Google Charts Implementation Guide¶

Overview¶

Comprehensive documentation for implementing Google Charts with AskRITA's UniversalChartData model. Each chart type includes sample data structures, implementation code for vanilla JavaScript, React, and Angular frameworks.

📊 Available Chart Types¶

Standard Charts¶

Bar Chart (Horizontal)¶

Perfect for: Category comparison with long names - Service area rankings - Top performing stores - Response volume by demographics - Use when: Limited vertical space or long category names

Column Chart (Vertical)¶

Perfect for: Categorical data comparison over time - Monthly survey responses - Satisfaction by business unit - Performance metrics comparison - Use when: Standard comparison visualization needed

Line Chart ¶

Perfect for: Trends over time and continuous data - Satisfaction trends over time - Response volume tracking - Performance monitoring - Use when: Time series analysis is primary focus

Pie Chart & Donut Chart ¶

Perfect for: Part-to-whole relationships - NPS distribution (Promoters/Passives/Detractors) - Response channel breakdown - Satisfaction level distribution - Use when: Showing proportions of a total (3-7 categories)

Scatter Plot ¶

Perfect for: Correlation analysis between two variables - Response time vs satisfaction - Store size vs performance - Demographics vs satisfaction - Use when: Exploring relationships between numeric variables

High-Impact Charts¶

ComboChart (Bar-and-Line)¶

Perfect for: Volume + Quality metrics with dual Y-axes - Response counts with NPS scores - Customer volume with satisfaction metrics - Campaign reach with engagement rates - Use when: Different scales (10x+ difference) between metrics

Gauge Charts ¶

Perfect for: Single KPI displays and dashboards - Current NPS score with target zones - Response rate percentages - Performance indicators - Use when: Real-time monitoring of single metrics

GeoChart (Geographic Maps)¶

Perfect for: Regional analysis and location-based data - Satisfaction scores by state/country - Response distribution by geography - Store performance by location - Use when: Geographic patterns are important

Advanced Visualizations¶

Sankey Diagrams ¶

Perfect for: Flow analysis and customer journeys - Survey completion funnels - Customer journey mapping - Process flow visualization - Use when: Understanding paths and drop-offs

TreeMap Charts ¶

Perfect for: Hierarchical data with quantities - Response volume by service area and department - Customer segments and sub-segments - Market share analysis - Use when: Part-to-whole relationships with hierarchy

Timeline Charts ¶

Perfect for: Event sequences and project timelines - Survey campaign schedules - Customer journey events - Project milestone tracking - Use when: Showing duration and sequence of events

Calendar Heatmap ¶

Perfect for: Daily patterns over extended periods - Daily response volume patterns - Satisfaction score trends by day - Campaign activity calendar - Use when: Identifying seasonal or daily patterns

Histogram ¶

Perfect for: Data distribution analysis - Response score distribution - Response time analysis - Customer demographics distribution - Use when: Understanding data distribution and outliers

Data Table with Sparklines ¶

Perfect for: Detailed data with embedded trends - Store performance dashboards - Customer segment analysis - Campaign performance tracking - Use when: Precise values needed alongside visual trends

🚀 Framework Integration¶

React Integration Guide ¶

Complete React implementation with: - Custom hooks for chart management - TypeScript support - Component patterns - Error boundaries - Real-time updates - Performance optimization

Angular Integration Guide ¶

Complete Angular implementation with: - Service-based architecture - Dependency injection - Component lifecycle management - RxJS integration - Testing strategies - Dashboard patterns

🎯 Quick Start Examples¶

Basic Implementation¶

// Load Google Charts
google.charts.load('current', {'packages':['corechart', 'gauge', 'geochart']});
google.charts.setOnLoadCallback(drawChart);

function drawChart() {
    var data = google.visualization.arrayToDataTable([
        ['Category', 'Value'],
        ['Satisfied', 75],
        ['Neutral', 15],
        ['Dissatisfied', 10]
    ]);

    var options = {
        title: 'Customer Satisfaction Distribution'
    };

    var chart = new google.visualization.PieChart(document.getElementById('chart_div'));
    chart.draw(data, options);
}

Ask RITA Integration¶

# Using AskRITA's UniversalChartData
from askrita.sqlagent.formatters.DataFormatter import UniversalChartData, ChartDataset, DataPoint

chart_data = UniversalChartData(
    type="combo",
    title="Response Volume vs NPS Score",
    labels=["Commercial", "Medicare", "Medicaid"],
    datasets=[
        ChartDataset(
            label="Response Count",
            data=[
                DataPoint(y=15420, category="Commercial"),
                DataPoint(y=8932, category="Medicare"),
                DataPoint(y=5621, category="Medicaid")
            ],
            yAxisId="left-axis"
        ),
        ChartDataset(
            label="NPS Score",
            data=[
                DataPoint(y=72, category="Commercial"),
                DataPoint(y=68, category="Medicare"),
                DataPoint(y=45, category="Medicaid")
            ],
            yAxisId="right-axis"
        )
    ]
)

📋 Implementation Checklist¶

For Each Chart Type¶

[ ] Data Structure - Define UniversalChartData format
[ ] HTML Setup - Create container element
[ ] Script Loading - Load required Google Charts packages
[ ] Data Transformation - Convert UniversalChartData to Google Charts format
[ ] Options Configuration - Set up chart styling and behavior
[ ] Event Handling - Add interactivity (optional)
[ ] Responsive Design - Handle window resize events
[ ] Error Handling - Graceful failure for missing data

Framework-Specific¶

[ ] React: Component lifecycle, hooks, TypeScript types — see React Integration
[ ] Angular: Services, dependency injection, observables — see Angular Integration

🎨 Design Guidelines¶

Color Schemes¶

// Brand Colors
const brandColors = {
    primary: '#1976D2',      // Blue
    secondary: '#FFFFFF',    // White
    accent: '#666666',       // Gray
    success: '#28a745',      // Green
    warning: '#ffc107',      // Yellow
    danger: '#dc3545'        // Red
};

// Satisfaction Score Colors
const satisfactionColors = {
    high: '#28a745',         // Green (80-100)
    medium: '#ffc107',       // Yellow (60-79)
    low: '#dc3545'          // Red (0-59)
};

// Multi-series Colors (accessible)
const seriesColors = [
    '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728',
    '#9467bd', '#8c564b', '#e377c2', '#7f7f7f'
];

Typography¶

const chartTypography = {
    title: {
        fontSize: 18,
        fontFamily: 'Arial, sans-serif',
        bold: true,
        color: '#333333'
    },
    axis: {
        fontSize: 12,
        fontFamily: 'Arial, sans-serif',
        color: '#666666'
    },
    legend: {
        fontSize: 11,
        fontFamily: 'Arial, sans-serif',
        color: '#333333'
    }
};

🔧 Common Patterns¶

Data Loading Pattern¶

async function loadChartData(endpoint) {
    try {
        const response = await fetch(endpoint);
        const universalData = await response.json();

        // Transform to Google Charts format
        const chartData = transformUniversalData(universalData);

        // Draw chart
        drawChart(chartData);
    } catch (error) {
        console.error('Failed to load chart data:', error);
        showErrorMessage('Unable to load chart data');
    }
}

Responsive Chart Pattern¶

function createResponsiveChart() {
    function drawChart() {
        const container = document.getElementById('chart_div');
        const width = container.offsetWidth;
        const height = Math.max(300, width * 0.6);

        const options = {
            ...baseOptions,
            width: width,
            height: height
        };

        chart.draw(data, options);
    }

    // Initial draw
    drawChart();

    // Redraw on resize
    window.addEventListener('resize', debounce(drawChart, 250));
}

Error Handling Pattern¶

function drawChartWithErrorHandling(data, options) {
    try {
        if (!data || data.length === 0) {
            showNoDataMessage();
            return;
        }

        const chart = new google.visualization.ComboChart(
            document.getElementById('chart_div')
        );

        google.visualization.events.addListener(chart, 'error', function(error) {
            console.error('Chart error:', error);
            showErrorMessage('Chart rendering failed');
        });

        chart.draw(data, options);
    } catch (error) {
        console.error('Chart initialization error:', error);
        showErrorMessage('Chart initialization failed');
    }
}

📚 Additional Resources¶

Google Charts Documentation¶

Ask RITA Integration¶

Configuration Options

Best Practices¶

🎯 Chart Selection Guide¶

By Data Type¶

Categorical Data: Bar, Column, Pie charts
Time Series: Line, Area, Calendar charts
Geographic: GeoChart
Hierarchical: TreeMap
Flow/Process: Sankey
Distribution: Histogram
Single Metrics: Gauge
Multi-dimensional: ComboChart, Scatter
Detailed Data: Table with Sparklines

By Use Case¶

Executive Dashboard: Gauge, ComboChart, Table with Sparklines
Regional Analysis: GeoChart, TreeMap
Customer Journey: Sankey, Timeline
Performance Tracking: Line, ComboChart, Calendar
Market Analysis: TreeMap, GeoChart, Scatter
Satisfaction Surveys: Gauge, Bar, ComboChart, Histogram
Campaign Management: Timeline, Calendar, Table
Data Distribution: Histogram, Scatter

By Audience¶

Executives: Simple, high-level (Gauge, ComboChart, Pie)
Analysts: Detailed, interactive (Sankey, TreeMap, Scatter, Histogram)
Operations: Real-time, actionable (Gauge, Line, Calendar)
Marketing: Engaging, visual (GeoChart, TreeMap, Timeline)
Data Scientists: Statistical, exploratory (Histogram, Scatter, Table)

By Data Size¶

Small Datasets (<50 points): Pie, Bar, Column
Medium Datasets (50-500 points): Line, Scatter, TreeMap
Large Datasets (500+ points): Histogram, Calendar, aggregated charts
Time Series: Line, Calendar (any size with aggregation)
Geographic: GeoChart (scales well with proper aggregation)

📊 Google Charts Quick Reference¶

#	Chart Type	Link
1	Bar-and-Line Chart	https://developers.google.com/chart/interactive/docs/gallery/combochart
2	Multi-Series Line Chart	https://developers.google.com/chart/interactive/docs/gallery/linechart
3	Grouped Column Chart	https://developers.google.com/chart/interactive/docs/gallery/columnchart
4	Horizontal Bar Chart	https://developers.google.com/chart/interactive/docs/gallery/barchart
5	Data Table with Sparklines	https://developers.google.com/chart/interactive/docs/gallery/table
6	Donut Chart	https://developers.google.com/chart/interactive/docs/gallery/piechart
7	Scatter Plot	https://developers.google.com/chart/interactive/docs/gallery/scatterchart
8	Stacked Area Chart	https://developers.google.com/chart/interactive/docs/gallery/areachart
9	Geographic Map	https://developers.google.com/chart/interactive/docs/gallery/geochart
10	Gauge Meter	https://developers.google.com/chart/interactive/docs/gallery/gauge
11	Flow Diagram	https://developers.google.com/chart/interactive/docs/gallery/sankey
12	Hierarchical Rectangles	https://developers.google.com/chart/interactive/docs/gallery/treemap
13	Event Timeline	https://developers.google.com/chart/interactive/docs/gallery/timeline
14	Calendar Heatmap	https://developers.google.com/chart/interactive/docs/gallery/calendar
15	Distribution Histogram	https://developers.google.com/chart/interactive/docs/gallery/histogram
16	Multi-Chart Dashboard	https://developers.google.com/chart/interactive/docs/gallery/controls
17	Interactive Controls	https://developers.google.com/chart/interactive/docs/gallery/controls
18	Linked Chart Views	https://developers.google.com/chart/interactive/docs/gallery/controls

Additional Resources: - All Chart Types Gallery - Dashboard Tutorial - Dual Y-Axis Examples

This comprehensive guide provides everything needed to implement professional-grade chart visualizations using Google Charts with Ask RITA's data structures!