feat(bokeh): implement bar-basic (#131)

## Summary
Implements `bar-basic` for **bokeh** library.

**Parent Issue:** #117
**Sub-Issue:** #121
**Base Branch:** `plot/bar-basic`
**Attempt:** 1/3

## Implementation
- `plots/bokeh/vbar/bar-basic/default.py`

## Features
- Uses `vbar` glyph for vertical bar chart
- Supports categorical x-axis with `x_range`
- Uses `ColumnDataSource` for data management
- Customizable colors, alpha, and edge colors
- Subtle grid on y-axis only (alpha=0.3)
- Y-axis starts at zero for accurate comparison
- Support for x-axis label rotation
- Proper input validation with helpful error messages
- Google-style docstrings with type hints

Co-authored-by: claude[bot] <41898282+claude[bot]@users.noreply.github.com>

Author: claude[bot]

plots/bokeh/vbar/bar-basic/default.py

@@ -0,0 +1,152 @@
+"""
+bar-basic: Basic Bar Chart
+Library: bokeh
+"""
+
+from typing import TYPE_CHECKING
+
+import pandas as pd
+from bokeh.io import export_png
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+
+
+if TYPE_CHECKING:
+    from bokeh.plotting import figure as Figure
+
+
+def create_plot(
+    data: pd.DataFrame,
+    category: str,
+    value: str,
+    figsize: tuple[int, int] = (1600, 900),
+    color: str = "steelblue",
+    edgecolor: str = "black",
+    alpha: float = 0.8,
+    title: str | None = None,
+    xlabel: str | None = None,
+    ylabel: str | None = None,
+    rotation: int = 0,
+    **kwargs,
+) -> "Figure":
+    """
+    Create a basic vertical bar chart.
+
+    A fundamental bar chart that visualizes categorical data with numeric values,
+    ideal for comparing quantities across discrete categories.
+
+    Args:
+        data: Input DataFrame with categorical and numeric columns
+        category: Column name for category labels (x-axis)
+        value: Column name for numeric values (bar heights)
+        figsize: Figure size as (width, height) in pixels. Defaults to (1600, 900).
+        color: Bar fill color. Defaults to "steelblue".
+        edgecolor: Bar edge color. Defaults to "black".
+        alpha: Transparency level for bars (0-1). Defaults to 0.8.
+        title: Plot title. Defaults to None.
+        xlabel: X-axis label. Defaults to category column name.
+        ylabel: Y-axis label. Defaults to value column name.
+        rotation: Rotation angle for x-axis labels in degrees. Defaults to 0.
+        **kwargs: Additional parameters passed to figure.
+
+    Returns:
+        Bokeh figure object with the bar chart.
+
+    Raises:
+        ValueError: If data is empty.
+        KeyError: If required columns are not found in data.
+
+    Example:
+        >>> data = pd.DataFrame({
+        ...     'category': ['A', 'B', 'C', 'D'],
+        ...     'value': [10, 25, 15, 30]
+        ... })
+        >>> fig = create_plot(data, 'category', 'value', title='Sample Bar Chart')
+    """
+    # Input validation
+    if data.empty:
+        raise ValueError("Data cannot be empty")
+
+    for col in [category, value]:
+        if col not in data.columns:
+            available = ", ".join(data.columns.tolist())
+            raise KeyError(f"Column '{col}' not found. Available: {available}")
+
+    # Prepare data - drop NaN values
+    plot_data = data[[category, value]].dropna()
+
+    # Get categories as list for x_range
+    categories = plot_data[category].astype(str).tolist()
+
+    # Create ColumnDataSource
+    source = ColumnDataSource(data={"categories": categories, "values": plot_data[value].tolist()})
+
+    # Set labels
+    x_label = xlabel if xlabel is not None else category
+    y_label = ylabel if ylabel is not None else value
+
+    # Create figure with categorical x-axis
+    p = figure(
+        width=figsize[0],
+        height=figsize[1],
+        x_range=categories,
+        title=title,
+        x_axis_label=x_label,
+        y_axis_label=y_label,
+        **kwargs,
+    )
+
+    # Calculate bar width (0.8 of available space)
+    bar_width = 0.8
+
+    # Add bars
+    p.vbar(
+        x="categories",
+        top="values",
+        width=bar_width,
+        source=source,
+        fill_color=color,
+        fill_alpha=alpha,
+        line_color=edgecolor,
+        line_width=1,
+    )
+
+    # Ensure y-axis starts at zero
+    p.y_range.start = 0
+
+    # Style grid - subtle y-grid only
+    p.xgrid.grid_line_color = None
+    p.ygrid.grid_line_alpha = 0.3
+
+    # Apply x-axis label rotation if specified
+    if rotation != 0:
+        from math import pi
+
+        p.xaxis.major_label_orientation = rotation * pi / 180
+
+    # Style axis labels
+    p.xaxis.axis_label_text_font_size = "12pt"
+    p.yaxis.axis_label_text_font_size = "12pt"
+    p.xaxis.major_label_text_font_size = "10pt"
+    p.yaxis.major_label_text_font_size = "10pt"
+
+    # Style title if present
+    if title:
+        p.title.text_font_size = "14pt"
+        p.title.align = "center"
+
+    return p
+
+
+if __name__ == "__main__":
+    # Sample data for testing
+    sample_data = pd.DataFrame(
+        {"category": ["Product A", "Product B", "Product C", "Product D", "Product E"], "value": [45, 78, 52, 91, 63]}
+    )
+
+    # Create plot
+    fig = create_plot(sample_data, "category", "value", title="Sales by Product", xlabel="Product", ylabel="Sales ($)")
+
+    # Save
+    export_png(fig, filename="plot.png")
+    print("Plot saved to plot.png")