Added density reducer to bin/group/hexbin

docs/transforms/bin.md

@@ -192,7 +192,7 @@ Plot.plot({
 ```
 :::
 
-The bin transform works with Plot’s [faceting system](../features/facets.md), partitioning bins by facet. Below, we compare the weight distributions of athletes within each sport using the *proportion-facet* reducer. Sports are sorted by median weight: gymnasts tend to be the lightest, and basketball players the heaviest.
+The bin transform works with Plot’s [faceting system](../features/facets.md), partitioning bins by facet. Below, we compare the weight distributions of athletes within each sport using the *density* reducer. Sports are sorted by median weight: gymnasts tend to be the lightest, and basketball players the heaviest.
 
 :::plot defer
 ```js-vue
@@ -202,7 +202,7 @@ Plot.plot({
   x: {grid: true},
   fy: {domain: d3.groupSort(olympians.filter((d) => d.weight), (g) => d3.median(g, (d) => d.weight), (d) => d.sport)},
   color: {scheme: "{{$dark ? "turbo" : "YlGnBu"}}"},
-  marks: [Plot.rect(olympians, Plot.binX({fill: "proportion-facet"}, {x: "weight", fy: "sport", inset: 0.5}))]
+  marks: [Plot.rect(olympians, Plot.binX({fill: "density"}, {x: "weight", fy: "sport", inset: 0.5}))]
 })
 ```
 :::
@@ -253,6 +253,7 @@ The following named reducers are supported:
 * *first* - the first value, in input order
 * *last* - the last value, in input order
 * *count* - the number of elements (frequency)
+* *density* – the count or sum normalized by series (*z*)
 * *distinct* - the number of distinct values
 * *sum* - the sum of values
 * *proportion* - the sum proportional to the overall total (weighted frequency)

docs/transforms/group.md

@@ -354,6 +354,7 @@ The following named reducers are supported:
 * *first* - the first value, in input order
 * *last* - the last value, in input order
 * *count* - the number of elements (frequency)
+the count or sum normalized by series (*z*)
 * *sum* - the sum of values
 * *proportion* - the sum proportional to the overall total (weighted frequency)
 * *proportion-facet* - the sum proportional to the facet total

docs/transforms/hexbin.md

@@ -183,6 +183,7 @@ The following named reducers are supported:
 * *first* - the first value, in input order
 * *last* - the last value, in input order
 * *count* - the number of elements (frequency)
+the count or sum normalized by series (*z*)
 * *distinct* - the number of distinct values
 * *sum* - the sum of values
 * *proportion* - the sum proportional to the overall total (weighted frequency)

src/reducer.d.ts

@@ -13,6 +13,7 @@ export type ReducerPercentile =
  * - *first* - the first value, in input order
  * - *last* - the last value, in input order
  * - *count* - the number of elements (frequency)
+ * - *density* – the count or sum normalized by series (*z*)
  * - *distinct* - the number of distinct values
  * - *sum* - the sum of values
  * - *proportion* - the sum proportional to the overall total (weighted frequency)
@@ -36,6 +37,7 @@ export type ReducerName =
   | "last"
   | "identity"
   | "count"
+  | "density"
   | "distinct"
   | "sum"
   | "proportion"

src/transforms/bin.js

@@ -179,6 +179,7 @@ function binn(
         if (sort) sort.scope("facet", facet);
         if (filter) filter.scope("facet", facet);
         for (const [f, I] of maybeGroup(facet, G)) {
+          for (const o of outputs) o.scope("group", I);
           for (const [k, g] of maybeGroup(I, K)) {
             for (const [b, extent] of bin(g)) {
               if (G) extent.z = f;

src/transforms/group.js

@@ -131,6 +131,7 @@ function groupn(
         if (sort) sort.scope("facet", facet);
         if (filter) filter.scope("facet", facet);
         for (const [f, I] of maybeGroup(facet, G)) {
+          for (const o of outputs) o.scope("group", I);
           for (const [y, gg] of maybeGroup(I, Y)) {
             for (const [x, g] of maybeGroup(gg, X)) {
               const extent = {data};
@@ -248,6 +249,8 @@ export function maybeReduce(reduce, value, fallback = invalidReduce) {
       return reduceIdentity;
     case "count":
       return reduceCount;
+    case "density":
+      return reduceDensity;
     case "distinct":
       return reduceDistinct;
     case "sum":
@@ -405,6 +408,18 @@ export const reduceCount = {
   }
 };
 
+export const reduceDensity = {
+  label: "Density",
+  scope: "group",
+  reduceIndex(I, V, context, extent) {
+    if (context === undefined) return I.length;
+    let proportion = I.length / context;
+    if ("y2" in extent && !("x2" in extent)) proportion /= extent.y2 - extent.y1;
+    else if ("x2" in extent && !("y2" in extent)) proportion /= extent.x2 - extent.x1;
+    return proportion;
+  }
+};
+
 const reduceDistinct = {
   label: "Distinct",
   reduceIndex(I, X) {

src/transforms/hexbin.js

@@ -64,6 +64,7 @@ export function hexbin(outputs = {fill: "count"}, {binWidth, ...options} = {}) {
       const binFacet = [];
       for (const o of outputs) o.scope("facet", facet);
       for (const [f, I] of maybeGroup(facet, G)) {
+        for (const o of outputs) o.scope("group", I);
         for (const {index: b, extent} of hbin(data, I, X, Y, binWidth)) {
           binFacet.push(++i);
           BX.push(extent.x);

test/plots/athletes-sport-weight.ts

@@ -8,7 +8,7 @@ export async function athletesSportWeight() {
     grid: true,
     color: {scheme: "YlGnBu", zero: true},
     marks: [
-      Plot.barX(athletes, Plot.binX({fill: "proportion-facet"}, {x: "weight", fy: "sport", thresholds: 60})),
+      Plot.barX(athletes, Plot.binX({fill: "density"}, {x: "weight", fy: "sport", thresholds: 60})),
       Plot.frame({anchor: "bottom", facetAnchor: "bottom"})
     ]
   });

test/plots/density-reducer.ts

@@ -0,0 +1,46 @@
+import * as Plot from "@observablehq/plot";
+import * as d3 from "d3";
+
+const pdf_normal = (x, mu = 0, sigma = 1) =>
+  Math.exp(-0.5 * Math.pow((x - mu) / sigma, 2)) / (sigma * Math.sqrt(2 * Math.PI));
+
+const densities = d3
+  .range(-6, 10, 0.1)
+  .map((x) => [0, 3].map((mu) => [1, 2].map((sigma) => ({x, mu, sigma, rho: pdf_normal(x, mu, sigma)}))))
+  .flat(3);
+
+const n_pts = 100000;
+
+const mus = Array.from({length: n_pts}, d3.randomBernoulli.source(d3.randomLcg(42))(0.2)).map((x) => 3 * x);
+const sigmas = Array.from({length: n_pts}, d3.randomBernoulli.source(d3.randomLcg(43))(0.3)).map((x) => 1 + x);
+const standardNormals = Array.from({length: n_pts}, d3.randomNormal.source(d3.randomLcg(44))(0, 1)).map(
+  (x, i) => x * sigmas[i] + mus[i]
+);
+
+const pts = standardNormals.map((value, i) => ({mu: mus[i], sigma: sigmas[i], value}));
+
+export async function densityReducer() {
+  return Plot.plot({
+    marks: [
+      Plot.areaY(
+        pts,
+        Plot.binX(
+          {y2: "density"},
+          {
+            x: "value",
+            fill: (x) => `μ = ${x.mu}`,
+            opacity: 0.5,
+            fy: (x) => `σ = ${x.sigma}`,
+            interval: 0.2,
+            curve: "step"
+          }
+        )
+      ),
+      Plot.line(densities, {x: "x", y: "rho", stroke: (x) => `μ = ${x.mu}`, fy: (x) => `σ = ${x.sigma}`}),
+      Plot.ruleY([0])
+    ],
+    fy: {label: null},
+    color: {legend: true, type: "categorical"},
+    grid: true
+  });
+}

test/plots/hexbin-r.ts

@@ -17,7 +17,7 @@ export async function hexbinR() {
     marks: [
       Plot.frame(),
       Plot.hexgrid(),
-      Plot.dot(penguins, Plot.hexbin({title: "count", r: "count", fill: "proportion-facet"}, xy))
+      Plot.dot(penguins, Plot.hexbin({title: "count", r: "count", fill: "density"}, xy))
     ]
   });
 }

test/plots/index.ts

@@ -65,6 +65,7 @@ export * from "./d3-survey-2015-comfort.js";
 export * from "./d3-survey-2015-why.js";
 export * from "./darker-dodge.js";
 export * from "./decathlon.js";
+export * from "./density-reducer.js";
 export * from "./diamonds-boxplot.js";
 export * from "./diamonds-carat-price-dots.js";
 export * from "./diamonds-carat-price.js";