AI-Assisted Visualization¶
AI-assisted visualization covers the use of AI tools to create data visualizations, from simple exploratory charts to publication-quality figures and diagrams.
Context & Background¶
AI tools can help with visualization at multiple levels:
- Code generation: Writing matplotlib, ggplot2, or other visualization code from descriptions
- Design suggestions: Recommending appropriate chart types for different data
- Formatting: Adjusting colors, labels, and layouts for publication standards
- Diagram creation: Generating Mermaid, D3, or TikZ diagrams from descriptions
- Interactive visualization: Building dashboards and interactive exploratory tools
Practical Implications¶
- Describe what you want: "Create a scatter plot of returns vs. market cap with a fitted line" works well
- Iterate on design: Use AI to quickly try different visualization approaches
- Know your journal's requirements: Specify resolution, color scheme, and format requirements upfront
- Review carefully: Check axis labels, scales, and data accuracy in AI-generated plots
Key Sources¶
- The Train Has Left the Station: Agentic AI and the Future of Social Science Research
- Claude Code for Academics: An AI Agent for Empirical Research
- Research in the Time of AI
- A Guide to Which AI to Use in the Agentic Era
- Data Analysis: Claude Code for Economists with Paul Goldsmith-Pinkham (Markus Academy Ep. 162-2)