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README.md

@@ -17,7 +17,7 @@ An interactive Shiny app for exploring AI exposure and employment trends across
 - **Occupation View**: Select an occupation by SSYK level and year to see employment statistics and % changes (1, 3, 5-year)
 - **AI Exposure**: Ranked bar chart of AI sub-domain exposure scores with percentile rankings and exposure levels
 - **Employment by Age Group**: Line chart of annual employment % change by age group over a custom year range
-- **Comparison View**: Benchmark multiple occupations side by side with an employment trend chart, summary table, and AI exposure radar chart
+- **Comparison View**: Benchmark multiple occupations side by side with an annual employment % change chart, summary table, and AI exposure radar chart
 - **Download**: Filter and export the full dataset as CSV, Parquet, or Excel
 
 ## Data Sources

app.py

@@ -19,7 +19,6 @@ from src.setup import (
     build_choices_by_level,
     download_extension,
     download_media_type,
-    empty_figure,
     export_filtered_data,
     lf,
 )
@@ -82,8 +81,7 @@ def comparison_data():
     """Return total employment per year/occupation for the comparison view."""
     occs = list(input.comp_occs())
     ages = list(input.comp_age())
-    if not occs or not ages:
-        return pl.DataFrame()
+    req(occs, ages)
     return calcs.get_comparison_employment(lf, occs, ages)
 
 
@@ -91,8 +89,7 @@ def comparison_data():
 def comp_radar_data():
     """Return mean AI percentile scores per occupation for the radar chart."""
     occs = list(input.comp_occs())
-    if not occs:
-        return pl.DataFrame()
+    req(occs)
     return calcs.get_comp_radar(lf, occs, int(input.comp_year()))
 
 
@@ -203,8 +200,6 @@ with ui.navset_pill(id="tab"):
                 @render.ui
                 def comparison_summary():
                     df = comparison_data()
-                    if df.is_empty():
-                        return ui.markdown("*Select occupations to generate a summary...*")
 
                     latest_yr = df["year"].max()
                     summary_rows = []
@@ -243,24 +238,18 @@ with ui.navset_pill(id="tab"):
 
             with ui.layout_columns(col_widths=[6, 6], gap="1rem"):
                 with ui.card(full_screen=True):
-                    ui.card_header("Employment Trends (Selected Occupations)")
+                    ui.card_header("Annual Employment Change (Selected Occupations)")
 
                     @render_plotly
                     def comparison_employment_plot():
-                        df = comparison_data()
-                        if df.is_empty():
-                            return empty_figure("Select occupations to compare", {"text": "#666"})
-                        return visuals.build_comparison_employment_plot(df.to_pandas())
+                        return visuals.build_comparison_employment_plot(comparison_data().to_pandas())
 
                 with ui.card(full_screen=True):
                     ui.card_header("Radar Comparison (AI Exposure Percentiles)")
 
                     @render_plotly
                     def comp_radar_plot():
-                        df = comp_radar_data()
-                        if df.is_empty():
-                            return empty_figure("Select occupations to compare", {"text": "#666"})
-                        return visuals.build_comp_radar_plot(df.to_pandas(), METRICS)
+                        return visuals.build_comp_radar_plot(comp_radar_data().to_pandas(), METRICS)
 
     with ui.nav_panel(title="3. Download"):
         ui.p(

src/calcs.py

@@ -139,7 +139,10 @@ def get_comparison_employment(
             & pl.col("age_group").is_in(age_groups),
         )
         .group_by(["year", "occupation"])
-        .agg(pl.col("count").sum())
+        .agg([
+            pl.col("count").sum(),
+            pl.col("pct_chg_1y").mean(),
+        ])
         .sort(["occupation", "year"])
         .collect()
     )

src/visuals.py

@@ -138,21 +138,32 @@ def build_age_chart(df: pd.DataFrame, occupation: str) -> go.Figure:
 
 
 def build_comparison_employment_plot(df: pd.DataFrame) -> go.Figure:
-    """Build a line chart comparing employment trends across selected occupations."""
+    """Build a line chart comparing 1-yr employment % change across selected occupations."""
     if df.empty:
         return go.Figure()
 
     fig = px.line(
         df,
         x="year",
-        y="count",
+        y="pct_chg_1y",
         color="occupation",
         markers=True,
-        labels={"count": "Total Employment", "year": "Year"},
+        custom_data=["count"],
+        labels={"pct_chg_1y": "Employment Change (%)", "year": "Year"},
+    )
+    fig.update_traces(
+        hovertemplate=(
+            "<b>%{fullData.name}</b><br>"
+            "Year: %{x}<br>"
+            "Change: %{y:.1f}%<br>"
+            "Employment: %{customdata[0]:,}<extra></extra>"
+        ),
     )
+    fig.add_hline(y=0, line_color="grey", line_width=1)
     fig.update_layout(
         **_BASE_LAYOUT,
         legend={"orientation": "h", "yanchor": "bottom", "y": -0.25, "xanchor": "center", "x": 0.5, "title": None},
+        yaxis={"ticksuffix": "%"},
     )
     fig.update_xaxes(gridcolor=_C_GRID, zeroline=False, dtick=1)
     fig.update_yaxes(gridcolor=_C_GRID, zeroline=False)