feat(letsplot): implement bar-categorical

[github-actions]

Implementation: bar-categorical - letsplot

Implements the letsplot version of bar-categorical.

File: plots/bar-categorical/implementations/letsplot.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a categorical bar chart showing fruit type frequency counts. Five vertical bars represent different fruits: Bananas (~40), Mangoes (~31), Grapes (~33), Oranges (~40), and Apples (~56). The bars are rendered in a muted blue color (#306998) with darker borders. The title "bar-categorical · letsplot · pyplots.ai" appears at the top left in bold. The x-axis is labeled "Fruit Type" with category names below each bar, and the y-axis is labeled "Count" with tick marks from 0 to 55. The plot uses a minimal theme with horizontal grid lines only, and the overall layout is clean with good proportions.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - Title is bold and large, axis labels and tick text are clearly readable at full resolution
• VQ-02: No Overlap (8/8) - No overlapping text, category labels are well-spaced
• VQ-03: Element Visibility (8/8) - Bars are appropriately sized with good width and clear visibility
• VQ-04: Color Accessibility (5/5) - Single color scheme with good contrast, no colorblind issues
• VQ-05: Layout Balance (5/5) - Plot fills canvas appropriately with balanced margins
• VQ-06: Axis Labels (1/2) - Labels are descriptive ("Fruit Type", "Count") but lack units
• VQ-07: Grid & Legend (1/2) - Horizontal grid only is appropriate for bar chart, but no legend needed; minor grid could be more subtle

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct categorical count bar chart using geom_bar()
• SC-02: Data Mapping (5/5) - Categories on x-axis, counts on y-axis (computed automatically)
• SC-03: Required Features (5/5) - Automatic count computation from raw categorical data
• SC-04: Data Range (3/3) - Y-axis shows full range of counts (0-55)
• SC-05: Legend Accuracy (2/2) - No legend needed for single-series bar chart
• SC-06: Title Format (2/2) - Correctly formatted as "bar-categorical · letsplot · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows variation in counts across categories, demonstrates the counting functionality well
• DQ-02: Realistic Context (5/7) - Fruit popularity is plausible but generic; could be more specific (e.g., store inventory, survey results)
• DQ-03: Appropriate Scale (5/5) - 200 samples with realistic probability weights producing sensible counts

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save structure
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Modern lets-plot API
• CQ-05: Output Correct (0/1) - Saves both plot.png and plot.html which is correct for letsplot

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses ggplot grammar correctly with geom_bar() and theme customization, but does not leverage lets-plot specific features like tooltips or interactivity

Strengths

• Clean implementation following ggplot2 grammar of graphics
• Automatic count computation using geom_bar() without stat="identity"
• Well-sized text elements (24pt title, 20pt axis labels, 16pt tick text) following library guidelines
• Good use of theme_minimal() with appropriate grid customization
• Proper figure sizing (1600x900 with scale=3 for 4800x2700 output)

Weaknesses

• Does not leverage lets-plot interactive features (tooltips, hover effects) that distinguish it from plotnine
• Data context is generic; could use a more specific real-world scenario

Verdict: APPROVED