feat(letsplot): implement residual-plot (#2367)

## Implementation: `residual-plot` - letsplot

Implements the **letsplot** version of `residual-plot`.

**File:** `plots/residual-plot/implementations/letsplot.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20528206816)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/residual-plot/implementations/letsplot.py

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+# ruff: noqa: F405
+"""pyplots.ai
+residual-plot: Residual Plot
+Library: lets-plot | Python 3.13
+Quality: pending | Created: 2025-12-26
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *  # noqa: F403, F405
+
+
+LetsPlot.setup_html()
+
+# Data: Generate realistic regression scenario with deliberate pattern in residuals
+np.random.seed(42)
+n = 150
+
+# Create feature with mild non-linearity to show residual patterns
+X = np.linspace(10, 100, n)
+noise = np.random.normal(0, 5, n)
+# Add slight heteroscedasticity: variance increases with X
+heteroscedastic_noise = noise * (0.5 + 0.01 * X)
+y_true = 2.5 * X + 0.02 * X**2 + heteroscedastic_noise + 50
+
+# Simple linear regression (manual implementation)
+X_mean = np.mean(X)
+y_mean = np.mean(y_true)
+slope = np.sum((X - X_mean) * (y_true - y_mean)) / np.sum((X - X_mean) ** 2)
+intercept = y_mean - slope * X_mean
+y_pred = slope * X + intercept
+
+# Calculate residuals
+residuals = y_true - y_pred
+
+# Calculate residual standard deviation for outlier bands
+residual_std = np.std(residuals)
+
+# Identify outliers (beyond ±2 standard deviations)
+outlier_threshold = 2 * residual_std
+is_outlier = np.abs(residuals) > outlier_threshold
+
+# Create DataFrame for plotting
+df = pd.DataFrame(
+    {"Fitted Values": y_pred, "Residuals": residuals, "Outlier": np.where(is_outlier, "Outlier (>2σ)", "Normal")}
+)
+
+# Create residual plot
+plot = (
+    ggplot(df, aes(x="Fitted Values", y="Residuals"))
+    # Reference line at y=0
+    + geom_hline(yintercept=0, color="#306998", size=1.5, linetype="solid")
+    # Outlier bands at ±2 standard deviations
+    + geom_hline(yintercept=outlier_threshold, color="#DC2626", size=1, linetype="dashed", alpha=0.7)
+    + geom_hline(yintercept=-outlier_threshold, color="#DC2626", size=1, linetype="dashed", alpha=0.7)
+    # Points colored by outlier status
+    + geom_point(aes(color="Outlier"), size=5, alpha=0.7)
+    # LOWESS smoothing line to detect patterns
+    + geom_smooth(method="loess", color="#FFD43B", size=2, se=False, span=0.6)
+    # Color scale for outliers
+    + scale_color_manual(values=["#306998", "#DC2626"], name="Point Type")
+    # Labels
+    + labs(title="residual-plot · letsplot · pyplots.ai", x="Fitted Values", y="Residuals (Observed - Predicted)")
+    # Theme
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_title=element_text(size=18),
+        legend_text=element_text(size=16),
+        legend_position="right",
+        panel_grid_major=element_line(color="#CCCCCC", size=0.5),
+        panel_grid_minor=element_blank(),
+    )
+    # Size for export (1600 × 900 base, scaled 3x = 4800 × 2700)
+    + ggsize(1600, 900)
+)
+
+# Save as PNG and HTML (path='.' ensures files are saved in current directory)
+ggsave(plot, "plot.png", path=".", scale=3)
+ggsave(plot, "plot.html", path=".")

plots/residual-plot/metadata/letsplot.yaml

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+library: letsplot
+specification_id: residual-plot
+created: '2025-12-26T19:38:27Z'
+updated: '2025-12-26T19:44:03Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20528206816
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/residual-plot/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/residual-plot/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/residual-plot/letsplot/plot.html
+quality_score: 93
+review:
+  strengths:
+  - Excellent implementation of all spec requirements including y=0 reference line,
+    LOWESS smoothing, ±2σ outlier bands, and outlier coloring
+  - Clear U-shaped LOWESS curve effectively demonstrates non-linearity detection in
+    residuals
+  - Good data generation with deliberate heteroscedasticity and non-linear term to
+    showcase residual analysis
+  - Clean ggplot2-style grammar with proper lets-plot idioms
+  - Proper sizing (1600x900 base with scale=3) for 4800x2700 output
+  - Both PNG and HTML outputs generated correctly
+  weaknesses:
+  - Grid alpha at 0.5 is slightly too prominent; recommended 0.2-0.4 for subtler appearance
+  - Wildcard import pattern, while necessary for lets-plot, could be more explicit
+  - Could leverage lets-plot interactive tooltips in HTML output for enhanced exploration