Published
Edited
Oct 22, 2021
4 stars
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texasCrisis = FileAttachment("texasCrisis2021.csv").csv({ typed: true })
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data = texasCrisis.map(d => {
return {
...d,
type: d.name.match(/Net generation from (.*) for/)[1]
}
})
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Inputs.table(data)
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// Type code here


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// set up our color scale to match EIA color schemes
color = ({
domain: ['solar', 'wind', 'coal', 'natural gas', 'nuclear', 'hydro', 'other'],
range: ["#FEC704","#5C9631", "#664F06","#BD7229","#A1333F","#0395D6","#ADADAD"]
})
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// Type your code here


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// type your code here


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import {Q, A, styles} with {renderSnippetOverride as renderSnippet} from "@observablehq/plot-exploration-penguins";
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function renderSnippetOverride(str) {
try {
// TODO: if you want to support more variables in the solutions you add them as a string and pass them in here
const func = new Function("data", "Plot", "d3", "width", "color", `return ${str}`)
return func(data, Plot, d3, width, color);
}
catch(e) {}
}
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styles
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Purpose-built for displays of data

Observable is your go-to platform for exploring data and creating expressive data visualizations. Use reactive JavaScript notebooks for prototyping and a collaborative canvas for visual data exploration and dashboard creation.
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