Public
Edited
Jul 30, 2023
maples
Type Table, then Shift-Enter. Ctrl-space for more options.
maples2003 = maples
.map((d) => ({
year: d.Year,
watershed: d.Watershed,
stemLength: d.StemLength,
leafMass: d.LeafDryMass,
stemMass: d.StemDryMass,
leafArea: d.CorrectedLeafArea
}))
.filter((d) => d.year == 2003)
maples2003
X
leafArea
Y
—
Color
watershed
Size
—
Facet X
—
Facet Y
watershed
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.
Plot.plot({
marks: [
Plot.dot(maples2003, {
x: "stemLength",
y: "stemMass",
fill: "watershed",
tip: true
}),
Plot.linearRegressionY(maples2003, {
x: "stemLength",
y: "stemMass",
stroke: "watershed"
})
]
})
import { PlotMatrix } with { maples2003 as data } from "@observablehq/autoplot-matrix"
PlotMatrix(maples2003)
ML = require("https://www.lactame.com/lib/ml/6.0.0/ml.min.js")
import { scale, asMatrix } from "@chrispahm/hierarchical-clustering"
import { viewof scores } with {
maples2003Scaled as food_scaled,
maples2003 as food
} from "@chrispahm/principal-component-analysis"
import { viewof loadings } with { maples2003Scaled as food_scaled } from "@chrispahm/principal-component-analysis"
maples2003Scaled = scale(maples2003.map(({ year, watershed, ...rest }) => rest))
maples20003PCA = new ML.PCA(maples2003Scaled.map(Object.values))
cumulativeVarience = maples20003PCA.getCumulativeVariance()
viewof loadings
viewof scores
scoresCombined = scores.map((d, i) => ({
...d,
year: maples2003[i].year,
watershed: maples2003[i].watershed
}))
scalingFactor = 6
Plot.plot({
marks: [
Plot.dot(scoresCombined, {
x: "PC1",
y: "PC2",
fill: "watershed",
r: 4,
opacity: 0.85,
stroke: "black",
strokeOpacity: 0.8,
tip: true
}),
Plot.arrow(loadings, {
x1: 0,
x2: (d) => d.PC1 * scalingFactor,
y1: 0,
y2: (d) => d.PC2 * scalingFactor
}),
Plot.text(loadings, {
x: (d) => d.PC1 * scalingFactor,
y: (d) => d.PC2 * scalingFactor,
text: "Variable",
dy: -5,
dx: 30,
fill: "black",
stroke: "white"
})
],
color: { legend: true }
})
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.
