or connection.
| Chart | Best when | Avoid when |
|---|---|---|
| Scatter | Two continuous variables; outlier identification | Categorical variables |
| Bubble | Three variables (x, y, size) | More than 50 bubbles (overplotting) |
| Heatmap | Dense matrix relationships (correlation, time × category) | Fewer than 4×4 cells |
| Parallel coordinates | Multi-dimensional comparison (5+ variables) | General audiences |
| Network graph | Entity relationships, social connections | Hierarchical data (use tree) |
| Chord diagram | Bidirectional flows between categories | More than 10 categories |
Flow and Process
Show movement, conversion, or paths.
| Chart | Best when | Avoid when |
|---|---|---|
| Sankey | Multi-stage flow with branching paths | Fewer than 3 stages (use stacked bar) |
| Funnel | Sequential drop-off (conversion, pipeline) | No sequential order |
| Gantt | Timeline with parallel activities | More than 30 tasks (filter or paginate) |
| Swimlane | Process flow with role/team assignments | Simple linear process |
Deviation
Show variance from a reference point.
| Chart | Best when | Avoid when |
|---|---|---|
| Diverging bar | Above/below target or median | No clear reference point |
| Lollipop | Deviation from baseline; cleaner than bars | Audience expects standard bars |
| Line + reference | Trend deviation from target over time | Multiple baselines |
| Gauge | Single KPI vs. target (dashboards, KPI cards) | More than 3 gauges on a page |
Module 2: The CSAR Evaluation Loop
Module 1.5: Storytelling Technique Router
Choose a technique because it solves a reading problem, not because it makes the chart look authored.
| Reading problem | Technique | Constraint |
|---|---|---|
| Reader cannot find the claim | Focal contrast on one story-carrying mark or series | Use one pre-attentive emphasis; keep context visible. |
| Legend lookup interrupts reading | Direct labels at line ends or beside marks | Check collisions and preserve a key when labels cannot fit. |
| Benchmark or threshold matters | Reference line, band, or structure | Label the reference and distinguish target from observed data. |
| Too many series obscure shape | Small multiples | Keep comparable scales unless independence is the point. |
| Order carries the argument | Sorting by value, time, or semantic order | Do not reorder cyclic or causal sequences arbitrarily. |
| Groups need comparison and context | Grouping or faceting | Prefer grouping for direct values; facets for shape and repeated structure. |
| Color may fail or print poorly | Redundant encoding through label, shape, position, stroke, or texture | Color is never the only carrier of critical meaning. |
| A specific event changes the story | Annotation with a concise evidence-linked note | Annotate the event, not every point. |
Theme choice follows technique and audience. It cannot repair a wrong chart family or unsupported claim.
When an AI tool generates a chart, evaluate before accepting. Compose with render-verify's Prose-coupling check for a two-layer safety net: CSAR asks did it pick the right chart family; render-verify asks did it render the right message.
| Step | Question | Action |
|---|---|---|
| Clarify | "What question am I answering?" | Write the question as a sentence before looking at the chart |
| Summarize | "The AI chose a [chart type]. Does it answer the question?" | Name the chart type and check it against Module 1 |
| Act | Accept, modify, or override | Accept if story-intent matches. Override if wrong goal group. Modify if right type but wrong emphasis |
| Reflect | "Why did I accept / override? What principle guided me?" | Document the rationale; builds judgment over time |
Override Decision Table
| AI chose | But your goal is | Override to | Rationale |
|---|---|---|---|
| Pie (8 slices) | Compare items | Horizontal bar, sorted | Pie with 8+ slices is unreadable |
| Clustered bar | Show trend | Line chart | Time series needs continuity |
| 3D column | Anything | 2D bar or line | 3D adds no information, distorts perception |
| Stacked bar | Compare individual series | Grouped bar or small multiples | Stacking hides individual values |
| Donut | Show precise values | Table with conditional formatting | Donut communicates rough proportion only |
| Line (20 series) | Compare trends | Small multiples or highlight 3 key series | Too many lines become spaghetti |
| Map | Compare values | Bar chart sorted by value | Maps encode position; bars encode length (more precise) |
Module 3: The 5-Visual Rule
Executive dashboards that work follow this constraint:
No more than 5 visuals per page. Each visual answers a different question. If you need more, you need a second page, not a denser layout.
| Slot | Role | Typical chart |
|---|---|---|
| 1-3 | KPI cards | Card, gauge, or sparkline |
| 4 | Hero chart | The main visual that carries the story |
| 5 | Supporting chart | A second angle on the same story |
A sixth visual is a table for drill-down, placed below the fold or on demand.
Composition by Audience
| Audience | Visuals | Time budget | Design priority |
|---|---|---|---|
| Executive | 3-5 | 30 seconds | KPIs first, hero chart, one action item |
| Manager | 5-8 | 2 minutes | Filters, comparison charts, trend lines |
| Analyst | 8-15 | Unlimited | Detail tables, drill-through, cross-filters |
| General | 3-4 | 1 minute | Annotated hero chart, simple narrative |
Module 4: Living Gallery References
Use these galleries to browse real examples when Module 1's tables aren't enough. The galleries are the living source of truth; this skill is the decision framework.
| Gallery | URL | Organized by | Best for |
|---|---|---|---|
| FT Visual Vocabulary | ft-interactive.github.io/visual-vocabulary | Communication goal | Choosing chart type by what you want to say |
| From Data to Viz | data-to-viz.com | Data type and relationship | Choosing chart type by what your data looks like |
| Data Viz Catalog | datavizcatalogue.com | Chart function | Understanding individual chart strengths/weaknesses |
| Vega-Lite Examples | vega.github.io/vega-lite/examples | Chart type | Declarative spec examples for programmatic generation |
| Storytelling with Data | community.storytellingwithdata.com/exercises | Story challenge | Real-world "makeover" exercises with before / after |
When to consult a gallery
- Module 1 suggests 2+ chart types and you cannot decide
- The story intent doesn't fit any standard goal group
- You need to see a real example of a chart type before committing
- Stakeholder needs visual evidence that the chart type works for their data
Module 5: Chart Selection Decision Tree
Run this algorithm when choosing charts for a dashboard, report, or book figure:
1. Write the Big Idea sentence (audience + action + evidence)
→ delegate to chart-big-idea; if you can't write it, the analysis isn't done
2. List the 3-5 questions the artifact must answer
Each question becomes one visual
3. For each question:
a. Identify the communication goal (Module 1 header)
b. Check the chart catalog table
c. Filter by data shape and audience
d. If AI generated a chart, run CSAR (Module 2)
e. If still unsure, consult a gallery (Module 4)
4. Apply the 5-Visual Rule (Module 3)
Cut the weakest visual if over budget
5. Arrange by narrative flow:
KPIs → Hero chart → Supporting → Detail
6. Author + render:
→ statistical charts: flint-chart (Vega-Lite / ECharts / Chart.js via Flint MCP)
→ structural / hand-authored SVG: figure-generator + print-svg-style-guide
→ AI-generated illustration: replicate-imagery
→ no renderer available (terminal, log, PR, context window): ascii-chart
7. Verify: render-verify (Prose-coupling + failure catalog)
Attribution
The chart catalog (Module 1), CSAR loop (Module 2), 5-visual rule (Module 3), gallery references (Module 4), and selection tree (Module 5) are adapted from an earlier visual-vocabulary curriculum, absorbed into this plugin in 2026. Its ASCII delivery lives here as ascii-chart. Its SVG composition patterns (panel primitive, pie sizing, dark-slate palette) are covered by print-svg-style-guide and figure-generator, with print-legibility math and semantic palette colors, so they are not duplicated here.
FT Visual Vocabulary, Data-to-Viz, Data Viz Catalog, Vega-Lite examples, and Storytelling with Data are cited under their published URLs; this skill is a decision framework built on top of those galleries, not a replacement for them.
Would Revise If
Revisit by 2026-10-30 (90 days) or sooner if:
- ≥3 chart types in Module 1 are reported as miscategorised or missing a "best when / avoid when" pair
- The CSAR override decision table produces the wrong verdict for a real chart choice ≥2 times in observation
- The 5-visual rule is contradicted by a working dashboard that ships more visuals without density complaints (rule too strict)
- One of the five galleries in Module 4 retires or moves; the URL check fails
- A heir installs
chart-vocabularyandvisual-vocabularyfrom the Mall on the same brain and reports confusion about which is canonical (the absorption story isn't landing) - The selection tree in Module 5 sends a heir to the wrong plugin skill ≥2 times (routing miscalibrated)
- Zero heirs invoke this skill in the 90-day window (decoration, not load-bearing)