Published
Edited
May 8, 2021
1 fork
md`# 2020-2019 Yield`
chart = {
const div = d3.create('div')
const legendContainer = div.append('div')
.attr('id', 'legend')
.style('margin-left', `${ margin.left + 10 }px`)
const svg = div.append('svg')
.attr('viewBox', [ 0, 0, width, height ])
svg.append("g")
.selectAll("g")
.data(series)
.join("g")
.attr("fill", d => color(d.key))
.selectAll("rect")
.data(d => d)
.join("rect")
.attr("x", (d, i) => x(d.data.name))
.attr("y", d => y(d[1]))
.attr("height", d => y(d[0]) - y(d[1]))
.attr("width", x.bandwidth())
svg.append('g')
.call(xAxis)
svg.append('g')
.call(yAxis)
legendContainer
.call(legend)
return div.node()
}
function legend(div) {
const elements = div.selectAll('div.element')
.data(series)
.join('div.element')
.style('float', 'left')
.style('position', 'relative')
.style('font-family', 'sans-serif')
.style('margin-right', '20px')
elements.append('span.color')
.style('position', 'absolute')
.style('left', 0)
.style('top', 0)
.style('background', d => color(d.key))
.style('width', '10px')
.style('height', '80%')
elements.append('span.legend-key')
.style('margin-left', '12px')
.text(d => d.key)
}
yAxis = g => g
.attr('transform', `translate(${ margin.left }, 0)`)
.call(d3.axisLeft(y).ticks(null, 's'))
.call(g => g.selectAll('.domain').remove())
xAxis = g => g
.attr('transform', `translate(0, ${ height - margin.bottom })`)
.call(d3.axisBottom(x).tickSizeOuter(0))
.call(g => g.selectAll('.domain').remove())
y = d3.scaleLinear()
.domain([ 0, d3.max(series, d => d3.max(d, d => d[1]))])
.rangeRound([ height - margin.bottom, margin.top ])
x = d3.scaleBand()
.domain(data.map(d => d.name))
.range([margin.left, width - margin.right])
.padding(0.1)
color = d3.scaleOrdinal()
.domain(series.map(d => d.key))
// TODO: annotate what is happening here
.range(d3.quantize(t => d3.interpolateSpectral(t * 0.8 + 0.1), series.length).reverse())
.unknown('brown')
height = 400
margin = ({ top: 50, right: 10, bottom: 20, left: 40 })
series = d3.stack().keys(data.columns.slice(1))(data) // TODO: annotate what is actually happening here
data = Object.assign(_data, { columns: _.keys(_data[0])})
/***
* As if we had pivoted on gender, asked for experience overall, and are charting the counts of each
* gender that responded with each possible score
*/
_data = [
{ name: 'Clinton', "Year 2020": 179,'Year 2019': 17, },
{ name: 'Benton', "Year 2020": 146, 'Year 2019': 52, },
{ name: 'Boone', "Year 2020": 162,'Year 2019': 33, },
{ name: 'Cedar', "Year 2020": 154,'Year 2019': 40, },
{ name: 'Polk', "Year 2020": 151, 'Year 2019': 46 },
{ name: 'Poweshiek', "Year 2020": 139,'Year 2019': 61, },
{ name: 'Scott', "Year 2020": 182, 'Year 2019': 7, },
{ name: 'Story', "Year 2020": 149, 'Year 2019': 40, },
{ name: 'Tama', "Year 2020": 134,'Year 2019': 71, },
{ name: 'Dallas', "Year 2020": 141,'Year 2019': 54, },
{ name: 'Guthrie', "Year 2020": 152, 'Year 2019': 45, },
{ name: 'Hamiliton', "Year 2020": 167,'Year 2019': 31 },
{ name: 'Hardin', "Year 2020": 157, 'Year 2019': 43, },
{ name: 'Johnson', "Year 2020": 162, 'Year 2019': 19, },
{ name: 'Jones', "Year 2020": 173,'Year 2019': 30, },
{ name: 'Linn', "Year 2020": 166,'Year 2019': 58, },
]
d3 = require('d3@5')
_ = require('lodash')
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.