allowed-tools: Task, Read, Write, Bash, Grep, Glob

argument-hint: [dataset] [chart_type]

description: Create data visualizations for the specified dataset

Data Visualization Command

Create comprehensive data visualizations for dataset $1 with chart type $2 using the visualization-specialist subagent.

Context

• Dataset location: @data_storage/$1
• Chart type: $2 (all, trends, distribution, correlation, comparison, custom)
• Current working directory: !pwd
• Visualization output directory: ./visualizations/
• Available libraries: matplotlib, seaborn, plotly, bokeh

Your Task

Use the visualization-specialist subagent to create informative visualizations:

1. Data Preparation

• Load and prepare the dataset
• Handle missing values and outliers
• Select appropriate variables for visualization
• Prepare data for different chart types

2. Visualization Planning

• Determine the best chart types for the data
• Plan color schemes and styling
• Consider the target audience and purpose
• Plan layout and composition

3. Chart Creation

• Create multiple complementary visualizations
• Ensure proper labeling and annotations
• Use appropriate scales and ranges
• Apply consistent styling and colors

4. Quality Assurance

• Test visualizations with different data scenarios
• Verify data accuracy in visualizations
• Check accessibility and readability
• Optimize for different screen sizes

Chart Types

All Visualizations

• Comprehensive dashboard with multiple chart types
• Overview of all key variables and relationships
• Executive summary visualizations
• Interactive exploration dashboard

Trends

• Time series line charts
• Moving average plots
• Trend decomposition
• Seasonal analysis charts

Distribution

• Histograms and density plots
• Box plots and violin plots
• Q-Q plots for normality
• Statistical distribution charts

Correlation

• Correlation heatmaps
• Scatter plot matrices
• Pair plots
• Regression analysis plots

Comparison

• Bar charts and column charts
• Grouped and stacked charts
• Small multiples
• Comparative analysis charts

Custom

• User-specified custom visualizations
• Domain-specific charts
• Interactive dashboards
• Animated visualizations

Expected Output

Visualization Files

• visualizations/dashboard_$1.html - Interactive dashboard
• visualizations/summary_$1.png - Summary charts
• visualizations/detailed_$1.pdf - Detailed analysis charts
• visualizations/charts_$1.py - Reproducible code

Documentation

• Chart Descriptions: Explanation of each visualization
• Data Sources: Documentation of data transformations
• Interpretation Guide: How to read and understand the charts
• Customization Options: How to modify and extend visualizations

Technical Requirements

File Formats

• Static Images: PNG (high-resolution), SVG (vector)
• Interactive: HTML with JavaScript (Plotly, D3.js)
• Print: PDF with high resolution
• Code: Python/R scripts for reproducibility

Design Standards

• Color Schemes: Colorblind-friendly palettes
• Typography: Clear, readable fonts
• Layout: Responsive and well-organized
• Accessibility: WCAG compliant where possible

Quality Assurance

Validation Checks

• Verify data accuracy in all visualizations
• Test with different screen sizes and devices
• Check color accessibility
• Ensure proper labeling and annotations

Performance

• Optimize file sizes for web display
• Ensure fast loading times
• Test interactivity and responsiveness
• Validate cross-browser compatibility

Example Usage

/visualize user_behavior.csv all
/visualize sales_data.csv trends
/visualize customer_data.csv distribution
/visualize financial_data.csv correlation
/visualize performance_data.csv comparison
/visualize custom_data.csv custom

Best Practices

Design Principles

• Data-Ink Ratio: Maximize the ratio of data-ink to total ink
• Chart Junk: Eliminate non-data ink and decorative elements
• Clarity: Ensure the message is immediately understandable
• Consistency: Use consistent styling across all visualizations

Data Integrity

• Validate data before visualization
• Handle missing values appropriately
• Use appropriate scales and ranges
• Document all data transformations

User Experience

• Consider the target audience
• Provide clear labels and legends
• Include interactive features where helpful
• Offer multiple views of the same data

Notes

• Dataset should be located in the data_storage/ directory
• Visualizations will be saved to visualizations/ directory
• Use Task tool to delegate to visualization-specialist subagent
• Consider using /analyze command first for data insights
• Interactive visualizations require web browser for viewing

Integration with Other Commands

• Use after /analyze for data-driven visualizations
• Combine with /report for comprehensive analysis reports
• Follow with `/generate