[ridgeline-basic] Basic Ridgeline Plot (Joy Plot)

[MarkusNeusinger]

Description

A ridgeline plot (also known as Joy Plot) displays the distribution of multiple groups by stacking partially overlapping density curves. Named after the iconic Joy Division album cover.

Key Features

• Multiple density distributions stacked vertically
• Partial overlap creates a mountain ridge appearance
• Efficient comparison of many distributions
• Beautiful aesthetic for presentations

Applications

• Comparing distributions across time periods
• Showing seasonal patterns (e.g., temperature by month)
• Population distributions by age groups
• Survey responses by demographic segments

Data Requirements

• One continuous numeric variable
• One categorical variable for grouping (creates multiple ridges)
• Works best with 5-20 groups
• Each group should have sufficient observations for smooth density

Visual Elements

• Stacked horizontal density curves
• Partial vertical overlap between curves
• Y-axis: group labels
• X-axis: variable values
• Optional: color gradient or distinct colors per group

[claude]

✅ Spec ID: ridgeline-basic

Rationale: Ridgeline plots (Joy Plots) are a distinct visualization type that stacks multiple density curves with vertical overlap - different from existing density-basic (single distribution) and violin-basic (mirrored densities per category).

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: ridgeline-basic

PR: #924

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specification.md

ridgeline-basic: Basic Ridgeline Plot

Description

A ridgeline plot (also known as Joy Plot, named after the Joy Division album cover) displays the distribution of multiple groups by stacking partially overlapping density curves vertically. This creates a mountain ridge appearance that allows efficient comparison of many distributions simultaneously while maintaining a compact and visually striking presentation.

Applications

• Comparing temperature distributions across months to reveal seasonal patterns
• Analyzing survey response distributions by demographic segments
• Visualizing population age distributions across different regions or time periods
• Showing how performance metrics vary across different teams or time windows

Data

• value (numeric) - The continuous variable to visualize as density curves
• group (categorical) - The grouping variable that creates separate ridges (5-20 groups recommended)
• Size: 50-500 observations per group for smooth density estimation
• Example: Monthly temperature readings, survey scores by age group, reaction times by condition

Notes

• Stack density curves vertically with partial overlap (typically 50-70% overlap)
• Y-axis should display group labels rather than numeric values
• Consider color gradients or distinct colors to differentiate ridges
• Ensure sufficient overlap for visual cohesion while maintaining readability
• Order groups meaningfully (chronological, alphabetical, or by distribution characteristic)

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Next: Add approved label to merge this specification to main.

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🤖 spec-create workflow

[github-actions]

🚀 Specification Merged!

Specification: ridgeline-basic
PR: #924 (merged)

The specification is now in main and synced to PostgreSQL.

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Next: Add generate:{library} labels to start generating implementations.

Available libraries: matplotlib, seaborn, plotly, bokeh, altair, plotnine, pygal, highcharts, letsplot

Or use workflow dispatch for bulk operations.

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🤖 spec-create workflow