Data Visualization Developer at The Associated Press
Public
Edited
Jan 9, 2024
1 star
function percentiles(numbers) {
const sorted = d3.sort(numbers);
return d3.range(0, 101).map((q) => d3.quantileSorted(sorted, q / 100));
}
function averageQuantiles(fields) {
households.sort((a,b) => a.TOTEXP - b.TOTEXP)
const thresholds = [.1, .2, .3, .4, .5, .6, .7, .8, .9, .99]
const indeces = [0, ...thresholds.map(x => d3.quantileIndex(households.map(d => d.TOTEXP), x))]
const results = []
for (let i = 1; i < indeces.length; i++) {
const slice = households.slice(indeces[i - 1], indeces[i])
const data = {key: thresholds[i - 1], values: []}
let sums = {}
fields.forEach(field => {
data.values.push({ category: field, emissions: d3.mean(slice, d => d[field]) })
})
results.push(data)
}
return results
}
averageQuantiles(Object.keys(households[0]).slice(4, 17))
budgetHierarchy = {
const inputData = averageQuantiles(Object.keys(households[0]).slice(4, 17))
const root = {key: 'root', values: inputData }
return d3.hierarchy(root, d => d.values)
.sum(d => d.emissions)
}
cumulative = {
const cumulative = [...quantiles]
cumulative.forEach((d,i) => {
d.cumCO2 = d3.sum(quantiles.slice(0, i), d => d.CO2)
})
return cumulative
}
lorenzData = {
const total = d3.max(cumulative, d => d.cumCO2) / .94
return [...cumulative.map(d => ({quantile: d.quantile, cumCO2: d["cumCO2"], cumPct: d['cumCO2'] / total })), {quantile: 1, cumCO2: total, cumPct: 1}]
}
CO2_categories_bottom99.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
d3 = require("d3@7", "d3-weighted-voronoi", "d3-voronoi-map", "d3-voronoi-treemap", 'seedrandom@2.4.3/seedrandom.min.js')
import {swatches} from '@d3/color-legend'
households.map(d => d.Total_CO2).sort((a,b) => a - b)
allHouseholds = [...households.map(d => d.Total_CO2).sort((a,b) => a - b), ...d3.range(128).map(d => d + 955) ]
{
const q = .01
households.sort((a,b) => a['Total_CO2'] - b['Total_CO2'])
const thresholds = d3.range(q, 1 + q, q).map(d => +(d).toFixed(2))
const indeces = [0, ...thresholds.map(x => d3.quantileIndex(allHouseholds, x))].slice(0, 100)
const results = []
for (let i = 1; i < indeces.length; i++) {
const slice = households.slice(indeces[i - 1], indeces[i])
const avgCO2 = d3.mean(slice, d => d.Total_CO2)
const avgExp = d3.mean(slice, d => d.TOTEXP)
const avgIncome = d3.mean(slice, d => d.FINCBTAX_M)
const goodsValues = {}
for (let key of goodsMultipliers.keys()) {
goodsValues[key] = +(d3.mean(slice, d => d[key]))?.toFixed(3)
}
const {Water, ...restProps} = goodsValues
results.push({quantile: +(i * q).toFixed(2), avgCO2: +(avgCO2).toFixed(3), avgIncome: +(avgIncome).toFixed(3), avgExp: +(avgExp).toFixed(3), ...restProps})
}
return [...results, top1]
}
getPercentile(74000 * .8623, households.map(d => d.FINCBTAX_M))
function getPercentile(val, data) {
const allData = [...data, ...d3.range(128).map((d) => d + 955)]
let count = 0
allData.forEach((v) => {
if (v < val) {
count++
} else if (v == val) {
count += 0.5
}
})
return (100 * count) / data.length
}
households.length / 100
d3.range(127)
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