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feat: add 5 new plot specifications
- scatter-categorical: Categorical Scatter Plot - line-styled: Styled Line Plot - line-markers: Line Plot with Markers - line-stepwise: Step Line Plot - histogram-stacked: Stacked Histogram 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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‎plots/histogram-stacked/specification.md‎

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# histogram-stacked: Stacked Histogram
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## Description
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A histogram showing multiple groups stacked on top of each other within each bin. The total bar height represents the combined frequency, while colored segments show individual group contributions.
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## Applications
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- Comparing distributions of multiple groups
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- Showing composition within histogram bins
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- Total frequency with group breakdown
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- Demographic or categorical comparisons
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## Data
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The visualization requires:
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- **Continuous variable**: Values to bin
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- **Category variable**: Group membership for stacking
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Example structure:
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```
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Value | Group
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------|-------
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12.5 | A
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18.3 | B
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15.7 | A
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22.1 | B
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14.2 | C
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...
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```
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## Notes
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- Same bin boundaries applied to all groups
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- Distinct colors for each group
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- Legend shows group labels
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- Total bar height = combined frequency
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- Consider ordering groups by size within bins

‎plots/histogram-stacked/specification.yaml‎

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spec_id: histogram-stacked
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title: Stacked Histogram
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created: '2025-12-30T10:30:00Z'
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updated: '2025-12-30T10:30:00Z'
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issue: 2601
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suggested: claude-code
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tags:
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plot_type: [histogram, distribution]
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domain: [statistics, demographics]
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features: [stacked, grouped, comparison]
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data_type: [numeric, categorical]

‎plots/line-markers/specification.md‎

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# line-markers: Line Plot with Markers
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## Description
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A line plot with visible markers at each data point, combining line and scatter plot features. This is particularly useful for sparse data where individual observations are significant.
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## Applications
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- Sparse datasets where each point matters
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- Highlighting specific data points on trend lines
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- Experimental data with discrete measurements
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- Quality control charts with individual readings
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## Data
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The visualization requires:
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- **X variable**: Continuous variable
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- **One or more Y variables**: Series with data points to mark
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Example structure:
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```
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X | Value
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-----|-------
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0 | 10.5
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1 | 12.3
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2 | 11.8
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3 | 14.2
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...
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```
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## Notes
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- Markers should be clearly visible against the line
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- Use different marker shapes for multiple series
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- Marker size should be proportional to line thickness
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- Consider filled vs unfilled markers for distinction

‎plots/line-markers/specification.yaml‎

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spec_id: line-markers
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title: Line Plot with Markers
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created: '2025-12-30T10:30:00Z'
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updated: '2025-12-30T10:30:00Z'
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issue: 2599
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suggested: claude-code
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tags:
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plot_type: [line, scatter]
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domain: [general, scientific]
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features: [markers, data-points, sparse-data]
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data_type: [numeric, continuous]

‎plots/line-stepwise/specification.md‎

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# line-stepwise: Step Line Plot
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## Description
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A step function plot where values remain constant until the next change, creating horizontal-then-vertical transitions. This visualization emphasizes discrete changes rather than interpolated values.
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## Applications
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- Discrete state changes over time
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- Price/value stepping (stock prices at close)
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- Cumulative counts that increase discretely
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- Digital signals and binary states
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- Piecewise constant functions
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## Data
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The visualization requires:
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- **X variable**: Continuous or discrete variable (often time)
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- **Y variable**: Values that change at specific points
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Example structure:
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```
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X | Value
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-----|-------
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0 | 100
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1 | 100
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2 | 150
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3 | 150
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4 | 125
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...
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```
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## Notes
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- Step alignment options: 'pre' (before), 'mid' (middle), 'post' (after)
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- Clear distinction from smooth line interpolation
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- Horizontal segments show value persistence
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- Vertical segments show instantaneous changes

‎plots/line-stepwise/specification.yaml‎

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spec_id: line-stepwise
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title: Step Line Plot
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created: '2025-12-30T10:30:00Z'
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updated: '2025-12-30T10:30:00Z'
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issue: 2600
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suggested: claude-code
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tags:
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plot_type: [line, step]
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domain: [general, signal-processing]
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features: [discrete, step-function, piecewise]
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data_type: [numeric, discrete]

‎plots/line-styled/specification.md‎

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# line-styled: Styled Line Plot
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## Description
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A line plot using different line styles (solid, dashed, dotted, dash-dot) to distinguish multiple data series. This is especially useful for black-and-white printing or when color distinction is insufficient.
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## Applications
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- Comparing trends in print publications
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- Distinguishing overlapping lines without color
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- Accessibility-friendly visualizations
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- Technical documentation with monochrome printing
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## Data
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The visualization requires:
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- **X variable**: Continuous variable (often time or sequence)
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- **Multiple Y variables**: Different series to compare
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Example structure:
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```
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X | Series A | Series B | Series C
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-----|----------|----------|----------
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0 | 10 | 15 | 12
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1 | 12 | 14 | 15
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2 | 15 | 13 | 18
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...
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```
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## Notes
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- Standard styles: solid, dashed, dotted, dash-dot
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- Include legend mapping styles to series names
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- Line width should be consistent across styles
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- Consider line style visibility at different scales

‎plots/line-styled/specification.yaml‎

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spec_id: line-styled
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title: Styled Line Plot
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created: '2025-12-30T10:30:00Z'
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updated: '2025-12-30T10:30:00Z'
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issue: 2598
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suggested: claude-code
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tags:
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plot_type: [line, comparison]
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domain: [general, publishing]
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features: [line-styles, multi-series, print-friendly]
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data_type: [numeric, continuous]

‎plots/scatter-categorical/specification.md‎

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# scatter-categorical: Categorical Scatter Plot
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## Description
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A scatter plot where points are colored according to a categorical variable. Each category has a distinct color, allowing visual comparison of patterns across groups. A legend maps colors to category names.
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## Applications
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- Visualizing relationships between variables across different groups
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- Comparing correlation patterns by category
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- Identifying clusters in multivariate data
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- Exploratory analysis with group membership
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## Data
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The visualization requires:
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- **X variable**: First continuous/numeric variable
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- **Y variable**: Second continuous/numeric variable
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- **Category variable**: Categorical grouping variable
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Example structure:
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```
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X | Y | Category
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------|-------|----------
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1.2 | 3.4 | Group A
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2.1 | 4.5 | Group B
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1.8 | 3.9 | Group A
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...
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```
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## Notes
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- Use distinct, colorblind-safe colors for categories
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- Include legend for category identification
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- Marker shapes can also vary by group for additional distinction
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- Consider alpha transparency for overlapping points

‎plots/scatter-categorical/specification.yaml‎

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spec_id: scatter-categorical
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title: Categorical Scatter Plot
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created: '2025-12-30T10:30:00Z'
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updated: '2025-12-30T10:30:00Z'
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issue: 2597
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suggested: claude-code
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tags:
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plot_type: [scatter, categorical]
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domain: [statistics, general]
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features: [grouped, color-coded, legend]
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data_type: [numeric, categorical]

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