feat(letsplot): implement scatter-annotated

[github-actions]

Implementation: scatter-annotated - letsplot

Implements the letsplot version of scatter-annotated.

File: plots/scatter-annotated/implementations/letsplot.py

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a scatter chart showing 15 company names plotted by Annual Revenue (x-axis, in $ millions, ranging from ~60 to ~540) against Year-over-Year Growth (y-axis, in %, ranging from 0 to 55). Each data point is a blue (#306998) circle with alpha=0.7, and company names appear as dark gray text labels positioned above each point. The title "scatter-annotated · lets-plot · pyplots.ai" appears at the top. Notable outliers include QuantumAI (low revenue, high growth ~48%), NeuraTech (high revenue ~480M, high growth ~42%), and MegaSoft (highest revenue ~520M, low growth ~8%). The plot uses a minimal theme with subtle gray grid lines.

Quality Score: 91/100

Criteria Checklist

Visual Quality (36/40 pts)

• VQ-01: Text Legibility (10/10) - all text clearly readable at full resolution
• VQ-02: No Overlap (4/8) - some labels are close together (ByteWorks/SmartScale, DataSys/LogiCore area) though still readable
• VQ-03: Element Visibility (8/8) - markers well-sized with appropriate alpha
• VQ-04: Color Accessibility (5/5) - single blue color, colorblind-safe
• VQ-05: Layout Balance (5/5) - good proportions, plot fills canvas well
• VQ-06: Axis Labels (2/2) - descriptive with units ("Annual Revenue ($ millions)", "Year-over-Year Growth (%)")
• VQ-07: Grid & Legend (2/2) - subtle grid, no legend needed

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - correct scatter plot with text annotations
• SC-02: Data Mapping (5/5) - X=revenue, Y=growth, correctly mapped
• SC-03: Required Features (5/5) - text labels present for all points
• SC-04: Data Range (3/3) - all data points visible within axes
• SC-05: Legend Accuracy (2/2) - N/A (no legend needed)
• SC-06: Title Format (2/2) - uses correct format "{spec-id} · {library} · pyplots.ai"

Data Quality (20/20 pts)

• DQ-01: Feature Coverage (8/8) - shows variety: high/low revenue, high/low growth, outliers
• DQ-02: Realistic Context (7/7) - plausible business scenario (company revenue vs growth)
• DQ-03: Appropriate Scale (5/5) - sensible values for revenue (50-520M) and growth (5-48%)

Code Quality (10/10 pts)

• CQ-01: KISS Structure (3/3) - imports → data → plot → save, no functions/classes
• CQ-02: Reproducibility (3/3) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - only necessary imports
• CQ-04: No Deprecated API (1/1) - uses current lets-plot API
• CQ-05: Output Correct (1/1) - saves as plot.png

Library Features (4/5 pts)

• LF-01: Uses geom_text with nudge_y, theme_minimal, ggsize, ggsave with scale parameter

Strengths

• Clean, readable visualization with all 15 company labels clearly visible
• Good use of geom_text with nudge_y for label positioning
• Appropriate point size and alpha for data density
• Realistic business scenario with meaningful outliers (startup high-growth, mature low-growth)
• Correct title format and axis labels with units

Weaknesses

• Some label pairs are close together (ByteWorks/SmartScale, DataSys/LogiCore/CloudNet region) - could benefit from adjustText-like positioning if available in lets-plot

Verdict: APPROVED