Curious human. Interested in the mechanics of how things work.
urbanDomain = d3.extent(iataData.map(row => row['urban_sqkm']));
freeDomain = d3.extent(iataData.map(row => row['free_sqkm']));
changeColor(dotPlot, 12, 'black')
dotPlot = Plot.plot({
width: 270,
height: 900,
insetRight: 100,
labelOffset: 100,
// grid: true,
x: {
domain: freeDomain
},
// fx: {
// labelOffset: -200,
// },
marks: [
Plot.frame({stroke: "#d8d8d8", strokeWidth: 1}),
//Plot.dotX(cars, Plot.dodgeX({x: "urban_10", title: "name", fill: "sub_continent", fy: "sub_continent"})),
// Plot.rectY(iataData, Plot.binX({y: "count"}, {x: "free_sqkm", fill: "sub_continent", fy: "sub_continent", })),
// Plot.areaY(iataData, Plot.binX({y: "count"}, {x: "urban_10", fill: d => colorScale(d['sub_continent']), curve: 'basis', fy: "sub_continent", opacity: 0.4})),
// Plot.ruleY([0]),
// Plot.dot(iataData, Plot.dodgeY({x: jitter(iataData, seed), padding:0.1, fill: d => colorScale(d['sub_continent']), fy: 'sub_continent', anchor: 'middle', r:1})),
Plot.dot(iataData, Plot.dodgeY({x: "free_sqkm", padding:0.01, fill: d => colorScale(d['sub_continent']), fy: 'sub_continent', anchor: 'middle', r:0.7})),
],
})
import {jitter} from "@spren9er/raincloud-plots-with-observable-plot"
viewof seed = Inputs.range([1, 100], { step: 1, label: 'Raw Data Seed', value: 1 })
seedrandom = require('seedrandom')
// changeColor(barplot, 10, 'black')
thresholds=10
combined= Plot.plot({
// inset: 20,
width: width/4,
marks:[
Plot.frame({stroke: "#d8d8d8", strokeWidth: 1}),
// Plot.rectY(iataData, Plot.binX({y: "count"}, {x: "urban_sqkm", thresholds: thresholds,fill: "sub_continent", fy: 'sub_continent'})),
Plot.dot(data, Plot.dodgeY({x: "urban_sqkm", padding:0.4, fill: 'green', fy: 'sub_continent', anchor, r:1.5})),
// Plot.areaY(
// data,
// Plot.binX(
// {
// y: 'count'
// },
// {
// x: 'free_sqkm',
// fill: d => colorScale(d['sub_continent']),
// thresholds: thresholds,
// curve: 'basis',
// opacity: 0.4,
// fy: 'sub_continent',
// }
// )
// )
]
})
viewof anchor = Inputs.radio(["top", "middle", "bottom"], {label: "Anchor", value: "bottom"})
changeColor(combined, "black", 12)
function changeColor (chart, color, fontsize) {
d3
.select(chart)
.select("[aria-label=x-axis]")
.selectAll("text")
.each(function (d) {
// d is the tick's value (in this case, a number)
d3.select(this)
.attr("fill", color)
.attr("font-size", fontsize)
.attr("font-family", "IBM Plex Sans");
})
d3
.select(chart)
.select("[aria-label=y-axis]")
.selectAll("text")
.each(function (d) {
// d is the tick's value (in this case, a number)
d3.select(this)
.attr("fill", color)
.attr("font-size", fontsize)
.attr("font-family", "IBM Plex Sans");
})
d3
.select(chart)
.select("[aria-label=x-axis]")
.selectAll("line")
.each(function (d) {
// d is the tick's value (in this case, a number)
d3.select(this)
.attr("stroke", color)
})
d3
.select(chart)
.select("[aria-label=y-axis]")
.selectAll("line")
.each(function (d) {
// d is the tick's value (in this case, a number)
d3.select(this)
.attr("stroke", color)
})
d3.select(chart)
.selectAll("text")
.each(function(d) {
d3.select(this)
.attr("font-family", "IBM Plex Sans");
})
}
// Plot.plot({
// width:width/1.5,
// height: 1000,
// marks: [
// Plot.dot(data, Plot.dodgeY({x: "urban_10", fill: "sub_continent", fy: "sub_continent", anchor}))
// ]
// })
// Plot.plot({
// insetRight: 10,
// marginLeft: 100,
// height: 400,
// width,
// marks: [
// Plot.dot(
// data,
// Plot.dodgeX({
// y: "sub_continent",
// r: "urban_10",
// title: d => `${d.NAME}\n${(d.rMVOP / 1e3).toFixed(1)}B`,
// fill: "currentColor"
// })
// ),
// // Plot.text(
// // data,
// // Plot.dodgeX({
// // filter: (d) => d.rMVOP > 5e3,
// // y: "sub_continent",
// // r: "urban_10",
// // text: d => (d.rMVOP / 1e3).toFixed(),
// // fill: "white",
// // fontWeight: "bold"
// // })
// // )
// ]
// })
// Plot.plot({
// height: 180,
// marks: [
// Plot.dot(iataGroupedByRegions, Plot.dodgeY({x: "sub_continent",}))
// ]
// })
data =
{
let df = iataGroupedByRegions.filter(row => sub_continents.includes(row['sub_continent']))
// for (let i in df){
// df[i]['combined_10'] = iataGroupedByRegions[i]['empty_10'] + iataGroupedByRegions[i]['arid_10'];
// df[i]['combined_sqkm'] = iataGroupedByRegions[i]['combined_10'] * 13 / 1e6;
// df[i]['urban_sqkm'] = iataGroupedByRegions[i]['urban_10'] * 13 / 1e6;
// }
return df;
}
range = ["#ff595e", "#ff924c", "#ffca3a", "#8ac926", "#1982c4", "#6a4c93",
"#E04E53", "#DD7275", "#FA8D91", "#FFA6A8",
"#FF924C", "#E0884F", "#FBA771", "#FFCBA9",
"#73AA1C", "#70A51E" ,"#9FC95C" ,"#B9E375", "#B9E375",
"#FFE9AF", "#FFCA3A", "#1982C4",
"#9D88BA",
"#BCE784", "#5DD39E", "#348AA7", "#513B56",
]
domain = ["EU", "AS", "SA", "AF", "NA", "OC",
"Central and East Europe", "North Europe", "South Europe", "West Europe",
"East Asia", "Southeast Asia", "South Asia", "West Asia",
"Central Africa", "East Africa", "North Africa", "South Africa", "West Africa",
"South America", 'Central America', "North America",
"Australia and New Zealand",
"Trains", "Electric aircraft", "Hydrogen aircraft", "Petroleum", ]
new Set(iataData.map(row => row['sub_continent']));
colorScale = d3.scaleOrdinal().domain(domain).range(range)
iataData = FileAttachment("iata-sqkm@3.csv").csv({typed: true})
import {sub_continents} from "4b75e48934b7ce6a"
import {iataGroupedByRegions} from "b7b073d13cec22e7"
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