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feat(plotnine): implement forest-basic (#2412)
## Implementation: `forest-basic` - plotnine Implements the **plotnine** version of `forest-basic`. **File:** `plots/forest-basic/implementations/plotnine.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20543282765)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/forest-basic/implementations/plotnine.py‎

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""" pyplots.ai
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forest-basic: Meta-Analysis Forest Plot
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Library: plotnine 0.15.2 | Python 3.13.11
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Quality: 92/100 | Created: 2025-12-27
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"""
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import pandas as pd
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from plotnine import (
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aes,
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element_blank,
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element_line,
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element_rect,
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element_text,
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geom_errorbarh,
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geom_point,
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geom_polygon,
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geom_text,
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geom_vline,
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ggplot,
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labs,
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scale_size_identity,
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scale_x_continuous,
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scale_y_continuous,
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theme,
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)
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# Data: Meta-analysis of RCTs comparing treatment vs control
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studies = pd.DataFrame(
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{
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"study": [
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"Smith 2018",
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"Johnson 2019",
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"Williams 2019",
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"Brown 2020",
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"Davis 2020",
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"Miller 2021",
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"Wilson 2021",
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"Moore 2022",
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"Taylor 2022",
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"Anderson 2023",
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],
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"effect_size": [-0.45, -0.22, -0.38, -0.15, -0.52, -0.31, -0.08, -0.41, -0.25, -0.35],
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"ci_lower": [-0.72, -0.48, -0.61, -0.42, -0.81, -0.55, -0.35, -0.68, -0.51, -0.58],
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"ci_upper": [-0.18, 0.04, -0.15, 0.12, -0.23, -0.07, 0.19, -0.14, 0.01, -0.12],
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"weight": [9.8, 11.2, 10.5, 8.7, 7.3, 10.9, 9.1, 8.4, 11.8, 12.3],
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}
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)
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# Calculate pooled estimate (weighted mean)
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pooled_effect = (studies["effect_size"] * studies["weight"]).sum() / studies["weight"].sum()
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pooled_se = 0.08 # Simplified standard error for pooled estimate
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pooled_lower = pooled_effect - 1.96 * pooled_se
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pooled_upper = pooled_effect + 1.96 * pooled_se
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# Create y positions (studies listed top to bottom, pooled at bottom)
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studies["y_pos"] = range(len(studies), 0, -1)
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# Scale marker sizes for visibility (based on weight, scaled for canvas)
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studies["marker_size"] = studies["weight"] * 0.5
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# Create diamond for pooled estimate
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diamond_y = 0
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diamond = pd.DataFrame(
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{
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"x": [pooled_lower, pooled_effect, pooled_upper, pooled_effect],
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"y": [diamond_y, diamond_y + 0.3, diamond_y, diamond_y - 0.3],
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}
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)
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# Create label data for study names and effect sizes
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studies["label"] = (
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studies["effect_size"].round(2).astype(str)
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+ " ["
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+ studies["ci_lower"].round(2).astype(str)
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+ ", "
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+ studies["ci_upper"].round(2).astype(str)
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+ "]"
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)
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# Fixed positions for text columns
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x_left = -1.4 # Left column for study names
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x_right = 0.55 # Right column for effect sizes
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# Add fixed positions to dataframe
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studies["x_left"] = x_left
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studies["x_right"] = x_right
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# Pooled estimate label data
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pooled_label_left = pd.DataFrame({"x": [x_left], "y": [diamond_y], "label": ["Pooled"]})
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pooled_label_right = pd.DataFrame(
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{"x": [x_right], "y": [diamond_y], "label": [f"{pooled_effect:.2f} [{pooled_lower:.2f}, {pooled_upper:.2f}]"]}
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)
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# Plot
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plot = (
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ggplot()
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# Reference line at null effect (0 for mean difference)
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+ geom_vline(xintercept=0, linetype="dashed", color="#888888", size=1)
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# Error bars (confidence intervals)
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+ geom_errorbarh(
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aes(y="y_pos", xmin="ci_lower", xmax="ci_upper"), data=studies, height=0.25, size=1.2, color="#306998"
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)
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# Point estimates (sized by weight)
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+ geom_point(aes(x="effect_size", y="y_pos", size="marker_size"), data=studies, color="#306998", fill="#306998")
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+ scale_size_identity()
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# Pooled estimate diamond
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+ geom_polygon(aes(x="x", y="y"), data=diamond, fill="#FFD43B", color="#306998", size=1.2)
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# Study labels at fixed left position
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+ geom_text(aes(x="x_left", y="y_pos", label="study"), data=studies, ha="left", size=12, color="#333333")
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# Effect size labels at fixed right position
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+ geom_text(aes(x="x_right", y="y_pos", label="label"), data=studies, ha="left", size=10, color="#333333")
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# Pooled label
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+ geom_text(
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aes(x="x", y="y", label="label"), data=pooled_label_left, ha="left", size=12, fontweight="bold", color="#333333"
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)
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+ geom_text(
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aes(x="x", y="y", label="label"),
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data=pooled_label_right,
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ha="left",
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size=10,
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fontweight="bold",
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color="#333333",
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)
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# Labels and theme
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+ labs(x="Mean Difference (Treatment - Control)", y="", title="forest-basic · plotnine · pyplots.ai")
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+ scale_x_continuous(breaks=[-0.8, -0.6, -0.4, -0.2, 0, 0.2, 0.4], limits=(-1.5, 1.3))
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+ scale_y_continuous(breaks=[], limits=(-1, 11.5))
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+ theme(
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figure_size=(16, 9),
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panel_background=element_rect(fill="white"),
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plot_background=element_rect(fill="white"),
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panel_grid_major_x=element_line(color="#EEEEEE", size=0.5),
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panel_grid_major_y=element_blank(),
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panel_grid_minor=element_blank(),
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axis_text_x=element_text(size=16, color="#333333"),
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axis_text_y=element_blank(),
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axis_title_x=element_text(size=20, color="#333333"),
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axis_title_y=element_blank(),
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plot_title=element_text(size=24, ha="center", color="#333333"),
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axis_ticks_major_y=element_blank(),
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legend_position="none",
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)
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)
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# Save
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plot.save("plot.png", dpi=300, verbose=False)

‎plots/forest-basic/metadata/plotnine.yaml‎

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library: plotnine
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specification_id: forest-basic
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created: '2025-12-27T19:23:19Z'
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updated: '2025-12-27T19:32:52Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20543282765
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issue: 0
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python_version: 3.13.11
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library_version: 0.15.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/forest-basic/plotnine/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/forest-basic/plotnine/plot_thumb.png
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preview_html: null
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quality_score: 92
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review:
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strengths:
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- Excellent implementation of forest plot structure with clear visual hierarchy
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- Proper diamond shape for pooled estimate using geom_polygon
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- Study labels and effect size values clearly displayed on left and right sides
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- Good color choices with Python blue (#306998) and yellow accent (#FFD43B)
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- Appropriate sizing for all text elements following quality guidelines
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- Clean, readable code structure following KISS principles
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- Realistic meta-analysis data context with meaningful study names
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weaknesses:
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- All individual study effects are negative; including some positive effects would
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better demonstrate the plot ability to show heterogeneous results
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- Grid lines only on x-axis (though appropriate for forest plots, full grid could
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be considered)

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