feat(plotly): implement bar-basic (#130)

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

**Parent Issue:** #117
**Sub-Issue:** #120
**Base Branch:** `plot/bar-basic`
**Attempt:** 1/3

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

## Features
- Basic vertical bar chart for categorical data comparison
- Customizable bar colors, edge colors, and transparency
- Configurable axis labels and title
- Y-axis starts at zero for accurate visual comparison
- Subtle grid lines (alpha=0.3) for readability
- Support for x-axis label rotation for long category names
- 16:9 aspect ratio (1600x900 pixels)

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

Author: claude[bot]

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

@@ -0,0 +1,125 @@
+"""
+bar-basic: Basic Bar Chart
+Library: plotly
+"""
+
+from typing import TYPE_CHECKING
+
+import pandas as pd
+import plotly.graph_objects as go
+
+
+if TYPE_CHECKING:
+    pass
+
+
+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,
+) -> go.Figure:
+    """
+    Create a basic vertical bar chart for categorical data comparison.
+
+    A fundamental bar chart that displays rectangular bars with heights
+    proportional to the values they represent. Each bar corresponds to
+    a category, making it ideal for comparing quantities across discrete
+    categories.
+
+    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) in pixels. Defaults to (1600, 900).
+        color: Bar fill color. Defaults to "steelblue".
+        edgecolor: Bar edge/outline 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 column name if None.
+        ylabel: Y-axis label. Defaults to column name if None.
+        rotation: Rotation angle for x-axis labels in degrees. Defaults to 0.
+        **kwargs: Additional parameters passed to go.Bar.
+
+    Returns:
+        Plotly Figure object containing 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': [45, 78, 52, 91]
+        ... })
+        >>> fig = create_plot(data, 'category', 'value', title='Sales by Product')
+    """
+    # 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 from column names
+    x_label = xlabel if xlabel is not None else category
+    y_label = ylabel if ylabel is not None else value
+
+    # Create figure
+    fig = go.Figure()
+
+    # Add bar trace
+    fig.add_trace(
+        go.Bar(
+            x=data[category],
+            y=data[value],
+            marker=dict(color=color, opacity=alpha, line=dict(color=edgecolor, width=1)),
+            **kwargs,
+        )
+    )
+
+    # Update layout with styling
+    fig.update_layout(
+        title=dict(text=title, x=0.5, xanchor="center") if title else None,
+        xaxis=dict(title=dict(text=x_label, font=dict(size=14)), tickangle=-rotation, tickfont=dict(size=12)),
+        yaxis=dict(
+            title=dict(text=y_label, font=dict(size=14)),
+            tickfont=dict(size=12),
+            rangemode="tozero",
+            showgrid=True,
+            gridwidth=1,
+            gridcolor="rgba(128, 128, 128, 0.3)",
+        ),
+        template="plotly_white",
+        width=figsize[0],
+        height=figsize[1],
+        showlegend=False,
+        margin=dict(l=80, r=40, t=80 if title else 40, b=80),
+    )
+
+    return fig
+
+
+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.write_image("plot.png", width=1600, height=900, scale=2)
+    print("Plot saved to plot.png")