Regression Scatterplot Recipe

Use this pattern to layer grouped linear fits and mean-response confidence bands over a point chart.

Minimal flow

Runnable: install ggaction and provide the named data array as plain row objects.

import { chart } from "ggaction";

const program = chart()
  .createCanvas({
    width: 760,
    height: 480,
    margin: { top: 40, right: 190, bottom: 70, left: 80 }
  })
  .createData({ values: cars })
  .createPointMark()
  .encodeX({ field: "Displacement", scale: { nice: true, zero: false } })
  .encodeY({ field: "Acceleration", scale: { nice: true, zero: false } })
  .encodeColor({ field: "Origin", scale: { palette: "tableau10" } })
  .encodeSize({ field: "Acceleration" })
  .encodeShape({ field: "Origin" })
  .encodeOpacity({ value: 0.27 })
  .filterMarks({
    field: "Origin",
    op: "oneOf",
    values: ["Japan", "USA"]
  })
  .createRegression()
  .createGuides();

You must decide

• Point x and y fields
• Optional nominal color or shape grouping field
• Optional source filter

The library infers

• The current eligible point layer
• Quantitative x/y fields and their shared coordinate and scales
• One grouping field when color and shape agree
• Immutable OLS rows, one 95% mean-response confidence band, and one line per group
• Shared axes, grid, categorical legend, and quantitative size legend

Pass target, x, y, or groupBy only when inference is ambiguous.

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Regression scatterplot tutorial · Regression API · Appearance encodings