---
name: food-figure
description: "Comprehensive figure system for food & nutrition manuscripts: analyzes the user's data, recommends the best figure(s) to make, then produces submission-grade graphics in Python or R at the target journal's spec. Handles all common scientific figure types (bar/box/violin, line/kinetic, scatter/regression, Bland–Altman, radar/sensory, chromatograms, TPA/rheology, dose–response, survival, PCA/PLS-DA, heatmaps/clustering, forest, microscopy plates, multi-panel). Use to make, create, design, revise, audit, or recommend figures/charts/plots for a food-science paper, or to work out what to plot from a dataset. If Python or R isn't chosen, ask once and remember it. Triggers: make a figure, create a figure, design a figure, what figure should I make, recommend a chart, plot my data, analyze my data and plot it, chart my results, food science figure, journal figure, scientific plotting, data visualization for a manuscript."
metadata:
version: "2.3.0"
verified: "2026-07"
related_skills: [journal-selector, food-paper, food-research]
references:
- references/chart-types.md
- references/data-to-figure.md
- references/design-principles.md
- references/figure-contract.md
- references/qa-checklist.md
- references/python-guide.md
- references/r-guide.md
- references/food-recipes.md
- references/journal-specs.md
- references/color-palettes.md
- references/figure-provenance.md
- references/figure-story-design.md
- references/ai-image-generation.md
examples:
- examples/python_food_figures.py
- examples/r_food_figures.R
scripts:
- scripts/analyze_data.py
- scripts/backend_pref.py
---
# Food-Figure — Data-Driven Figure System for Food & Nutrition Science
Turn a dataset (or a described result) into the right submission-grade figure.
**The chart serves the scientific logic; polish is subordinate to making the
core conclusion clear, defensible, and reviewable.** Original work; architecture
informed by open community figure skills (see the repo README Acknowledgements).
Load reference files **as needed** (progressive disclosure) — don't read them all
up front. The map is in the frontmatter `references` list.
## Workflow
```mermaid
flowchart TD
A[Data or described result] --> B[1. Analyze the data
scripts/analyze_data.py -> profile]
B --> C[2. Recommend figures
references/data-to-figure.md]
C --> D[3. Figure contract
references/figure-contract.md]
D --> E{Backend?}
E -- unknown --> Eq[Ask 'Python or R?' once
scripts/backend_pref.py]
E -- known --> F
Eq --> F[4. Render
python-guide.md OR r-guide.md + food-recipes.md]
F --> G[5. Export at journal spec
references/journal-specs.md]
G --> H[6. QA
references/qa-checklist.md]
H --> OUT[Journal-ready SVG/PDF/TIFF + editable source]
```
### 1 — Analyze the data
If the user supplies a data file (CSV/TSV/Excel) or table, profile it first:
run `scripts/analyze_data.py ` to get, per column, the type
(numeric/categorical/datetime), cardinality, missingness, distribution summary,
and the detected structure (grouping factors, repeated measures, time/dose axis,
wide sensory/composition matrix). If the user only describes a result, elicit the
same: what varies, what's measured, n, and the error type. See
`references/data-to-figure.md`.
### 2 — Recommend the figure(s)
From the profile, propose the **best** figure type(s) with a one-line rationale
each, and say what each would show. Prefer the figure that makes the paper's
claim most directly; note honest alternatives. The decision rules and a full
catalog are in `references/data-to-figure.md` and `references/chart-types.md`.
Never force a chart the data can't support (e.g. bar-of-means where a
distribution matters → box/violin + points).
### 3 — Figure contract + provenance (before code)
Fix the conclusion, evidence logic, export needs (target journal), and review
risks (`references/figure-contract.md`). Open a **figure trace card**
(`references/figure-provenance.md`): the real data source, the script that makes
the figure, what it shows, and the claim it supports — so the plotted values match
the reported statistics. Choose the palette by data type
(`references/color-palettes.md`).
For a dense Figure 1/2-style request, first design the complete evidence story
with `references/figure-story-design.md`: experimental sequence, evidence
hierarchy, non-redundant panel questions, source-data map, opening schematic, and
an integrated synthesis panel. Do not start by filling a grid with chart types.
### 4 — Backend gate (blocking)
- **Data figures → Python or R (always).** Resolve the backend by priority:
explicit request → language of the user's input files/data → saved preference
(`python scripts/backend_pref.py get`) → ask once ("Python or R? I'll remember
this") and save it (`backend_pref.py set python|r`). The chosen backend does
**all** data graphics, preview, and export; the other may only help with data
prep/conversion.
- **AI image route (opt-in, schematics only).** **Only if the user explicitly
asks** to generate the image with **Gemini, ChatGPT, or Claude** (or another
named image model) — and **only for conceptual visuals** (mechanism diagrams,
graphical abstracts, process schematics) — use that model instead. **Never** use
an AI image model for a data-bearing figure, and never let it invent data. See
`references/ai-image-generation.md`.
### 5 — Render & export
Use the selected backend's guide (`python-guide.md` = matplotlib/seaborn/
subplot_mosaic/statsmodels; `r-guide.md` = ggplot2/patchwork/ComplexHeatmap/
ggrepel + svglite/cairo_pdf/ragg) plus `food-recipes.md` for the food/nutrition
figure types. **Start from the template library** — `examples/python_food_figures.py`
or `examples/r_food_figures.R` — which has a ready function for every figure type;
adapt it to the user's data. Export **vector** (PDF/SVG) for line/bar/scatter and
**TIFF (LZW)** at the journal DPI for raster/microscopy; keep an editable source.
Pull DPI, column width, font, and format from the target journal via
`references/journal-specs.md`; if no journal is set, default to 300 dpi, ~90/190 mm
widths, TIFF+PDF, Arial 7–9 pt.
### 6 — QA
Run `references/qa-checklist.md` before delivering (error bars defined + n;
statistics shown consistently; axes honest; colorblind-safe; labels legible at
final size; matches journal spec; every panel cited). **Privacy:** any code or
legend you hand back must use **relative paths**, never local machine paths — scan
with `python3 scripts/privacy_scan.py` (see
`food-paper/references/privacy-and-confidentiality.md`).
### Deliver
Hand back, per figure: the file(s) (vector + raster), the **figure trace card**,
a **self-contained caption** (APA 7.0 or the journal's style — see
`references/figure-provenance.md`), and the plotting code. For a `.tex` build,
include the `\includegraphics` environment.
## Modes
- **recommend** — analyze data and suggest figures, no rendering yet.
- **make** (default) — full pipeline to a rendered, exported figure + caption + trace card.
- **revise / audit** — critique or fix an existing figure against the QA checklist and journal spec.
- **multi-panel** — compose labelled panels (a, b, c) that share a logical thread.
- **figure-story** — design and render an 8–12 panel journal-style evidence
narrative from experimental design through primary results, diagnostics,
robustness, and an integrated conclusion.
- **schematic** — a graphical abstract / mechanism diagram: Python/R by default, or the opt-in AI-image route (`references/ai-image-generation.md`) if the user asks.
## Scope
Reproducible, code-generated, submission-grade scientific figures for food &
nutrition. Not for dashboards or Illustrator/Figma-first artwork. A
schematic/graphical-abstract (mechanism diagram) is a drawing task: keep it in
Python/R by default, or — only if the user explicitly asks — generate it with an
AI image model (Gemini/ChatGPT/Claude) per `references/ai-image-generation.md`.
Data figures are always Python/R.
## Handoff
Called by `food-paper`'s `viz_designer` at the journal spec; figures feed the
manuscript's Results.