viewof raw_cars = aq.from(cars).view(5)
viewof cars_xy = raw_cars.derive({
x: aq.escape(d => d[cars_x]),
y: aq.escape(d => d[cars_y])
}).view(2)
cars_kmeans = cars_xy.objects()
viewof cars_cluster_chart = {
kmeans(cars_kmeans, cars_clusters)
return vl.markPoint()
.data(cars_kmeans)
.encode(
vl.x().fieldQ("x").title(cars_x),
vl.y().fieldQ("y").title(cars_y),
vl.color().fieldN("cluster"),
vl.tooltip([cars_x, cars_y, 'Name', 'cluster'])
)
.render()
}
viewof iris_cluster_chart = {
kmeans(iris_kmeans, iris_clusters)
return vl.markPoint()
.data(iris_kmeans)
.encode(
vl.x().fieldQ("x").title(iris_x).scale({zero: false, nice: true}),
vl.y().fieldQ("y").title(iris_y).scale({zero: false, nice: true}),
vl.color().fieldN("cluster"),
vl.tooltip([iris_x, iris_y, 'species', 'cluster'])
)
.render()
}
import {kmeans} from "@spond/k-means-clustering-algorithm"
import { aq, op } from '@uwdata/arquero'
import { vl } from "@vega/vega-lite-api-v5"
import {Range, Select} from "@observablehq/inputs"
d3 = require("d3@7")
Purpose-built for displays of data
Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
