R Econometrics MCP Server

# RMCP Visual Analytics Example **New in v0.3.7**: This example demonstrates how RMCP now displays professional-quality plots and visualizations directly in Claude conversations, revolutionizing data analysis workflows. ## 🎯 Revolutionary Visual Analytics ### Direct Image Display in Claude RMCP visualization tools now return both comprehensive statistical analysis **and** publication-quality images directly in Claude: ```markdown # When you ask Claude: "Create a correlation heatmap of my sales, marketing, and customer satisfaction data" # RMCP responds with: # 1. 📊 Interactive heatmap displayed inline with color-coded correlation strengths # 2. 📋 Statistical analysis: correlation matrix with exact values and significance tests # 3. 💡 Insights: "Strong positive correlation (r=0.89) between marketing and sales" # 4. 🎨 Professional ggplot2 styling ready for presentations ``` ### 🎨 Enhanced Visualization Tools (All 6 Support Inline Display) **🔥 correlation_heatmap**: Color-coded correlation matrices with statistical significance testing - Perfect for: Exploring relationships between multiple variables - Visual: Color intensity shows correlation strength (-1 to +1) - Analysis: p-values, confidence intervals, sample sizes **📈 scatter_plot**: Interactive scatter plots with trend lines and grouping - Perfect for: Regression analysis, outlier detection, group comparisons - Visual: Points, trend lines, confidence bands, group colors - Analysis: Correlation coefficients, R², regression equations **📊 histogram**: Distribution analysis with density overlays - Perfect for: Understanding data distributions, checking normality - Visual: Bars with density curves, group overlays - Analysis: Mean, median, skewness, kurtosis statistics **📦 boxplot**: Quartile analysis with outlier detection - Perfect for: Comparing distributions, finding outliers - Visual: Boxes, whiskers, outlier points, group comparisons - Analysis: Quartiles, IQR, outlier counts, group statistics **⏱️ time_series_plot**: Temporal analysis with trend forecasting - Perfect for: Time series analysis, trend identification - Visual: Lines, points, smooth trends, confidence bands - Analysis: Trend statistics, seasonal patterns, forecasts **🔍 regression_plot**: Comprehensive diagnostic plots (4-panel) - Perfect for: Model validation, assumption checking - Visual: Residuals vs fitted, Q-Q plots, scale-location, leverage - Analysis: Model diagnostics, outliers, influential points ### Usage Examples #### 1. Correlation Heatmap (no file needed) ```json { "tool": "correlation_heatmap", "arguments": { "data": { "sales": [100, 150, 200, 250, 300], "marketing": [10, 15, 25, 30, 40], "temperature": [20, 25, 30, 35, 40] }, "method": "pearson", "title": "Sales Correlation Analysis" } } ``` **Returns:** - **Text**: Correlation matrix with values, statistics - **Image**: Color-coded heatmap displayed directly in Claude #### 2. Scatter Plot with Grouping ```json { "tool": "scatter_plot", "arguments": { "data": { "x": [1, 2, 3, 4, 5, 6, 7, 8], "y": [2, 4, 3, 6, 5, 8, 7, 10], "group": ["A", "A", "B", "B", "A", "A", "B", "B"] }, "x": "x", "y": "y", "group": "group", "title": "Sales vs Marketing by Region" } } ``` **Returns:** - **Text**: Correlation coefficient, data points count - **Image**: Scatter plot with color-coded groups and trend lines ### Optional File Saving You can still save plots to files if needed: ```json { "tool": "correlation_heatmap", "arguments": { "data": {...}, "file_path": "/path/to/save/heatmap.png", "return_image": true } } ``` This saves the plot to a file **and** displays it inline in Claude. ### Technical Details #### Image Format - **Format**: PNG images with white background - **Encoding**: Base64 for transmission - **Resolution**: Configurable (default 800x600 pixels) - **Quality**: 100 DPI for crisp display #### MCP Content Response Tools now return multiple content types: ```json { "content": [ { "type": "text", "text": "{\"correlation_matrix\": [[1.0, 0.95], [0.95, 1.0]], ...}" }, { "type": "image", "data": "iVBORw0KGgoAAAANSUhEUgAAA...", "mimeType": "image/png" } ] } ``` #### Configuration Options All visualization tools support these parameters: ```json { "return_image": true, // Enable/disable inline images (default: true) "file_path": "plot.png", // Optional: also save to file "width": 800, // Image width in pixels "height": 600 // Image height in pixels } ``` ### Benefits 1. **Immediate Visual Feedback**: See plots instantly without file management 2. **Streamlined Workflow**: Analysis and visualization in one conversation 3. **Better Context**: Images appear alongside statistical results 4. **No File Management**: No need to handle file paths or external viewers 5. **Responsive**: Works in any environment where Claude runs ### Backward Compatibility - **Existing scripts**: All existing RMCP scripts continue to work unchanged - **File paths**: Still supported for users who want to save plots - **API**: No breaking changes to tool interfaces This enhancement makes RMCP visualizations much more accessible and user-friendly, providing immediate visual feedback for statistical analyses directly within your Claude conversation.