Building and Using Tools to See.
UVA Architecture + Data Science. IT Director. Instructor.
<svg width="500" height="200">
<g id="layer1"></g> <!-- one at a time -->
<g id="layer2" transform="translate(300,0)"></g> <!-- all together -->
</svg>
viewof track = Inputs.range([0, 4], {label: "Track", step: 1, value: 0})
data = FileAttachment("trackFeatures_sample.csv").csv()
fields = ["danceability","energy","speechiness","liveness","valence"]
onetrack = [data[track]] // just one record out of the set. "track" index chosen from range slider above
radarElem = d3.select(svgContainer).select("#layer1") // the svg element to put the radar in
radar = dvRadar(radarElem2,data,fields,200,options)
options = ({
maxValue: 1,
idField: "id",
transitionMS: 1000,
axisLabelShow: true
})
radarElem2 = d3.select(svgContainer).select("#layer2") // the svg element to put the radar in
radar2 = dvRadar(radarElem,onetrack,fields,200,options)
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.