Public
Edited
Jul 28, 2023
maples = d3.csv("https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-hbr.157.3&entityid=5c415a399b00430d35d13c31312f1e53", d3.autoType)
maples
Type Table, then Shift-Enter. Ctrl-space for more options.
maples2003 = maples
.filter(d => d.Year == 2003)
.map( d=> ({
year: d.Year,
watershed: d.Watershed,
stemLength: d.StemLength,
leafMass: d.LeafDryMass,
stemMass: d.StemDryMass,
leafArea: d.CorrectedLeafArea
}))
maples2003
X
leafArea
Y
—
Color
—
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"}),
Plot.linearRegressionY(maples2003, {x: "stemLength", y: "stemMass", stroke: "watershed"}),
Plot.frame()
]
})
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")
reference = maples2003.filter(d => d.watershed == 'Reference')
referenceLM = new ML.SimpleLinearRegression(
reference.map(d => d.stemLength),
reference.map(d => d.stemMass)
)
W1 = maples2003.filter(d => d.watershed == 'W1')
W1LM = new ML.SimpleLinearRegression(
W1.map(d => d.stemLength),
W1.map(d => d.stemMass)
)
import {scale, asMatrix} from "@chrispahm/hierarchical-clustering"
maplesScaled = scale(maples2003.map(({ year, watershed, ...rest}) => rest))
maplesArray = maplesScaled.map(Object.values)
maplesClusters = ML.KMeans(maplesArray, 2)
maplesKmeans = maples2003.map((d,i) => ({...d, clusterNo: maplesClusters.clusters[i]}))
clusterchart = Plot.plot({ // Remember to name if you want to use embeds!
marks: [
Plot.text(maplesKmeans, {
x: "stemLength",
y: "stemMass",
text: "clusterNo",
fontSize: "15px",
fontWeight: 500,
fill: "watershed",
tip: true
})
],
color: { legend: true }
})
maplesPCA = new ML.PCA(maplesArray)
maplesPCA.getExplainedVariance()
maplesPCA.getCumulativeVariance()
import { viewof loadings } with { maplesScaled as food_scaled } from "@chrispahm/principal-component-analysis"
viewof loadings
import { viewof scores } with { maplesArray as food_scaled, maples2003 as food} from "@chrispahm/principal-component-analysis"
viewof scores
// Do some wrangling to get the month and season alongside scores:
scoresCombined = scores.map((d, i) => ({
...d,
year: maples2003[i].year,
watershed: maples2003[i].watershed
}))
scalingFactor = 4
pcachart = Plot.plot({
marks: [
Plot.dot(scoresCombined, { x: "PC1", y: "PC2", fill: "watershed", r: 5 }),
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 }
})
Calcium addition appears to increase the overall dimensions and mass of stems and leaves in the seedlings in the treated watershed compared to untreated watershed.
Purpose-built for displays of data
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