Skip to main content

Creating Advanced Charts and Visualizations

This guide explains how to use Coginiti's advanced charting and visualization capabilities, including new chart types, interactive filtering, and data consistency features that provide powerful insights into your data analysis results.

Overview​

Coginiti's advanced visualization system transforms query results into interactive charts and graphs that help you discover patterns, trends, and insights in your data. The enhanced charting capabilities provide professional-grade visualizations with real-time filtering and data consistency across different views.

Key Features​

Advanced Chart Types:

  • Candlestick Charts - Perfect for financial and time-series data analysis
  • Box Plot Charts - Ideal for outlier detection and distribution analysis
  • Traditional Charts - Bar, line, pie, scatter plots, and more

Interactive Data Control:

  • Column Selection Panel - Choose which data columns to visualize
  • Real-time Filtering - Apply filters that update both chart and data table
  • Data Consistency - Synchronized views between visual and tabular data

Enhanced Grid Interface:

  • Data Type-Aware Alignment - Text left-aligned, numbers right-aligned
  • Spreadsheet-like Behavior - Familiar interface for data review
  • Improved Readability - Better data interpretation and quality assurance

Getting Started with Charts​

Creating Your First Chart​

Step 1: Execute Query​

  1. Write and execute your SQL query in the code editor
  2. Review the results in the data grid to ensure expected output
  3. Verify data types and column structure for visualization needs

Step 2: Access Chart Interface​

  1. Click the "Chart" tab in the results panel
  2. The chart interface opens with automatic chart type suggestions
  3. Chart sidebar panel appears for configuration options

Step 3: Configure Basic Chart​

  1. Select chart type from available options
  2. Choose data columns for X and Y axes
  3. Apply any initial filters if needed
  4. Review the generated visualization

Chart Interface Layout​

Main Chart Area​

  • Primary visualization space showing your selected chart
  • Interactive elements for zooming and data point inspection
  • Responsive design that adapts to different screen sizes
  • Chart type selection with preview icons
  • Column selection controls for data mapping
  • Filter configuration options
  • Chart customization settings

Data Consistency Controls​

  • View synchronization toggle to link chart and table views
  • Filter application buttons for real-time updates
  • Data subset selection for focused analysis

Advanced Chart Types​

Candlestick Charts​

Candlestick charts are essential for financial data analysis and time-series exploration, providing comprehensive view of price movements and trends.

When to Use Candlestick Charts​

  • Financial Data Analysis - Stock prices, trading volumes, market trends
  • Time-Series Data - Any data with open, high, low, close values over time
  • Performance Metrics - KPIs with range and final values
  • Operational Data - Service levels, response times with ranges

Required Data Structure​

Your query results must include these specific columns:

SELECT 
    date_column,           -- Time dimension (X-axis)
    open_value,           -- Opening value
    high_value,           -- Highest value in period
    low_value,            -- Lowest value in period
    close_value           -- Closing value
FROM your_table
ORDER BY date_column;

Example: Stock Price Analysis​

SELECT 
    trading_date,
    opening_price,
    daily_high,
    daily_low,
    closing_price,
    volume
FROM stock_prices 
WHERE symbol = 'AAPL'
  AND trading_date >= CURRENT_DATE - INTERVAL '30 days'
ORDER BY trading_date;

Configuration Steps​

  1. Select "Candlestick" chart type from the sidebar
  2. Map data columns:
    • Date/Time: trading_date
    • Open: opening_price
    • High: daily_high
    • Low: daily_low
    • Close: closing_price
  3. Configure time range if filtering is needed
  4. Apply chart settings and review visualization

Candlestick Chart Interpretation​

Green/White Candles: Close price higher than open price (bullish) Red/Black Candles: Close price lower than open price (bearish) Wicks/Shadows: Show the full range between high and low values Body Size: Indicates the difference between open and close values

Box Plot Charts​

Box plot charts excel at showing data distribution, identifying outliers, and comparing distributions across different categories.

When to Use Box Plots​

  • Outlier Detection - Identify data points that fall outside normal ranges
  • Distribution Analysis - Understand data spread and quartile ranges
  • Comparative Analysis - Compare distributions across different groups
  • Quality Assurance - Detect anomalies in data quality checks

Required Data Structure​

Box plots work with numerical data and optional grouping columns:

SELECT 
    category_column,      -- Optional grouping dimension
    numerical_value       -- Values for distribution analysis
FROM your_table
WHERE conditions;

Example: Sales Performance Analysis​

SELECT 
    sales_region,
    monthly_sales_amount,
    deal_size,
    days_to_close
FROM sales_data 
WHERE sales_date >= CURRENT_DATE - INTERVAL '1 year'
  AND deal_status = 'closed_won';

Configuration Steps​

  1. Select "Box Plot" chart type from available options
  2. Choose numerical column for distribution analysis
  3. Select grouping column (optional) for comparative analysis
  4. Configure outlier detection sensitivity if available
  5. Apply settings and review distribution visualization

Box Plot Interpretation​

Box Elements:

  • Bottom of Box: First quartile (Q1, 25th percentile)
  • Middle Line: Median (Q2, 50th percentile)
  • Top of Box: Third quartile (Q3, 75th percentile)
  • Whiskers: Extend to min/max values within 1.5 * IQR
  • Outlier Points: Values beyond whisker boundaries

Analysis Insights:

  • Box Height: Shows interquartile range (data spread)
  • Median Position: Indicates data skewness
  • Whisker Length: Shows data range and potential outliers
  • Outlier Density: Indicates data quality issues or interesting exceptions

Interactive Data Control​

Column Selection Panel​

The sidebar panel provides comprehensive control over which data appears in your visualizations.

Selecting Columns for Visualization​

  1. Open column selection panel in the chart sidebar
  2. Review available columns from your query results
  3. Check/uncheck columns to include or exclude from visualization
  4. Observe real-time updates in both chart and data table
  5. Fine-tune selection based on visual clarity and analysis needs

Column Selection Strategies​

Focus on Key Metrics:

  • Include primary measures essential for analysis
  • Exclude noise columns that don't add analytical value
  • Group related metrics for comparative visualization

Progressive Analysis:

  • Start with core dimensions and primary metrics
  • Add complexity gradually to avoid visual overload
  • Use subsets for detailed drill-down analysis

Column Management Best Practices​

  • Limit columns to what's necessary for current analysis
  • Use descriptive column aliases in SQL for clearer chart labels
  • Consider data types when selecting columns for specific chart types
  • Test combinations to find optimal visualization approaches

Real-time Filtering​

Apply filters that simultaneously update both chart visualizations and underlying data tables for consistent analysis.

Filter Types Available​

Range Filters:

  • Numerical ranges for metrics and measures
  • Date/time ranges for temporal analysis
  • Custom boundaries for focused analysis

Category Filters:

  • Multi-select options for dimensional data
  • Include/exclude patterns for flexible selection
  • Search-based filtering for large category sets

Conditional Filters:

  • Comparison operators (greater than, less than, equals)
  • Pattern matching for text-based filtering
  • Null/not null options for data quality analysis

Applying Filters​

  1. Access filter controls in the chart sidebar
  2. Select filter type appropriate for your data column
  3. Set filter criteria using available controls
  4. Apply filter to see immediate updates in both chart and table
  5. Adjust criteria as needed for optimal analysis focus

Filter Combination Strategies​

Layered Analysis:

Step 1: Apply date range filter (last 6 months)
Step 2: Add regional filter (specific geographic areas)  
Step 3: Include performance filter (top quartile results)
Step 4: Analyze filtered subset with appropriate chart type

Comparative Filtering:

  • Create multiple filter sets for comparison analysis
  • Use category filters to isolate different segments
  • Apply temporal filters for time-based comparisons
  • Combine dimensional filters for multi-faceted analysis

Data Consistency Across Views​

Ensure that changes in chart configuration are reflected in tabular data views and vice versa.

Synchronized View Benefits​

Analytical Confidence:

  • Consistent data between visual and tabular representations
  • Verified insights through multiple view perspectives
  • Quality assurance through cross-view validation

Workflow Efficiency:

  • Seamless transitions between chart and table analysis
  • No data discrepancies between different view modes
  • Single source of truth for filtered and selected data

How Synchronization Works​

Column Selection Sync:

  • Chart column changes immediately reflect in data table
  • Table column visibility matches chart configuration
  • Consistent data subset across all views

Filter Application Sync:

  • Chart filters apply to tabular data automatically
  • Table sorting/filtering updates chart visualization
  • Real-time consistency maintained throughout analysis session

Managing View Consistency​

Toggle Synchronization:

  • Enable/disable sync based on analysis needs
  • Independent exploration when synchronization is off
  • Linked analysis when synchronization is active

Verification Techniques:

  • Cross-check insights between chart and table views
  • Validate filter effects in both visualization modes
  • Confirm data accuracy through multiple perspectives

Enhanced Grid Interface​

Data Type-Aware Alignment​

The improved grid interface automatically aligns data based on type for better readability and analysis.

Automatic Alignment Rules​

Text Data (Left-Aligned):

  • String columns - Names, descriptions, categories
  • Date/time values - Timestamps and date columns
  • Categorical data - Status codes, classification values

Numerical Data (Right-Aligned):

  • Integer values - Counts, IDs, quantities
  • Decimal numbers - Amounts, percentages, ratios
  • Financial data - Currency values, calculations

Benefits of Smart Alignment​

Improved Readability:

  • Natural scanning patterns for different data types
  • Easier comparison of numerical values
  • Reduced cognitive load when reviewing results

Quality Assurance:

  • Visual data type validation through alignment patterns
  • Quick identification of data type issues
  • Consistent presentation across different result sets

Spreadsheet-like Behavior​

The enhanced grid provides familiar spreadsheet functionality for data analysis and manipulation.

Familiar Interface Elements​

Column Operations:

  • Resizable columns for optimal data viewing
  • Column sorting by clicking headers
  • Column reordering through drag-and-drop

Data Navigation:

  • Keyboard navigation using arrow keys
  • Page scrolling for large result sets
  • Search functionality within results

Data Selection:

  • Cell selection for copying specific values
  • Row selection for copying entire records
  • Range selection for bulk operations

Enhanced Data Review​

Visual Scanning:

  • Consistent alignment improves data scanning speed
  • Clear column boundaries for easy navigation
  • Optimal spacing for readability

Data Validation:

  • Type-based formatting helps identify anomalies
  • Consistent presentation aids in quality checks
  • Quick visual verification of data accuracy

Chart Customization and Configuration​

Chart Type Selection​

Choose the optimal chart type based on your data characteristics and analysis objectives.

Chart Type Guidelines​

Time-Series Data:

  • Line Charts - Trends over time
  • Candlestick Charts - Financial time-series with OHLC data
  • Area Charts - Cumulative values over time

Categorical Comparisons:

  • Bar Charts - Comparing values across categories
  • Column Charts - Vertical category comparisons
  • Pie Charts - Parts-of-whole relationships

Distribution Analysis:

  • Box Plots - Distribution characteristics and outliers
  • Histograms - Frequency distributions
  • Scatter Plots - Relationships between variables

Specialized Analysis:

  • Heatmaps - Two-dimensional data patterns
  • Bubble Charts - Three-dimensional relationships
  • Gauge Charts - Single value performance indicators

Advanced Configuration Options​

Axis Configuration​

X-Axis Settings:

  • Scale type (linear, logarithmic, categorical)
  • Label formatting and rotation
  • Range specification for focused analysis

Y-Axis Settings:

  • Multiple Y-axes for different value scales
  • Axis labels and formatting options
  • Scale adjustments for optimal visualization

Visual Customization​

Color Schemes:

  • Predefined palettes for consistent branding
  • Custom colors for specific requirements
  • Accessibility considerations for color-blind users

Chart Elements:

  • Legend positioning and formatting
  • Grid lines and reference lines
  • Data labels and annotations

Interactive Features​

Zoom and Pan:

  • Mouse wheel zooming for detailed analysis
  • Click and drag panning for large datasets
  • Reset controls for returning to full view

Data Point Interaction:

  • Hover tooltips with detailed information
  • Click actions for drill-down analysis
  • Selection highlighting for focused exploration

Best Practices for Data Visualization​

Query Design for Visualization​

Optimizing Queries for Charts​

Data Structure Planning:

-- Good: Well-structured data for visualization
SELECT 
    DATE_TRUNC('month', order_date) as month,
    product_category,
    SUM(revenue) as total_revenue,
    COUNT(*) as order_count,
    AVG(order_value) as avg_order_value
FROM sales_data 
WHERE order_date >= CURRENT_DATE - INTERVAL '12 months'
GROUP BY 1, 2
ORDER BY 1, 2;

Column Naming:

  • Use descriptive aliases for clear chart labels
  • Avoid technical abbreviations in visualization columns
  • Include units in column names when appropriate
  • Consider axis label length for readability

Data Preparation Strategies​

Aggregation Levels:

  • Choose appropriate aggregation for chart clarity
  • Avoid over-aggregation that loses important details
  • Balance granularity with visual comprehension

Data Filtering:

  • Pre-filter irrelevant data in SQL rather than chart filters
  • Handle null values appropriately for visualization
  • Consider outlier treatment in query logic

Chart Selection Guidelines​

Matching Chart Types to Analysis Goals​

Trend Analysis:

  • Line charts for continuous time-series trends
  • Candlestick charts for detailed time-series with ranges
  • Area charts for cumulative trend visualization

Comparison Analysis:

  • Bar charts for category-to-category comparisons
  • Column charts for temporal comparisons
  • Box plots for distribution comparisons

Relationship Analysis:

  • Scatter plots for correlation exploration
  • Bubble charts for multi-dimensional relationships
  • Heatmaps for pattern identification

Avoiding Common Visualization Mistakes​

Data Overload:

  • Limit data points for clarity and readability
  • Use aggregation to reduce visual complexity
  • Focus on key insights rather than showing all data

Inappropriate Chart Types:

  • Don't use pie charts for more than 5-6 categories
  • Avoid 3D effects that distort data interpretation
  • Choose appropriate scales that don't mislead

Performance Optimization​

Large Dataset Handling​

Query Optimization:

  • Limit result sets to reasonable sizes for visualization
  • Use appropriate aggregation in SQL queries
  • Implement pagination for very large datasets

Chart Performance:

  • Consider chart type performance with large datasets
  • Use sampling techniques for exploratory analysis
  • Implement progressive loading for complex visualizations

Browser Performance​

Memory Management:

  • Close unused chart tabs to free memory
  • Refresh charts periodically for long analysis sessions
  • Monitor browser performance with large visualizations

Rendering Optimization:

  • Use appropriate chart complexity for available resources
  • Consider chart refresh frequency with real-time data
  • Optimize filter application for responsive interaction

Troubleshooting Charts and Visualizations​

Common Chart Issues​

Chart Not Displaying​

Symptoms: Empty chart area or error messages

Solutions:

  1. Verify data results - Ensure query returns data
  2. Check column selection - Confirm appropriate columns selected
  3. Review data types - Ensure compatible types for selected chart
  4. Clear filters - Remove any restrictive filters

Incorrect Chart Rendering​

Symptoms: Chart displays wrong data or format

Solutions:

  1. Review column mapping - Verify X/Y axis assignments
  2. Check data types - Ensure numerical data for numerical charts
  3. Validate data structure - Confirm required columns for chart type
  4. Reset chart configuration - Start with default settings

Performance Issues​

Symptoms: Slow chart loading or interaction

Solutions:

  1. Reduce data volume - Apply filters or aggregation in SQL
  2. Simplify chart type - Use simpler charts for large datasets
  3. Optimize query - Improve query performance for faster results
  4. Check browser resources - Close other applications if needed

Data-Specific Troubleshooting​

Candlestick Chart Issues​

Missing Candles:

  • Verify OHLC columns are properly mapped
  • Check for null values in required columns
  • Ensure proper date/time ordering in data

Incorrect Price Display:

  • Validate data ranges for realistic financial values
  • Check decimal precision in numerical columns
  • Verify currency conversion if applicable

Box Plot Problems​

No Box Visible:

  • Ensure sufficient data points for statistical calculations
  • Check for data variance (constant values won't show boxes)
  • Verify numerical data types for calculation columns

Excessive Outliers:

  • Review data quality for potential errors
  • Consider data transformation or filtering
  • Adjust outlier sensitivity if configurable

Filter and Selection Issues​

Filters Not Working​

Symptoms: Filter changes don't affect chart or data

Solutions:

  1. Check filter compatibility with selected data types
  2. Verify filter syntax for pattern-based filters
  3. Confirm data synchronization is enabled
  4. Reset filters and reapply incrementally

Column Selection Problems​

Symptoms: Column changes don't update visualizations

Solutions:

  1. Refresh chart view to force update
  2. Check column data types for chart compatibility
  3. Verify column contains data (not all nulls)
  4. Reset column selection to defaults

Summary​

You have successfully mastered Coginiti's advanced charts and visualizations! Key achievements:

✅ Advanced Chart Types: Candlestick and Box Plot charts for specialized analysis ✅ Interactive Controls: Column selection and real-time filtering capabilities ✅ Data Consistency: Synchronized views between charts and tabular data ✅ Enhanced Interface: Data type-aware grid alignment and spreadsheet-like behavior ✅ Customization: Professional chart configuration and visual optimization ✅ Best Practices: Effective query design and chart selection strategies

Your data analysis workflow now includes powerful visualization capabilities that transform query results into actionable insights through professional-grade charts and interactive data exploration tools.