Data Visualization Pro (Automaton)

AI-powered data visualization tool with 6 chart types (bar, line, pie, scatter, heatmap, radar), CSV/JSON import, AI-driven chart recommendations, interactive dashboards, and export to PNG/SVG/PDF. Use when creating charts, visualizing datasets, generating reports, or building data dashboards. Triggers on "chart", "graph", "visualize data", "plot", "dashboard", "data viz".

Data Visualization Pro

AI-powered data visualization with smart chart recommendations.

Features

• 6 Chart Types: Bar, Line, Pie, Scatter, Heatmap, Radar
• AI Chart Recommendations: Analyzes your data and suggests the best chart type
• CSV/JSON Import: Drop in your data file and visualize instantly
• Interactive Dashboards: Combine multiple charts into a single view
• Export: PNG, SVG, PDF — publication-ready output
• Responsive: Works on desktop and mobile

Quick Start

1. Visualize a CSV file

Visualize this data: [paste CSV or provide file path]

The agent will:

1. Parse the data (CSV, JSON, or raw text)
2. Analyze column types (numeric, categorical, temporal)
3. Recommend the best chart type
4. Generate an interactive visualization

2. Create a specific chart

Create a bar chart comparing Q1-Q4 revenue for 2024 and 2025

3. Build a dashboard

Build a dashboard from sales-data.csv with:
- Revenue trend (line chart)
- Regional breakdown (pie chart)
- Product comparison (bar chart)

Chart Selection Guide

Data Pattern	Recommended Chart	When to Use
Trends over time	Line	Time-series, stock prices, growth
Category comparison	Bar	Revenue by region, product sales
Part-of-whole	Pie	Market share, budget allocation
Correlation	Scatter	Height vs weight, price vs demand
Multi-variable	Radar	Product comparison, skill assessment
Density/matrix	Heatmap	Correlation matrix, geographic data

AI Recommendation Engine

The AI analyzes your data to recommend the optimal visualization:

1. Column type detection: Numeric, categorical, temporal, boolean
2. Relationship analysis: Correlation strength, distribution shape
3. Data volume assessment: Row count determines complexity level
4. Pattern recognition: Trends, clusters, outliers, proportions

Sample Datasets Included

• sample-data.csv — Mixed business metrics
• sample-categories.csv — Category comparison data
• sample-correlation.csv — Multi-variable correlation data
• sample-proportions.csv — Part-of-whole data

Technical Stack

• Frontend: React + TypeScript + Vite
• Charts: Recharts (built on D3.js)
• Styling: Tailwind CSS
• Export: html2canvas + jsPDF
• Build: 382KB production build

Web App

Try the live demo: https://courageous-bonbon-d1af15.netlify.app

Usage Tips

• For large datasets (>10K rows), use aggregation before visualizing
• AI recommendations work best with 3-20 columns
• Export at 2x resolution for print-quality output
• Use the dashboard view to tell a complete data story