Data visualization (dataset)

--- 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 ``bash /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