fix: compute position stacking per-facet-panel instead of globally

src/execute/position.rs

@@ -58,6 +58,7 @@ pub fn apply_position_adjustments(
 #[cfg(test)]
 mod tests {
     use super::*;
+    use crate::plot::facet::{Facet, FacetLayout};
     use crate::plot::layer::{Geom, Position};
     use crate::plot::{AestheticValue, Mappings, ParameterValue, Scale, ScaleType};
     use polars::prelude::*;
@@ -322,4 +323,182 @@ mod tests {
             assert!((-0.3..=0.3).contains(&v));
         }
     }
+
+    #[test]
+    fn test_stack_resets_per_facet_panel() {
+        // Stacking should compute independently within each facet panel.
+        // Without this, bars in the second facet panel stack on top of
+        // cumulative values from the first panel (see issue #244).
+        //
+        // Two facet panels (F1, F2) each with the same x="A" and two
+        // fill groups (X, Y). Stacking within each panel should start from 0.
+        let df = df! {
+            "__ggsql_aes_pos1__" => ["A", "A", "A", "A"],
+            "__ggsql_aes_pos2__" => [10.0, 20.0, 30.0, 40.0],
+            "__ggsql_aes_pos2end__" => [0.0, 0.0, 0.0, 0.0],
+            "__ggsql_aes_fill__" => ["X", "Y", "X", "Y"],
+            "__ggsql_aes_facet1__" => ["F1", "F1", "F2", "F2"],
+        }
+        .unwrap();
+
+        let mut layer = crate::plot::Layer::new(Geom::bar());
+        layer.mappings = {
+            let mut m = Mappings::new();
+            m.insert(
+                "pos1",
+                AestheticValue::standard_column("__ggsql_aes_pos1__"),
+            );
+            m.insert(
+                "pos2",
+                AestheticValue::standard_column("__ggsql_aes_pos2__"),
+            );
+            m.insert(
+                "pos2end",
+                AestheticValue::standard_column("__ggsql_aes_pos2end__"),
+            );
+            m.insert(
+                "fill",
+                AestheticValue::standard_column("__ggsql_aes_fill__"),
+            );
+            m.insert(
+                "facet1",
+                AestheticValue::standard_column("__ggsql_aes_facet1__"),
+            );
+            m
+        };
+        layer.partition_by = vec![
+            "__ggsql_aes_fill__".to_string(),
+            "__ggsql_aes_facet1__".to_string(),
+        ];
+        layer.position = Position::stack();
+        layer.data_key = Some("__ggsql_layer_0__".to_string());
+
+        let mut spec = Plot::new();
+        spec.scales.push(make_discrete_scale("pos1"));
+        spec.scales.push(make_continuous_scale("pos2"));
+        spec.facet = Some(Facet::new(FacetLayout::Wrap {
+            variables: vec!["facet_var".to_string()],
+        }));
+        let mut data_map = HashMap::new();
+        data_map.insert("__ggsql_layer_0__".to_string(), df);
+
+        let mut spec_with_layer = spec;
+        spec_with_layer.layers.push(layer);
+
+        apply_position_adjustments(&mut spec_with_layer, &mut data_map).unwrap();
+
+        let result_df = data_map.get("__ggsql_layer_0__").unwrap();
+
+        // Sort by facet then fill so we can assert in predictable order
+        let result_df = result_df
+            .clone()
+            .lazy()
+            .sort_by_exprs(
+                [col("__ggsql_aes_facet1__"), col("__ggsql_aes_fill__")],
+                SortMultipleOptions::default(),
+            )
+            .collect()
+            .unwrap();
+
+        let pos2 = result_df
+            .column("__ggsql_aes_pos2__")
+            .unwrap()
+            .f64()
+            .unwrap();
+        let pos2end = result_df
+            .column("__ggsql_aes_pos2end__")
+            .unwrap()
+            .f64()
+            .unwrap();
+
+        let pos2_vals: Vec<f64> = pos2.into_iter().flatten().collect();
+        let pos2end_vals: Vec<f64> = pos2end.into_iter().flatten().collect();
+
+        // Expected (sorted by facet, fill):
+        // F1/X: pos2end=0,  pos2=10  (first in panel, starts at 0)
+        // F1/Y: pos2end=10, pos2=30  (stacks on X)
+        // F2/X: pos2end=0,  pos2=30  (first in panel, should reset to 0)
+        // F2/Y: pos2end=30, pos2=70  (stacks on X)
+        assert_eq!(
+            pos2end_vals[2], 0.0,
+            "F2 panel first bar should start at 0, not carry over from F1. pos2end={:?}, pos2={:?}",
+            pos2end_vals, pos2_vals
+        );
+    }
+
+    #[test]
+    fn test_dodge_ignores_facet_columns_in_group_count() {
+        // Dodge should compute n_groups per facet panel, not globally.
+        // With fill=["X","Y"] and facet=["F1","F2"], dodge should see
+        // 2 groups (X, Y) not 4 (X-F1, X-F2, Y-F1, Y-F2).
+        //
+        // With 2 groups and default width 0.9:
+        //   adjusted_width = 0.9 / 2 = 0.45
+        //   offsets: -0.225 (group X), +0.225 (group Y)
+        //
+        // If facet columns incorrectly inflate n_groups to 4:
+        //   adjusted_width = 0.9 / 4 = 0.225
+        //   offsets would be different (spread across 4 positions)
+        let df = df! {
+            "__ggsql_aes_pos1__" => ["A", "A", "A", "A"],
+            "__ggsql_aes_pos2__" => [10.0, 20.0, 30.0, 40.0],
+            "__ggsql_aes_pos2end__" => [0.0, 0.0, 0.0, 0.0],
+            "__ggsql_aes_fill__" => ["X", "Y", "X", "Y"],
+            "__ggsql_aes_facet1__" => ["F1", "F1", "F2", "F2"],
+        }
+        .unwrap();
+
+        let mut layer = crate::plot::Layer::new(Geom::bar());
+        layer.mappings = {
+            let mut m = Mappings::new();
+            m.insert(
+                "pos1",
+                AestheticValue::standard_column("__ggsql_aes_pos1__"),
+            );
+            m.insert(
+                "pos2",
+                AestheticValue::standard_column("__ggsql_aes_pos2__"),
+            );
+            m.insert(
+                "pos2end",
+                AestheticValue::standard_column("__ggsql_aes_pos2end__"),
+            );
+            m.insert(
+                "fill",
+                AestheticValue::standard_column("__ggsql_aes_fill__"),
+            );
+            m.insert(
+                "facet1",
+                AestheticValue::standard_column("__ggsql_aes_facet1__"),
+            );
+            m
+        };
+        layer.partition_by = vec![
+            "__ggsql_aes_fill__".to_string(),
+            "__ggsql_aes_facet1__".to_string(),
+        ];
+        layer.position = Position::dodge();
+        layer.data_key = Some("__ggsql_layer_0__".to_string());
+
+        let mut spec = Plot::new();
+        spec.scales.push(make_discrete_scale("pos1"));
+        spec.scales.push(make_continuous_scale("pos2"));
+        spec.facet = Some(Facet::new(FacetLayout::Wrap {
+            variables: vec!["facet_var".to_string()],
+        }));
+        let mut data_map = HashMap::new();
+        data_map.insert("__ggsql_layer_0__".to_string(), df);
+
+        spec.layers.push(layer);
+
+        apply_position_adjustments(&mut spec, &mut data_map).unwrap();
+
+        // With 2 groups (X, Y), adjusted_width should be 0.45
+        let adjusted = spec.layers[0].adjusted_width.unwrap();
+        assert!(
+            (adjusted - 0.45).abs() < 0.001,
+            "adjusted_width should be 0.45 (2 groups), got {} (facet columns inflated group count)",
+            adjusted
+        );
+    }
 }

src/plot/layer/position/dodge.rs

@@ -6,7 +6,10 @@
 //! - If only pos2 is discrete → dodge vertically (pos2offset)
 //! - If both are discrete → 2D grid dodge (both offsets, arranged in a grid)
 
-use super::{compute_dodge_offsets, is_continuous_scale, Layer, PositionTrait, PositionType};
+use super::{
+    compute_dodge_offsets, is_continuous_scale, non_facet_partition_cols, Layer, PositionTrait,
+    PositionType,
+};
 use crate::plot::types::{DefaultParamValue, ParamConstraint, ParamDefinition, ParameterValue};
 use crate::{naming, DataFrame, GgsqlError, Plot, Result};
 use polars::prelude::*;
@@ -159,8 +162,10 @@ fn apply_dodge_with_width(
         return Ok((df, None));
     }
 
-    // Compute group indices
-    let group_info = match compute_group_indices(&df, &layer.partition_by)? {
+    // Compute group indices, excluding facet columns so group count
+    // reflects within-panel groups (not cross-panel composites)
+    let group_cols = non_facet_partition_cols(&layer.partition_by, spec);
+    let group_info = match compute_group_indices(&df, &group_cols)? {
         Some(info) => info,
         None => return Ok((df, None)),
     };

src/plot/layer/position/jitter.rs

@@ -15,8 +15,8 @@
 //! - `normal`: normal/Gaussian distribution with ~95% of points within the width
 
 use super::{
-    compute_dodge_offsets, compute_group_indices, is_continuous_scale, Layer, PositionTrait,
-    PositionType,
+    compute_dodge_offsets, compute_group_indices, is_continuous_scale, non_facet_partition_cols,
+    Layer, PositionTrait, PositionType,
 };
 use crate::plot::types::{DefaultParamValue, ParamConstraint, ParamDefinition, ParameterValue};
 use crate::{naming, DataFrame, GgsqlError, Plot, Result};
@@ -491,9 +491,11 @@ fn apply_jitter(df: DataFrame, layer: &Layer, spec: &Plot) -> Result<DataFrame>
     let mut rng = rand::thread_rng();
     let n_rows = df.height();
 
-    // Compute group info for dodge-first behavior
+    // Compute group info for dodge-first behavior, excluding facet columns
+    // so group count reflects within-panel groups
+    let group_cols = non_facet_partition_cols(&layer.partition_by, spec);
     let group_info = if dodge {
-        compute_group_indices(&df, &layer.partition_by)?
+        compute_group_indices(&df, &group_cols)?
     } else {
         None
     };

src/plot/layer/position/mod.rs

@@ -104,6 +104,32 @@ pub fn compute_dodge_offsets(
     }
 }
 
+/// Filter facet columns out of partition_by for position adjustments that
+/// compute group indices (dodge, jitter).
+///
+/// Facet columns in partition_by inflate the group count — e.g., 2 fill groups
+/// across 2 facet panels would be seen as 4 composite groups instead of 2.
+/// Position adjustments should operate per-panel, so facet columns must be excluded.
+pub fn non_facet_partition_cols(partition_by: &[String], spec: &Plot) -> Vec<String> {
+    let facet_cols: std::collections::HashSet<String> = spec
+        .facet
+        .as_ref()
+        .map(|f| {
+            f.layout
+                .internal_facet_names()
+                .into_iter()
+                .map(|aes| crate::naming::aesthetic_column(&aes))
+                .collect()
+        })
+        .unwrap_or_default();
+
+    partition_by
+        .iter()
+        .filter(|col| !facet_cols.contains(*col))
+        .cloned()
+        .collect()
+}
+
 // Re-export position implementations
 pub use dodge::{compute_group_indices, Dodge, GroupIndices};
 pub use identity::Identity;

src/plot/layer/position/stack.rs

@@ -266,22 +266,33 @@ fn apply_stack(df: DataFrame, layer: &Layer, spec: &Plot, mode: StackMode) -> Re
     // 2. stack_end_col = lag(stack_col, 1, 0) - the bar bottom/start (previous stack top)
     // The cumsum naturally stacks across the grouping column values
 
+    // Build the partition columns for .over(): group column + facet columns.
+    // Facet columns must be included so stacking resets per facet panel,
+    // matching ggplot2 where position adjustments are computed per-panel.
+    let mut over_cols: Vec<Expr> = vec![col(&group_col)];
+    if let Some(ref facet) = spec.facet {
+        for aes in facet.layout.internal_facet_names() {
+            let facet_col = naming::aesthetic_column(&aes);
+            over_cols.push(col(&facet_col));
+        }
+    }
+
     // Treat NA heights as 0 for stacking
     // Compute cumulative sums (shared by all modes)
     let lf = lf
         .with_column(col(&stack_col).fill_null(lit(0.0)).alias(&stack_col))
         .with_column(
             col(&stack_col)
                 .cum_sum(false)
-                .over([col(&group_col)])
+                .over(&over_cols)
                 .alias("__cumsum__"),
         )
         .with_column(
             col(&stack_col)
                 .cum_sum(false)
                 .shift(lit(1))
                 .fill_null(lit(0.0))
-                .over([col(&group_col)])
+                .over(&over_cols)
                 .alias("__cumsum_lag__"),
         );
 
@@ -293,15 +304,15 @@ fn apply_stack(df: DataFrame, layer: &Layer, spec: &Plot, mode: StackMode) -> Re
             vec!["__cumsum__", "__cumsum_lag__"],
         ),
         StackMode::Fill(target) => {
-            let total = col(&stack_col).sum().over([col(&group_col)]);
+            let total = col(&stack_col).sum().over(&over_cols);
             (
                 (col("__cumsum__") / total.clone() * lit(target)).alias(&stack_col),
                 (col("__cumsum_lag__") / total * lit(target)).alias(&stack_end_col),
                 vec!["__cumsum__", "__cumsum_lag__"],
             )
         }
         StackMode::Center => {
-            let half_total = col(&stack_col).sum().over([col(&group_col)]) / lit(2.0);
+            let half_total = col(&stack_col).sum().over(&over_cols) / lit(2.0);
             (
                 (col("__cumsum__") - half_total.clone()).alias(&stack_col),
                 (col("__cumsum_lag__") - half_total).alias(&stack_end_col),