Web Datavis researcher by day, Indie Game designer by night
Public
Edited
Oct 6, 2023
Plot.plot({
color: { legend: true },
x: { type: "log", nice: true },
y: { type: "log", nice: true },
aspectRatio: 2,
facetAnchor: null,
marks: [
Plot.frame(),
Plot.dot(jp_lca_dat, {
x: x_axis,
y: y_axis,
r: 2,
stroke: "Group",
strokeWidth: 1,
symbol: 'plus'
}),
Plot.density(jp_lca_dat, {
x: x_axis,
y: y_axis,
fy: 'Group',
bandwidth: 30,
thresholds: 10,
stroke: "Group",
strokeOpacity: 0.2,
fill: "Group",
fillOpacity: 0.15,
mixBlendMode: "multiply",
weight: 1
}),
Plot.axisX({
anchor: "bottom",
facetAnchor: null
})
]
})
// from the Clark_et_al_2022_PNAS_SM zip file
jp_lca_dat = (await FileAttachment("jp_lca_dat.csv").csv({typed: true})).map(d => ({...d, Weight: +(d.Weight.replace('%',''))}))
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.
