The end-to-end solution for building and hosting better data apps, dashboards, and reports.
myCategoricalColors = ["orange", "#56B4e9", "#009e73", "tomato", "steelblue", "#f0e442"] // Edit these values, or add more!
Plot.plot({
marks: [
Plot.dot(cars, {
x: "power (hp)",
y: "economy (mpg)",
fill: "cylinders",
r: 5
})
],
color: { range: myCategoricalColors } // Your custom color scheme here
})
myContinuousColors = ["orange", "#9700ff"] // Edit these colors!
Plot.plot({
marks: [
Plot.dot(cars, {
x: "power (hp)",
y: "economy (mpg)",
fill: "economy (mpg)",
r: 5
})
],
color: {
range: myContinuousColors, // Your custom color scheme here
interpolate: "hcl"
}
})
<style>
@import url('https://fonts.googleapis.com/css2?family=Lato&display=swap');
</style>
myGoogleFonts
Plot.plot({
style: {fontFamily: "Lato"}, // Update with your imported Google font to change all in chart
y: {
grid: true,
label: "↑ Frequency (%)",
transform: (d) => d * 100
},
marks: [
Plot.barY(alphabet, { x: "letter", y: "frequency" }),
Plot.text(alphabet, {
x: "letter",
y: "frequency",
text: (d) => (d.frequency * 100).toFixed(1),
dy: -5
}),
Plot.ruleY([0])
]
})
kalam = FileAttachment("Kalam-Regular.ttf")
<style>
@font-face {
font-family: Kalam;
src: url('${await kalam.url()}');
}
</style>
myDownloadedFonts
Plot.plot({
y: {
grid: true,
label: "↑ Frequency (%)",
transform: (d) => d * 100
},
marks: [
Plot.barY(alphabet, { x: "letter", y: "frequency" }),
Plot.text(alphabet, {
x: "letter",
y: "frequency",
text: (d) => (d.frequency * 100).toFixed(1),
dy: -5,
fontFamily: "Kalam",
fontSize: 14,
fill: "red",
dy: -10
}),
Plot.ruleY([0])
]
})
logo = FileAttachment("observable-logo.png").url()
Plot.plot({
marks: [
Plot.dot(cars, { x: "power (hp)", y: "economy (mpg)" }),
Plot.frame(),
Plot.image([logo], { src: [logo], frameAnchor: "top-right", dx: -80, width: 130 }) // Add, position and size your logo
],
})
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.