feat(ggplot2): implement errorbar-basic

[github-actions]

Implementation: errorbar-basic - r/ggplot2

Implements the r/ggplot2 version of errorbar-basic.

File: plots/errorbar-basic/implementations/r/ggplot2.R

Parent Issue: #973

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🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

Light render (plot-light.png): Warm off-white #FAF8F1 background. Title "errorbar-basic · r · ggplot2 · anyplot.ai" in dark ink at ~50% canvas width — clearly readable. Y-axis label "Enzyme Activity (μmol / min)" and X-axis label "Temperature" in dark ink at 10pt, proportional and readable. Tick labels (20°C–50°C, 10–40) in secondary ink at 8pt — readable. Caption "Error bars: ±1 SD (n = 12 replicates per condition)" at bottom-left, legible. Seven data points with symmetric #009E73 error bars capped at both ends, overlaid with matching point markers (size=3.5). Bell-curve pattern peaks at 35°C. Only horizontal grid lines; top/right spines absent. All text readable against the light background — no light-on-light issues.

Dark render (plot-dark.png): Warm near-black #1A1A17 background. Title, axis labels, and tick labels flip to light ink (#F0EFE8 / #B8B7B0) — all clearly readable against the dark surface. No dark-on-dark failures. Data colors are identical to the light render: error bars and point markers remain #009E73 brand green, clearly visible. Grid lines subtle but present. Caption readable. Both renders theme-correct; only chrome flips between themes.

Both paragraphs are required. A review that only describes one render is invalid.

Score: 88/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	12	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	7	10
Total	88	100

Visual Quality (29/30)

• VQ-01: Text Legibility (7/8) — Font sizes explicitly set (title=12, axis=10, ticks=8); readable in both themes. Caption at 7pt is near the floor but legible.
• VQ-02: No Overlap (6/6) — No overlapping elements in either render.
• VQ-03: Element Visibility (6/6) — 7 sparse data points; markers size=3.5 and error bars with caps all clearly visible.
• VQ-04: Color Accessibility (2/2) — Single series #009E73; no CVD concerns.
• VQ-05: Layout & Canvas (4/4) — Canvas 3200×1800, no clipping, generous margins, no overflow.
• VQ-06: Axis Labels & Title (2/2) — Y: "Enzyme Activity (μmol / min)" with units; X: "Temperature"; caption describes error bars; title format correct.
• VQ-07: Palette Compliance (2/2) — First series #009E73 ✓; backgrounds #FAF8F1 light / #1A1A17 dark ✓; chrome flips correctly.

Design Excellence (12/20)

• DE-01: Aesthetic Sophistication (5/8) — Intentional Imprint palette, full theme-adaptive token set, scientific context creates authentic look. No visual emphasis on enzyme optimum at 35°C reduces ceiling.
• DE-02: Visual Refinement (4/6) — Top/right spines removed via theme_minimal, y-only subtle grid, axis lines retained, generous explicit margins (20, 24, 16, 20).
• DE-03: Data Storytelling (3/6) — Bell-curve pattern naturally visible and caption contextualizes error bars, but no explicit focal-point emphasis (e.g., annotation or enlarged marker at 35°C peak).

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct error bar plot type.
• SC-02: Required Features (4/4) — Visible caps (width=0.22), consistent widths, point markers for means, symmetric ±1 SD errors, caption.
• SC-03: Data Mapping (3/3) — Temperature on X, enzyme activity on Y, all 7 conditions shown.
• SC-04: Title & Legend (3/3) — Title: errorbar-basic · r · ggplot2 · anyplot.ai ✓. No legend (correct for single series).

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Symmetric error bars, caps, mean markers, categorical X axis, caption.
• DQ-02: Realistic Context (5/5) — Enzyme activity vs temperature is a standard biochemistry scenario; neutral and plausible.
• DQ-03: Appropriate Scale (4/4) — Activity 8–36 μmol/min with SD 1.4–3.2 are scientifically plausible.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Flat script, no functions or classes.
• CQ-02: Reproducibility (2/2) — set.seed(42) present; data is fully deterministic.
• CQ-03: Clean Imports (2/2) — Only ggplot2 and ragg imported; both used.
• CQ-04: Code Elegance (2/2) — Appropriate complexity, no fake UI, clean structure.
• CQ-05: Output & API (1/1) — Saves as plot-%s.png via sprintf; ragg device; correct canvas dimensions.

Library Mastery (7/10)

• LM-01: Idiomatic Usage (4/5) — Idiomatic ggplot2: ggplot() + geom_errorbar(aes(ymin, ymax)) + geom_point() + labs() + theme_minimal() + theme(). Correct ggsave with ragg device.
• LM-02: Distinctive Features (3/5) — geom_errorbar (ggplot2-native), layer composition of errorbar + point, factor() for ordered categorical X axis, ragg PNG device.

Score Caps Applied

• None

Strengths

• Correct error bar plot with visible caps and consistent widths matching spec exactly
• Complete theme-adaptive chrome — all INK/INK_SOFT tokens correctly applied to title, axis labels, ticks, grid, and caption in both renders
• Realistic scientific enzyme activity dataset with a natural bell-curve narrative peaking at 35°C
• Clean KISS code: flat script, only ggplot2 + ragg imported, deterministic data, correct 3200×1800 canvas
• Palette compliant: single series uses #009E73 brand green; backgrounds #FAF8F1 light / #1A1A17 dark

Weaknesses

• set.seed(42) is called but data is entirely hardcoded — seed has no effect; either remove or use actual random simulation
• ELEVATED_BG token defined but unused (no legend in single-series plot) — harmless dead code, remove for clarity
• No visual emphasis on the 35°C peak (enzyme optimum) — an annotation or slightly larger point marker would create a stronger focal point and lift DE-03
• Grid color uses hardcoded hex approximations (#D5D4CD / #3D3D38) rather than the INK_SOFT token — minor inconsistency with the rest of the theme token approach

Issues Found

1. DE-03 MEDIUM: No focal point emphasis at the temperature optimum (35°C)
   • Fix: Add a text annotation ("Peak: 35°C") or use a slightly larger point marker at the peak to guide the viewer's eye
2. CQ-02 MINOR: set.seed(42) has no effect on hardcoded data
   • Fix: Either remove set.seed(42) or replace hardcoded arrays with rnorm-generated data that actually uses the seed

AI Feedback for Next Attempt

The implementation is clean, correct, and theme-adaptive. To reach the next level: (1) add a brief annotation or enlarged marker at 35°C to create a visual focal point; (2) replace the hardcoded grid-color hex with the INK_SOFT token (color = INK_SOFT at reduced alpha via alpha or a grDevices::adjustcolor(INK_SOFT, alpha.f = 0.5)); (3) remove set.seed(42) if data stays hardcoded, or switch to rnorm-based simulation.

Verdict: APPROVED

[github-actions]

🔧 Repair Attempt 1/4

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

Light render (plot-light.png): Warm off-white #FAF8F1 background — correct. Title "errorbar-basic · r · ggplot2 · anyplot.ai" in dark #1A1A17 ink, clearly readable. Axis label "Enzyme Activity (μmol / min)" with units on the Y-axis; "Temperature" on the X-axis — both in 10pt dark ink, well-proportioned. Tick labels (20°C–50°C and numeric Y values) in #4A4A44 INK_SOFT at 8pt, readable. Caption "Error bars: ±1 SD (n = 12 replicates per condition)" in 7pt soft ink at bottom left. All seven conditions display brand green #009E73 error bars with visible horizontal caps and center-value points. The 35°C point is rendered at size=6 (vs. size=3.5 for the others) to highlight the enzyme optimum; the annotation "enzyme optimum" appears above it in secondary ink. Bell-curve response pattern is clearly visible. All text readable against the light background — no light-on-light issues.

Dark render (plot-dark.png): Warm near-black #1A1A17 background — correct. Title in light #F0EFE8 ink, clearly readable. Axis labels and tick labels in #B8B7B0 INK_SOFT light secondary ink — all readable. Annotation "enzyme optimum" in light secondary ink. No dark-on-dark failures detected. Data colors are identical to the light render: all error bars and points in #009E73 brand green. Grid lines visible as subtle light-gray horizontal rules. Caption readable in light ink at bottom left. Both renders pass legibility.

Score: 90/100

Category	Score	Max
Visual Quality	29	30
Design Excellence	13	20
Spec Compliance	15	15
Data Quality	15	15
Code Quality	10	10
Library Mastery	8	10
Total	90	100

Visual Quality (29/30)

• VQ-01: Text Legibility (7/8) — All font sizes explicitly set (title=12pt, axis.title=10pt, axis.text=8pt, caption=7pt, annotation=3.2mm). All text readable in both themes. Annotation text at size=3.2mm is small but visible; minor deduction for borderline scaled readability.
• VQ-02: No Overlap (6/6) — No overlapping text or data elements. Annotation well-separated above the 35°C peak. Tick labels well-spaced.
• VQ-03: Element Visibility (6/6) — Error bars with caps clearly visible. Points sized appropriately for 7 data points (size=3.5 standard, size=6.0 focal). Error bar widths=0.22 consistent.
• VQ-04: Color Accessibility (2/2) — Single series in Imprint brand green #009E73, colorblind-safe. Good contrast in both themes.
• VQ-05: Layout & Canvas (4/4) — Canvas gate passed (3200×1800). Balanced margins. Nothing cut off. Plot fills canvas well.
• VQ-06: Axis Labels & Title (2/2) — Y: "Enzyme Activity (μmol / min)" with units. X: "Temperature" descriptive.
• VQ-07: Palette Compliance (2/2) — First series #009E73 correct. Light bg #FAF8F1, dark bg #1A1A17. All chrome theme-adaptive in both renders.

Design Excellence (13/20)

• DE-01: Aesthetic Sophistication (5/8) — Above well-configured default: enlarged focal point for emphasis, clean annotation, Imprint palette correctly applied. Not yet FiveThirtyEight-level visual sophistication.
• DE-02: Visual Refinement (4/6) — L-shaped axis frame (no top/right), horizontal-only major grid with alpha-adjusted color via adjustcolor(), no minor grid, generous margins. Clear refinement above ggplot2 defaults.
• DE-03: Data Storytelling (4/6) — Enlarged 35°C point + "enzyme optimum" annotation actively guides viewer to the key insight. Bell-curve temperature response pattern is clearly communicated. Methodological caption adds credibility.

Spec Compliance (15/15)

• SC-01: Plot Type (5/5) — Correct error bar chart with center-value markers and visible horizontal caps.
• SC-02: Required Features (4/4) — Error bars with caps ✓, consistent widths ✓, center markers ✓, varying error magnitudes ✓.
• SC-03: Data Mapping (3/3) — X: categorical temperature, Y: enzyme activity. All 7 conditions visible with full data range.
• SC-04: Title & Legend (3/3) — Title exactly "errorbar-basic · r · ggplot2 · anyplot.ai". No legend needed for single series.

Data Quality (15/15)

• DQ-01: Feature Coverage (6/6) — Shows all aspects: error bars with caps, varying error magnitudes (SD 1.4–3.2), bell-curve response with a clear peak demonstrating a complete temperature-response curve.
• DQ-02: Realistic Context (5/5) — Real biochemistry scenario: enzyme kinetics temperature optimum study. Non-controversial, scientifically meaningful and comprehensible.
• DQ-03: Appropriate Scale (4/4) — Activity values 8.3–35.6 μmol/min plausible. 20°C–50°C range appropriate for mesophilic enzyme. SDs 1.4–3.2 reasonable for n=12 biological replicates. Optimum at 35°C near physiological temperature — factually grounded.

Code Quality (10/10)

• CQ-01: KISS Structure (3/3) — Linear: imports → tokens → data → plot → save. No functions or classes.
• CQ-02: Reproducibility (2/2) — Fully deterministic hardcoded data vectors; no random generation.
• CQ-03: Clean Imports (2/2) — Only ggplot2 and ragg imported, both used.
• CQ-04: Code Elegance (2/2) — Clean idiomatic R. Double geom_point() layering for emphasis is an elegant ggplot2 pattern.
• CQ-05: Output & API (1/1) — Saves as plot-{THEME}.png via sprintf(). Uses modern linewidth= API.

Library Mastery (8/10)

• LM-01: Idiomatic Usage (5/5) — Expert grammar of graphics: geom_errorbar + layered geom_point, annotate(), scale_y_continuous(expand=expansion()), theme_minimal() base with additive theme customization.
• LM-02: Distinctive Features (3/5) — Uses adjustcolor() trick for grid line alpha (ggplot2-specific workaround since theme() doesn't expose alpha for grid lines), expansion() for axis padding, and double geom_point() layering for emphasis. ggplot2-idiomatic but not uniquely distinctive features.

Score Caps Applied

• None — no caps triggered. DE-01=5 > 2, so the DE-01≤2 + DE-02≤2 cap does not apply.

Strengths

• Correct Imprint palette (#009E73) used throughout with properly themed chrome in both renders
• Enlarged focal point at 35°C plus "enzyme optimum" annotation provides clear visual hierarchy guiding the viewer to the key insight
• Perfect spec compliance: error bars with visible caps, consistent widths, and center-value markers
• Excellent real-world biochemistry dataset (enzyme kinetics temperature response) with scientifically plausible values and appropriate error magnitudes
• Expert ggplot2 grammar: geom_errorbar + layered geom_point, annotate(), expansion(), adjustcolor() for grid alpha
• All font sizes explicitly set with proper theme-adaptive chrome in both renders
• Methodological caption explaining error bar meaning adds scientific credibility

Weaknesses

• Annotation text size=3.2 (≈9pt) is small relative to the canvas — consider increasing to size=3.8–4.0 for better readability at scaled sizes
• Design, while clean and above defaults, is still close to a well-configured library default — a more deliberate emphasis technique (e.g., subtle reference band for the optimum zone) could push DE-01 higher
• LM-02: adjustcolor() grid trick and expansion() are ggplot2-idiomatic but a uniquely distinctive ggplot2 feature (e.g., stat_smooth, geom_ribbon for CI bands) would score higher

Issues Found

1. VQ-01 MINOR: Annotation text size=3.2mm is small — borderline readable at scaled sizes
   • Fix: Increase annotation size to size=3.8 or size=4.0

AI Feedback for Next Attempt

This is a strong implementation that passes on attempt 2. If a repair were needed: (1) increase annotation text to size=3.8–4.0 for better readability, (2) consider a geom_ribbon or shaded reference zone to add a uniquely ggplot2-distinctive feature, (3) a subtle annotate("rect", ...) highlighting the optimal temperature range (33°C–37°C) could elevate both DE-01 and LM-02.

Verdict: APPROVED