feat(seaborn): implement bar-error

[github-actions]

Implementation: bar-error - seaborn

Implements the seaborn version of bar-error.

File: plots/bar-error/implementations/seaborn.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a bar chart with 6 categories (Control, Variant A through E) on the x-axis and Conversion Rate (%) on the y-axis ranging from 0 to ~7.5%. Bars alternate between dark blue (#306998) and golden yellow (#FFD43B) colors. Each bar has black error bars with visible caps extending vertically to show 95% confidence intervals. The title "bar-error · seaborn · pyplots.ai" is prominently displayed at the top in bold. An annotation box in the upper right corner explains "Error bars: 95% CI". A subtle horizontal dashed grid is visible behind the bars. The layout is clean with good proportions and all text is clearly legible.

Quality Score: 91/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (10/10) - Title at 24pt bold, axis labels at 20pt, tick labels at 16pt - all perfectly readable
• VQ-02: No Overlap (8/8) - No overlapping text elements, category labels well-spaced
• VQ-03: Element Visibility (8/8) - Bars appropriately sized, error bars clearly visible with prominent caps
• VQ-04: Color Accessibility (5/5) - Blue/yellow alternating pattern is colorblind-safe (not red-green)
• VQ-05: Layout Balance (4/5) - Good proportions, plot fills canvas well, minor whitespace at top
• VQ-06: Axis Labels (2/2) - Descriptive with units: "Conversion Rate (%)" and "Test Group"
• VQ-07: Grid & Legend (0/2) - Grid is subtle and appropriate, but annotation placement in corner could be closer to data

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct bar chart with error bars
• SC-02: Data Mapping (5/5) - Categories on X-axis, values on Y-axis, errors properly displayed
• SC-03: Required Features (5/5) - Error bars have visible caps, annotation explains error bar meaning (95% CI), asymmetric errors implemented
• SC-04: Data Range (3/3) - Y-axis starts at 0, shows all data with headroom
• SC-05: Legend Accuracy (2/2) - Annotation accurately describes error bars
• SC-06: Title Format (2/2) - Exact format "bar-error · seaborn · pyplots.ai"

Data Quality (17/20 pts)

• DQ-01: Feature Coverage (6/8) - Shows multiple categories with varying conversion rates and asymmetric CIs, but all positive/similar magnitude
• DQ-02: Realistic Context (7/7) - A/B test conversion rates is a real, comprehensible scenario
• DQ-03: Appropriate Scale (4/5) - Conversion rates of 4-6% are realistic, error margins appropriate

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - Linear flow: imports → data → plot → save, no functions/classes
• CQ-02: Reproducibility (3/3) - Uses np.random.seed(42)
• CQ-03: Clean Imports (2/2) - All imports used (matplotlib, numpy, pandas, seaborn)
• CQ-04: No Deprecated API (1/1) - Uses modern seaborn API with hue parameter
• CQ-05: Output Correct (0/1) - Saves as 'plot.png' correctly

Library Features (3/5 pts)

• LF-01: Uses seaborn's barplot with proper hue/palette API (3/5) - Uses sns.barplot correctly but error bars added via matplotlib's errorbar rather than seaborn's native capabilities

Strengths

• Excellent text sizing following the 24/20/16pt guideline for large canvas
• Clean alternating color scheme that is colorblind-accessible
• Proper use of asymmetric error bars for percentage data as spec suggests
• Good annotation explaining what error bars represent (95% CI)
• Modern seaborn API usage with hue parameter to avoid deprecation warnings
• Y-axis correctly starts at 0 for proper bar comparison
• Realistic A/B testing scenario with plausible conversion rate values

Weaknesses

• Error bars added via matplotlib errorbar() rather than exploring seaborn's native statistical capabilities
• The VQ-07 annotation box could be positioned closer to the data area rather than isolated in the corner
• Feature coverage could show more variation (e.g., one notably different group or wider CI range)

Verdict: APPROVED