feat(letsplot): implement bar-basic

[github-actions]

Implementation: bar-basic - letsplot

Implements the letsplot version of bar-basic.

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

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a vertical bar chart showing sales data for 6 product categories: Electronics ($45,200), Clothing ($32,800), Home & Garden ($28,500), Sports ($21,300), Books ($18,900), and Toys ($15,600). All bars use a consistent blue color (#306998) with dollar-formatted value labels positioned above each bar. The x-axis labels are rotated 45 degrees for readability. The plot uses a minimal theme with subtle horizontal gridlines (no vertical gridlines). The title "bar-basic · letsplot · pyplots.ai" is centered at the top. The y-axis shows "Sales ($)" and x-axis shows "Product Category".

Quality Score: 99/100

Criteria Checklist

Visual Quality (40/40 pts)

• VQ-01: Text Legibility (10/10) - All text perfectly readable at full resolution
• VQ-02: No Overlap (8/8) - No overlapping text, rotated x-labels avoid collision
• VQ-03: Element Visibility (8/8) - Bars well-sized with appropriate width (0.6)
• VQ-04: Color Accessibility (5/5) - Single blue color, no colorblind issues
• VQ-05: Layout Balance (5/5) - Good proportions and margins
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Sales ($)", "Product Category"
• VQ-07: Grid & Legend (2/2) - Subtle horizontal grid, no legend needed

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct vertical bar chart
• SC-02: Data Mapping (5/5) - Categories on X, values on Y correctly mapped
• SC-03: Required Features (5/5) - Value labels, consistent bar widths, single color as spec suggests
• SC-04: Data Range (3/3) - All data visible within y-axis limits (0-55000)
• SC-05: Legend Accuracy (2/2) - N/A for single-color chart
• SC-06: Title Format (2/2) - Correct: "bar-basic · letsplot · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - Shows 6 varied categories with descending values demonstrating comparison
• DQ-02: Realistic Context (7/7) - Real scenario: product sales by category with plausible values
• DQ-03: Appropriate Scale (5/5) - Realistic sales values ($15K-$45K range)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - Simple linear structure: imports → data → plot → save
• CQ-02: Reproducibility (3/3) - Deterministic data (no random values needed)
• CQ-03: Clean Imports (2/2) - Only necessary imports
• CQ-04: No Deprecated API (1/1) - Current API usage
• CQ-05: Output Correct (1/1) - Saves plot.png and plot.html

Library Features (4/5 pts)

• LF-01: Uses distinctive library features (4/5) - Good use of ggplot grammar, geom_text with label_format for currency, theme_minimal, custom theme elements

Strengths

• Excellent use of geom_text with label_format for currency formatting ($X,XXX)
• Clean ggplot2-style grammar with proper layering
• Appropriate use of pd.Categorical to preserve category order
• Well-configured theme with proper font sizes (16pt axis text, 20pt titles, 24pt plot title)
• Correct 16:9 aspect ratio (1600x900 base, scaled 3x to 4800x2700)
• Value labels positioned above bars using position_nudge for clarity

Weaknesses

• Could use scale_y_continuous with labels parameter for formatted y-axis tick values (currently shows raw numbers like 45,000 instead of $45K)

Verdict: APPROVED