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Oct 28, 2022
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Examples for Data Analysts
Discovering Date PatternsCorrelation over timeTargets vs. ActualsUser Retention with SegmentsIntroduction to dataSpreadsheet-based data collaboration gets supercharged with ObservableSummary TableConnecting to databasesObservable for Excel Users: IntroductionData Loading and Cleanup
Adding Columns to Data
Analyzing DataModeling in ObservablePlot for Matplotlib Users: OverviewUpset Plots with Observable PlotDistributions and summary statistics - a collection of Plot examplesSankey With Animated GradientsHacking Power BI Charts with PlotNF Difference ChartGreenhouse gas emission projectionsConcentration values vs. TimePlot: regression
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Observable for Excel Users
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import {fixedStates} from "@observablehq/excel-data-loading"
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laserStrikes = addColumn(fixedStates, 'year', row => row['Incident Date'].getFullYear())
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d3.groups(laserStrikes, d => d.year)
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strikesByYear = d3.groups(laserStrikes, d => d.year)
.map(d => {
return {
year: d[0],
count: d[1].length
}
})
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viewof table = Inputs.table(strikesByYear, {sort: "year"})
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Plot.plot({
marks: [
Plot.barY(strikesByYear, {x: "year", y: "count", fill: "steelblue"}),
Plot.ruleY([0])
]
})
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laserStrikesCleaned = laserStrikes.filter(d => d.year > 2016)
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Plot.plot({
marks: [
Plot.barY(laserStrikesCleaned, Plot.groupX({y: "count"}, {x: "year", fill: "steelblue"})),
Plot.ruleY([0])
]
})
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function addColumn(tableData, newColumnName, newColumnFunction) {
return tableData.map(row => {
let returnObject = row;
returnObject[newColumnName] = newColumnFunction(row)
return returnObject
})
}
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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.
Learn more