diff --git a/02_quant_analysis.py b/02_quant_analysis.py
index 055bca7..19ba3e5 100644
--- a/02_quant_analysis.py
+++ b/02_quant_analysis.py
@@ -42,7 +42,7 @@ def _(JPMCSurvey, QSF_FILE, RESULTS_FILE):
     return S, data_all
 
 
-@app.cell
+@app.cell(hide_code=True)
 def _(Path, RESULTS_FILE, data_all, mo):
     mo.md(f"""
     # Load Data
@@ -68,12 +68,6 @@ def _(check_progress, data_all, duration_validation, mo):
     return
 
 
-@app.cell
-def _(data_all, duration_validation):
-    duration_validation(data_all)
-    return
-
-
 @app.cell(hide_code=True)
 def _(mo):
     mo.md(r"""
@@ -120,8 +114,9 @@ def _(S, mo):
     return (filter_form,)
 
 
-@app.cell
+@app.cell(hide_code=True)
 def _(S, data_all, filter_form, mo):
+    mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
     _d = S.filter_data(data_all, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer'])
 
     # Stop execution and prevent other cells from running if no data is selected
@@ -162,7 +157,7 @@ def _(S, char_rank, mo):
     ### 1. Which character personality is ranked best?
 
 
-    {mo.ui.plotly(S.plot_top3_ranking_distribution(char_rank, x_label='Character Personality', width=1000))}
+    {mo.ui.altair_chart(S.plot_top3_ranking_distribution(char_rank, x_label='Character Personality'))}
     """)
     return
 
@@ -173,7 +168,7 @@ def _(S, char_rank, mo):
     ### 2. Which character personality is ranked 1st the most?
 
 
-    {mo.ui.plotly(S.plot_most_ranked_1(char_rank, title="Most Popular Character<br>(Number of Times Ranked 1st)", x_label='Character Personality', width=1000))}
+    {mo.ui.altair_chart(S.plot_most_ranked_1(char_rank, title="Most Popular Character<br>(Number of Times Ranked 1st)", x_label='Character Personality', width=1000))}
     """)
     return
 
@@ -186,7 +181,7 @@ def _(S, calculate_weighted_ranking_scores, char_rank, mo):
     ### 3. Which character personality most popular based on weighted scores?
 
 
-    {mo.ui.plotly(S.plot_weighted_ranking_score(char_rank_weighted, title="Most Popular Character - Weighted Popularity Score<br>(1st=3pts, 2nd=2pts, 3rd=1pt)", x_label='Voice', width=1000))}
+    {mo.ui.altair_chart(S.plot_weighted_ranking_score(char_rank_weighted, title="Most Popular Character - Weighted Popularity Score<br>(1st=3pts, 2nd=2pts, 3rd=1pt)", x_label='Voice', width=1000))}
     """)
     return
 
@@ -211,7 +206,7 @@ def _(S, mo, v_18_8_3):
     mo.md(f"""
     ### Which 8 voices are chosen the most out of 18? 
 
-    {mo.ui.plotly(S.plot_voice_selection_counts(v_18_8_3, height=500, width=1000))}
+    {mo.ui.altair_chart(S.plot_voice_selection_counts(v_18_8_3, height=500, width=1000))}
     """)
     return
 
@@ -223,7 +218,7 @@ def _(S, mo, v_18_8_3):
 
     How many times does each voice end up in the top 3? ( this is based on the survey question where participants need to choose 3 out of the earlier selected 8 voices. So how often each of the 18 stimuli ended up in participants’ Top 3, after they first selected 8 out of 18. 
 
-    {mo.ui.plotly(S.plot_top3_selection_counts(v_18_8_3, height=500, width=1000))}
+    {mo.ui.altair_chart(S.plot_top3_selection_counts(v_18_8_3, height=500, width=1000))}
     """)
     return
 
@@ -242,7 +237,7 @@ def _(S, mo, top3_voices):
 
     (not best 3 out of 8 question)  
 
-    {mo.ui.plotly(S.plot_ranking_distribution(top3_voices, x_label='Voice', width=1000))}
+    {mo.ui.altair_chart(S.plot_ranking_distribution(top3_voices, x_label='Voice', width=1000))}
     """)
     return
 
@@ -254,7 +249,7 @@ def _(S, mo, top3_voices_weighted):
     - E.g. 1 point for place 3. 2 points for place 2 and 3 points for place 1.  The voice with most points is ranked best. 
     Distribution of the rankings for each voice:
 
-    {mo.ui.plotly(S.plot_weighted_ranking_score(top3_voices_weighted, title="Most Popular Voice - Weighted Popularity Score<br>(1st = 3pts, 2nd = 2pts, 3rd = 1pt)", height=500, width=1000))}
+    {mo.ui.altair_chart(S.plot_weighted_ranking_score(top3_voices_weighted, title="Most Popular Voice - Weighted Popularity Score<br>(1st = 3pts, 2nd = 2pts, 3rd = 1pt)", height=500, width=1000))}
     """)
     return
 
@@ -266,7 +261,7 @@ def _(S, mo, top3_voices):
 
     (not always the voice with most points)
 
-    {mo.ui.plotly(S.plot_most_ranked_1(top3_voices, title="Most Popular Voice<br>(Number of Times Ranked 1st)", x_label='Voice', width=1000))}
+    {mo.ui.altair_chart(S.plot_most_ranked_1(top3_voices, title="Most Popular Voice<br>(Number of Times Ranked 1st)", x_label='Voice', width=1000))}
     """)
     return
 
@@ -307,7 +302,7 @@ def _(S, mo, pl, ss_long):
         content += f"""
     ### {i+1}) {trait.replace(":", " ↔ ")}
 
-    {mo.ui.plotly(S.plot_speaking_style_trait_scores(trait_d, title=trait.replace(":", " ↔ "), height=550))}
+    {mo.ui.altair_chart(S.plot_speaking_style_trait_scores(trait_d, title=trait.replace(":", " ↔ "), height=550))}
     """
 
     mo.md(content)
@@ -334,7 +329,7 @@ def _(S, mo, vscales):
     mo.md(f"""
     ### How does each voice score on a scale from 1-10?
 
-    {mo.ui.plotly(S.plot_average_scores_with_counts(vscales, x_label='Voice', width=1000))}
+    {mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales, x_label='Voice', width=1000))}
     """)
     return
 
@@ -404,7 +399,7 @@ def _(S, SPEAKING_STYLES, joined_df, mo):
         _content += f"""
     #### Speaking Style **{style}**:
 
-    {mo.ui.plotly(fig)}
+    {mo.ui.altair_chart(fig)}
 
     """
     mo.md(_content)
@@ -469,7 +464,7 @@ def _(S, SPEAKING_STYLES, df_style, mo, top3_voices, utils):
 
         #### Speaking Style **{_style}**:
 
-        {mo.ui.plotly(_fig)}
+        {mo.ui.altair_chart(_fig)}
 
         """
 
diff --git a/plots.py b/plots.py
index c4da523..07c7269 100644
--- a/plots.py
+++ b/plots.py
@@ -1,9 +1,10 @@
-"""Plotting functions for Voice Branding analysis."""
+"""Plotting functions for Voice Branding analysis using Altair."""
 
 import re
 from pathlib import Path
 
-import plotly.graph_objects as go
+import altair as alt
+import pandas as pd
 import polars as pl
 from theme import ColorPalette
 
@@ -75,8 +76,115 @@ class JPMCPlotsMixin:
             
         return "_".join(parts)
 
-    def _save_plot(self, fig: go.Figure, title: str) -> None:
-        """Save plot to PNG file if fig_save_dir is set."""
+    def _get_filter_description(self) -> str:
+        """Generate a human-readable description of active filters."""
+        parts = []
+        
+        # Mapping of attribute name to (display_name, value, options_attr)
+        filters = [
+            ('Age', getattr(self, 'filter_age', None), 'options_age'),
+            ('Gender', getattr(self, 'filter_gender', None), 'options_gender'),
+            ('Consumer', getattr(self, 'filter_consumer', None), 'options_consumer'),
+            ('Ethnicity', getattr(self, 'filter_ethnicity', None), 'options_ethnicity'),
+            ('Income', getattr(self, 'filter_income', None), 'options_income'),
+        ]
+        
+        for display_name, value, options_attr in filters:
+            if value is None:
+                continue
+            
+            # Ensure value is a list for uniform handling
+            if not isinstance(value, list):
+                value = [value]
+            
+            if len(value) == 0:
+                continue
+
+            # Check if all options are selected (equivalent to no filter)
+            master_list = getattr(self, options_attr, None)
+            if master_list and set(value) == set(master_list):
+                 continue
+            
+            # Use original values for display (full list)
+            clean_values = [str(v) for v in value]
+            val_str = ", ".join(clean_values)
+            # Use UPPERCASE for category name to distinguish from values
+            parts.append(f"{display_name.upper()}: {val_str}")
+            
+        if not parts:
+            return ""
+            
+        # Join with clear separator - double space for visual break
+        return "Filters:  " + "  —  ".join(parts)
+
+    def _add_filter_footnote(self, chart: alt.Chart) -> alt.Chart:
+        """Add a footnote with active filters to the chart.
+        
+        Uses chart subtitle for filter text to avoid layout issues with vconcat.
+        Returns the modified chart (or original if no filters).
+        """
+        filter_text = self._get_filter_description()
+        
+        # Skip if no filters active - return original chart
+        if not filter_text:
+            return chart
+        
+        # Wrap text into multiple lines at ~100 chars, but don't break mid-word
+        max_line_length = 100
+        words = filter_text.split()
+        lines = []
+        current_line = ""
+        
+        for word in words:
+            test_line = f"{current_line} {word}".strip() if current_line else word
+            if len(test_line) <= max_line_length:
+                current_line = test_line
+            else:
+                if current_line:
+                    lines.append(current_line)
+                current_line = word
+        if current_line:
+            lines.append(current_line)
+        
+        # Get existing title from chart spec
+        chart_spec = chart.to_dict()
+        existing_title = chart_spec.get('title', '')
+        
+        # Handle different title formats (string vs dict)
+        if isinstance(existing_title, str):
+            title_config = {
+                'text': existing_title,
+                'subtitle': lines,
+                'subtitleColor': 'gray',
+                'subtitleFontSize': 10,
+                'anchor': 'start',
+            }
+        elif isinstance(existing_title, dict):
+            title_config = existing_title.copy()
+            title_config['subtitle'] = lines
+            title_config['subtitleColor'] = 'gray'
+            title_config['subtitleFontSize'] = 10
+            title_config['anchor'] = 'start'
+        else:
+            # No existing title, just add filters as subtitle
+            title_config = {
+                'text': '',
+                'subtitle': lines,
+                'subtitleColor': 'gray',
+                'subtitleFontSize': 10,
+                'anchor': 'start',
+            }
+        
+        return chart.properties(title=title_config)
+
+    def _save_plot(self, chart: alt.Chart, title: str) -> alt.Chart:
+        """Save chart to PNG file if fig_save_dir is set.
+        
+        Returns the (potentially modified) chart with filter footnote added.
+        """
+        # Add filter footnote - returns combined chart if filters active
+        chart = self._add_filter_footnote(chart)
+
         if hasattr(self, 'fig_save_dir') and self.fig_save_dir:
             path = Path(self.fig_save_dir)
             
@@ -88,7 +196,11 @@ class JPMCPlotsMixin:
                 path.mkdir(parents=True, exist_ok=True)
                 
             filename = f"{self._sanitize_filename(title)}.png"
-            fig.write_image(path / filename, width=fig.layout.width, height=fig.layout.height)
+            
+            # Save using vl-convert backend
+            chart.save(str(path / filename), format='png', scale_factor=2.0)
+        
+        return chart
 
     def _ensure_dataframe(self, data: pl.LazyFrame | pl.DataFrame | None) -> pl.DataFrame:
         """Ensure data is an eager DataFrame, collecting if necessary."""
@@ -100,198 +212,156 @@ class JPMCPlotsMixin:
             return df.collect()
         return df
 
+    def _clean_voice_label(self, col_name: str) -> str:
+        """Extract and clean voice name from column name for display.
+        
+        Handles patterns like:
+        - 'Voice_Scale__The_Coach' -> 'The Coach'
+        - 'Character_Ranking_The_Coach' -> 'The Coach'
+        - 'Top_3_Voices_ranking__Familiar_Friend' -> 'Familiar Friend'
+        """
+        # First split by __ if present
+        label = col_name.split('__')[-1] if '__' in col_name else col_name
+        # Remove common prefixes
+        label = label.replace('Character_Ranking_', '')
+        label = label.replace('Top_3_Voices_ranking_', '')
+        # Replace underscores with spaces
+        label = label.replace('_', ' ').strip()
+        return label
+
     def plot_average_scores_with_counts(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
-        title: str = "General Impression (1-10)<br>Per Voice with Number of Participants Who Rated It",
+        title: str = "General Impression (1-10)\nPer Voice with Number of Participants Who Rated It",
         x_label: str = "Stimuli",
         y_label: str = "Average General Impression Rating (1-10)",
         color: str = ColorPalette.PRIMARY,
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Create a bar plot showing average scores and count of non-null values for each column.
-        """
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Create a bar plot showing average scores and count of non-null values for each column."""
         df = self._ensure_dataframe(data)
 
-        # Exclude _recordId column
+        # Calculate stats for each column (exclude _recordId)
         stats = []
         for col in [c for c in df.columns if c != '_recordId']:
             avg_score = df[col].mean()
             non_null_count = df[col].drop_nulls().len()
+            label = self._clean_voice_label(col)
             stats.append({
-                'column': col,
+                'voice': label,
                 'average': avg_score,
                 'count': non_null_count
             })
 
-        # Sort by average score in descending order
-        stats_df = pl.DataFrame(stats).sort('average', descending=True)
+        # Convert to pandas for Altair (sort by average descending)
+        stats_df = pl.DataFrame(stats).sort('average', descending=True).to_pandas()
 
-        # Extract voice identifiers from column names (e.g., "V14" from "Voice_Scale_1_10__V14")
-        labels = [col.split('__')[-1] if '__' in col else col for col in stats_df['column']]
-
-        # Create the plot
-        fig = go.Figure()
-
-        fig.add_trace(go.Bar(
-            x=labels,
-            y=stats_df['average'],
-            text=stats_df['count'],
-            textposition='inside',
-            textfont=dict(size=10, color='black'),
-            marker_color=color,
-            hovertemplate='<b>%{x}</b><br>Average: %{y:.2f}<br>Count: %{text}<extra></extra>'
-        ))
-
-        fig.update_layout(
-            title=title,
-            xaxis_title=x_label,
-            yaxis_title=y_label,
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                tickangle=-45
-            ),
-            yaxis=dict(
-                range=[0, 10],
-                showgrid=True,
-                gridcolor=ColorPalette.GRID
-            ),
-            font=dict(size=11)
+        # Base bar chart
+        bars = alt.Chart(stats_df).mark_bar(color=color).encode(
+            x=alt.X('voice:N', title=x_label, sort='-y'),
+            y=alt.Y('average:Q', title=y_label, scale=alt.Scale(domain=[0, 10])),
+            tooltip=[
+                alt.Tooltip('voice:N', title='Voice'),
+                alt.Tooltip('average:Q', title='Average', format='.2f'),
+                alt.Tooltip('count:Q', title='Count')
+            ]
         )
 
-        self._save_plot(fig, title)
-        return fig
+        # Text overlay for counts
+        text = alt.Chart(stats_df).mark_text(
+            dy=-5,
+            color='black',
+            fontSize=10
+        ).encode(
+            x=alt.X('voice:N', sort='-y'),
+            y=alt.Y('average:Q'),
+            text=alt.Text('count:Q')
+        )
+
+        # Combine layers
+        chart = (bars + text).properties(
+            title=title,
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
+        )
+
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_top3_ranking_distribution(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
-        title: str = "Top 3 Rankings Distribution<br>Count of 1st, 2nd, and 3rd Place Votes per Voice",
+        title: str = "Top 3 Rankings Distribution\nCount of 1st, 2nd, and 3rd Place Votes per Voice",
         x_label: str = "Voices",
         y_label: str = "Number of Mentions in Top 3",
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Create a stacked bar chart showing how often each voice was ranked 1st, 2nd, or 3rd.
-        """
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Create a stacked bar chart showing how often each voice was ranked 1st, 2nd, or 3rd."""
         df = self._ensure_dataframe(data)
 
-        # Exclude _recordId column
+        # Calculate stats per column
         stats = []
         for col in [c for c in df.columns if c != '_recordId']:
-            # Count occurrences of each rank (1, 2, 3)
-            # We ensure we're just counting the specific integer values
             rank1 = df.filter(pl.col(col) == 1).height
             rank2 = df.filter(pl.col(col) == 2).height
             rank3 = df.filter(pl.col(col) == 3).height
             total = rank1 + rank2 + rank3
 
-            # Only include if it received at least one vote (optional, but keeps chart clean)
             if total > 0:
-                stats.append({
-                    'column': col,
-                    'Rank 1': rank1,
-                    'Rank 2': rank2,
-                    'Rank 3': rank3,
-                    'Total': total
-                })
+                label = self._clean_voice_label(col)
+                # Add 3 rows (one per rank)
+                stats.append({'voice': label, 'rank': 'Rank 1 (1st Choice)', 'count': rank1, 'total': total})
+                stats.append({'voice': label, 'rank': 'Rank 2 (2nd Choice)', 'count': rank2, 'total': total})
+                stats.append({'voice': label, 'rank': 'Rank 3 (3rd Choice)', 'count': rank3, 'total': total})
 
-        # Sort by Total count descending (Most popular overall)
-        # Tie-break with Rank 1 count
-        stats_df = pl.DataFrame(stats).sort(['Total', 'Rank 1'], descending=[True, True])
-
-        # Extract voice identifiers from column names
-        labels = [col.split('__')[-1] if '__' in col else col for col in stats_df['column']]
-
-        fig = go.Figure()
-
-        # Add traces for Rank 1, 2, and 3.
-        # Stack order: Rank 1 at bottom (Base) -> Rank 2 -> Rank 3
-        # This makes it easy to compare the "First Choice" volume across bars.
-
-        fig.add_trace(go.Bar(
-            name='Rank 1 (1st Choice)',
-            x=labels,
-            y=stats_df['Rank 1'],
-            marker_color=ColorPalette.RANK_1,
-            hovertemplate='<b>%{x}</b><br>Rank 1: %{y}<extra></extra>'
-        ))
-
-        fig.add_trace(go.Bar(
-            name='Rank 2 (2nd Choice)',
-            x=labels,
-            y=stats_df['Rank 2'],
-            marker_color=ColorPalette.RANK_2,
-            hovertemplate='<b>%{x}</b><br>Rank 2: %{y}<extra></extra>'
-        ))
-
-        fig.add_trace(go.Bar(
-            name='Rank 3 (3rd Choice)',
-            x=labels,
-            y=stats_df['Rank 3'],
-            marker_color=ColorPalette.RANK_3,
-            hovertemplate='<b>%{x}</b><br>Rank 3: %{y}<extra></extra>'
-        ))
-
-        fig.update_layout(
-            barmode='stack',
+        # Convert to long format, sort by total
+        stats_df = pl.DataFrame(stats).to_pandas()
+        
+        # Interactive legend selection - click to filter
+        selection = alt.selection_point(fields=['rank'], bind='legend')
+        
+        # Create stacked bar chart with interactive legend
+        chart = alt.Chart(stats_df).mark_bar().encode(
+            x=alt.X('voice:N', title=x_label, sort=alt.EncodingSortField(field='total', op='sum', order='descending')),
+            y=alt.Y('count:Q', title=y_label, stack='zero'),
+            color=alt.Color('rank:N',
+                           scale=alt.Scale(domain=['Rank 1 (1st Choice)', 'Rank 2 (2nd Choice)', 'Rank 3 (3rd Choice)'],
+                                         range=[ColorPalette.RANK_1, ColorPalette.RANK_2, ColorPalette.RANK_3]),
+                           legend=alt.Legend(orient='top', direction='horizontal', title=None)),
+            order=alt.Order('rank:N', sort='ascending'),
+            opacity=alt.condition(selection, alt.value(1), alt.value(0.2)),
+            tooltip=[
+                alt.Tooltip('voice:N', title='Voice'),
+                alt.Tooltip('rank:N', title='Rank'),
+                alt.Tooltip('count:Q', title='Count')
+            ]
+        ).add_params(selection).properties(
             title=title,
-            xaxis_title=x_label,
-            yaxis_title=y_label,
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                tickangle=-45
-            ),
-            yaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID
-            ),
-            legend=dict(
-                orientation="h",
-                yanchor="bottom",
-                y=1.02,
-                xanchor="right",
-                x=1,
-                traceorder="normal"
-            ),
-            font=dict(size=11)
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
         )
 
-        self._save_plot(fig, title)
-        return fig
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_ranking_distribution(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
-        title: str = "Rankings Distribution<br>(1st to 4th Place)",
+        title: str = "Rankings Distribution\n(1st to 4th Place)",
         x_label: str = "Item",
         y_label: str = "Number of Votes",
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Create a stacked bar chart showing the distribution of rankings (1st to 4th) for characters or voices.
-        Sorted by the number of Rank 1 votes.
-        """
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Create a stacked bar chart showing the distribution of rankings (1st to 4th)."""
         df = self._ensure_dataframe(data)
 
         stats = []
-        # Identify ranking columns (assume all columns except _recordId)
         ranking_cols = [c for c in df.columns if c != '_recordId']
 
         for col in ranking_cols:
-            # Count occurrences of each rank (1, 2, 3, 4)
-            # Using height/len to count rows in the filtered frame
             r1 = df.filter(pl.col(col) == 1).height
             r2 = df.filter(pl.col(col) == 2).height
             r3 = df.filter(pl.col(col) == 3).height
@@ -299,248 +369,154 @@ class JPMCPlotsMixin:
             total = r1 + r2 + r3 + r4
 
             if total > 0:
-                stats.append({
-                    'column': col,
-                    'Rank 1': r1,
-                    'Rank 2': r2,
-                    'Rank 3': r3,
-                    'Rank 4': r4
-                })
+                label = self._clean_voice_label(col)
+                stats.append({'item': label, 'rank': 'Rank 1 (Best)', 'count': r1, 'rank1': r1})
+                stats.append({'item': label, 'rank': 'Rank 2', 'count': r2, 'rank1': r1})
+                stats.append({'item': label, 'rank': 'Rank 3', 'count': r3, 'rank1': r1})
+                stats.append({'item': label, 'rank': 'Rank 4 (Worst)', 'count': r4, 'rank1': r1})
 
         if not stats:
-            return go.Figure()
+            return alt.Chart(pd.DataFrame({'text': ['No data']})).mark_text().encode(text='text:N')
 
-        # Sort by Rank 1 (Most "Best" votes) descending to show the winner first
-        # Secondary sort by Rank 2
-        stats_df = pl.DataFrame(stats).sort(['Rank 1', 'Rank 2'], descending=[True, True])
+        stats_df = pl.DataFrame(stats).to_pandas()
 
-        # Clean up labels: Remove prefix and underscores
-        # e.g. "Character_Ranking_The_Coach" -> "The Coach"
-        labels = [
-            col.replace('Character_Ranking_', '').replace('Top_3_Voices_ranking__', '').replace('_', ' ').strip() 
-            for col in stats_df['column']
-        ]
+        # Interactive legend selection - click to filter
+        selection = alt.selection_point(fields=['rank'], bind='legend')
 
-        fig = go.Figure()
-
-        # Rank 1 (Best)
-        fig.add_trace(go.Bar(
-            name='Rank 1 (Best)',
-            x=labels,
-            y=stats_df['Rank 1'],
-            marker_color=ColorPalette.RANK_1,
-            hovertemplate='<b>%{x}</b><br>Rank 1: %{y}<extra></extra>'
-        ))
-
-        # Rank 2
-        fig.add_trace(go.Bar(
-            name='Rank 2',
-            x=labels,
-            y=stats_df['Rank 2'],
-            marker_color=ColorPalette.RANK_2,
-            hovertemplate='<b>%{x}</b><br>Rank 2: %{y}<extra></extra>'
-        ))
-
-        # Rank 3
-        fig.add_trace(go.Bar(
-            name='Rank 3',
-            x=labels,
-            y=stats_df['Rank 3'],
-            marker_color=ColorPalette.RANK_3,
-            hovertemplate='<b>%{x}</b><br>Rank 3: %{y}<extra></extra>'
-        ))
-
-        # Rank 4 (Worst)
-        # Using a neutral grey as a fallback for the lowest rank to keep focus on top ranks
-        fig.add_trace(go.Bar(
-            name='Rank 4 (Worst)',
-            x=labels,
-            y=stats_df['Rank 4'],
-            marker_color=ColorPalette.RANK_4, 
-            hovertemplate='<b>%{x}</b><br>Rank 4: %{y}<extra></extra>'
-        ))
-
-        fig.update_layout(
-            barmode='stack',
+        chart = alt.Chart(stats_df).mark_bar().encode(
+            x=alt.X('item:N', title=x_label, sort=alt.EncodingSortField(field='rank1', order='descending')),
+            y=alt.Y('count:Q', title=y_label, stack='zero'),
+            color=alt.Color('rank:N',
+                           scale=alt.Scale(domain=['Rank 1 (Best)', 'Rank 2', 'Rank 3', 'Rank 4 (Worst)'],
+                                         range=[ColorPalette.RANK_1, ColorPalette.RANK_2, ColorPalette.RANK_3, ColorPalette.RANK_4]),
+                           legend=alt.Legend(orient='top', direction='horizontal', title=None)),
+            order=alt.Order('rank:N', sort='ascending'),
+            opacity=alt.condition(selection, alt.value(1), alt.value(0.2)),
+            tooltip=[
+                alt.Tooltip('item:N', title='Item'),
+                alt.Tooltip('rank:N', title='Rank'),
+                alt.Tooltip('count:Q', title='Count')
+            ]
+        ).add_params(selection).properties(
             title=title,
-            xaxis_title=x_label,
-            yaxis_title=y_label,
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                tickangle=-45
-            ),
-            yaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID
-            ),
-            legend=dict(
-                orientation="h",
-                yanchor="bottom",
-                y=1.02,
-                xanchor="right",
-                x=1,
-                traceorder="normal"
-            ),
-            font=dict(size=11)
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
         )
 
-        self._save_plot(fig, title)
-        return fig
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_most_ranked_1(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
-        title: str = "Most Popular Choice<br>(Number of Times Ranked 1st)",
+        title: str = "Most Popular Choice\n(Number of Times Ranked 1st)",
         x_label: str = "Item",
         y_label: str = "Count of 1st Place Rankings",
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Create a bar chart showing which item (character/voice) was ranked #1 the most.
-        Top 3 items are highlighted.
-        """
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Create a bar chart showing which item was ranked #1 the most. Top 3 highlighted."""
         df = self._ensure_dataframe(data)
 
         stats = []
-        # Identify ranking columns (assume all columns except _recordId)
         ranking_cols = [c for c in df.columns if c != '_recordId']
 
         for col in ranking_cols:
-            # Count occurrences of rank 1
             count_rank_1 = df.filter(pl.col(col) == 1).height
-            
-            stats.append({
-                'column': col,
-                'count': count_rank_1
-            })
+            label = self._clean_voice_label(col)
+            stats.append({'item': label, 'count': count_rank_1})
 
-        # Sort by count descending
+        # Convert and sort
         stats_df = pl.DataFrame(stats).sort('count', descending=True)
-
-        # Clean up labels
-        labels = [
-            col.replace('Character_Ranking_', '').replace('Top_3_Voices_ranking__', '').replace('_', ' ').strip() 
-            for col in stats_df['column']
-        ]
-
-        # Assign colors: Top 3 get PRIMARY (Blue), others get NEUTRAL (Grey)
-        colors = [
-            ColorPalette.PRIMARY if i < 3 else ColorPalette.NEUTRAL
-            for i in range(len(stats_df))
-        ]
-
-        fig = go.Figure()
         
-        fig.add_trace(go.Bar(
-            x=labels,
-            y=stats_df['count'],
-            text=stats_df['count'],
-            textposition='inside',
-            textfont=dict(size=10, color='white'),
-            marker_color=colors,
-            hovertemplate='<b>%{x}</b><br>1st Place Votes: %{y}<extra></extra>'
-        ))
+        # Add rank column for coloring (1-3 vs 4+)
+        stats_df = stats_df.with_row_index('rank_index')
+        stats_df = stats_df.with_columns(
+            pl.when(pl.col('rank_index') < 3)
+            .then(pl.lit('Top 3'))
+            .otherwise(pl.lit('Other'))
+            .alias('category')
+        ).to_pandas()
 
-        fig.update_layout(
+        # Bar chart with conditional color
+        chart = alt.Chart(stats_df).mark_bar().encode(
+            x=alt.X('item:N', title=x_label, sort='-y'),
+            y=alt.Y('count:Q', title=y_label),
+            color=alt.Color('category:N',
+                           scale=alt.Scale(domain=['Top 3', 'Other'],
+                                         range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
+                           legend=None),
+            tooltip=[
+                alt.Tooltip('item:N', title='Item'),
+                alt.Tooltip('count:Q', title='1st Place Votes')
+            ]
+        ).properties(
             title=title,
-            xaxis_title=x_label,
-            yaxis_title=y_label,
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                tickangle=-45
-            ),
-            yaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID
-            ),
-            font=dict(size=11)
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
         )
 
-        self._save_plot(fig, title)
-        return fig
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_weighted_ranking_score(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
-        title: str = "Weighted Popularity Score<br>(1st=3pts, 2nd=2pts, 3rd=1pt)",
+        title: str = "Weighted Popularity Score\n(1st=3pts, 2nd=2pts, 3rd=1pt)",
         x_label: str = "Character Personality",
         y_label: str = "Total Weighted Score",
         color: str = ColorPalette.PRIMARY,
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Create a bar chart showing the weighted ranking score for each character.
-        """
-        weighted_df = self._ensure_dataframe(data)
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Create a bar chart showing the weighted ranking score for each character."""
+        weighted_df = self._ensure_dataframe(data).to_pandas()
 
-        fig = go.Figure()
-
-        fig.add_trace(go.Bar(
-            x=weighted_df['Character'],
-            y=weighted_df['Weighted Score'],
-            text=weighted_df['Weighted Score'],
-            textposition='inside',
-            textfont=dict(size=11, color='white'),
-            marker_color=color,
-            hovertemplate='<b>%{x}</b><br>Score: %{y}<extra></extra>'
-        ))
-
-        fig.update_layout(
-            title=title,
-            xaxis_title=x_label,
-            yaxis_title=y_label,
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                tickangle=-45
-            ),
-            yaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID
-            ),
-            font=dict(size=11)
+        # Bar chart
+        bars = alt.Chart(weighted_df).mark_bar(color=color).encode(
+            x=alt.X('Character:N', title=x_label),
+            y=alt.Y('Weighted Score:Q', title=y_label),
+            tooltip=[
+                alt.Tooltip('Character:N'),
+                alt.Tooltip('Weighted Score:Q', title='Score')
+            ]
         )
 
-        self._save_plot(fig, title)
-        return fig
+        # Text overlay
+        text = bars.mark_text(
+            dy=-5,
+            color='white',
+            fontSize=11
+        ).encode(
+            text='Weighted Score:Q'
+        )
+
+        chart = (bars + text).properties(
+            title=title,
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
+        )
+
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_voice_selection_counts(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
         target_column: str = "8_Combined",
-        title: str = "Most Frequently Chosen Voices<br>(Top 8 Highlighted)",
+        title: str = "Most Frequently Chosen Voices\n(Top 8 Highlighted)",
         x_label: str = "Voice",
         y_label: str = "Number of Times Chosen",
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Create a bar plot showing the frequency of voice selections.
-        """
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Create a bar plot showing the frequency of voice selections."""
         df = self._ensure_dataframe(data)
 
         if target_column not in df.columns:
-            return go.Figure()
+            return alt.Chart(pd.DataFrame({'text': [f"Column '{target_column}' not found"]})).mark_text().encode(text='text:N')
 
-        # Process the data:
-        # 1. Select the relevant column and remove nulls
-        # 2. Split the comma-separated string into a list
-        # 3. Explode the list so each voice gets its own row
-        # 4. Strip whitespace ensuring "Voice 1" and " Voice 1" match
-        # 5. Count occurrences
+        # Process data: split, explode, count
         stats_df = (
             df.select(pl.col(target_column))
             .drop_nulls()
@@ -551,67 +527,52 @@ class JPMCPlotsMixin:
             .group_by(target_column)
             .agg(pl.len().alias("count"))
             .sort("count", descending=True)
+            .with_row_index('rank_index')
+            .with_columns(
+                pl.when(pl.col('rank_index') < 8)
+                .then(pl.lit('Top 8'))
+                .otherwise(pl.lit('Other'))
+                .alias('category')
+            )
+            .to_pandas()
         )
 
-        # Define colors: Top 8 get PRIMARY, rest get NEUTRAL
-        colors = [
-            ColorPalette.PRIMARY if i < 8 else ColorPalette.NEUTRAL 
-            for i in range(len(stats_df))
-        ]
-
-        fig = go.Figure()
-
-        fig.add_trace(go.Bar(
-            x=stats_df[target_column],
-            y=stats_df['count'],
-            text=stats_df['count'],
-            textposition='outside',
-            marker_color=colors,
-            hovertemplate='<b>%{x}</b><br>Selections: %{y}<extra></extra>'
-        ))
-
-        fig.update_layout(
+        chart = alt.Chart(stats_df).mark_bar().encode(
+            x=alt.X(f'{target_column}:N', title=x_label, sort='-y'),
+            y=alt.Y('count:Q', title=y_label),
+            color=alt.Color('category:N',
+                           scale=alt.Scale(domain=['Top 8', 'Other'],
+                                         range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
+                           legend=None),
+            tooltip=[
+                alt.Tooltip(f'{target_column}:N', title='Voice'),
+                alt.Tooltip('count:Q', title='Selections')
+            ]
+        ).properties(
             title=title,
-            xaxis_title=x_label,
-            yaxis_title=y_label,
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                tickangle=-45
-            ),
-            yaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID
-            ),
-            font=dict(size=11),
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
         )
 
-        self._save_plot(fig, title)
-        return fig
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_top3_selection_counts(
         self,
         data: pl.LazyFrame | pl.DataFrame | None = None,
         target_column: str = "3_Ranked",
-        title: str = "Most Frequently Chosen Top 3 Voices<br>(Top 3 Highlighted)",
+        title: str = "Most Frequently Chosen Top 3 Voices\n(Top 3 Highlighted)",
         x_label: str = "Voice",
         y_label: str = "Count of Mentions in Top 3",
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Question: Which 3 voices are chosen the most out of 18?
-        """
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Question: Which 3 voices are chosen the most out of 18?"""
         df = self._ensure_dataframe(data)
 
         if target_column not in df.columns:
-            return go.Figure()
+            return alt.Chart(pd.DataFrame({'text': [f"Column '{target_column}' not found"]})).mark_text().encode(text='text:N')
 
-        # Process the data:
-        # Same logic as plot_voice_selection_counts: explode comma-separated string
         stats_df = (
             df.select(pl.col(target_column))
             .drop_nulls()
@@ -622,46 +583,35 @@ class JPMCPlotsMixin:
             .group_by(target_column)
             .agg(pl.len().alias("count"))
             .sort("count", descending=True)
+            .with_row_index('rank_index')
+            .with_columns(
+                pl.when(pl.col('rank_index') < 3)
+                .then(pl.lit('Top 3'))
+                .otherwise(pl.lit('Other'))
+                .alias('category')
+            )
+            .to_pandas()
         )
 
-        # Define colors: Top 3 get PRIMARY, rest get NEUTRAL
-        colors = [
-            ColorPalette.PRIMARY if i < 3 else ColorPalette.NEUTRAL 
-            for i in range(len(stats_df))
-        ]
-
-        fig = go.Figure()
-
-        fig.add_trace(go.Bar(
-            x=stats_df[target_column],
-            y=stats_df['count'],
-            text=stats_df['count'],
-            textposition='outside',
-            marker_color=colors,
-            hovertemplate='<b>%{x}</b><br>In Top 3: %{y} times<extra></extra>'
-        ))
-
-        fig.update_layout(
+        chart = alt.Chart(stats_df).mark_bar().encode(
+            x=alt.X(f'{target_column}:N', title=x_label, sort='-y'),
+            y=alt.Y('count:Q', title=y_label),
+            color=alt.Color('category:N',
+                           scale=alt.Scale(domain=['Top 3', 'Other'],
+                                         range=[ColorPalette.PRIMARY, ColorPalette.NEUTRAL]),
+                           legend=None),
+            tooltip=[
+                alt.Tooltip(f'{target_column}:N', title='Voice'),
+                alt.Tooltip('count:Q', title='In Top 3')
+            ]
+        ).properties(
             title=title,
-            xaxis_title=x_label,
-            yaxis_title=y_label,
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                tickangle=-45
-            ),
-            yaxis=dict(
-                showgrid=True,
-                gridcolor=ColorPalette.GRID
-            ),
-            font=dict(size=11),
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
         )
 
-        self._save_plot(fig, title)
-        return fig
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_speaking_style_trait_scores(
         self,
@@ -671,19 +621,17 @@ class JPMCPlotsMixin:
         right_anchor: str = None,
         title: str = "Speaking Style Trait Analysis",
         height: int | None = None,
-        width: int | None = None,
-    ) -> go.Figure:
-        """
-        Plot scores for a single speaking style trait across multiple voices.
-        """
+        width: int | str | None = None,
+    ) -> alt.Chart:
+        """Plot scores for a single speaking style trait across multiple voices."""
         df = self._ensure_dataframe(data)
 
         if df.is_empty():
-            return go.Figure()
+            return alt.Chart(pd.DataFrame({'text': ['No data']})).mark_text().encode(text='text:N')
             
         required_cols = ["Voice", "score"]
         if not all(col in df.columns for col in required_cols):
-             return go.Figure()
+            return alt.Chart(pd.DataFrame({'text': ['Missing required columns']})).mark_text().encode(text='text:N')
 
         # Calculate stats: Mean, Count
         stats = (
@@ -693,93 +641,61 @@ class JPMCPlotsMixin:
                 pl.col("score").mean().alias("mean_score"),
                 pl.col("score").count().alias("count")
             ])
-            .sort("mean_score", descending=False) # Ascending for display bottom-to-top
+            .sort("mean_score", descending=False)  # Ascending for bottom-to-top display
+            .to_pandas()
         )
 
-        # Attempt to extract anchors from DF if not provided
+        # Extract anchors from data if not provided
         if (left_anchor is None or right_anchor is None) and "Left_Anchor" in df.columns:
             head = df.filter(pl.col("Left_Anchor").is_not_null()).head(1)
             if not head.is_empty():
-                if left_anchor is None: left_anchor = head["Left_Anchor"][0]
-                if right_anchor is None: right_anchor = head["Right_Anchor"][0]
+                if left_anchor is None:
+                    left_anchor = head["Left_Anchor"][0]
+                if right_anchor is None:
+                    right_anchor = head["Right_Anchor"][0]
 
         if trait_description is None:
             if left_anchor and right_anchor:
                 trait_description = f"{left_anchor.split('|')[0]} vs. {right_anchor.split('|')[0]}"
+            elif "Description" in df.columns:
+                head = df.filter(pl.col("Description").is_not_null()).head(1)
+                trait_description = head["Description"][0] if not head.is_empty() else ""
             else:
-                 # Try getting from Description column
-                 if "Description" in df.columns:
-                     head = df.filter(pl.col("Description").is_not_null()).head(1)
-                     if not head.is_empty():
-                          trait_description = head["Description"][0]
-                     else:
-                          trait_description = ""
-                 else:
-                     trait_description = ""
+                trait_description = ""
 
-        fig = go.Figure()
-
-        fig.add_trace(go.Bar(
-            y=stats["Voice"], # Y is Voice
-            x=stats["mean_score"], # X is Score
-            orientation='h',
-            text=stats["count"],
-            textposition='inside',
-            textangle=0,
-            textfont=dict(size=16, color='white'),
-            texttemplate='%{text}', # Count on bar
-            marker_color=ColorPalette.PRIMARY,
-            hovertemplate='<b>%{y}</b><br>Average: %{x:.2f}<br>Count: %{text}<extra></extra>'
-        ))
-
-        # Add annotations for anchors
-        annotations = []
-        
-        # Place anchors at the bottom
-        if left_anchor:
-            annotations.append(dict(
-                xref='x', yref='paper',
-                x=1, y=-0.2, # Below axis
-                xanchor='left', yanchor='top',
-                text=f"<b>1: {left_anchor.split('|')[0]}</b>",
-                showarrow=False,
-                font=dict(size=10, color='gray')
-            ))
-        if right_anchor:
-            annotations.append(dict(
-                xref='x', yref='paper',
-                x=5, y=-0.2, # Below axis
-                xanchor='right', yanchor='top',
-                text=f"<b>5: {right_anchor.split('|')[0]}</b>",
-                showarrow=False,
-                font=dict(size=10, color='gray')
-            ))
-
-        fig.update_layout(
-             title=dict(
-                text=f"{title}<br><sub>{trait_description}</sub><br><sub>(Numbers on bars indicate respondent count)</sub>",
-                y=0.92
-            ),
-            xaxis_title="Average Score (1-5)",
-            yaxis_title="Voice",
-            height=height if height else getattr(self, 'plot_height', 500),
-            width=width if width else getattr(self, 'plot_width', 1000),
-            plot_bgcolor=ColorPalette.BACKGROUND,
-            xaxis=dict(
-                range=[1, 5],
-                showgrid=True,
-                gridcolor=ColorPalette.GRID,
-                zeroline=False
-            ),
-            yaxis=dict(
-                showgrid=False
-            ),
-            margin=dict(b=120),
-            annotations=annotations,
-            font=dict(size=11)
+        # Horizontal bar chart
+        bars = alt.Chart(stats).mark_bar(color=ColorPalette.PRIMARY).encode(
+            x=alt.X('mean_score:Q', title='Average Score (1-5)', scale=alt.Scale(domain=[1, 5])),
+            y=alt.Y('Voice:N', title='Voice', sort='-x'),
+            tooltip=[
+                alt.Tooltip('Voice:N'),
+                alt.Tooltip('mean_score:Q', title='Average', format='.2f'),
+                alt.Tooltip('count:Q', title='Count')
+            ]
         )
-        self._save_plot(fig, title)
-        return fig
+
+        # Count text inside bars
+        text = bars.mark_text(
+            align='center',
+            baseline='middle',
+            color='white',
+            fontSize=16
+        ).encode(
+            text='count:Q'
+        )
+
+        # Combine
+        chart = (bars + text).properties(
+            title={
+                "text": title,
+                "subtitle": [trait_description, "(Numbers on bars indicate respondent count)"]
+            },
+            width=width if width else 'container',
+            height=height or getattr(self, 'plot_height', 400)
+        )
+
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_speaking_style_correlation(
         self,
@@ -787,10 +703,10 @@ class JPMCPlotsMixin:
         style_traits: list[str],
         data: pl.LazyFrame | pl.DataFrame | None = None,
         title: str | None = None,
-    ) -> go.Figure:
-        """
-        Plots the correlation between Speaking Style Trait Scores (1-5) and Voice Scale (1-10) using a Bar Chart.
-        """
+        width: int | str | None = None,
+        height: int | None = None,
+    ) -> alt.Chart:
+        """Plots correlation between Speaking Style Trait Scores (1-5) and Voice Scale (1-10)."""
         df = self._ensure_dataframe(data)
 
         if title is None:
@@ -798,80 +714,47 @@ class JPMCPlotsMixin:
 
         trait_correlations = []
         
-        # 1. Calculate Correlations
+        # Calculate correlations
         for i, trait in enumerate(style_traits):
-            # Match against Right_Anchor which contains the positive trait description
-            # Use exact match for reliability
-            subset = df.filter(
-                pl.col("Right_Anchor") == trait
-            )
-            
-            # Drop Nulls for correlation calculation
+            subset = df.filter(pl.col("Right_Anchor") == trait)
             valid_data = subset.select(["score", "Voice_Scale_Score"]).drop_nulls()
             
             if valid_data.height > 1:
-                # Calculate Pearson Correlation
                 corr_val = valid_data.select(pl.corr("score", "Voice_Scale_Score")).item()
-                
-                # Trait Label for Plot
+                # Wrap trait text at '|' for display
+                trait_display = trait.replace('|', '\n')
                 trait_correlations.append({
-                    "trait_full": trait,
-                    "trait_short": f"Trait {i+1}", 
+                    "trait_display": trait_display,
+                    "trait_index": f"Trait {i+1}",
                     "correlation": corr_val if corr_val is not None else 0.0
                 })
         
-        # 2. Build Plot Data
         if not trait_correlations:
-            # Return empty fig with title
-            fig = go.Figure()
-            fig.update_layout(title=f"No data for {style_color} Style")
-            return fig
+            return alt.Chart(pd.DataFrame({'text': [f"No data for {style_color} Style"]})).mark_text().encode(text='text:N')
             
-        plot_df = pl.DataFrame(trait_correlations)
-        
-        # Determine colors based on correlation sign
-        colors = []
-        for val in plot_df["correlation"]:
-            if val >= 0:
-                colors.append("green") # Positive
-            else:
-                colors.append("red")   # Negative
-                
-        fig = go.Figure()
-        
-        fig.add_trace(go.Bar(
-            x=[f"Trait {i+1}" for i in range(len(plot_df))], # Simple Labels on Axis
-            y=plot_df["correlation"],
-            text=[f"{val:.2f}" for val in plot_df["correlation"]],
-            textposition='outside', # Or auto
-            marker_color=colors,
-            hovertemplate="<b>%{customdata}</b><br>Correlation: %{y:.2f}<extra></extra>",
-            customdata=plot_df["trait_full"] # Full text on hover
-        ))
-        
-        # Wrap text at the "|" separator for cleaner line breaks
-        def wrap_text_at_pipe(text):
-            parts = [p.strip() for p in text.split("|")]
-            return "<br>".join(parts)
-            
-        x_labels = [wrap_text_at_pipe(t) for t in plot_df["trait_full"]]
-        
-        # Update trace to use full labels
-        fig.data[0].x = x_labels
-        
-        fig.update_layout(
+        plot_df = pl.DataFrame(trait_correlations).to_pandas()
+
+        # Conditional color based on sign
+        chart = alt.Chart(plot_df).mark_bar().encode(
+            x=alt.X('trait_display:N', title=None, axis=alt.Axis(labelAngle=0)),
+            y=alt.Y('correlation:Q', title='Correlation', scale=alt.Scale(domain=[-1, 1])),
+            color=alt.condition(
+                alt.datum.correlation >= 0,
+                alt.value('green'),
+                alt.value('red')
+            ),
+            tooltip=[
+                alt.Tooltip('trait_display:N', title='Trait'),
+                alt.Tooltip('correlation:Q', format='.2f')
+            ]
+        ).properties(
             title=title,
-            yaxis_title="Correlation",
-            yaxis=dict(range=[-1, 1], zeroline=True, zerolinecolor="black"),
-            xaxis=dict(tickangle=0), # Keep flat if possible
-            height=400, # Use fixed default from original
-            width=1000,
-            template="plotly_white",
-            showlegend=False
+            width=width if width else 'container',
+            height=height or 350
         )
-        
-        self._save_plot(fig, title)
-        return fig
+
+        chart = self._save_plot(chart, title)
+        return chart
 
     def plot_speaking_style_ranking_correlation(
         self,
@@ -879,10 +762,10 @@ class JPMCPlotsMixin:
         style_traits: list[str],
         data: pl.LazyFrame | pl.DataFrame | None = None,
         title: str | None = None,
-    ) -> go.Figure:
-        """
-        Plots the correlation between Speaking Style Trait Scores (1-5) and Voice Ranking Points (0-3).
-        """
+        width: int | str | None = None,
+        height: int | None = None,
+    ) -> alt.Chart:
+        """Plots correlation between Speaking Style Trait Scores (1-5) and Voice Ranking Points (0-3)."""
         df = self._ensure_dataframe(data)
 
         if title is None:
@@ -890,72 +773,41 @@ class JPMCPlotsMixin:
         
         trait_correlations = []
         
-        # 1. Calculate Correlations
         for i, trait in enumerate(style_traits):
-            # Match against Right_Anchor which contains the positive trait description
             subset = df.filter(pl.col("Right_Anchor") == trait)
-            
-            # Drop Nulls for correlation calculation
             valid_data = subset.select(["score", "Ranking_Points"]).drop_nulls()
             
             if valid_data.height > 1:
-                # Calculate Pearson Correlation
                 corr_val = valid_data.select(pl.corr("score", "Ranking_Points")).item()
-                
+                trait_display = trait.replace('|', '\n')
                 trait_correlations.append({
-                    "trait_full": trait,
-                    "trait_short": f"Trait {i+1}",
+                    "trait_display": trait_display,
+                    "trait_index": f"Trait {i+1}",
                     "correlation": corr_val if corr_val is not None else 0.0
                 })
         
-        # 2. Build Plot Data
         if not trait_correlations:
-            fig = go.Figure()
-            fig.update_layout(title=f"No data for {style_color} Style")
-            return fig
+            return alt.Chart(pd.DataFrame({'text': [f"No data for {style_color} Style"]})).mark_text().encode(text='text:N')
             
-        plot_df = pl.DataFrame(trait_correlations)
-        
-        # Determine colors based on correlation sign
-        colors = []
-        for val in plot_df["correlation"]:
-            if val >= 0:
-                colors.append("green")
-            else:
-                colors.append("red")
-                
-        fig = go.Figure()
-        
-        fig.add_trace(go.Bar(
-            x=[f"Trait {i+1}" for i in range(len(plot_df))],
-            y=plot_df["correlation"],
-            text=[f"{val:.2f}" for val in plot_df["correlation"]],
-            textposition='outside',
-            marker_color=colors,
-            hovertemplate="<b>%{customdata}</b><br>Correlation: %{y:.2f}<extra></extra>",
-            customdata=plot_df["trait_full"]
-        ))
-        
-        # Wrap text at the "|" separator for cleaner line breaks
-        def wrap_text_at_pipe(text):
-            parts = [p.strip() for p in text.split("|")]
-            return "<br>".join(parts)
-            
-        x_labels = [wrap_text_at_pipe(t) for t in plot_df["trait_full"]]
-        
-        # Update trace to use full labels
-        fig.data[0].x = x_labels
-        
-        fig.update_layout(
+        plot_df = pl.DataFrame(trait_correlations).to_pandas()
+
+        chart = alt.Chart(plot_df).mark_bar().encode(
+            x=alt.X('trait_display:N', title=None, axis=alt.Axis(labelAngle=0)),
+            y=alt.Y('correlation:Q', title='Correlation', scale=alt.Scale(domain=[-1, 1])),
+            color=alt.condition(
+                alt.datum.correlation >= 0,
+                alt.value('green'),
+                alt.value('red')
+            ),
+            tooltip=[
+                alt.Tooltip('trait_display:N', title='Trait'),
+                alt.Tooltip('correlation:Q', format='.2f')
+            ]
+        ).properties(
             title=title,
-            yaxis_title="Correlation",
-            yaxis=dict(range=[-1, 1], zeroline=True, zerolinecolor="black"),
-            xaxis=dict(tickangle=0),
-            height=400,
-            width=1000,
-            template="plotly_white",
-            showlegend=False
+            width=width if width else 'container',
+            height=height or 350
         )
-        
-        self._save_plot(fig, title)
-        return fig
+
+        chart = self._save_plot(chart, title)
+        return chart
diff --git a/pyproject.toml b/pyproject.toml
index 6c70c31..5fb45e7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,6 @@ readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
     "altair>=6.0.0",
-    "kaleido>=1.2.0",
     "marimo>=0.18.0",
     "matplotlib>=3.10.8",
     "modin[dask]>=0.37.1",
@@ -15,12 +14,13 @@ dependencies = [
     "openai>=2.9.0",
     "openpyxl>=3.1.5",
     "pandas>=2.3.3",
-    "plotly>=6.5.1",
     "polars>=1.37.1",
+    "pyarrow>=23.0.0",
     "pysqlite3>=0.6.0",
     "pyzmq>=27.1.0",
     "requests>=2.32.5",
     "taguette>=1.5.1",
+    "vl-convert-python>=1.9.0.post1",
     "wordcloud>=1.9.5",
 ]
 
diff --git a/theme.py b/theme.py
index fac1325..aa15845 100644
--- a/theme.py
+++ b/theme.py
@@ -23,3 +23,58 @@ class ColorPalette:
     TEXT = "black"
     GRID = "lightgray"
     BACKGROUND = "white"
+
+
+def jpmc_altair_theme():
+    """JPMC brand theme for Altair charts."""
+    return {
+        'config': {
+            'view': {
+                'continuousWidth': 1000,
+                'continuousHeight': 500,
+                'strokeWidth': 0
+            },
+            'background': ColorPalette.BACKGROUND,
+            'axis': {
+                'grid': True,
+                'gridColor': ColorPalette.GRID,
+                'labelFontSize': 11,
+                'titleFontSize': 12,
+                'labelColor': ColorPalette.TEXT,
+                'titleColor': ColorPalette.TEXT,
+                'labelLimit': 200  # Allow longer labels before truncation
+            },
+            'axisX': {
+                'labelAngle': -45,
+                'labelLimit': 200  # Allow longer x-axis labels
+            },
+            'axisY': {
+                'labelAngle': 0
+            },
+            'legend': {
+                'orient': 'top',
+                'direction': 'horizontal',
+                'titleFontSize': 11,
+                'labelFontSize': 11
+            },
+            'title': {
+                'fontSize': 14,
+                'color': ColorPalette.TEXT,
+                'anchor': 'start',
+                'subtitleFontSize': 10,
+                'subtitleColor': 'gray'
+            },
+            'bar': {
+                'color': ColorPalette.PRIMARY
+            }
+        }
+    }
+
+
+# Register Altair theme
+try:
+    import altair as alt
+    alt.themes.register('jpmc', jpmc_altair_theme)
+    alt.themes.enable('jpmc')
+except ImportError:
+    pass  # Altair not installed
diff --git a/uv.lock b/uv.lock
index ada9950..0883f2b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -220,19 +220,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/0a/4c/925909008ed5a988ccbb72dcc897407e5d6d3bd72410d69e051fc0c14647/charset_normalizer-3.4.4-py3-none-any.whl", hash = "sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f", size = 53402, upload-time = "2025-10-14T04:42:31.76Z" },
 ]
 
-[[package]]
-name = "choreographer"
-version = "1.2.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "logistro" },
-    { name = "simplejson" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/74/47/64a035c6f764450ea9f902cbeba14c8c70316c2641125510066d8f912bfa/choreographer-1.2.1.tar.gz", hash = "sha256:022afd72b1e9b0bcb950420b134e70055a294c791b6f36cfb47d89745b701b5f", size = 43399, upload-time = "2025-11-09T23:04:44.749Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/b7/9f/d73dfb85d7a5b1a56a99adc50f2074029468168c970ff5daeade4ad819e4/choreographer-1.2.1-py3-none-any.whl", hash = "sha256:9af5385effa3c204dbc337abf7ac74fd8908ced326a15645dc31dde75718c77e", size = 49338, upload-time = "2025-11-09T23:04:43.154Z" },
-]
-
 [[package]]
 name = "click"
 version = "8.3.1"
@@ -578,15 +565,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a4/ed/1f1afb2e9e7f38a545d628f864d562a5ae64fe6f7a10e28ffb9b185b4e89/importlib_resources-6.5.2-py3-none-any.whl", hash = "sha256:789cfdc3ed28c78b67a06acb8126751ced69a3d5f79c095a98298cd8a760ccec", size = 37461, upload-time = "2025-01-03T18:51:54.306Z" },
 ]
 
-[[package]]
-name = "iniconfig"
-version = "2.3.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/72/34/14ca021ce8e5dfedc35312d08ba8bf51fdd999c576889fc2c24cb97f4f10/iniconfig-2.3.0.tar.gz", hash = "sha256:c76315c77db068650d49c5b56314774a7804df16fee4402c1f19d6d15d8c4730", size = 20503, upload-time = "2025-10-18T21:55:43.219Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/cb/b1/3846dd7f199d53cb17f49cba7e651e9ce294d8497c8c150530ed11865bb8/iniconfig-2.3.0-py3-none-any.whl", hash = "sha256:f631c04d2c48c52b84d0d0549c99ff3859c98df65b3101406327ecc7d53fbf12", size = 7484, upload-time = "2025-10-18T21:55:41.639Z" },
-]
-
 [[package]]
 name = "itsdangerous"
 version = "2.2.0"
@@ -715,22 +693,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/41/45/1a4ed80516f02155c51f51e8cedb3c1902296743db0bbc66608a0db2814f/jsonschema_specifications-2025.9.1-py3-none-any.whl", hash = "sha256:98802fee3a11ee76ecaca44429fda8a41bff98b00a0f2838151b113f210cc6fe", size = 18437, upload-time = "2025-09-08T01:34:57.871Z" },
 ]
 
-[[package]]
-name = "kaleido"
-version = "1.2.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "choreographer" },
-    { name = "logistro" },
-    { name = "orjson" },
-    { name = "packaging" },
-    { name = "pytest-timeout" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/38/ad/76eec859b71eda803a88ea50ed3f270281254656bb23d19eb0a39aa706a0/kaleido-1.2.0.tar.gz", hash = "sha256:fa621a14423e8effa2895a2526be00af0cf21655be1b74b7e382c171d12e71ef", size = 64160, upload-time = "2025-11-04T21:24:23.833Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/4b/97/f6de8d4af54d6401d6581a686cce3e3e2371a79ba459a449104e026c08bc/kaleido-1.2.0-py3-none-any.whl", hash = "sha256:c27ed82b51df6b923d0e656feac221343a0dbcd2fb9bc7e6b1db97f61e9a1513", size = 68997, upload-time = "2025-11-04T21:24:21.704Z" },
-]
-
 [[package]]
 name = "kiwisolver"
 version = "1.4.9"
@@ -812,15 +774,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/db/bc/83e112abc66cd466c6b83f99118035867cecd41802f8d044638aa78a106e/locket-1.0.0-py2.py3-none-any.whl", hash = "sha256:b6c819a722f7b6bd955b80781788e4a66a55628b858d347536b7e81325a3a5e3", size = 4398, upload-time = "2022-04-20T22:04:42.23Z" },
 ]
 
-[[package]]
-name = "logistro"
-version = "2.0.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/08/90/bfd7a6fab22bdfafe48ed3c4831713cb77b4779d18ade5e248d5dbc0ca22/logistro-2.0.1.tar.gz", hash = "sha256:8446affc82bab2577eb02bfcbcae196ae03129287557287b6a070f70c1985047", size = 8398, upload-time = "2025-11-01T02:41:18.81Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/54/20/6aa79ba3570bddd1bf7e951c6123f806751e58e8cce736bad77b2cf348d7/logistro-2.0.1-py3-none-any.whl", hash = "sha256:06ffa127b9fb4ac8b1972ae6b2a9d7fde57598bf5939cd708f43ec5bba2d31eb", size = 8555, upload-time = "2025-11-01T02:41:17.587Z" },
-]
-
 [[package]]
 name = "loro"
 version = "1.10.3"
@@ -1287,59 +1240,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/60/69/68b0b15515c74b31e93eaf40ea4a8297097386ccbe7e2fd4c7b0dd5aacd3/opentelemetry_api-1.10.0-py3-none-any.whl", hash = "sha256:ee56d74d8576807d86e0791bcfa44ec2c9abeec3f5ea080de09fd5fe7c442655", size = 47996, upload-time = "2022-03-11T00:12:18.859Z" },
 ]
 
-[[package]]
-name = "orjson"
-version = "3.11.5"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/04/b8/333fdb27840f3bf04022d21b654a35f58e15407183aeb16f3b41aa053446/orjson-3.11.5.tar.gz", hash = "sha256:82393ab47b4fe44ffd0a7659fa9cfaacc717eb617c93cde83795f14af5c2e9d5", size = 5972347, upload-time = "2025-12-06T15:55:39.458Z" }
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