feat(altair): implement bar-basic (#129)

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

- Basic vertical bar chart for categorical data
- Y-axis starts at zero for accurate comparison
- Subtle grid lines (gridOpacity=0.3) on y-axis
- Configurable color, transparency, and label rotation
- Tooltips for interactive exploration
- Full input validation with clear error messages

**Parent Issue:** #117
**Sub-Issue:** #122
**Base Branch:** `plot/bar-basic`
**Attempt:** 1/3

## Implementation
- `plots/altair/bar/bar-basic/default.py`

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

Author: claude[bot]

plots/altair/bar/bar-basic/default.py

@@ -0,0 +1,107 @@
+"""
+bar-basic: Basic Bar Chart
+Library: altair
+"""
+
+import altair as alt
+import pandas as pd
+
+
+def create_plot(
+    data: pd.DataFrame,
+    category: str,
+    value: str,
+    *,
+    color: str = "steelblue",
+    alpha: float = 0.8,
+    title: str | None = None,
+    xlabel: str | None = None,
+    ylabel: str | None = None,
+    rotation: int = 0,
+    **kwargs,
+) -> alt.Chart:
+    """
+    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 containing the data to plot.
+        category: Column name for categorical x-axis values.
+        value: Column name for numeric y-axis values.
+        color: Bar fill color. Defaults to "steelblue".
+        alpha: Transparency level for bars (0-1). Defaults to 0.8.
+        title: Plot title. Defaults to None.
+        xlabel: X-axis label. Defaults to column name if None.
+        ylabel: Y-axis label. Defaults to column name if None.
+        rotation: Rotation angle for x-axis labels. Defaults to 0.
+        **kwargs: Additional parameters passed to chart properties.
+
+    Returns:
+        Altair Chart object.
+
+    Raises:
+        ValueError: If data is empty.
+        KeyError: If required columns are not found in data.
+
+    Example:
+        >>> data = pd.DataFrame({
+        ...     'category': ['A', 'B', 'C'],
+        ...     'value': [10, 20, 15]
+        ... })
+        >>> chart = create_plot(data, 'category', 'value', title='Example')
+    """
+    # 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)
+            raise KeyError(f"Column '{col}' not found. Available: {available}")
+
+    # Determine axis labels
+    x_label = xlabel if xlabel is not None else category
+    y_label = ylabel if ylabel is not None else value
+
+    # Build x-axis configuration
+    x_axis = alt.Axis(title=x_label, labelAngle=-rotation if rotation != 0 else 0)
+
+    # Build y-axis configuration with subtle grid
+    y_axis = alt.Axis(title=y_label, grid=True, gridOpacity=0.3)
+
+    # Create the bar chart
+    chart = (
+        alt.Chart(data)
+        .mark_bar(color=color, opacity=alpha)
+        .encode(
+            x=alt.X(f"{category}:N", axis=x_axis, sort=None),
+            y=alt.Y(f"{value}:Q", axis=y_axis, scale=alt.Scale(domain=[0, data[value].max() * 1.1])),
+            tooltip=[alt.Tooltip(f"{category}:N", title=x_label), alt.Tooltip(f"{value}:Q", title=y_label)],
+        )
+        .properties(width=800, height=450)
+    )
+
+    # Add title if provided
+    if title is not None:
+        chart = chart.properties(title=title)
+
+    # Configure chart appearance
+    chart = chart.configure_axis(labelFontSize=12, titleFontSize=14).configure_title(fontSize=16, anchor="middle")
+
+    return chart
+
+
+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")
+
+    # Save
+    fig.save("plot.png", scale_factor=2.0)
+    print("Plot saved to plot.png")