feat(letsplot): implement forest-basic

plots/forest-basic/implementations/letsplot.py

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+""" pyplots.ai
+forest-basic: Meta-Analysis Forest Plot
+Library: letsplot 4.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-27
+"""
+
+import pandas as pd
+from lets_plot import *
+
+
+LetsPlot.setup_html()
+
+# Data: Meta-analysis of clinical trials comparing treatment vs control
+# Effect sizes are log odds ratios (log OR) - null effect at 0
+studies = [
+    {"study": "Smith 2018", "effect_size": 0.35, "ci_lower": 0.05, "ci_upper": 0.65, "weight": 12.5},
+    {"study": "Johnson 2019", "effect_size": -0.12, "ci_lower": -0.45, "ci_upper": 0.21, "weight": 10.2},
+    {"study": "Williams 2019", "effect_size": 0.48, "ci_lower": 0.18, "ci_upper": 0.78, "weight": 11.8},
+    {"study": "Brown 2020", "effect_size": 0.22, "ci_lower": -0.15, "ci_upper": 0.59, "weight": 9.5},
+    {"study": "Davis 2020", "effect_size": 0.55, "ci_lower": 0.20, "ci_upper": 0.90, "weight": 8.7},
+    {"study": "Miller 2021", "effect_size": 0.15, "ci_lower": -0.18, "ci_upper": 0.48, "weight": 11.0},
+    {"study": "Wilson 2021", "effect_size": 0.42, "ci_lower": 0.12, "ci_upper": 0.72, "weight": 12.0},
+    {"study": "Moore 2022", "effect_size": 0.28, "ci_lower": -0.08, "ci_upper": 0.64, "weight": 9.8},
+    {"study": "Taylor 2022", "effect_size": 0.65, "ci_lower": 0.28, "ci_upper": 1.02, "weight": 7.5},
+    {"study": "Anderson 2023", "effect_size": 0.18, "ci_lower": -0.12, "ci_upper": 0.48, "weight": 12.8},
+]
+
+df = pd.DataFrame(studies)
+
+# Calculate pooled estimate (weighted average)
+total_weight = df["weight"].sum()
+pooled_effect = (df["effect_size"] * df["weight"]).sum() / total_weight
+pooled_se = 0.08  # Simplified SE for visualization
+pooled_ci_lower = pooled_effect - 1.96 * pooled_se
+pooled_ci_upper = pooled_effect + 1.96 * pooled_se
+
+# Order studies by effect size and assign y positions
+df = df.sort_values("effect_size", ascending=True).reset_index(drop=True)
+df["y_pos"] = range(len(df), 0, -1)
+
+# Scale weights for marker sizes (proportional to study weight)
+df["marker_size"] = df["weight"] / df["weight"].max() * 8 + 2
+
+# Create the forest plot
+plot = (
+    ggplot()
+    # Vertical reference line at null effect (0 for log OR)
+    + geom_vline(xintercept=0, color="#888888", size=1, linetype="dashed")
+    # Confidence interval lines (whiskers)
+    + geom_segment(aes(x="ci_lower", xend="ci_upper", y="y_pos", yend="y_pos"), data=df, color="#306998", size=1.5)
+    # Point estimates (squares proportional to weight)
+    + geom_point(
+        aes(x="effect_size", y="y_pos", size="marker_size"),
+        data=df,
+        color="#306998",
+        shape=15,  # Square marker
+    )
+    # Study labels on y-axis
+    + scale_y_continuous(breaks=df["y_pos"].tolist(), labels=df["study"].tolist())
+    # Diamond for pooled estimate
+    + geom_polygon(
+        aes(x="x", y="y"),
+        data=pd.DataFrame(
+            {"x": [pooled_ci_lower, pooled_effect, pooled_ci_upper, pooled_effect], "y": [-0.5, -1.0, -0.5, 0.0]}
+        ),
+        fill="#FFD43B",
+        color="#306998",
+        size=1,
+    )
+    # Labels and title
+    + labs(x="Log Odds Ratio (95% CI)", y="", title="forest-basic · letsplot · pyplots.ai")
+    # Theme and sizing
+    + theme_minimal()
+    + theme(
+        plot_title=element_text(size=24, face="bold"),
+        axis_title_x=element_text(size=20),
+        axis_text_x=element_text(size=16),
+        axis_text_y=element_text(size=16),
+        legend_position="none",
+        panel_grid_major_y=element_blank(),
+        panel_grid_minor=element_blank(),
+    )
+    + scale_size_identity()
+    + ggsize(1600, 900)
+)
+
+# Add text annotation for pooled estimate using geom_text
+pooled_label_df = pd.DataFrame(
+    {
+        "x": [pooled_effect],
+        "y": [-1.8],
+        "label": [f"Pooled: {pooled_effect:.2f} [{pooled_ci_lower:.2f}, {pooled_ci_upper:.2f}]"],
+    }
+)
+plot = plot + geom_text(aes(x="x", y="y", label="label"), data=pooled_label_df, size=14, color="#306998")
+
+# Save as PNG (scale 3x for 4800 × 2700 px)
+ggsave(plot, "plot.png", scale=3, path=".")
+
+# Save as HTML for interactivity
+ggsave(plot, "plot.html", path=".")

plots/forest-basic/metadata/letsplot.yaml

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+library: letsplot
+specification_id: forest-basic
+created: '2025-12-27T19:22:44Z'
+updated: '2025-12-27T20:06:47Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20543284156
+issue: 0
+python_version: 3.13.11
+library_version: 4.8.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/forest-basic/letsplot/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/forest-basic/letsplot/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/forest-basic/letsplot/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Excellent implementation of the forest plot structure with all required elements
+    (point estimates, CIs, pooled diamond, null reference line)
+  - Clean, readable code following KISS principles with logical data organization
+  - Good use of lets-plot ggplot2-style grammar including geom_polygon for the diamond
+    shape
+  - Appropriate text sizing for 4800x2700 output (title 24pt, axis text 16pt, axis
+    title 20pt)
+  - Realistic meta-analysis data with meaningful variation in effect sizes and weights
+  - Proper marker sizing proportional to study weight using scale_size_identity()
+  weaknesses:
+  - Marker size variation based on weight is subtle and could be more visually distinct
+    (range of 2-10 is narrow)
+  - Grid styling could be improved - currently using panel_grid_major_y=element_blank()
+    but might benefit from subtle horizontal grid lines for readability
+  - The plot could benefit from lets-plot interactive features like tooltips showing
+    exact values on hover