Independent Data Visualisation Designer
{
const rectWidth = 50
const svg = html`
<svg width=${rectWidth * barData.length} height=100 style='border: 1px dashed'>
<-- nothing but emptiness -->
</svg>
`
d3.select(svg).selectAll('rect')
.data(barData).enter().append('rect')
// calculate x-position based on its index
.attr('x', (d, i) => i * rectWidth)
// set height based on the bound datum
.attr('height', d => d)
// rest of attributes are constant values
.attr('width', rectWidth)
.attr('stroke-width', 3)
.attr('stroke', 'plum')
.attr('fill', 'pink')
return svg
}
movies
pathObj
colorObj
{
const svg = html`<svg width=${width} height=${svgHeight}></svg>`
// YOUR CODE HERE ✨
d3.select(svg)
.selectAll('path')
.data(movies)
.enter().append('path')
.attr('transform', (d,i) => 'translate(${calculateGridPos(i)})')
//.attr('transform', (d,i) => 'translate(50,50)')
.attr('d', d => pathObj[d.rated])
.attr('fill', d=> colorObj[d.genres[0]] || colorObj.Other)
.attr('stroke', d=> colorObj[d.genres[0]] || colorObj.Other)
.attr('fill-opacity', 0.1)
.attr('stroke-width', 4)
return scrollSVG(svg)
}
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.