straight-liner plot analysis
This commit is contained in:
388
plots.py
388
plots.py
@@ -1115,6 +1115,7 @@ class QualtricsPlotsMixin:
|
||||
title: str = "Speaking Style Trait Analysis",
|
||||
height: int | None = None,
|
||||
width: int | str | None = None,
|
||||
color_gender: bool = False,
|
||||
) -> alt.Chart:
|
||||
"""Plot scores for a single speaking style trait across multiple voices."""
|
||||
df = self._ensure_dataframe(data)
|
||||
@@ -1156,36 +1157,71 @@ class QualtricsPlotsMixin:
|
||||
else:
|
||||
trait_description = ""
|
||||
|
||||
# Horizontal bar chart - use x2 to explicitly start bars at x=1
|
||||
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]), axis=alt.Axis(grid=True)),
|
||||
x2=alt.datum(1), # Bars start at x=1 (left edge of domain)
|
||||
y=alt.Y('Voice:N', title='Voice', sort='-x', axis=alt.Axis(grid=False)),
|
||||
tooltip=[
|
||||
alt.Tooltip('Voice:N'),
|
||||
alt.Tooltip('mean_score:Q', title='Average', format='.2f'),
|
||||
alt.Tooltip('count:Q', title='Count')
|
||||
]
|
||||
)
|
||||
if color_gender:
|
||||
stats['gender'] = stats['Voice'].apply(self._get_voice_gender)
|
||||
|
||||
bars = alt.Chart(stats).mark_bar().encode(
|
||||
x=alt.X('mean_score:Q', title='Average Score (1-5)', scale=alt.Scale(domain=[1, 5]), axis=alt.Axis(grid=True)),
|
||||
x2=alt.datum(1), # Bars start at x=1 (left edge of domain)
|
||||
y=alt.Y('Voice:N', title='Voice', sort='-x', axis=alt.Axis(grid=False)),
|
||||
color=alt.Color('gender:N',
|
||||
scale=alt.Scale(domain=['Male', 'Female'],
|
||||
range=[ColorPalette.GENDER_MALE, ColorPalette.GENDER_FEMALE]),
|
||||
legend=alt.Legend(orient='top', direction='horizontal', title='Gender')),
|
||||
tooltip=[
|
||||
alt.Tooltip('Voice:N'),
|
||||
alt.Tooltip('mean_score:Q', title='Average', format='.2f'),
|
||||
alt.Tooltip('count:Q', title='Count'),
|
||||
alt.Tooltip('gender:N', title='Gender')
|
||||
]
|
||||
)
|
||||
|
||||
text = alt.Chart(stats).mark_text(
|
||||
align='left',
|
||||
baseline='middle',
|
||||
dx=5,
|
||||
fontSize=12
|
||||
).encode(
|
||||
x='mean_score:Q',
|
||||
y=alt.Y('Voice:N', sort='-x'),
|
||||
text='count:Q',
|
||||
color=alt.condition(
|
||||
alt.datum.gender == 'Female',
|
||||
alt.value(ColorPalette.GENDER_FEMALE),
|
||||
alt.value(ColorPalette.GENDER_MALE)
|
||||
)
|
||||
)
|
||||
else:
|
||||
# Horizontal bar chart - use x2 to explicitly start bars at x=1
|
||||
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]), axis=alt.Axis(grid=True)),
|
||||
x2=alt.datum(1), # Bars start at x=1 (left edge of domain)
|
||||
y=alt.Y('Voice:N', title='Voice', sort='-x', axis=alt.Axis(grid=False)),
|
||||
tooltip=[
|
||||
alt.Tooltip('Voice:N'),
|
||||
alt.Tooltip('mean_score:Q', title='Average', format='.2f'),
|
||||
alt.Tooltip('count:Q', title='Count')
|
||||
]
|
||||
)
|
||||
|
||||
# Count text at end of bars (right-aligned inside bar)
|
||||
text = alt.Chart(stats).mark_text(
|
||||
align='right',
|
||||
baseline='middle',
|
||||
color='white',
|
||||
fontSize=12,
|
||||
dx=-5 # Slight padding from bar end
|
||||
).encode(
|
||||
x='mean_score:Q',
|
||||
y=alt.Y('Voice:N', sort='-x'),
|
||||
text='count:Q'
|
||||
)
|
||||
# Count text at end of bars
|
||||
text = alt.Chart(stats).mark_text(
|
||||
align='left',
|
||||
baseline='middle',
|
||||
color='black',
|
||||
fontSize=12,
|
||||
dx=5
|
||||
).encode(
|
||||
x='mean_score:Q',
|
||||
y=alt.Y('Voice:N', sort='-x'),
|
||||
text='count:Q'
|
||||
)
|
||||
|
||||
# Combine layers
|
||||
chart = (bars + text).properties(
|
||||
title={
|
||||
"text": self._process_title(title),
|
||||
"subtitle": [trait_description, "(Numbers on bars indicate respondent count)"]
|
||||
"subtitle": [trait_description, "(Numbers near bars indicate respondent count)"]
|
||||
},
|
||||
width=width or 800,
|
||||
height=height or getattr(self, 'plot_height', 400)
|
||||
@@ -1194,6 +1230,101 @@ class QualtricsPlotsMixin:
|
||||
chart = self._save_plot(chart, title)
|
||||
return chart
|
||||
|
||||
def plot_speaking_style_trait_scores_comparison(
|
||||
self,
|
||||
data_all: pl.LazyFrame | pl.DataFrame,
|
||||
data_clean: pl.LazyFrame | pl.DataFrame,
|
||||
trait_description: str = None,
|
||||
title: str = "Speaking Style Trait Analysis (Comparison)",
|
||||
height: int | None = None,
|
||||
width: int | str | None = None,
|
||||
) -> alt.Chart:
|
||||
"""Plot scores comparing All Respondents vs Cleaned data (excl. straight-liners) in grouped bars."""
|
||||
|
||||
# Helper to process each dataframe
|
||||
def get_stats(d, group_label):
|
||||
df = self._ensure_dataframe(d)
|
||||
if df.is_empty(): return None
|
||||
|
||||
return (
|
||||
df.filter(pl.col("score").is_not_null())
|
||||
.group_by("Voice")
|
||||
.agg([
|
||||
pl.col("score").mean().alias("mean_score"),
|
||||
pl.col("score").count().alias("count")
|
||||
])
|
||||
.with_columns(pl.lit(group_label).alias("dataset"))
|
||||
.to_pandas()
|
||||
)
|
||||
|
||||
stats_all = get_stats(data_all, "All Respondents")
|
||||
stats_clean = get_stats(data_clean, "Excl. Straight-Liners")
|
||||
|
||||
if stats_all is None or stats_clean is None:
|
||||
return alt.Chart(pd.DataFrame({'text': ['No data']})).mark_text().encode(text='text:N')
|
||||
|
||||
# Combine
|
||||
stats = pd.concat([stats_all, stats_clean])
|
||||
|
||||
# Determine sort order using "All Respondents" data (Desc)
|
||||
sort_order = stats_all.sort_values('mean_score', ascending=False)['Voice'].tolist()
|
||||
|
||||
# Add gender and combined category for color
|
||||
stats['gender'] = stats['Voice'].apply(self._get_voice_gender)
|
||||
stats['color_group'] = stats.apply(
|
||||
lambda x: f"{x['gender']} - {x['dataset']}", axis=1
|
||||
)
|
||||
|
||||
# Define Color Scale
|
||||
domain = [
|
||||
'Male - All Respondents', 'Male - Excl. Straight-Liners',
|
||||
'Female - All Respondents', 'Female - Excl. Straight-Liners'
|
||||
]
|
||||
range_colors = [
|
||||
ColorPalette.GENDER_MALE, ColorPalette.GENDER_MALE_RANK_3,
|
||||
ColorPalette.GENDER_FEMALE, ColorPalette.GENDER_FEMALE_RANK_3
|
||||
]
|
||||
|
||||
# Base chart
|
||||
base = alt.Chart(stats).encode(
|
||||
y=alt.Y('Voice:N', title='Voice', sort=sort_order, axis=alt.Axis(grid=False)),
|
||||
)
|
||||
|
||||
bars = base.mark_bar().encode(
|
||||
x=alt.X('mean_score:Q', title='Average Score (1-5)', scale=alt.Scale(domain=[1, 5]), axis=alt.Axis(grid=True)),
|
||||
x2=alt.datum(1),
|
||||
yOffset=alt.YOffset('dataset:N', sort=['All Respondents', 'Excl. Straight-Liners']),
|
||||
color=alt.Color('color_group:N',
|
||||
scale=alt.Scale(domain=domain, range=range_colors),
|
||||
legend=alt.Legend(title='Dataset', orient='top', columns=2)),
|
||||
tooltip=[
|
||||
alt.Tooltip('Voice:N'),
|
||||
alt.Tooltip('dataset:N', title='Dataset'),
|
||||
alt.Tooltip('mean_score:Q', title='Average', format='.2f'),
|
||||
alt.Tooltip('count:Q', title='Count'),
|
||||
alt.Tooltip('gender:N', title='Gender')
|
||||
]
|
||||
)
|
||||
|
||||
text = base.mark_text(align='left', baseline='middle', dx=5, fontSize=9).encode(
|
||||
x=alt.X('mean_score:Q'),
|
||||
yOffset=alt.YOffset('dataset:N', sort=['All Respondents', 'Excl. Straight-Liners']),
|
||||
text=alt.Text('count:Q'),
|
||||
color=alt.Color('color_group:N', scale=alt.Scale(domain=domain, range=range_colors), legend=None)
|
||||
)
|
||||
|
||||
chart = (bars + text).properties(
|
||||
title={
|
||||
"text": self._process_title(title),
|
||||
"subtitle": [trait_description if trait_description else "", "(Lighter shade = Straight-liners removed)"]
|
||||
},
|
||||
width=width or 800,
|
||||
height=height or getattr(self, 'plot_height', 600)
|
||||
)
|
||||
|
||||
chart = self._save_plot(chart, title)
|
||||
return chart
|
||||
|
||||
def plot_speaking_style_scale_correlation(
|
||||
self,
|
||||
style_color: str,
|
||||
@@ -2495,5 +2626,214 @@ class QualtricsPlotsMixin:
|
||||
height=height or getattr(self, 'plot_height', 400),
|
||||
)
|
||||
|
||||
chart = self._save_plot(chart, title)
|
||||
return chart
|
||||
|
||||
def plot_straight_liner_repeat_offenders(
|
||||
self,
|
||||
cumulative_df: pl.DataFrame | pd.DataFrame,
|
||||
title: str = "Straight-Liner Repeat Offenders\n(Cumulative Distribution)",
|
||||
height: int | None = None,
|
||||
width: int | str | None = None,
|
||||
total_respondents: int | None = None,
|
||||
) -> alt.Chart:
|
||||
"""Plot the cumulative distribution of straight-liner repeat offenders.
|
||||
|
||||
Shows how many respondents straight-lined at N or more question
|
||||
groups, for every observed threshold.
|
||||
|
||||
Parameters:
|
||||
cumulative_df: DataFrame with columns ``threshold`` (int),
|
||||
``count`` (int) and ``pct`` (float, 0-100). Each row
|
||||
represents "≥ threshold question groups".
|
||||
title: Chart title.
|
||||
height: Chart height in pixels.
|
||||
width: Chart width in pixels.
|
||||
total_respondents: If provided, shown in the subtitle for
|
||||
context.
|
||||
|
||||
Returns:
|
||||
The Altair chart object (already saved if ``fig_save_dir``
|
||||
is configured).
|
||||
"""
|
||||
if isinstance(cumulative_df, pl.DataFrame):
|
||||
plot_df = cumulative_df.to_pandas()
|
||||
else:
|
||||
plot_df = cumulative_df.copy()
|
||||
|
||||
# Build readable x-axis labels ("≥1", "≥2", …)
|
||||
plot_df["label"] = plot_df["threshold"].apply(lambda t: f"≥{t}")
|
||||
|
||||
# Explicit sort order so Altair keeps ascending threshold
|
||||
sort_order = plot_df.sort_values("threshold")["label"].tolist()
|
||||
|
||||
# --- Bars: respondent count ---
|
||||
bars = alt.Chart(plot_df).mark_bar(
|
||||
color=ColorPalette.PRIMARY
|
||||
).encode(
|
||||
x=alt.X(
|
||||
"label:N",
|
||||
title="Number of Straight-Lined Question Groups",
|
||||
sort=sort_order,
|
||||
axis=alt.Axis(grid=False),
|
||||
),
|
||||
y=alt.Y(
|
||||
"count:Q",
|
||||
title="Number of Respondents",
|
||||
axis=alt.Axis(grid=True),
|
||||
),
|
||||
tooltip=[
|
||||
alt.Tooltip("label:N", title="Threshold"),
|
||||
alt.Tooltip("count:Q", title="Respondents"),
|
||||
alt.Tooltip("pct:Q", title="% of Total", format=".1f"),
|
||||
],
|
||||
)
|
||||
|
||||
# --- Text: count + percentage above each bar ---
|
||||
text = alt.Chart(plot_df).mark_text(
|
||||
dy=-10, color="black", fontSize=11
|
||||
).encode(
|
||||
x=alt.X("label:N", sort=sort_order),
|
||||
y=alt.Y("count:Q"),
|
||||
text=alt.Text("count_label:N"),
|
||||
)
|
||||
|
||||
# Build a combined label column "N (xx.x%)"
|
||||
plot_df["count_label"] = plot_df.apply(
|
||||
lambda r: f"{int(r['count'])} ({r['pct']:.1f}%)", axis=1
|
||||
)
|
||||
|
||||
# Rebuild text layer with the updated df
|
||||
text = alt.Chart(plot_df).mark_text(
|
||||
dy=-10, color="black", fontSize=11
|
||||
).encode(
|
||||
x=alt.X("label:N", sort=sort_order),
|
||||
y=alt.Y("count:Q"),
|
||||
text=alt.Text("count_label:N"),
|
||||
)
|
||||
|
||||
# --- Subtitle ---
|
||||
subtitle_parts = []
|
||||
if total_respondents is not None:
|
||||
subtitle_parts.append(
|
||||
f"Total respondents: {total_respondents}"
|
||||
)
|
||||
subtitle_parts.append(
|
||||
"Each bar shows how many respondents straight-lined "
|
||||
"at least that many question groups"
|
||||
)
|
||||
subtitle = " | ".join(subtitle_parts)
|
||||
|
||||
title_config = {
|
||||
"text": self._process_title(title),
|
||||
"subtitle": subtitle,
|
||||
"subtitleColor": "gray",
|
||||
"subtitleFontSize": 10,
|
||||
"anchor": "start",
|
||||
}
|
||||
|
||||
chart = alt.layer(bars, text).properties(
|
||||
title=title_config,
|
||||
width=width or 800,
|
||||
height=height or getattr(self, "plot_height", 400),
|
||||
)
|
||||
|
||||
chart = self._save_plot(chart, title)
|
||||
return chart
|
||||
|
||||
def plot_straight_liner_per_question(
|
||||
self,
|
||||
per_question_df: pl.DataFrame | pd.DataFrame,
|
||||
title: str = "Straight-Lining Frequency per Question Group",
|
||||
height: int | None = None,
|
||||
width: int | str | None = None,
|
||||
total_respondents: int | None = None,
|
||||
) -> alt.Chart:
|
||||
"""Plot how often each question group is straight-lined.
|
||||
|
||||
Parameters:
|
||||
per_question_df: DataFrame with columns ``question`` (str,
|
||||
human-readable name), ``count`` (int) and ``pct``
|
||||
(float, 0-100). Sorted descending by count.
|
||||
title: Chart title.
|
||||
height: Chart height in pixels.
|
||||
width: Chart width in pixels.
|
||||
total_respondents: Shown in subtitle for context.
|
||||
|
||||
Returns:
|
||||
The Altair chart (saved if ``fig_save_dir`` is set).
|
||||
"""
|
||||
if isinstance(per_question_df, pl.DataFrame):
|
||||
plot_df = per_question_df.to_pandas()
|
||||
else:
|
||||
plot_df = per_question_df.copy()
|
||||
|
||||
# Sort order: largest count at top. Altair y-axis nominal sort places
|
||||
# the first list element at the top, so descending order is correct.
|
||||
sort_order = plot_df.sort_values("count", ascending=False)["question"].tolist()
|
||||
|
||||
# Combined label "N (xx.x%)"
|
||||
plot_df["count_label"] = plot_df.apply(
|
||||
lambda r: f"{int(r['count'])} ({r['pct']:.1f}%)", axis=1
|
||||
)
|
||||
|
||||
# --- Horizontal Bars ---
|
||||
bars = alt.Chart(plot_df).mark_bar(
|
||||
color=ColorPalette.PRIMARY,
|
||||
).encode(
|
||||
y=alt.Y(
|
||||
"question:N",
|
||||
title=None,
|
||||
sort=sort_order,
|
||||
axis=alt.Axis(grid=False, labelLimit=250, labelAngle=0),
|
||||
),
|
||||
x=alt.X(
|
||||
"count:Q",
|
||||
title="Number of Straight-Liners",
|
||||
axis=alt.Axis(grid=True),
|
||||
),
|
||||
tooltip=[
|
||||
alt.Tooltip("question:N", title="Question"),
|
||||
alt.Tooltip("count:Q", title="Straight-Liners"),
|
||||
alt.Tooltip("pct:Q", title="% of Respondents", format=".1f"),
|
||||
],
|
||||
)
|
||||
|
||||
# --- Text labels to the right of bars ---
|
||||
text = alt.Chart(plot_df).mark_text(
|
||||
align="left", dx=4, color="black", fontSize=10,
|
||||
).encode(
|
||||
y=alt.Y("question:N", sort=sort_order),
|
||||
x=alt.X("count:Q"),
|
||||
text=alt.Text("count_label:N"),
|
||||
)
|
||||
|
||||
# --- Subtitle ---
|
||||
subtitle_parts = []
|
||||
if total_respondents is not None:
|
||||
subtitle_parts.append(f"Total respondents: {total_respondents}")
|
||||
subtitle_parts.append(
|
||||
"Count and share of respondents who straight-lined each question group"
|
||||
)
|
||||
subtitle = " | ".join(subtitle_parts)
|
||||
|
||||
title_config = {
|
||||
"text": self._process_title(title),
|
||||
"subtitle": subtitle,
|
||||
"subtitleColor": "gray",
|
||||
"subtitleFontSize": 10,
|
||||
"anchor": "start",
|
||||
}
|
||||
|
||||
# Scale height with number of questions for readable bar spacing
|
||||
n_questions = len(plot_df)
|
||||
auto_height = max(400, n_questions * 22)
|
||||
|
||||
chart = alt.layer(bars, text).properties(
|
||||
title=title_config,
|
||||
width=width or 700,
|
||||
height=height or auto_height,
|
||||
)
|
||||
|
||||
chart = self._save_plot(chart, title)
|
||||
return chart
|
||||
Reference in New Issue
Block a user