[ridgeline-basic] Ridgeline plot for distribution comparison

[MarkusNeusinger]

Description

A ridgeline plot (also known as joy plot) showing multiple overlapping density distributions arranged vertically, allowing comparison of distributions across categories.

Applications

• Comparing salary distributions across different job titles
• Visualizing temperature distributions across months
• Showing survey response distributions by demographic groups

Data

• One categorical variable for grouping (y-axis labels)
• One continuous variable for the distribution (x-axis)
• Optional: color encoding for additional grouping

Notes

• Distributions should overlap slightly for visual appeal
• Consider transparency/alpha for overlapping areas
• Named after Joy Division album cover artwork

[claude]

✅ Spec Ready: ridgeline-basic

Branch: plot/ridgeline-basic

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Analysis

Why this spec makes sense:
Ridgeline plots are excellent for comparing distributions across multiple categories in a compact, visually striking format. They reveal patterns that would be hard to see in side-by-side histograms or box plots, making them valuable for exploratory data analysis and presentation.

Spec-ID reasoning:
ridgeline-basic uses the plot type (ridgeline) as the primary identifier with basic as the variant since this is the foundational ridgeline implementation without additional modifiers like color mapping or interactivity.

Similar existing specs:

• histogram-basic - shows single distribution, ridgeline shows multiple distributions stacked
• box-basic - compares distributions via summary statistics, ridgeline shows full density shapes
  These are related distribution visualization methods but ridgeline offers a unique overlapping density view.

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spec.md

# ridgeline-basic: Ridgeline Plot

## Description

A ridgeline plot (also known as joy plot) displays multiple overlapping density distributions arranged vertically, allowing effective comparison of distributions across categories. Each distribution is plotted as a filled density curve, slightly overlapping with adjacent rows to create a visually appealing layered effect. This visualization excels at revealing distribution shapes, modes, and shifts across groups.

## Applications

- Comparing salary distributions across different job titles to identify compensation patterns
- Visualizing temperature distributions across months to show seasonal climate patterns
- Showing survey response distributions by demographic groups to understand opinion variations

## Data

- `category` (categorical) - grouping variable for vertical arrangement (y-axis labels)
- `value` (numeric, continuous) - the variable whose distribution is shown (x-axis)
- Size: 5-20 categories, 50-500+ observations per category
- Example: Monthly temperature readings with month as category and temperature as value

## Notes

- Distributions should overlap slightly (10-30%) for visual appeal without obscuring data
- Use semi-transparent fills (alpha 0.6-0.8) to maintain visibility of overlapping areas
- Named after the Joy Division album cover "Unknown Pleasures" featuring pulsar radio signals
- Consider gradient coloring from bottom to top for additional visual interest
- Kernel density estimation (KDE) is typically used to smooth the distributions

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Next: Add approved label to start code generation.

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🤖 plot-prepare workflow

[github-actions]

🚀 Implementation Status

Library	Status	PR	Quality
matplotlib	✅ Done	-	-
seaborn	✅ Done	-	-
plotnine	✅ Done	-	-
plotly	✅ Done	-	-
bokeh	✅ Done	-	-
altair	✅ Done	-	-
highcharts	✅ Done	-	-
pygal	✅ Done	-	-
letsplot	✅ Done	-	-

✅ All 9 libraries completed successfully!

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🤖 plot-generate workflow