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Public
Edited
Jan 20, 2023
4 forks
19 stars
6
Curated Datasets
Pizza ParadiseUSAFacts Higher Education DataStack Overflow Developer Survey DataMonthly Retail Trade Report DataCurated datasetsStreetEasy Real Estate DataFAA Laser Reports DataVisualization Publication Dataset
DarkCideS: Bats in Caves Data
The MoMA Collection DataU.S. Geographic DataPlot Test DataNOAA Hourly Weather DataEIA Electricity DataBandcamp Sales DataGoogle Merchandise Sales Data
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Templates
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db.describe()
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db
select * from species
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db
select * from distributions
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Plot.plot({
marks: [
Plot.barX(distributions, Plot.groupY({x: "count"}, {y: "Continent", sort: {y: "x", reverse: true, limit: 10}}))
],
marginLeft: 100,
grid: true
})
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db
select
round(Longitude, 1) as Long,
round(Latitude, 1) as Lat,
count(*)::INT as count
from distributions
group by Long,Lat
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map = Plot.plot({
projection: "equal-earth",
width: 975,
inset: 1,
insetBottom: 10,
length: { range: [0, 50] },
marks: [
Plot.geo(countries, { fill: "#ddd" }),
Plot.geo(countrymesh, { stroke: "#fff" }),
Plot.spike(bylocation, {
x: "Long",
y: "Lat",
filter: (d) => !(d.Long === 0 && d.Lat === 0), // null island
stroke: "red",
length: "count"
}),
Plot.sphere(),
legendSpike([25, 50, 100, 150], { stroke: "red" })
]
})
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db
select * from distributions
join species on distributions."Species.name" = species."Species.name"
where species.Urban = 1
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db
select
round(Longitude, 1) as Long,
round(Latitude, 1) as Lat,
count(*)::INT as count
from distributions
join species on distributions."Species.name" = species."Species.name"
where species.Urban = 1
group by Long,Lat
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Plot.plot({
projection: "equal-earth",
width: 975,
inset: 1,
length: { range: [0, 50] },
marks: [
Plot.geo(countries, { fill: "#ddd" }),
Plot.geo(countrymesh, { stroke: "#fff" }),
Plot.spike(urbanbylocation, {
x: "Long",
y: "Lat",
filter: d => !(d.Long===0 && d.Lat===0), // null island
stroke: "red",
length: "count",
}),
Plot.sphere(),
legendSpike([1, 2, 3, 4, 5], { stroke: "red" })
]
})
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db
select * from landscapes
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Plot.plot({
projection: "equal-earth",
inset: 1,
width,
insetBottom: 10,
r: {
range: [1, 30]
},
marks: [
Plot.geo(countries, { fill: "#ddd" }),
Plot.geo(countrymesh, { stroke: "#fff" }),
Plot.dot(landscapes, {
x: "Longitude",
y: "Latitude",
fill: "red",
fillOpacity: .25,
r: "Tree.dens",
stroke: '#fff',
strokeWidth: .1,
}),
Plot.sphere(),
legendBubble([5e4, 10e4, 25e4], { stroke: "red" })
]
})
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db
select * from parasites
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db = DuckDBClient.of({
species: FileAttachment("Dataset1@1.csv"),
distributions: FileAttachment("Dataset2@2.csv"),
landscapes: FileAttachment("Dataset3@1.csv"),
parasites: { file: FileAttachment("Dataset4@3.csv"), header: true },
})
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world = FileAttachment("countries-50m.json").json()
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countries = topojson.feature(world, world.objects.countries)
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countrymesh = topojson.mesh(world, world.objects.countries, (a, b) => a !== b)
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One platform to build and deploy the best data apps

Experiment and prototype by building visualizations in live JavaScript notebooks. Collaborate with your team and decide which concepts to build out.
Use Observable Framework to build data apps locally. Use data loaders to build in any language or library, including Python, SQL, and R.
Seamlessly deploy to Observable. Test before you ship, use automatic deploy-on-commit, and ensure your projects are always up-to-date.
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