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Statistical Visualization

Generate publication-quality statistical visualizations in R using ggplot2, patchwork, and plotly.

Updated Mar 7, 2026
Search Tags:#data visualization#visualization#r#publication#ggplot2#3d plots
One-click install
npx skills add https://github.com/ntluong95/agent-skills-statistics --skill statistical-visualization
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Statistical Visualization
Source: https://github.com/ntluong95/agent-skills-statistics/tree/main/skills/r-language/visualization
Command: npx skills add https://github.com/ntluong95/agent-skills-statistics --skill statistical-visualization

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users generate publication-quality data visualizations in R, ensuring clarity, aesthetic appeal, and adherence to journal standards.

Core Features & Use Cases

  • ggplot2 Customization: Apply consistent themes, labels, and color palettes for professional figures.
  • Multi-Panel Figures: Combine multiple plots efficiently using patchwork or cowplot.
  • 3D Surface Plots: Generate interactive or static 3D visualizations, particularly for DLNM models.
  • Use Case: You need to create a figure for a journal submission showing the relationship between air pollution and health outcomes across different lag days, including a 3D surface plot and a multi-panel time series.

Quick Start

Use the visualization skill to create a ggplot2 scatter plot with custom labels and a minimal theme.

Frequently Asked Questions about Statistical Visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create publication-quality plots in R using ggplot2?▼

To create publication-quality plots in R, use ggplot2 to apply consistent themes, labels, and colorblind-safe palettes. This ensures figures meet professional journal submission standards while maintaining aesthetic clarity.

How do I combine multiple plots into a multi-panel figure in R?▼

You can combine multiple plots into a multi-panel figure in R using the patchwork or cowplot packages. This allows efficient arrangement of several ggplot2 visualizations into a single cohesive publication-ready layout.

Can I generate 3D surface plots for DLNM models in R?▼

Yes, you can generate 3D surface plots for DLNM models in R using plotly. This supports creating interactive or static visualizations to show relationships across different lag days, particularly for air pollution and health outcome analyses.

Does this visualization approach support colorblind-safe palettes for journal submission?▼

This visualization approach explicitly supports colorblind-safe palettes for journal submission. It adheres to best practices for themes and labels, ensuring your statistical figures meet professional publication requirements.

What is the best way to visualize air pollution and health outcomes across lag days?▼

The best way to visualize air pollution and health outcomes across lag days is generating a 3D surface plot alongside multi-panel time series figures. This combination effectively represents DLNM model relationships for journal submissions.

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