Research Visualization Pack

What it does

Generates code for publication-ready visualizations in both R (ggplot2) and Python (matplotlib/seaborn), with APA-style defaults baked in.

Chart types included

Group comparisons

• Raincloud plots (raw data + violin + boxplot)
• Bar charts with error bars (SD, SE, 95% CI)
• Interaction plots for factorial designs

Correlations

• Scatter plots with regression lines and CIs
• Correlation matrices (corrplot / seaborn heatmap)
• Partial correlation networks

Longitudinal data

• Spaghetti plots (individual trajectories)
• Growth curve visualization
• Change score plots

Clinical / applied

• ROC curves
• Bland-Altman plots
• Survival/Kaplan-Meier curves

Style

All outputs default to APA 7 figure standards: white background, no gridlines, accessible color palette, 300 DPI export.

How to use

Describe your data structure and the story you want to tell. Receive annotated, runnable code.