feat(highcharts): implement scatter-categorical

[github-actions]

Implementation: scatter-categorical - highcharts

Implements the highcharts version of scatter-categorical.

File: plots/scatter-categorical/implementations/highcharts.py

---

🤖 impl-generate workflow

[claude]

AI Review - Attempt 1/3

Image Description

The plot displays a categorical scatter plot showing the relationship between Nitrogen Applied (kg/ha) on the x-axis and Plant Growth (cm) on the y-axis. Three fertilizer types are distinguished by color: Fertilizer A (blue #306998), Fertilizer B (yellow #FFD43B), and Fertilizer C (purple #9467BD). The title "scatter-categorical · highcharts · pyplots.ai" is prominently displayed at the top center. A vertical legend in the top-right corner identifies each category. The plot shows clear differentiation between groups - Fertilizer B (yellow) shows the highest growth values, Fertilizer A (blue) shows moderate growth, and Fertilizer C (purple) shows lower, more scattered values. Grid lines are subtle and do not distract from the data. Markers are circular with white borders and appropriately sized for the data density (~120 points total).

Quality Score: 92/100

Criteria Checklist

Visual Quality (37/40 pts)

• VQ-01: Text Legibility (9/10) - Title at 48px, axis labels at 36px, tick labels at 28px - all very readable. Slightly oversized for the canvas but clear.
• VQ-02: No Overlap (8/8) - No overlapping text elements, all labels fully readable
• VQ-03: Element Visibility (7/8) - Markers with radius 12 are well-sized for ~120 points, white borders add distinction. Minor overlap in dense regions but acceptable.
• VQ-04: Color Accessibility (5/5) - Blue/Yellow/Purple palette is colorblind-safe (no red-green)
• VQ-05: Layout Balance (4/5) - Plot fills most of the canvas with balanced margins. Bottom margin (300px) creates some extra whitespace.
• VQ-06: Axis Labels (2/2) - Descriptive labels with units: "Nitrogen Applied (kg/ha)" and "Plant Growth (cm)"
• VQ-07: Grid & Legend (2/2) - Grid subtle (rgba(0,0,0,0.1)), legend well-positioned in top-right

Spec Compliance (25/25 pts)

• SC-01: Plot Type (8/8) - Correct scatter plot type
• SC-02: Data Mapping (5/5) - X=Nitrogen (continuous), Y=Growth (continuous), Color=Fertilizer type (categorical) - correctly assigned
• SC-03: Required Features (5/5) - All spec features present: distinct colors per category, legend for identification
• SC-04: Data Range (3/3) - Axes show all data points without clipping
• SC-05: Legend Accuracy (2/2) - Legend labels match data series names
• SC-06: Title Format (2/2) - Correct format: "scatter-categorical · highcharts · pyplots.ai"

Data Quality (18/20 pts)

• DQ-01: Feature Coverage (7/8) - Shows three distinct categories with different correlation patterns. Fertilizer B shows steeper slope and higher values, C shows more variance. Good variety but could show more extreme outliers.
• DQ-02: Realistic Context (7/7) - Plant growth study with fertilizer types is a real, neutral scientific scenario
• DQ-03: Appropriate Scale (4/5) - Nitrogen 15-85 kg/ha and growth 0-70 cm are plausible. Some growth values could be slightly more realistic (growth values starting near 0 would be more realistic).

Code Quality (9/10 pts)

• CQ-01: KISS Structure (3/3) - No functions or classes, follows imports → data → plot → save pattern
• CQ-02: Reproducibility (3/3) - np.random.seed(42) is set
• CQ-03: Clean Imports (2/2) - All imports are used
• CQ-04: No Deprecated API (1/1) - Uses current highcharts-core API
• CQ-05: Output Correct (0/1) - Saves as "plot.png" but also creates plot.html (acceptable but the HTML variable naming reuses f-string incorrectly - interactive_html variable contains html_str which uses original chart dimensions)

Library Features (3/5 pts)

• LF-01: Uses distinctive library features (3/5) - Uses Highcharts scatter series with proper options, hover states, and generates both PNG and HTML output. Could leverage more Highcharts features like tooltips with custom formatting.

Strengths

• Excellent colorblind-safe palette (blue/yellow/purple)
• Clear visual separation between categories with distinct patterns
• Proper title format following pyplots.ai convention
• Realistic plant growth study scenario with meaningful data
• Good marker sizing with white borders for distinction
• Both static PNG and interactive HTML outputs generated

Weaknesses

• Interactive HTML reuses the 4800x2700 chart JS literal instead of regenerating at smaller size, causing potential sizing issues
• No alpha transparency on markers despite spec suggestion for overlapping points
• Legend positioned at fixed pixel offset rather than responsive positioning

Verdict: APPROVED