Former physicist turned programmer with research experience building complex computational models for physical systems.
csubs = (await FileAttachment("csub-thefts-by-state-and-loss-type.csv").csv()).map(d=>({
...d,
period:d3.utcParse('%Y')(d.period),
count:+d.count
}))
csubs
Type Table, then Shift-Enter. Ctrl-space for more options.
count_by_state = d3.rollup(csubs, v => d3.sum(v, d => d.count), d => d.state)
count_by_state.get('FL')
Plot.plot({
color:{legend:true},
marks:[
Plot.lineY(csubs, Plot.groupX({y: "sum"}, {x: "period", y: "count", stroke:'loss_type'})
)
]
})
transit = csubs.filter(d=>d.loss_type=='LOSS IN TRANSIT')
transit.map(d=>({
...d,
pop: state_pop.filter(v=> v.STATE == +statetofips[d.state])[0]
}))
viewof overlap = Inputs.range([0, 9], {step: 0.1, label: "Overlap"})
Plot.plot({
// height: 700,
// width,
x: {axis: "top"},
y: {domain: [0, d3.max(transit, d => d.count) / (overlap + 1)]},
axis:null,
marks:[
Plot.areaY(transit, {x:'period', y:'count', fy:'state', curve: "basis", fill: "#ddd"}),
Plot.lineY(transit, {x:'period', y:'count', fy:'state', curve: "basis", stroke: "#000"}),
Plot.text(transit, Plot.selectFirst({x: d3.min(transit, d => d.period), fy: "state", text: "state", dx: -3, frameAnchor: "right"}))
]
})
NST-EST2022-ALLDATA.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
state_pop = nstEst2022Alldata.map(d=>({
STATE:d.STATE,
POP:d.POPESTIMATE2022
})).filter(d=>d.STATE!=0)
csubs.state.get('FL')
Plot.plot({
projection: "albers-usa",
width,
color:{legend:true},
marks: [
Plot.geo(nation),
Plot.geo(states, { strokeOpacity: 0.25 , fill: d=>count_by_state.get(fipstostate[d.id])}),
]
})
fipstostate[10]
+statetofips['AL']
statetofips = FileAttachment("stateCodeToFips@1.json").json()
fipstostate = Object.fromEntries(
Object.entries(
(await FileAttachment("stateCodeToFips@1.json").json())
).map(a => a.reverse())
)
states
fipstostate1 = FileAttachment("fipsToState.json").json()
import {us} from "@observablehq/us-geographic-data"
nation = topojson.feature(us, us.objects.nation)
counties = topojson.feature(us, us.objects.counties)
states = topojson.feature(us, us.objects.states)
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