report layout

02_quant_analysis.py

@@ -1,7 +1,7 @@
 import marimo
 
 __generated_with = "0.19.2"
-app = marimo.App(width="medium")
+app = marimo.App(width="full")
 
 
 @app.cell
@@ -21,6 +21,7 @@ def _():
         SPEAKING_STYLES,
         calculate_weighted_ranking_scores,
         check_progress,
+        check_straight_liners,
         duration_validation,
         mo,
         pl,
@@ -58,7 +59,7 @@ def _(JPMCSurvey, QSF_FILE, RESULTS_FILE, mo):
 
 
 @app.cell
-def _(Path, RESULTS_FILE, mo):
+def _(Path, RESULTS_FILE, data_all, mo):
     mo.md(f"""
 
     ---
@@ -66,15 +67,42 @@ def _(Path, RESULTS_FILE, mo):
 
     **Dataset:** `{Path(RESULTS_FILE).name}`
 
+    **Responses**: `{data_all.collect().shape[0]}`
+
 
     """)
     return
 
 
 @app.cell
-def _(check_progress, data_all, duration_validation, mo):
+def _():
+    sl_ss_max_score = 5
+    sl_v1_10_max_score = 10
+    return sl_ss_max_score, sl_v1_10_max_score
+
+
+@app.cell
+def _(
+    S,
+    check_progress,
+    check_straight_liners,
+    data_all,
+    duration_validation,
+    mo,
+    sl_ss_max_score,
+    sl_v1_10_max_score,
+):
+    _ss_all = S.get_ss_green_blue(data_all)[0].join(S.get_ss_orange_red(data_all)[0], on='_recordId')
+    _sl_ss_c, sl_ss_df = check_straight_liners(_ss_all, max_score=sl_ss_max_score)
+
+    _sl_v1_10_c, sl_v1_10_df = check_straight_liners(
+        S.get_voice_scale_1_10(data_all)[0], 
+        max_score=sl_v1_10_max_score
+    )
+
+
     mo.md(f"""
-    ## Data Validation
+    # Data Validation
 
     {check_progress(data_all)}
 
@@ -83,29 +111,28 @@ def _(check_progress, data_all, duration_validation, mo):
     {duration_validation(data_all)}
 
 
+    ## Speaking Style - Straight Liners
+    {_sl_ss_c}
 
+
+    ## Voice Score Scale 1-10 - Straight Liners
+    {_sl_v1_10_c}
     """)
     return
 
 
 @app.cell
-def _(mo):
+def _(data_all):
-    mo.md(r"""
+    # # Drop any Voice Scale 1-10 responses with straight-lining, using sl_v1_10_df _responseId values
-    ### ⚠️  ToDo: "straight-liner" detection and removal
+    # records_to_drop = sl_v1_10_df.select('Record ID').to_series().to_list()
-    """)
-    return
 
+    # data_validated = data_all.filter(~pl.col('_recordId').is_in(records_to_drop))
 
-@app.cell
+    # mo.md(f"""
-def _(mo):
+    # Dropped `{len(records_to_drop)}` responses with straight-lining in Voice Scale 1-10 evaluation.
-    mo.md(r"""
+    # """)
-    ---
+    data_validated = data_all
-
+    return (data_validated,)
-    # Data Filter
-
-    Use to select a subset of the data for the following analysis
-    """)
-    return
 
 
 @app.cell(hide_code=True)
@@ -140,17 +167,19 @@ def _(S, mo):
     ''')
 
 
-    return (filter_form,)
+    return
 
 
 @app.cell
-def _(S, data_all, filter_form, mo):
+def _(data_validated):
-    mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**"))
+    # 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'])
+    # _d = S.filter_data(data_validated, 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
+    # # Stop execution and prevent other cells from running if no data is selected
-    mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))
+    # mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**"))
-    data = _d
+    # data = _d
+
+    data = data_validated
 
     data.collect()
     return (data,)
@@ -359,33 +388,48 @@ def _(S, data, mo):
     return (vscales,)
 
 
+@app.cell
+def _(pl, vscales):
+    # Count non-null values per row
+    nn_vscale = vscales.with_columns(
+        non_null_count = pl.sum_horizontal(pl.all().exclude("_recordID").is_not_null())
+    )
+    nn_vscale.collect()['non_null_count'].describe()
+    return
+
+
 @app.cell(hide_code=True)
 def _(S, mo, vscales):
     mo.md(f"""
     ### How does each voice score on a scale from 1-10?
 
-    {mo.ui.altair_chart(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, domain=[1,10], title="Voice General Impression (Scale 1-10)"))}
-    """)
-    return
-
-
-@app.cell(hide_code=True)
-def _():
-    return
-
-
-@app.cell
-def _(mo):
-    mo.md(r"""
- 
     """)
     return
 
 
 @app.cell
-def _(mo):
+def _(S, mo, utils, vscales):
-    mo.md(r"""
+    _target_cols=[c for c in vscales.collect().columns if c not in ['_recordId']]
+    vscales_row_norm = utils.normalize_row_values(vscales.collect(), target_cols=_target_cols)
 
+    mo.md(f"""
+    ### Voice scale 1-10 normalized per respondent?
+
+    {mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales_row_norm, x_label='Voice', width=1000, domain=[1,10], title="Voice General Impression (Scale 1-10) - Normalized per Respondent"))}
+    """)
+    return
+
+
+@app.cell
+def _(S, mo, utils, vscales):
+    _target_cols=[c for c in vscales.collect().columns if c not in ['_recordId']]
+    vscales_global_norm = utils.normalize_global_values(vscales.collect(), target_cols=_target_cols)
+
+    mo.md(f"""
+    ### Voice scale 1-10 normalized per respondent?
+
+    {mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales_global_norm, x_label='Voice', width=1000, domain=[1,10], title="Voice General Impression (Scale 1-10) - Normalized Across All Respondents"))}
     """)
     return
 

03_quant_report.py

@@ -0,0 +1,154 @@
+import marimo
+
+__generated_with = "0.19.2"
+app = marimo.App(width="medium")
+
+with app.setup:
+    import marimo as mo
+    import polars as pl
+    from pathlib import Path
+
+    from validation import check_progress, duration_validation, check_straight_liners
+    from utils import JPMCSurvey, combine_exclusive_columns, calculate_weighted_ranking_scores
+    import utils
+
+    from speaking_styles import SPEAKING_STYLES
+
+
+@app.cell
+def _():
+
+    file_browser = mo.ui.file_browser(
+        initial_path="./data/exports", multiple=False, restrict_navigation=True, filetypes=[".csv"], label="Select 'Labels' File"
+    )
+    file_browser
+
+    return (file_browser,)
+
+
+@app.cell
+def _(file_browser):
+    mo.stop(file_browser.path(index=0) is None, mo.md("**⚠️ Please select a `_Labels.csv` file above to proceed**"))
+    RESULTS_FILE = Path(file_browser.path(index=0))
+    QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf'
+    return QSF_FILE, RESULTS_FILE
+
+
+@app.cell
+def _(QSF_FILE, RESULTS_FILE):
+    S = JPMCSurvey(RESULTS_FILE, QSF_FILE)
+    try:
+        data_all = S.load_data()
+    except NotImplementedError as e:
+        mo.stop(True, mo.md(f"**⚠️ {str(e)}**"))
+    return S, data_all
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+    # Load Data
+
+    **Dataset:** `{Path(RESULTS_FILE).name}`
+
+    **Responses**: `{data_all.collect().shape[0]}`
+    """)
+    return
+
+
+@app.cell
+def _(S, data_all):
+    sl_ss_max_score = 5
+    sl_v1_10_max_score = 10
+
+    _ss_all = S.get_ss_green_blue(data_all)[0].join(S.get_ss_orange_red(data_all)[0], on='_recordId')
+    _sl_ss_c, sl_ss_df = check_straight_liners(_ss_all, max_score=sl_ss_max_score)
+
+    _sl_v1_10_c, sl_v1_10_df = check_straight_liners(
+        S.get_voice_scale_1_10(data_all)[0], 
+        max_score=sl_v1_10_max_score
+    )
+
+
+    mo.md(f"""
+    # Data Validation
+
+    {check_progress(data_all)}
+
+
+
+    {duration_validation(data_all)}
+
+
+    ## Speaking Style - Straight Liners
+    {_sl_ss_c}
+
+
+    ## Voice Score Scale 1-10 - Straight Liners
+    {_sl_v1_10_c}
+    """)
+    return
+
+
+@app.cell
+def _(data_all):
+    # # Drop any Voice Scale 1-10 responses with straight-lining, using sl_v1_10_df _responseId values
+    # records_to_drop = sl_v1_10_df.select('Record ID').to_series().to_list()
+
+    # data_validated = data_all.filter(~pl.col('_recordId').is_in(records_to_drop))
+
+    # mo.md(f"""
+    # Dropped `{len(records_to_drop)}` responses with straight-lining in Voice Scale 1-10 evaluation.
+    # """)
+    data_validated = data_all
+    return (data_validated,)
+
+
+@app.cell(hide_code=True)
+def _():
+
+
+    return
+
+
+@app.cell
+def _(data_validated):
+    data = data_validated
+
+    data.collect()
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+
+    # Introduction (Respondent Demographics)
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+
+    # Brand Character Results
+    """)
+    return
+
+
+@app.cell(hide_code=True)
+def _():
+    mo.md(r"""
+    ---
+
+    # Spoken Voice Results
+    """)
+    return
+
+
+if __name__ == "__main__":
+    app.run()

99_example_ingest_qualtrics_export.py

@@ -205,7 +205,7 @@ def _(mo):
 @app.cell
 def _(data, survey):
     vscales = survey.get_voice_scale_1_10(data)[0].collect()
-    vscales
+    print(vscales.head())
     return (vscales,)
 
 

plots.py

@@ -13,6 +13,12 @@ import hashlib
 class JPMCPlotsMixin:
     """Mixin class for plotting functions in JPMCSurvey."""
 
+    def _process_title(self, title: str) -> str | list[str]:
+        """Process title to handle <br> tags for Altair."""
+        if isinstance(title, str) and '<br>' in title:
+            return title.split('<br>')
+        return title
+
     def _sanitize_filename(self, title: str) -> str:
         """Convert plot title to a safe filename."""
         # Remove HTML tags
@@ -156,8 +162,8 @@ class JPMCPlotsMixin:
         chart_spec = chart.to_dict()
         existing_title = chart_spec.get('title', '')
         
-        # Handle different title formats (string vs dict)
+        # Handle different title formats (string vs dict vs list)
-        if isinstance(existing_title, str):
+        if isinstance(existing_title, (str, list)):
             title_config = {
                 'text': existing_title,
                 'subtitle': lines,
@@ -260,6 +266,7 @@ class JPMCPlotsMixin:
         color: str = ColorPalette.PRIMARY,
         height: int | None = None,
         width: int | str | None = None,
+        domain: list[float] | 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)
@@ -279,10 +286,13 @@ class JPMCPlotsMixin:
         # Convert to pandas for Altair (sort by average descending)
         stats_df = pl.DataFrame(stats).sort('average', descending=True).to_pandas()
         
+        if domain is None:
+            domain = [stats_df['average'].min(), stats_df['average'].max()]
+
         # 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])),
+            y=alt.Y('average:Q', title=y_label, scale=alt.Scale(domain=domain)),
             tooltip=[
                 alt.Tooltip('voice:N', title='Voice'),
                 alt.Tooltip('average:Q', title='Average', format='.2f'),
@@ -303,7 +313,7 @@ class JPMCPlotsMixin:
 
         # Combine layers
         chart = (bars + text).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -360,7 +370,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='Count')
             ]
         ).add_params(selection).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -420,7 +430,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='Count')
             ]
         ).add_params(selection).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -473,7 +483,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='1st Place Votes')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -514,7 +524,7 @@ class JPMCPlotsMixin:
         )
 
         chart = (bars + text).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -571,7 +581,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='Selections')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -627,7 +637,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('count:Q', title='In Top 3')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or getattr(self, 'plot_height', 400)
         )
@@ -713,7 +723,7 @@ class JPMCPlotsMixin:
         # Combine layers
         chart = (bars + text).properties(
             title={
-                "text": title,
+                "text": self._process_title(title),
                 "subtitle": [trait_description, "(Numbers on bars indicate respondent count)"]
             },
             width=width or 800,
@@ -776,7 +786,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('correlation:Q', format='.2f')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or 350
         )
@@ -832,7 +842,7 @@ class JPMCPlotsMixin:
                 alt.Tooltip('correlation:Q', format='.2f')
             ]
         ).properties(
-            title=title,
+            title=self._process_title(title),
             width=width or 800,
             height=height or 350
         )

utils.py

@@ -349,6 +349,87 @@ def calculate_weighted_ranking_scores(df: pl.LazyFrame) -> pl.DataFrame:
     return pl.DataFrame(scores).sort('Weighted Score', descending=True)
 
 
+def normalize_row_values(df: pl.DataFrame, target_cols: list[str]) -> pl.DataFrame:
+    """
+    Normalizes values in the specified columns row-wise to 0-10 scale (Min-Max normalization).
+    Formula: ((x - row_min) / (row_max - row_min)) * 10
+    
+    Nulls are preserved as nulls. If all non-null values in a row are equal (max == min),
+    those values become 5.0 (midpoint of the scale).
+    
+    Parameters
+    ----------
+    df : pl.DataFrame
+        Input dataframe.
+    target_cols : list[str]
+        List of column names to normalize.
+        
+    Returns
+    -------
+    pl.DataFrame
+        DataFrame with target columns normalized row-wise.
+    """
+    # Calculate row min and max across target columns (ignoring nulls)
+    row_min = pl.min_horizontal([pl.col(c).cast(pl.Float64) for c in target_cols])
+    row_max = pl.max_horizontal([pl.col(c).cast(pl.Float64) for c in target_cols])
+    row_range = row_max - row_min
+    
+    # Build normalized column expressions
+    norm_exprs = []
+    for col in target_cols:
+        norm_exprs.append(
+            pl.when(row_range == 0)
+            .then(
+                # If range is 0 (all values equal), return 5.0 for non-null, null for null
+                pl.when(pl.col(col).is_null()).then(None).otherwise(5.0)
+            )
+            .otherwise(
+                ((pl.col(col).cast(pl.Float64) - row_min) / row_range) * 10
+            )
+            .alias(col)
+        )
+    
+    return df.with_columns(norm_exprs)
+
+
+def normalize_global_values(df: pl.DataFrame, target_cols: list[str]) -> pl.DataFrame:
+    """
+    Normalizes values in the specified columns globally to 0-10 scale.
+    Formula: ((x - global_min) / (global_max - global_min)) * 10
+    Ignores null values (NaNs).
+    """
+    # Ensure eager for scalar extraction
+    was_lazy = isinstance(df, pl.LazyFrame)
+    if was_lazy:
+        df = df.collect()
+        
+    if len(target_cols) == 0:
+        return df.lazy() if was_lazy else df
+
+    # Calculate global stats efficiently by stacking all columns
+    # Cast to Float64 to ensure numeric calculations
+    stats = df.select([pl.col(c).cast(pl.Float64) for c in target_cols]).melt().select([
+        pl.col("value").min().alias("min"),
+        pl.col("value").max().alias("max")
+    ])
+    
+    global_min = stats["min"][0]
+    global_max = stats["max"][0]
+    
+    # Handle edge case where all values are same or none exist
+    if global_min is None or global_max is None or global_max == global_min:
+        return df.lazy() if was_lazy else df
+
+    global_range = global_max - global_min
+
+    res = df.with_columns([
+        (((pl.col(col).cast(pl.Float64) - global_min) / global_range) * 10).alias(col)
+        for col in target_cols
+    ])
+    
+    return res.lazy() if was_lazy else res
+
+
 class JPMCSurvey(JPMCPlotsMixin):
     """Class to handle JPMorgan Chase survey data."""
     
@@ -589,10 +670,12 @@ class JPMCSurvey(JPMCPlotsMixin):
         return subset, None
     
     
-    def get_voice_scale_1_10(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
+    def get_voice_scale_1_10(self, q: pl.LazyFrame, drop_cols=['Voice_Scale_1_10__V46']) -> Union[pl.LazyFrame, None]:
         """Extract columns containing the Voice Scale 1-10 ratings for the Chase virtual assistant. 
         
         Returns subquery that can be chained with other polars queries.
+        
+        Drops scores for V46 as it was improperly configured in the survey and thus did not show up for respondents.
         """
         
         QIDs_map = {}
@@ -602,6 +685,12 @@ class JPMCSurvey(JPMCPlotsMixin):
                 # Convert "Voice 16 Scale 1-10_1" to "Scale_1_10__Voice_16"
                 QIDs_map[qid] = f"Voice_Scale_1_10__V{val['QName'].split()[1]}"
         
+        for col in drop_cols:
+            if col in QIDs_map.values():
+                # remove from QIDs_map
+                qid_to_remove = [k for k,v in QIDs_map.items() if v == col][0]
+                del QIDs_map[qid_to_remove]
+        
         return self._get_subset(q, list(QIDs_map.keys()), rename_cols=False).rename(QIDs_map), None
     
     

validation.py

@@ -1,13 +1,14 @@
 import marimo as mo
 import polars as pl
-
+import altair as alt
+from theme import ColorPalette
 
 def check_progress(data):
     """Check if all responses are complete based on 'progress' column."""
     if data.collect().select(pl.col('progress').unique()).shape[0] == 1:
-        return """### Responses Complete: \n\n✅ All responses are complete (progress = 100) """
+        return """## Responses Complete: \n\n✅ All responses are complete (progress = 100) """
     
-    return "### Responses Complete: \n\n⚠️ There are incomplete responses (progress < 100) ⚠️"
+    return "## Responses Complete: \n\n⚠️ There are incomplete responses (progress < 100) ⚠️"
 
 
 def duration_validation(data):
@@ -30,9 +31,9 @@ def duration_validation(data):
     outlier_data = _d.filter(pl.col('outlier_duration') == True).collect()
 
     if outlier_data.shape[0] == 0:
-        return "### Duration Outliers: \n\n✅ No duration outliers detected"
+        return "## Duration Outliers: \n\n✅ No duration outliers detected"
 
-    return f"""### Duration Outliers:
+    return f"""## Duration Outliers:
     
     **⚠️ Potential outliers detected based on response duration ⚠️**
     
@@ -68,13 +69,25 @@ def check_straight_liners(data, max_score=3):
     schema_names = data.collect_schema().names()
     
     # regex groupings
-    pattern = re.compile(r"(.*__V\d+)__Choice_\d+")
+    pattern_choice = re.compile(r"(.*__V\d+)__Choice_\d+")
+    pattern_scale = re.compile(r"Voice_Scale_1_10__V\d+")
+    
     groups = {}
     
     for col in schema_names:
-        match = pattern.search(col)
+        # Check for Choice pattern (SS_...__Vxx__Choice_y)
-        if match:
+        match_choice = pattern_choice.search(col)
-            group_key = match.group(1)
+        if match_choice:
+            group_key = match_choice.group(1)
+            if group_key not in groups:
+                groups[group_key] = []
+            groups[group_key].append(col)
+            continue
+            
+        # Check for Voice Scale pattern (Voice_Scale_1_10__Vxx)
+        # All of these form a single group "Voice_Scale_1_10"
+        if pattern_scale.search(col):
+            group_key = "Voice_Scale_1_10"
             if group_key not in groups:
                 groups[group_key] = []
             groups[group_key].append(col)
@@ -85,6 +98,13 @@ def check_straight_liners(data, max_score=3):
     if not multi_attribute_groups:
         return "### Straight-lining Checks: \n\nℹ️ No multi-attribute question groups found."
 
+    # Cast all involved columns to Float64 (strict=False) to handle potential string columns
+    # and 1-10 scale floats (e.g. 5.5). Float64 covers integers as well.
+    all_group_cols = [col for cols in multi_attribute_groups.values() for col in cols]
+    data = data.with_columns([
+        pl.col(col).cast(pl.Float64, strict=False) for col in all_group_cols
+    ])
+
     # Build expressions
     expressions = []
     
@@ -108,8 +128,9 @@ def check_straight_liners(data, max_score=3):
         ).alias(f"__is_straight__{key}")
         
         value_expr = safe_val.alias(f"__val__{key}")
+        has_data = (list_expr.list.len() > 0).alias(f"__has_data__{key}")
         
-        expressions.extend([is_straight, value_expr])
+        expressions.extend([is_straight, value_expr, has_data])
         
     # collect data with checks
     # We only need _recordId and the check columns
@@ -120,33 +141,200 @@ def check_straight_liners(data, max_score=3):
     # Process results into a nice table
     outliers = []
     
-    for key in multi_attribute_groups.keys():
+    for key, group_cols in multi_attribute_groups.items():
         flag_col = f"__is_straight__{key}"
         val_col = f"__val__{key}"
         
         filtered = checked_data.filter(pl.col(flag_col))
         
         if filtered.height > 0:
-            rows = filtered.select(["_recordId", val_col]).rows()
+            # Sort group_cols logic
-            for row in rows:
+            # If Choice columns, sort by choice number.
+            # If Voice Scale columns (no Choice_), sort by Voice ID (Vxx)
+            if all("__Choice_" in c for c in group_cols):
+                 key_func = lambda c: int(c.split('__Choice_')[-1])
+            else:
+                 # Extract digits from Vxx
+                 def key_func(c):
+                     m = re.search(r"__V(\d+)", c)
+                     return int(m.group(1)) if m else 0
+            
+            sorted_group_cols = sorted(group_cols, key=key_func)
+            
+            # Select relevant columns: Record ID, Value, and the sorted group columns
+            subset = filtered.select(["_recordId", val_col] + sorted_group_cols)
+            
+            for row in subset.iter_rows(named=True):
+                # Create ordered list of values, using 'NaN' for missing data
+                resp_list = [row[c] if row[c] is not None else 'NaN' for c in sorted_group_cols]
+                
                 outliers.append({
-                    "Record ID": row[0],
+                    "Record ID": row["_recordId"],
                     "Question Group": key,
-                    "Value": row[1]
+                    "Value": row[val_col],
+                    "Responses": str(resp_list)
                 })
 
     if not outliers:
-        return f"### Straight-lining Checks: \n\n✅ No straight-liners detected (value <= {max_score})"
+        return f"### Straight-lining Checks: \n\n✅ No straight-liners detected (value <= {max_score})", None
         
     outlier_df = pl.DataFrame(outliers)
 
-    return f"""### Straight-lining Checks:
+    # --- Analysis & Visualization ---
     
-    **⚠️ Potential straight-liners detected ⚠️**
+    total_respondents = checked_data.height
     
-    Respondents selected the same value (<= {max_score}) for all attributes in the following groups:
+    # 1. & 3. Percentage Calculation
+    group_stats = []
+    value_dist_data = []
+
+    # Calculate Straight-Liners for ALL groups found in Data
+    # Condition: Respondent straight-lined ALL questions that they actually answered (ignoring empty/skipped questions)
+    # Logic: For every group G: if G has data (len > 0), then G must be straight.
+    # Also, the respondent must have answered at least one question group.
+    
+    conditions = []
+    has_any_data_exprs = []
+    
+    for key in multi_attribute_groups.keys():
+        flag_col = f"__is_straight__{key}"
+        data_col = f"__has_data__{key}"
+        
+        # If has_data is True, is_straight MUST be True for it to count as valid straight-lining behavior for that user.
+        # Equivalent: (not has_data) OR is_straight
+        cond = (~pl.col(data_col)) | pl.col(flag_col)
+        conditions.append(cond)
+        has_any_data_exprs.append(pl.col(data_col))
+
+    all_straight_count = checked_data.filter(
+        pl.all_horizontal(conditions) & pl.any_horizontal(has_any_data_exprs)
+    ).height
+    all_straight_pct = (all_straight_count / total_respondents) * 100
+
+    for key in multi_attribute_groups.keys():
+        flag_col = f"__is_straight__{key}"
+        val_col = f"__val__{key}"
+        
+        # Filter for straight-liners in this specific group
+        sl_sub = checked_data.filter(pl.col(flag_col))
+        count = sl_sub.height
+        pct = (count / total_respondents) * 100
+        
+        group_stats.append({
+            "Question Group": key,
+            "Straight-Liner %": pct,
+            "Count": count
+        })
+        
+        # Get Value Distribution for this group's straight-liners
+        if count > 0:
+            # Group by the Value they straight-lined
+            dist = sl_sub.group_by(val_col).agg(pl.len().alias("count"))
+            for row in dist.iter_rows(named=True):
+                 value_dist_data.append({
+                     "Question Group": key,
+                     "Value": row[val_col],
+                     "Count": row["count"]
+                 })
+
+    stats_df = pl.DataFrame(group_stats)
+    dist_df = pl.DataFrame(value_dist_data)
+    
+    # Plot 1: % of Responses with Straight-Liners per Question
+    # Vertical bars with Count label on top
+    base_pct = alt.Chart(stats_df).encode(
+        x=alt.X("Question Group", sort=alt.EncodingSortField(field="Straight-Liner %", order="descending"))
+    )
+    
+    bars_pct = base_pct.mark_bar(color=ColorPalette.PRIMARY).encode(
+        y=alt.Y("Straight-Liner %:Q", axis=alt.Axis(format=".1f", title="Share of all responses [%]")),
+        tooltip=["Question Group", alt.Tooltip("Straight-Liner %:Q", format=".1f"), "Count"]
+    )
+    
+    text_pct = base_pct.mark_text(dy=-10).encode(
+        y=alt.Y("Straight-Liner %:Q"),
+        text=alt.Text("Count")
+    )
+    
+    chart_pct = (bars_pct + text_pct).properties(
+        title="Share of Responses with Straight-Liners per Question", 
+        width=800,
+        height=300
+    )
+    
+    # Plot 2: Value Distribution (Horizontal Stacked Normalized Bar)
+    # Question Groups sorted by Total Count
+    # Values stacked 1 (left) -> 5 (right)
+    # Legend on top
+    # Total count at bar end
+    
+    # Sort order for Y axis (Question Group) based on total Count (descending)
+    # Explicitly calculate sort order from stats_df to ensure consistency across layers
+    # High counts at the top
+    sorted_groups = stats_df.sort("Count", descending=True)["Question Group"].to_list()
+    
+    # Base chart for Bars
+    # Use JPMC-aligned colors (blues) instead of default categorical rainbow
+    # Remove legend title as per plots.py style
+    bars_dist = alt.Chart(dist_df).mark_bar().encode(
+        y=alt.Y("Question Group", sort=sorted_groups),
+        x=alt.X("Count", stack="normalize", axis=alt.Axis(format="%"), title="Share of SL Responses"),
+        color=alt.Color("Value:O", 
+                        title=None, # explicit removal of title like in plots.py
+                        scale=alt.Scale(scheme="blues"), # Professional blue scale
+                        legend=alt.Legend(orient="top", direction="horizontal")
+                       ),
+        order=alt.Order("Value", sort="ascending"), # Ensures 1 is Left, 5 is Right
+        tooltip=["Question Group", "Value", "Count"]
+    )
+    
+    # Text layer for Total Count (using stats_df which already has totals)
+    # using same sort for Y
+    text_dist = alt.Chart(stats_df).mark_text(align='left', dx=5).encode(
+        y=alt.Y("Question Group", sort=sorted_groups),
+        x=alt.datum(1.0), # Position at 100%
+        text=alt.Text("Count")
+    )
+
+    chart_dist = (bars_dist + text_dist).properties(
+        title="Distribution of Straight-Lined Values",
+        width=800,
+        height=500
+    )
+
+    analysis_md = f"""
+    ### Straight-Lining Analysis
+    
+    *"Straight-lining" is defined here as selecting the same response value for all attributes within a multi-attribute question group.*
+    
+    *   **Total Respondents**: {total_respondents}
+    *   **Respondents straight-lining ALL questions presented to them**: {all_straight_pct:.2f}% ({all_straight_count} respondents)
     
-    {mo.ui.table(outlier_df)}
     """
     
+    return (mo.vstack([
+        mo.md(f"**⚠️ Potential straight-liners detected ⚠️**\n\n"),
+        mo.ui.table(outlier_df),
+        mo.md(analysis_md),
+        alt.vconcat(chart_pct, chart_dist).resolve_legend(color="independent")
+    ]), outlier_df)
 
+
+
+if __name__ == "__main__":
+    
+    from utils import JPMCSurvey
+    
+    RESULTS_FILE = "data/exports/OneDrive_2026-01-28/1-28-26 Afternoon/JPMC_Chase Brand Personality_Quant Round 1_January 28, 2026_Afternoon_Labels.csv"
+    QSF_FILE = "data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf"
+    
+    S = JPMCSurvey(RESULTS_FILE, QSF_FILE)
+    data = S.load_data()
+    
+    # print("Checking Green Blue:")
+    # print(check_straight_liners(S.get_ss_green_blue(data)[0]))
+    # print("Checking Orange Red:")
+    # print(check_straight_liners(S.get_ss_orange_red(data)[0]))
+    
+    print("Checking Voice Scale 1-10:")
+    print(check_straight_liners(S.get_voice_scale_1_10(data)[0]))