feat(letsplot): implement scatter-annotated

plots/scatter-annotated/implementations/letsplot.py

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+""" pyplots.ai
+scatter-annotated: Annotated Scatter Plot with Text Labels
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data - Company performance metrics (neutral business context)
+np.random.seed(42)
+
+companies = [
+    "Acme Corp",
+    "TechFlow",
+    "DataSys",
+    "CloudNet",
+    "NeuraTech",
+    "ByteWorks",
+    "InfoPlex",
+    "CyberDyn",
+    "QuantumAI",
+    "LogiCore",
+    "MegaSoft",
+    "SynergyX",
+    "DigiHub",
+    "SmartScale",
+    "CoreLogic",
+]
+
+# Revenue (millions) and growth rate (percentage)
+revenue = np.random.uniform(50, 500, len(companies))
+growth = np.random.uniform(5, 45, len(companies))
+
+# Add some outliers for visual interest
+revenue[4] = 480  # NeuraTech - high revenue
+growth[4] = 42  # NeuraTech - high growth
+revenue[8] = 120  # QuantumAI - low revenue
+growth[8] = 48  # QuantumAI - exceptional growth (startup)
+revenue[10] = 520  # MegaSoft - highest revenue
+growth[10] = 8  # MegaSoft - low growth (mature company)
+
+df = pd.DataFrame({"company": companies, "revenue": revenue, "growth": growth})
+
+# Create annotated scatter plot
+plot = (
+    ggplot(df, aes(x="revenue", y="growth"))
+    + geom_point(size=8, color="#306998", alpha=0.7)
+    + geom_text(aes(label="company"), size=12, nudge_y=2.5, color="#333333")
+    + scale_y_continuous(limits=[0, 55])
+    + labs(
+        x="Annual Revenue ($ millions)",
+        y="Year-over-Year Growth (%)",
+        title="scatter-annotated · lets-plot · pyplots.ai",
+    )
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        panel_grid_major=element_line(color="#cccccc", size=0.5),
+        panel_grid_minor=element_line(color="#eeeeee", size=0.3),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale 3x for 4800x2700 px)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save interactive HTML version
+ggsave(plot, "plot.html", path=".")

plots/scatter-annotated/metadata/letsplot.yaml

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+library: letsplot
+specification_id: scatter-annotated
+created: '2025-12-30T17:53:28Z'
+updated: '2025-12-30T18:05:30Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20602455677
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-annotated/letsplot/plot.html
+quality_score: 91
+review:
+  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