[strip-basic] Basic Strip Plot (Jittered Points)

[MarkusNeusinger]

Description

A strip plot displays individual data points for each category with random horizontal jitter to reduce overlap. Unlike swarm plots which intelligently spread points, strip plots add random noise to reveal density through point accumulation.

Key Features

• All individual observations visible as points
• Random jitter along categorical axis to reduce overplotting
• Simple and fast to render (unlike beeswarm algorithms)
• Shows both distribution and individual values

Applications

• Visualizing small to medium datasets by group
• Quick exploratory data analysis
• Showing raw data alongside summary statistics
• When individual observations need to be identifiable

Data Requirements

• category (categorical) - Group labels
• value (numeric) - Continuous variable
• Size: 10-200 observations per category works best

Visual Elements

• Points plotted at their true y-values
• Random horizontal offset (jitter) within each category
• Adjustable jitter amount to control spread
• Optional: transparency for overlapping points

[claude]

✅ Spec ID: strip-basic

Rationale: Strip plot is a distinct plot type for showing individual observations with jitter by category - different from scatter (x-y relationship), swarm (algorithmic spreading), or other distribution plots.

Add label approved to begin generation.

[github-actions]

✅ Specification Ready: strip-basic

PR: #985

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specification.md

strip-basic: Basic Strip Plot

Description

A strip plot displays individual data points for each category along a single axis, with random horizontal jitter applied to reduce overplotting. Unlike box plots or violin plots that show summary statistics, strip plots reveal every observation, making them ideal for small to medium datasets where individual values matter. The random jitter spreads points horizontally within each category to show density through point accumulation.

Applications

• Comparing distributions of test scores across different classrooms or teaching methods
• Visualizing patient response times to treatments across different drug groups
• Exploring salary distributions by department in organizational analysis
• Quality control inspection of measurements across production batches

Data

• category (categorical) - Group labels for the x-axis (e.g., treatment groups, departments)
• value (numeric) - Continuous measurement for the y-axis
• Size: 10-200 observations per category works best
• Example: Survey response scores grouped by demographic category

Notes

• Use moderate jitter width (0.1-0.3) to spread points without overlapping adjacent categories
• Apply transparency (alpha 0.5-0.7) when points overlap frequently
• Consider adding horizontal lines for group means or medians as reference
• For very large datasets (>200 per category), consider swarm plots or violin plots instead

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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: strip-basic
PR: #985 (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