Recommend chart types for data storytelling across plotting frameworks.
npx skills add https://github.com/drmowinckels/dotfiles --skill dataviz-guide
Please help me install this Agent Skill. Skill: dataviz-guide Source: https://github.com/drmowinckels/dotfiles/tree/main/config/claude/skills/dataviz-guide Command: npx skills add https://github.com/drmowinckels/dotfiles --skill dataviz-guide
Selecting the right chart to communicate data stories clearly is challenging; this guide helps analysts, researchers, and developers select effective visualisations and avoid common pitfalls.
Describe your data and goal to receive a chart recommendation.
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Choosing the right chart type involves matching your data story goal—comparison, distribution, trend, part-to-whole, or relationship—to a suitable visualization. This guide provides recommendations across these categories to ensure clear data storytelling.
Adapting data visualizations for a non-technical audience involves selecting chart types that simplify complex relationships while avoiding common pitfalls. This framework tailors recommendations specifically for layperson audiences to enhance comprehension.
Yes, this chart selection guide works with both ggplot2 and Plotly. It is a framework-agnostic design guide that provides explicit chart-type references and best practices applicable across ggplot2, matplotlib, d3, and Plotly.
To avoid common data visualization pitfalls when plotting trends, follow explicit best practices and do's-and-don'ts for trend scenarios. This guide highlights specific design principles to prevent misleading visualizations.
You need a chart-selection framework for data analysis when you must communicate data stories clearly across technical or lay audiences. It helps analysts and developers select effective visualizations tailored to specific data relationships.
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