Public
Edited
Jul 21, 2023
// 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 || 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",
r: "body_mass_g",
opacity: 0.5,
tip: true
}),
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.
viewof chooseVariable = Inputs.radio(["bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g"], {label: "Select variable", value: "body_mass_g"})
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):
penguinsComplete = penguins.filter(d => d.bill_length_mm != null)
// Create a scaled version of the values (non-numeric will be NaN, which is fine..):
penguinsScale = scale(penguinsComplete)
// Convert the array of objects to an array of arrays:
penguinsArray = penguinsScale.map(
(d) => [d.bill_length_mm, d.bill_depth_mm, d.body_mass_g, d.flipper_length_mm]
)
// Use ml.js KMeans() method to perform k-means clustering for k centroids:
penguinsCluster = ML.KMeans(penguinsArray, 3)
// Combine the cluster values for each element with the original female penguins data:
penguinsKmeans = penguinsComplete.map((d, i) => ({...d, clusterNo: penguinsCluster.clusters[i]}))
myChart = Plot.plot({ // Remember to name if you want to use embeds!
marks: [
Plot.text(penguinsKmeans, {
x: "body_mass_g",
y: "flipper_length_mm",
text: "clusterNo",
fontSize: "15px",
fontWeight: 500,
fill: "species",
tip: true
})
],
color: { legend: true }
})
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.
