Public
Edited
Jul 18, 2023
// Access gentoo data here:
gentoo = d3.csv("https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.220.7&entityid=e03b43c924f226486f2f0ab6709d2381", d3.autoType)
// Access Adélie data here:
adelie = d3.csv("https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.219.5&entityid=002f3893385f710df69eeebe893144ff", d3.autoType)
// Access chinstrap data here:
chinstrap = d3.csv("https://portal.edirepository.org/nis/dataviewer?packageid=knb-lter-pal.221.8&entityid=fe853aa8f7a59aa84cdd3197619ef462", 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({
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)
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:
// Use ml.js KMeans() method to perform k-means clustering for k centroids:
// Combine the cluster values for each element with the original female penguins data:
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.
