feat(letsplot): implement area-stacked-percent (#2689)

## Implementation: `area-stacked-percent` - letsplot

Implements the **letsplot** version of `area-stacked-percent`.

**File:** `plots/area-stacked-percent/implementations/letsplot.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20595341903)*

---------

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

Author: github-actions[bot]

plots/area-stacked-percent/implementations/letsplot.py

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+""" pyplots.ai
+area-stacked-percent: 100% Stacked Area Chart
+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,
+    aes,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_area,
+    ggplot,
+    ggsize,
+    labs,
+    scale_fill_manual,
+    scale_x_continuous,
+    scale_y_continuous,
+    theme,
+    theme_minimal,
+)
+from lets_plot.export import ggsave
+
+
+LetsPlot.setup_html()
+
+# Data - Market share evolution over 8 years
+np.random.seed(42)
+
+years = list(range(2016, 2024))
+
+# Simulate market share trends (values will be normalized to 100%)
+company_a = [40, 38, 42, 45, 48, 52, 55, 58]  # Growing leader
+company_b = [35, 36, 33, 30, 28, 25, 23, 22]  # Declining
+company_c = [15, 16, 15, 16, 15, 14, 13, 12]  # Stable small player
+company_d = [10, 10, 10, 9, 9, 9, 9, 8]  # Smallest, slight decline
+
+# Normalize to 100%
+totals = [a + b + c + d for a, b, c, d in zip(company_a, company_b, company_c, company_d)]
+company_a_pct = [a / t * 100 for a, t in zip(company_a, totals)]
+company_b_pct = [b / t * 100 for b, t in zip(company_b, totals)]
+company_c_pct = [c / t * 100 for c, t in zip(company_c, totals)]
+company_d_pct = [d / t * 100 for d, t in zip(company_d, totals)]
+
+# Create long-format dataframe for lets-plot
+df = pd.DataFrame(
+    {
+        "Year": years * 4,
+        "Share": company_a_pct + company_b_pct + company_c_pct + company_d_pct,
+        "Company": ["Company A"] * 8 + ["Company B"] * 8 + ["Company C"] * 8 + ["Company D"] * 8,
+    }
+)
+
+# Set category order for proper stacking
+df["Company"] = pd.Categorical(
+    df["Company"], categories=["Company D", "Company C", "Company B", "Company A"], ordered=True
+)
+
+# Plot
+plot = (
+    ggplot(df, aes(x="Year", y="Share", fill="Company"))
+    + geom_area(position="fill", alpha=0.85)
+    + scale_fill_manual(values=["#9B59B6", "#2ECC71", "#FFD43B", "#306998"])
+    + scale_x_continuous(breaks=list(range(2016, 2024)))
+    + scale_y_continuous(format=".0%")
+    + labs(x="Year", y="Market Share (%)", title="area-stacked-percent · letsplot · pyplots.ai")
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=26),
+        axis_title=element_text(size=22),
+        axis_text=element_text(size=18),
+        legend_title=element_text(size=18),
+        legend_text=element_text(size=16),
+        panel_grid_major=element_line(color="#DDDDDD", size=0.3),
+        panel_grid_minor=element_blank(),
+        panel_background=element_rect(fill="#FAFAFA"),
+    )
+    + ggsize(1600, 900)
+)
+
+# Save PNG (scale=3 gives 4800x2700)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save HTML for interactivity
+ggsave(plot, "plot.html", path=".")

plots/area-stacked-percent/metadata/letsplot.yaml

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+library: letsplot
+specification_id: area-stacked-percent
+created: '2025-12-30T11:30:04Z'
+updated: '2025-12-30T11:36:56Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20595341903
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/area-stacked-percent/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/area-stacked-percent/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/area-stacked-percent/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent visual clarity with well-chosen colors that are colorblind-friendly
+  - Perfect 100% stacked area representation matching the spec exactly
+  - Realistic and informative market share scenario that clearly shows composition
+    changes over time
+  - Clean ggplot2-style code structure using lets-plot grammar of graphics
+  weaknesses:
+  - Year labels display with comma separators (2,016 instead of 2016) which looks
+    unnatural for year values
+  - The HTML export setup_html() call is unnecessary overhead for PNG-only output