Vega-Lite API v5Vega-Lite API v4Vega-Lite with Inline CSV DataVega-Lite Cross-Filter ExampleVega-Lite Annotated Time SeriesVega-Lite Composing SelectionsPSR B1919+21 with Vega-LiteVega-Lite London Tube LinesVega-Lite Cartographic ProjectionsVega-Lite Airport ConnectionsVega-Lite Pyramid PieVega-Lite Wind VectorsVega-Lite Population ChangeVega-Lite with Streaming Data UpdatesVega-Lite Parallel CoordinatesVega-Lite Interactive Index ChartVega-Lite Adjacency MatrixVega-Lite Donut Chart with LabelsVega-Lite Spike MapVega-Lite Scatterplot Pan & ZoomVega-Lite Repeat LayerVega-Lite LAB Color SpaceVega-Lite Input BindingVega-Lite Population PyramidsVega-Lite Isotype dot plotVega-Lite Distribution PlotsVega Lite API - Timeline Focus + ContextVega-Lite Percent Of Total
Vega-Lite API Interactive Splom
viewof chart = {
const brush = vl
.selectInterval("brush")
.resolve("union")
.on("[mousedown[event.shiftKey], window:mouseup] > window:mousemove!")
.translate(
"[mousedown[event.shiftKey], window:mouseup] > window:mousemove!"
)
.zoom("wheel![event.shiftKey]");
const grid = vl
.selectInterval("grid")
.resolve("global")
.translate(
"[mousedown[!event.shiftKey], window:mouseup] > window:mousemove!"
)
.zoom("wheel![!event.shiftKey]")
.bind("scales");
return vl
.markPoint({tooltip: true})
.select(brush, grid)
.encode(
vl.x().fieldQ(vl.repeat("column")),
vl.y().fieldQ(vl.repeat("row")),
vl.color().if(brush, vl.fieldN("Origin")).value("#ddd")
)
.width((width / (attribs.length + 1)) * 0.9)
.height((width / (attribs.length + 1)) * 0.9)
.repeat({
row: attribs,
column: attribs.reverse()
})
.data(cars)
.render();
}
attribs = ["Horsepower", "Acceleration", "Miles_per_Gallon"]
viewof chart2 = {
const brush = vl.selectInterval("brush").resolve("union");
return vl
.markPoint({tooltip: true})
.select(brush)
.encode(
vl.x().fieldQ(vl.repeat("column")),
vl.y().fieldQ(vl.repeat("row")),
vl.color().if(brush, vl.fieldN("Origin")).value("#ddd")
)
.width((width / (attribs.length + 1)) * 0.9)
.height((width / (attribs.length + 1)) * 0.9)
.repeat({
row: attribs,
column: attribs.reverse()
})
.data(cars)
.render();
}
vegaEmbed({
$schema: "https://vega.github.io/schema/vega-lite/v5.json",
repeat: {
row: ["Horsepower", "Acceleration", "Miles_per_Gallon"],
column: ["Miles_per_Gallon", "Acceleration", "Horsepower"]
},
spec: {
data: { values: cars },
mark: "point",
params: [
{
name: "brush",
select: {
type: "interval",
resolve: "union",
on: "[mousedown[event.shiftKey], window:mouseup] > window:mousemove!",
translate:
"[mousedown[event.shiftKey], window:mouseup] > window:mousemove!",
zoom: "wheel![event.shiftKey]"
}
},
{
name: "grid",
select: {
type: "interval",
resolve: "global",
translate:
"[mousedown[!event.shiftKey], window:mouseup] > window:mousemove!",
zoom: "wheel![!event.shiftKey]"
},
bind: "scales"
}
],
encoding: {
x: { field: { repeat: "column" }, type: "quantitative" },
y: {
field: { repeat: "row" },
type: "quantitative",
axis: { minExtent: 30 }
},
color: {
condition: {
param: "brush",
field: "Origin",
type: "nominal"
},
value: "grey"
}
}
}
})
import { vl } from "@vega/vega-lite-api-v5"
vegaEmbed = require("vega-embed@6")
cars = (await require("vega-datasets"))["cars.json"]()
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