chart-generator

What this skill does

# Chart Generator Skill

Generate charts and visualizations from data using various charting libraries and formats.

## Instructions

You are a data visualization expert. When invoked:

1. **Analyze Data**:
   - Understand data structure and types
   - Identify appropriate chart types
   - Detect data patterns and trends
   - Calculate aggregations and statistics
   - Determine visualization goals

2. **Generate Charts**:
   - Create bar, line, pie, scatter plots
   - Generate heatmaps and tree maps
   - Create histograms and box plots
   - Build time series visualizations
   - Design multi-dimensional charts

3. **Style and Customize**:
   - Apply color schemes and themes
   - Add labels, legends, and annotations
   - Format axes and gridlines
   - Customize tooltips and interactions
   - Ensure accessibility and readability

4. **Export and Embed**:
   - Save as PNG, SVG, PDF
   - Generate interactive HTML charts
   - Embed in markdown reports
   - Create chart APIs
   - Support responsive design

## Usage Examples

```
@chart-generator data.csv --type bar
@chart-generator --line --time-series
@chart-generator --pie --group-by category
@chart-generator --scatter x:age y:income
@chart-generator --heatmap --correlation
@chart-generator --interactive --html
```

## Chart Types and Use Cases

### When to Use Each Chart Type

| Chart Type | Best For | Example Use Case |
|------------|----------|------------------|
| Bar Chart | Comparing categories | Sales by product |
| Line Chart | Trends over time | Revenue over months |
| Pie Chart | Part-to-whole relationships | Market share |
| Scatter Plot | Relationships between variables | Height vs Weight |
| Histogram | Distribution of values | Age distribution |
| Box Plot | Statistical distribution | Salary ranges by department |
| Heatmap | Matrix data, correlations | Feature correlations |
| Area Chart | Cumulative trends | Stacked revenue streams |
| Bubble Chart | 3-dimensional data | Sales vs Profit vs Market Share |
| Treemap | Hierarchical data | Disk space usage |

## Python - Matplotlib

```python
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

def create_bar_chart(data, x_col, y_col, title='Bar Chart', output='chart.png'):
    """
    Create a bar chart
    """
    plt.figure(figsize=(10, 6))

    if isinstance(data, pd.DataFrame):
        x = data[x_col]
        y = data[y_col]
    else:
        x = data['labels']
        y = data['values']

    bars = plt.bar(x, y, color='steelblue', alpha=0.8)

    # Add value labels on bars
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width()/2., height,
                f'{height:.1f}',
                ha='center', va='bottom')

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(x_col if isinstance(data, pd.DataFrame) else 'Category', fontsize=12)
    plt.ylabel(y_col if isinstance(data, pd.DataFrame) else 'Value', fontsize=12)
    plt.xticks(rotation=45, ha='right')
    plt.grid(axis='y', alpha=0.3)
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_line_chart(data, x_col, y_col, title='Line Chart', output='chart.png'):
    """
    Create a line chart
    """
    plt.figure(figsize=(12, 6))

    if isinstance(data, pd.DataFrame):
        x = data[x_col]
        y = data[y_col]
    else:
        x = data['x']
        y = data['y']

    plt.plot(x, y, marker='o', linewidth=2, markersize=6, color='steelblue')

    # Add grid
    plt.grid(True, alpha=0.3)

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(x_col if isinstance(data, pd.DataFrame) else 'X', fontsize=12)
    plt.ylabel(y_col if isinstance(data, pd.DataFrame) else 'Y', fontsize=12)
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_pie_chart(data, labels_col, values_col, title='Pie Chart', output='chart.png'):
    """
    Create a pie chart
    """
    plt.figure(figsize=(10, 8))

    if isinstance(data, pd.DataFrame):
        labels = data[labels_col]
        values = data[values_col]
    else:
        labels = data['labels']
        values = data['values']

    # Create color palette
    colors = plt.cm.Set3(np.linspace(0, 1, len(labels)))

    # Create pie chart
    wedges, texts, autotexts = plt.pie(
        values,
        labels=labels,
        autopct='%1.1f%%',
        startangle=90,
        colors=colors,
        explode=[0.05] * len(labels)  # Slightly separate slices
    )

    # Style percentage text
    for autotext in autotexts:
        autotext.set_color('white')
        autotext.set_fontweight('bold')
        autotext.set_fontsize(10)

    plt.title(title, fontsize=16, fontweight='bold')
    plt.axis('equal')
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_scatter_plot(data, x_col, y_col, color_col=None, size_col=None,
                       title='Scatter Plot', output='chart.png'):
    """
    Create a scatter plot
    """
    plt.figure(figsize=(10, 8))

    if isinstance(data, pd.DataFrame):
        x = data[x_col]
        y = data[y_col]
        c = data[color_col] if color_col else None
        s = data[size_col] if size_col else 50
    else:
        x = data['x']
        y = data['y']
        c = None
        s = 50

    scatter = plt.scatter(x, y, c=c, s=s, alpha=0.6, cmap='viridis')

    if color_col:
        plt.colorbar(scatter, label=color_col)

    # Add trend line
    z = np.polyfit(x, y, 1)
    p = np.poly1d(z)
    plt.plot(x, p(x), "r--", alpha=0.8, label='Trend')

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(x_col if isinstance(data, pd.DataFrame) else 'X', fontsize=12)
    plt.ylabel(y_col if isinstance(data, pd.DataFrame) else 'Y', fontsize=12)
    plt.grid(True, alpha=0.3)
    plt.legend()
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_histogram(data, column, bins=30, title='Histogram', output='chart.png'):
    """
    Create a histogram
    """
    plt.figure(figsize=(10, 6))

    if isinstance(data, pd.DataFrame):
        values = data[column]
    else:
        values = data

    n, bins, patches = plt.hist(values, bins=bins, color='steelblue',
                                 alpha=0.7, edgecolor='black')

    # Add mean line
    mean_val = np.mean(values)
    plt.axvline(mean_val, color='red', linestyle='dashed', linewidth=2,
                label=f'Mean: {mean_val:.2f}')

    # Add median line
    median_val = np.median(values)
    plt.axvline(median_val, color='green', linestyle='dashed', linewidth=2,
                label=f'Median: {median_val:.2f}')

    plt.title(title, fontsize=16, fontweight='bold')
    plt.xlabel(column if isinstance(data, pd.DataFrame) else 'Value', fontsize=12)
    plt.ylabel('Frequency', fontsize=12)
    plt.legend()
    plt.grid(axis='y', alpha=0.3)
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()

    return output

def create_box_plot(data, columns, title='Box Plot', output='chart.png'):
    """
    Create a box plot
    """
    plt.figure(figsize=(10, 6))

    if isinstance(data, pd.DataFrame):
        data_to_plot = [data[col].dropna() for col in columns]
        labels = columns
    else:
        data_to_plot = data
        labels = [f'Group {i+1}' for i in range(len(data))]

    bp = plt.boxplot(data_to_plot, labels=labels, patch_artist=True)

    # Color boxes
    for patch in bp['boxes']:
        patch.set_facecolor('lightblue')
        patch.set_alpha(0.7)

    plt.title(title, fontsize=16, fontweight='bold')
    plt.ylabel('Value', fontsize=12)
    plt.grid(axis='y', alpha=0.3)
    plt.xticks(rotation=45, ha='right')
    plt.tight_layout()

    plt.savefig(output, dpi=300, bbox_inches='tight')
    plt.close()