feat(plotnine): implement bar-basic (#132)

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

- Uses `geom_bar` with `stat="identity"` for value-based bar heights
- Includes input validation for empty data and missing columns
- Configurable colors, alpha, rotation, and figure size
- Subtle horizontal grid lines (alpha=0.3) on y-axis only
- Y-axis starts at zero for accurate comparison

**Parent Issue:** #117
**Sub-Issue:** #123
**Base Branch:** `plot/bar-basic`
**Attempt:** 1/3

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

## Quality Checklist
- [x] X-axis displays category labels clearly
- [x] Y-axis shows numeric scale starting at zero
- [x] Both axes are labeled (defaults to column names)
- [x] Grid lines subtle (alpha=0.3) on y-axis only
- [x] Bars have consistent width and spacing
- [x] Type hints on all parameters
- [x] Google-style docstring with Args, Returns, Raises, Example
- [x] Input validation with clear error messages

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

Author: claude[bot]

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

@@ -0,0 +1,102 @@
+"""
+bar-basic: Basic Bar Chart
+Library: plotnine
+
+A fundamental vertical bar chart that visualizes categorical data with numeric values.
+"""
+
+import pandas as pd
+from plotnine import aes, geom_bar, ggplot, labs, scale_y_continuous, theme, theme_minimal
+from plotnine.ggplot import ggplot as ggplot_type
+from plotnine.themes.elements import element_blank, element_line, element_text
+
+
+def create_plot(
+    data: pd.DataFrame,
+    category: str,
+    value: str,
+    figsize: tuple[float, float] = (10, 6),
+    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,
+) -> ggplot_type:
+    """
+    Create a basic vertical bar chart.
+
+    Args:
+        data: Input DataFrame containing the data to plot
+        category: Column name for category labels (x-axis)
+        value: Column name for numeric values (bar heights)
+        figsize: Figure size as (width, height)
+        color: Bar fill color
+        edgecolor: Bar edge color
+        alpha: Transparency level for bars (0-1)
+        title: Plot title (optional)
+        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
+        **kwargs: Additional parameters passed to geom_bar
+
+    Returns:
+        plotnine ggplot 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]
+        ... })
+        >>> fig = create_plot(data, 'category', 'value', title='Sample 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)
+            raise KeyError(f"Column '{col}' not found. Available: {available}")
+
+    # Set default labels to column names if not provided
+    x_label = xlabel if xlabel is not None else category
+    y_label = ylabel if ylabel is not None else value
+
+    # Create the plot
+    plot = (
+        ggplot(data, aes(x=category, y=value))
+        + geom_bar(stat="identity", fill=color, color=edgecolor, alpha=alpha, **kwargs)
+        + labs(x=x_label, y=y_label, title=title)
+        + scale_y_continuous(expand=(0, 0, 0.05, 0))  # Start y-axis at zero
+        + theme_minimal()
+        + theme(
+            figure_size=figsize,
+            axis_text_x=element_text(rotation=rotation, ha="right" if rotation else "center"),
+            panel_grid_major_x=element_blank(),  # Remove vertical grid lines
+            panel_grid_minor=element_blank(),  # Remove minor grid lines
+            panel_grid_major_y=element_line(alpha=0.3),  # Subtle horizontal grid
+        )
+    )
+
+    return plot
+
+
+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", dpi=300)
+    print("Plot saved to plot.png")