Public
Edited
Jul 19, 2023
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// Access Adélie data here:
adelie = d3.csv(
"https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.219.5&entityid=002f3893385f710df69eeebe893144ff",
d3.autoType
)
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// Access chinstrap data here:
chinstrap = d3.csv(
"https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.221.8&entityid=fe853aa8f7a59aa84cdd3197619ef462",
d3.autoType
)
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// Make combined version, penguinsCombo, here
penguinsCombo = adelie.concat(gentoo, chinstrap)
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// Create the wrangled version of penguins here:
penguins = penguinsCombo.map((d) => ({
species: d.Species.split(" ")[0],
island: d.Island,
sex: d.Sex == null || d.Sex == "." ? null : d.Sex,
bill_length_mm: d["Culmen Length (mm)"],
bill_depth_mm: d["Culmen Depth (mm)"],
body_mass_g: d["Body Mass (g)"],
flipper_length_mm: d["Flipper Length (mm)"]
}))
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// penguins = penguinsKeyCopy
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import { chart } from "@d3/zoomable-sunburst"
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chart
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import {aq, op} from "@uwdata/arquero"
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// Convert your array of objects to an Arquero table here:
penguinsTable = aq.from(penguins)
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// Write Arquero code to perform the steps above here:
penguinsTable
.filter((d) => d.sex == "female")
.select("species", "bill_depth_mm", "bill_length_mm")
.derive({ bill_ratio: (d) => d.bill_length_mm / d.bill_depth_mm })
.groupby("species")
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penguins
X
bill_length_mm
Y
bill_depth_mm
Color
species
Size
Facet X
Facet Y
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.

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Plot.plot({
color: { legend: true },
marks: [
Plot.dot(penguins, {
x: "bill_length_mm",
y: "bill_depth_mm",
fill: "species",
tip: true,
opacity: 0.5,
r: "body_mass_g"
}),
Plot.frame()
],
color: { range: ["teal", "darkorange", "orchid"] },
r: { domain: d3.extent(penguins.map((d) => d.body_mass_g)), range: [1, 20] }
})
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import {PlotMatrix} with {data} from "@observablehq/autoplot-matrix"
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// Make a copy of penguins here, stored as data:
data = penguins
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// Make the pairplot with PlotMatrix here:
PlotMatrix(data)
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penguins
X
body_mass_g
Y
Color
species
Size
Facet X
Facet Y
species
Mark
Auto
Type Chart, then Shift-Enter. Ctrl-space for more options.

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Plot.plot({
color: { legend: true },
marks: [
Plot.frame({ strokeOpacity: 0.1 }),
Plot.rectY(
penguins,
Plot.binX(
{ y: "count" },
{ fy: "species", x: chooseVariable, fill: "species", tip: true }
)
),
Plot.ruleY([0])
]
})
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viewof chooseVariable = Inputs.radio(
["bill_depth_mm", "bill_length_mm", "flipper_length_mm", "body_mass_g"],
{ label: "Select variable:", value: "body_mass_g" }
)
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import {scale} from "@chrispahm/hierarchical-clustering"
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// Make a subset of penguins with complete cases (filter out values where bill length is null):

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// Create a scaled version of the values (non-numeric will be NaN, which is fine..):

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// Convert the array of objects to an array of arrays:

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import { penguinsArray } from "@observablehq/ds-workflows-in-js-session-2-key"
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penguinsArray
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// Use ml.js KMeans() method to perform k-means clustering for k centroids:
penguinsClusters = ML.KMeans(penguinsArray, 3)
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// Combine the cluster values for each element with the original female penguins data:

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ML = require("https://www.lactame.com/lib/ml/6.0.0/ml.min.js")
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import {penguinsKeyCopy} from "@observablehq/ds-workflows-in-js-session-2-key"
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noUse = FileAttachment("fiddlerCrabBodySize.csv") // Note: this is only added here so that the file is attached in the forked version
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