// Access gentoo data here:
gentoo = d3.csv(
"https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.220.7&entityid=e03b43c924f226486f2f0ab6709d2381",
d3.autoType
)
// Make combined version, penguinsCombo, here
penguinsCombo = adelie.concat(gentoo, chinstrap)
// 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.toLowerCase(),
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)"]
}))
// penguins = penguinsKeyCopy
import {aq, op} from "@uwdata/arquero"
// Convert your array of objects to an Arquero table here:
penguinsTable = aq.from(penguins)
// 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")
.rollup({mean_bill_ratio: d => op.mean(d.bill_ratio)})
.view()
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.
Plot.plot({
color: { legend: true },
marks: [
Plot.dot(penguins, {
x: "bill_length_mm",
y: "bill_depth_mm",
fill: "species",
tip: true,
r: "body_mass_g",
opacity: 0.5
}),
Plot.frame()
],
color: {range: ["teal", "darkorange", "orchid"]},
r: {domain: d3.extent(penguins.map(d => d.body_mass_g)), range: [1, 20]}
})
import {PlotMatrix} with {data} from "@observablehq/autoplot-matrix"
// Make a copy of penguins here, stored as data:
data = penguins
// Make the pairplot with PlotMatrix here:
PlotMatrix(data)
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.
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])
]
})
import {scale} from "@chrispahm/hierarchical-clustering"
// Make a subset of penguins with complete cases (filter out values where bill length is null):
// Create a scaled version of the values (non-numeric will be NaN, which is fine..):
// Convert the array of objects to an array of arrays:
import { penguinsArray } from "@observablehq/ds-workflows-in-js-session-2-key"
// Use ml.js KMeans() method to perform k-means clustering for k centroids:
penguinsClusters = ML.KMeans(penguinsArray, 3)
// Combine the cluster values for each element with the original female penguins data:
penguinsClusters.clusters
penguinsKmeans = penguins.filter((d) => d.bill_length_mm != null)
data1 = penguinsKmeans.map((d,i) => ({
...d,
clusterNo: penguinsClusters.clusters[i]
}))
data1
Type Table, then Shift-Enter. Ctrl-space for more options.
ML = require("https://www.lactame.com/lib/ml/6.0.0/ml.min.js")
import {penguinsKeyCopy} from "@observablehq/ds-workflows-in-js-session-2-key"
noUse = FileAttachment("fiddlerCrabBodySize.csv") // Note: this is only added here so that the file is attached in the forked version
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.
