Tufte Viz

by aparente

skill

|

Skill Details

Repository Files

2 files in this skill directory


name: tufte-viz description: | Ideate and critique data visualizations using Edward Tufte's principles from "The Visual Display of Quantitative Information." Use this skill when: (1) Designing new data visualizations or charts (2) Critiquing or improving existing visualizations (3) Reviewing dashboards or reports for graphical integrity (4) Deciding between visualization approaches (5) Reducing chartjunk or improving data-ink ratio (6) Planning small multiples or high-density displays Applies principles: data-ink ratio, chartjunk elimination, graphical integrity, lie factor, small multiples, and data density.

Tufte Visualization Ideation

Apply Edward Tufte's principles to design clear, honest, high-density data visualizations.

Workflow

For new visualizations:

  1. Clarify the data story

    • What comparisons matter?
    • What's the key insight to communicate?
    • Who's the audience?
  2. Select approach using Tufte principles:

    • High comparison need → Small multiples
    • Dense data → Consider data tables, sparklines
    • Time-series → Line charts with minimal grid
    • Part-to-whole → Avoid pie charts; prefer bar/table
  3. Design with data-ink in mind

    • Start minimal, add only what's necessary
    • Every element must earn its ink
    • Default to grayscale; use color purposefully
  4. Apply the Tufte test (see references/tufte-principles.md)

For critiquing visualizations:

  1. Check graphical integrity

    • Calculate lie factor if proportions seem off
    • Verify baselines and scales
    • Look for 3D distortion
  2. Identify chartjunk

    • Decorative elements
    • Heavy grids
    • Unnecessary 3D effects
    • Moiré patterns
  3. Evaluate data-ink ratio

    • What can be erased?
    • What's redundant?
  4. Suggest improvements with specific before/after recommendations

Key Principles Reference

For detailed principles, read: references/tufte-principles.md

Quick checklist:

  • Lie Factor ≈ 1.0 (no visual distortion)
  • Maximum data-ink ratio
  • Zero chartjunk
  • Clear labeling
  • Enables comparison
  • Reveals multiple levels of detail
  • Appropriate data density

Related Skills

Attack Tree Construction

Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

skill

Grafana Dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

skill

Matplotlib

Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.

skill

Scientific Visualization

Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.

skill

Seaborn

Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.

skill

Shap

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Query Writing

For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Scientific Visualization

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

skill

Skill Information

Category:Skill
Last Updated:1/22/2026