feat(letsplot): implement line-retention-cohort

[github-actions]

Implementation: line-retention-cohort - letsplot

Implements the letsplot version of line-retention-cohort.

File: plots/line-retention-cohort/implementations/letsplot.py

Parent Issue: #4572

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays 5 retention curves (Jan–May 2025) on a clean minimal background. Lines use a monochromatic blue palette graduating from light blue (Jan 2025, oldest) to dark navy (May 2025, newest). All curves start at 100% at week 0 and decay over 12 weeks, with newer cohorts retaining better. Points are marked on each line. A horizontal gray dashed reference line sits at 20%. The title reads "line-retention-cohort · letsplot · pyplots.ai" centered at top. X-axis is labeled "Weeks Since Signup" (0–12, even ticks), Y-axis "Retained Users (%)" (0–100, gridlines at 20% intervals). Legend on the right shows cohort names with sample sizes (e.g., "Jan 2025 (n=1,245)"). Subtle gray major gridlines, no minor gridlines. Overall layout is well-proportioned with good canvas utilization.

Score: 85/100

Category	Score	Max
Visual Quality	27	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	14	15
Code Quality	10	10
Library Mastery	6	10
Total	85	100

Visual Quality (27/30)

• VQ-01: Text Legibility (7/8) - All font sizes explicitly set (title=28, axis_title=22, axis_text=18, legend=16), all clearly readable
• VQ-02: No Overlap (6/6) - No overlapping elements anywhere
• VQ-03: Element Visibility (5/6) - Lines (size=2.5) and points (size=4) are well-visible; some lines converge and are hard to distinguish due to similar blue shades
• VQ-04: Color Accessibility (3/4) - Monochromatic blue palette is distinguishable by lightness but not ideal for all viewers; 5 shades of blue can be challenging
• VQ-05: Layout & Canvas (4/4) - Excellent proportions, plot fills canvas well, legend positioned cleanly
• VQ-06: Axis Labels & Title (2/2) - "Weeks Since Signup" and "Retained Users (%)" are descriptive with units

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) - Custom monochromatic blue palette with intentional light-to-dark gradient creates cohesive look; above defaults but not publication-level
• DE-02: Visual Refinement (4/6) - theme_minimal removes spines, major grid customized to subtle gray, minor grid removed, legend title blank
• DE-03: Data Storytelling (4/6) - Color gradient (lighter=older, darker=newer) naturally guides viewer to see improving retention over time; 20% threshold line adds benchmark context

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct line chart with multiple cohort curves
• SC-02: Required Features (4/4) - All curves start at 100% ✓, distinct colors ✓, legend with cohort label and size ✓, Y-axis 0-100% with gridlines ✓, X-axis = time since signup ✓, lighter colors for older cohorts ✓, dashed reference line at 20% ✓
• SC-03: Data Mapping (3/3) - X=weeks since signup, Y=retention percentage, correct mapping
• SC-04: Title & Legend (3/3) - Title format correct, legend labels show "Jan 2025 (n=1,245)" format

Data Quality (14/15)

• DQ-01: Feature Coverage (5/6) - 5 cohorts with varying decay rates showing clear differentiation; noise adds realism; could show more varied curve shapes
• DQ-02: Realistic Context (5/5) - Monthly signup cohorts with realistic user counts (1,100–1,510), standard product analytics scenario
• DQ-03: Appropriate Scale (4/4) - Retention values follow realistic exponential decay, cohort sizes are plausible, 12-week tracking window is industry standard

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data → plot → save flow, no functions or classes
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set
• CQ-03: Clean Imports (2/2) - All imports used (numpy, pandas, lets_plot)
• CQ-04: Code Elegance (2/2) - Clean, appropriate complexity, no over-engineering
• CQ-05: Output & API (1/1) - Saves as plot.png with ggsave, current API

Library Mastery (6/10)

• LM-01: Idiomatic Usage (4/5) - Good ggplot2-style grammar: aes, geom_line, geom_point, scale_color_manual, theme, labs
• LM-02: Distinctive Features (2/5) - Uses ggsize and ggsave with scale (letsplot-specific) plus HTML export, but doesn't leverage more distinctive features like tooltips or interactive capabilities

Score Caps Applied

• None

Strengths

• Perfect spec compliance — all required features present including reference line, cohort sizes in legend, and lighter colors for older cohorts
• Effective data storytelling through monochromatic color gradient that naturally conveys temporal ordering
• Clean, well-structured code following KISS principles with proper reproducibility

Weaknesses

• Monochromatic blue palette, while aesthetically cohesive, makes it harder to distinguish individual cohorts — especially the middle three lines
• Library mastery doesn't leverage letsplot-specific interactive features (tooltips for HTML output, geom_text for endpoint labels)
• Could push design further with endpoint value annotations or a more varied colorblind-friendly palette

Issues Found

1. VQ-04 MODERATE: Monochromatic blue palette makes middle cohorts hard to distinguish
   • Fix: Use a more varied colorblind-safe palette (e.g., 5 distinct hues) while keeping the visual hierarchy through line weight or alpha
2. LM-02 LOW: Generic ggplot2 usage without leveraging letsplot-distinctive features
   • Fix: Add geom_text for endpoint retention labels, or leverage interactive tooltips for the HTML version
3. DE-01 MODERATE: Good but not publication-ready aesthetic
   • Fix: Consider adding endpoint annotations, refining the color palette for stronger differentiation, or adding a subtle subtitle with context

AI Feedback for Next Attempt

Use a more varied color palette (5 distinct hues) instead of monochromatic blue to improve distinguishability while maintaining visual hierarchy through line weight or alpha. Add geom_text endpoint labels showing final retention values to enhance storytelling. Consider leveraging letsplot-specific features like interactive tooltips for the HTML export.

Verdict: REJECTED

[github-actions]

🔧 Repair Attempt 1/3

Applied fixes based on AI review feedback.

Status: Repair completed, re-triggering review...

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🤖 impl-repair

[claude]

AI Review - Attempt 2/3

Image Description

The plot displays 5 retention curves for monthly signup cohorts (Jan–May 2025) on a clean white background. All curves start at 100% at week 0 and decay over 12 weeks with exponential profiles. Colors progress from light blue (Jan 2025, oldest) through green (Feb), orange (Mar), red (Apr), to dark navy blue (May 2025, newest). Newer cohorts have thicker lines, creating clear visual hierarchy. Data points are marked along each curve. Endpoint percentage labels (12%, 15%, 17%, 23%, 31%) are displayed at week 12, color-matched to their respective cohorts. A dashed gray horizontal line at y=20 marks the "20% threshold" benchmark. The legend on the right lists each cohort with sample size (e.g., "Jan 2025 (n=1,245)"). Title reads "line-retention-cohort · letsplot · pyplots.ai". X-axis: "Weeks Since Signup", Y-axis: "Retained Users (%)". Subtle light gray major gridlines on a minimal theme.

Score: 91/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	15	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	91	100

Visual Quality (29/30)

• VQ-01: Text Legibility (8/8) - All font sizes explicitly set: title=28, axis_title=22, axis_text=18, legend_text=16. All text clearly readable.
• VQ-02: No Overlap (5/6) - Endpoint labels at week 12 use overlap-prevention logic (min_gap=3.5), but the 12%/15%/17% labels are still fairly tight together.
• VQ-03: Element Visibility (6/6) - Lines well-sized with progressive widths (1.5–3.0), points at size=4 are clearly visible.
• VQ-04: Color Accessibility (4/4) - Distinct colorblind-friendly palette with good separation: light blue, green, orange, red, dark blue.
• VQ-05: Layout & Canvas (4/4) - Plot fills canvas well, x-axis extended to 14.5 to accommodate endpoint labels without crowding.
• VQ-06: Axis Labels & Title (2/2) - "Weeks Since Signup" and "Retained Users (%)" are descriptive with units.

Design Excellence (15/20)

• DE-01: Aesthetic Sophistication (6/8) - Custom palette with intentional light-to-bold progression emphasizing newer cohorts. Endpoint labels and threshold annotation add professional polish beyond defaults.
• DE-02: Visual Refinement (4/6) - theme_minimal() applied, minor grid removed, major grid subtle (#EBEBEB, 0.4px), legend title hidden, white background. Good refinement but spines still present.
• DE-03: Data Storytelling (5/6) - Strong visual hierarchy: newer cohorts have thicker/bolder lines, endpoint labels show final retention %, threshold reference line contextualizes performance. Data clearly shows improving retention over time.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) - Correct line chart with multiple cohort curves showing retention decay.
• SC-02: Required Features (4/4) - All spec features present: curves start at 100%, distinct colors per cohort, legend with cohort label and size, y-axis 0–100% with gridlines, x-axis as time since signup, varying line thickness for emphasis, dashed reference line at 20%.
• SC-03: Data Mapping (3/3) - X=weeks since signup, Y=retention percentage, correctly mapped.
• SC-04: Title & Legend (3/3) - Title format "line-retention-cohort · letsplot · pyplots.ai" correct. Legend labels match spec format "Jan 2025 (n=1,245)".

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) - 5 cohorts with different decay rates showing clear variation; exponential decay with noise creates realistic curves.
• DQ-02: Realistic Context (5/5) - Monthly signup cohorts with realistic sizes (1,102–1,510), plausible retention decay rates for a SaaS product.
• DQ-03: Appropriate Scale (4/4) - Retention values sensible (12–31% at week 12), cohort sizes realistic for product analytics.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) - Clean imports → data generation → plot construction → save flow. Per-cohort loop for line widths is justified.
• CQ-02: Reproducibility (2/2) - np.random.seed(42) set.
• CQ-03: Clean Imports (2/2) - All imports used (numpy, pandas, lets_plot).
• CQ-04: Code Elegance (2/2) - Clean code with thoughtful endpoint label overlap prevention. Appropriate complexity.
• CQ-05: Output & API (1/1) - Saves as plot.png with scale=3 and plot.html.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) - Good ggplot grammar usage with aes(), geom_line(), geom_point(), scale_color_manual(), theme_minimal(). Per-group loop for varying line widths is necessary since lets-plot doesn't support size as an aesthetic mapping in this context.
• LM-02: Distinctive Features (3/5) - Uses layer_tooltips() for interactive hover info (lets-plot specific), HTML export alongside PNG, ggsize() for sizing.

Score Caps Applied

• None

Strengths

• Excellent data storytelling through progressive line weights and color intensity emphasizing newer cohorts
• Endpoint labels with overlap prevention provide clear final-retention context
• 20% threshold reference line adds analytical value
• Full spec compliance with all required features implemented
• Interactive tooltips leverage lets-plot's distinctive capabilities

Weaknesses

• Endpoint labels for lower cohorts (12%, 15%, 17%) are slightly tight despite overlap prevention

Issues Found

None critical — implementation meets quality threshold.

AI Feedback for Next Attempt

N/A — implementation approved.

Verdict: APPROVED