Interactive Data Visualization Playground

A powerful data exploration tool that transforms CSV files into beautiful, interactive visualizations. Simply drag and drop your data files to instantly create charts, discover patterns, and gain insights from your data.

Features

Supported Visualization Types

• Bar Charts: Compare categories and values
• Line Charts: Show trends over time
• Scatter Plots: Explore relationships between variables
• Heatmaps: Visualize correlation matrices
• Histograms: Understand data distributions
• Box Plots: Analyze statistical summaries

Interactive Capabilities

• Zoom & Pan: Explore data at different scales
• Hover Details: See precise values on mouseover
• Filtering: Focus on specific data subsets
• Color Coding: Encode additional dimensions
• Axis Customization: Adjust scales and ranges
• Export Options: Save as PNG, SVG, or PDF

How It Works

The playground automatically analyzes your CSV structure and suggests appropriate visualizations:

Data Processing Pipeline

interface DataAnalysis {
  columns: ColumnInfo[]
  rowCount: number
  dataTypes: Record<string, 'numeric' | 'categorical' | 'temporal'>
  suggestedCharts: ChartType[]
  correlations: CorrelationMatrix
}

function analyzeData(csvData: string): DataAnalysis {
  const parsed = parseCSV(csvData)
  const columns = detectColumnTypes(parsed)
  const suggestions = suggestVisualizations(columns)

  return {
    columns,
    rowCount: parsed.length,
    dataTypes: inferDataTypes(columns),
    suggestedCharts: suggestions,
    correlations: calculateCorrelations(parsed)
  }
}

Smart Chart Recommendations

The system analyzes your data structure and recommends the most appropriate visualizations:

• Single numeric column → Histogram
• Categorical + numeric → Bar chart
• Two numeric columns → Scatter plot
• Time series data → Line chart
• Multiple numeric columns → Correlation heatmap

Advanced Features

Statistical Analysis

Automatic calculation of key statistics:

• Mean, median, mode
• Standard deviation and variance
• Quartiles and outliers
• Correlation coefficients
• Trend analysis

Interactive Filtering

interface FilterState {
  numericRanges: Record<string, [number, number]>
  categoricalSelections: Record<string, string[]>
  dateRanges: Record<string, [Date, Date]>
}

function applyFilters(data: DataPoint[], filters: FilterState): DataPoint[] {
  return data.filter(point => {
    // Apply numeric range filters
    for (const [column, range] of Object.entries(filters.numericRanges)) {
      if (point[column] < range[0] || point[column] > range[1]) {
        return false
      }
    }

    // Apply categorical filters
    for (const [column, selections] of Object.entries(filters.categoricalSelections)) {
      if (!selections.includes(point[column])) {
        return false
      }
    }

    return true
  })
}

Sample Datasets

Try the playground with these example datasets:

Sales Performance Data

• Monthly sales figures across different regions
• Product categories and performance metrics
• Time series analysis and trend identification

Weather Station Data

• Temperature, humidity, and precipitation readings
• Multi-variate analysis and correlation discovery
• Seasonal pattern visualization

Stock Market Data

• Historical price movements and trading volumes
• Technical indicator calculations
• Comparative analysis across different stocks

Technical Implementation

Built with modern web technologies for smooth performance:

D3.js Integration

class ScatterPlot {
  private svg: d3.Selection<SVGSVGElement, unknown, null, undefined>
  private data: DataPoint[]

  constructor(container: HTMLElement, data: DataPoint[]) {
    this.data = data
    this.svg = d3.select(container)
      .append('svg')
      .attr('width', 800)
      .attr('height', 600)

    this.render()
  }

  render() {
    const xScale = d3.scaleLinear()
      .domain(d3.extent(this.data, d => d.x))
      .range([50, 750])

    const yScale = d3.scaleLinear()
      .domain(d3.extent(this.data, d => d.y))
      .range([550, 50])

    this.svg.selectAll('circle')
      .data(this.data)
      .enter()
      .append('circle')
      .attr('cx', d => xScale(d.x))
      .attr('cy', d => yScale(d.y))
      .attr('r', 5)
      .on('mouseover', this.showTooltip)
      .on('mouseout', this.hideTooltip)
  }
}

Performance Optimization

• Virtual scrolling for large datasets
• Canvas rendering for high-density scatter plots
• Web Workers for intensive calculations
• Streaming CSV parsing for large files
• Efficient data structures for real-time filtering

Use Cases

Business Analytics

• Sales performance analysis
• Customer behavior patterns
• Marketing campaign effectiveness
• Financial trend analysis

Scientific Research

• Experimental data exploration
• Statistical hypothesis testing
• Pattern recognition in datasets
• Publication-ready visualizations

Educational Applications

• Teaching data science concepts
• Statistical analysis exercises
• Interactive data exploration
• Hypothesis generation and testing

Getting Started

1. Drop your CSV file onto the upload area
2. Review the data preview and column types
3. Select a visualization type from the recommendations
4. Customize the chart using the control panel
5. Interact with your data using zoom, filter, and selection tools
6. Export your visualization in your preferred format

The playground handles most CSV formats automatically, including:

• Different delimiter types (comma, semicolon, tab)
• Various date formats
• Missing value handling
• Automatic type inference
• Large file processing (up to 100MB)

Perfect for data analysts, researchers, students, and anyone who wants to quickly explore and visualize their data without complex software setup.