feat(letsplot): implement scatter-animated-controls (#3091)

## Implementation: `scatter-animated-controls` - letsplot

Implements the **letsplot** version of `scatter-animated-controls`.

**File:** `plots/scatter-animated-controls/implementations/letsplot.py`

**Parent Issue:** #3067

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620306159)*

---------

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/scatter-animated-controls/implementations/letsplot.py

@@ -0,0 +1,95 @@
+# ruff: noqa: F405
+"""pyplots.ai
+scatter-animated-controls: Animated Scatter Plot with Play Controls
+Library: letsplot | Python 3.13
+Quality: pending | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from lets_plot import *  # noqa: F403
+
+
+LetsPlot.setup_html()
+
+# Data - Simulated country-level metrics over 20 years (Gapminder-style)
+np.random.seed(42)
+
+countries = [
+    "Northland",
+    "Eastoria",
+    "Westopia",
+    "Southaven",
+    "Centralia",
+    "Alpinia",
+    "Deltania",
+    "Oceanica",
+    "Valleysia",
+    "Highlands",
+]
+years = list(range(2000, 2020))
+
+data_rows = []
+for country in countries:
+    # Base values with country-specific characteristics
+    base_gdp = np.random.uniform(5000, 40000)
+    base_life = np.random.uniform(55, 75)
+    base_pop = np.random.uniform(5, 200)  # millions
+
+    # Growth trends
+    gdp_growth = np.random.uniform(0.02, 0.06)
+    life_growth = np.random.uniform(0.002, 0.008)
+    pop_growth = np.random.uniform(0.005, 0.02)
+
+    for i, year in enumerate(years):
+        # Add some noise and trends
+        gdp = base_gdp * (1 + gdp_growth) ** i * (1 + np.random.normal(0, 0.05))
+        life_exp = min(85, base_life + life_growth * i * 100 + np.random.normal(0, 0.5))
+        pop = base_pop * (1 + pop_growth) ** i
+
+        data_rows.append(
+            {"country": country, "year": year, "gdp_per_capita": gdp, "life_expectancy": life_exp, "population": pop}
+        )
+
+df = pd.DataFrame(data_rows)
+
+# lets-plot does not have built-in animation like Plotly
+# Per spec: "Libraries without animation support should implement a static faceted version"
+# Create a faceted view showing key time points
+
+# Select key years for faceted display
+key_years = [2000, 2005, 2010, 2015, 2019]
+df_key = df[df["year"].isin(key_years)].copy()
+df_key["year_label"] = df_key["year"].astype(str)
+
+# Create the faceted plot showing temporal evolution
+plot = (
+    ggplot(df_key, aes(x="gdp_per_capita", y="life_expectancy"))
+    + geom_point(aes(color="country", size="population"), alpha=0.85)
+    + scale_size(range=[6, 20], name="Population (M)")
+    + scale_color_brewer(palette="Paired", name="Country")
+    + scale_x_log10()
+    + facet_wrap("year_label", ncol=5)
+    + labs(
+        title="scatter-animated-controls \u00b7 lets-plot \u00b7 pyplots.ai",
+        x="GDP per Capita (log scale, USD)",
+        y="Life Expectancy (years)",
+    )
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=28, face="bold"),
+        axis_title=element_text(size=18),
+        axis_text=element_text(size=14),
+        legend_title=element_text(size=16),
+        legend_text=element_text(size=14),
+        strip_text=element_text(size=18, face="bold"),
+        legend_position="right",
+    )
+    + ggsize(1600, 900)
+)
+
+# Save as PNG (scale 3x for 4800x2700)
+ggsave(plot, "plot.png", path=".", scale=3)
+
+# Save as HTML for interactivity
+ggsave(plot, "plot.html", path=".")

plots/scatter-animated-controls/metadata/letsplot.yaml

@@ -0,0 +1,28 @@
+library: letsplot
+specification_id: scatter-animated-controls
+created: '2025-12-31T13:53:17Z'
+updated: '2025-12-31T14:16:05Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20620306159
+issue: 3067
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/scatter-animated-controls/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/scatter-animated-controls/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/scatter-animated-controls/letsplot/plot.html
+quality_score: 90
+review:
+  strengths:
+  - Excellent implementation of Gapminder-style visualization with realistic country
+    data
+  - Correct fallback approach using faceted view for library without native animation
+    support
+  - Well-formatted title following exact spec requirements
+  - Good use of lets-plot grammar of graphics features (scales, faceting, theming)
+  - Proper sizing with ggsize(1600, 900) and scale=3 for 4800x2700 output
+  - Clean, readable code structure following KISS principle
+  weaknesses:
+  - Spec suggests large year indicator per panel for Gapminder storytelling feel -
+    facet strip labels work but larger text could enhance the visualization
+  - Some point overlap in the 2019 panel where multiple countries converge at higher
+    GDP/life expectancy values