best_testfile_visualstudio.csv
Type Table, then Shift-Enter. Ctrl-space for more options.
myGraph = Plot.plot({
title: `Letters`,
height: chartHeight,
width: chartWidth,
marginBottom: 50,
marks: [
Plot.dot(chartData, Plot.dodgeY({
x: "Date",
title: "Letter_ID",
tip: true,
}))
],
x: {
tickRotate: -45,
grid: true,
}
})
chartData = {
var recipientFilter = data.filter( d => recipientName.includes(d.Recipient_Name))
var timeFilter = recipientFilter.filter(d => d.year >= timeInterval[0] && d.year <= timeInterval[1])
if (locationFilter != null)
return timeFilter.filter(d => d.Dispatch_Location == locationFilter )//then filter
else //if there is no categery filter
return timeFilter
}
recipients = _.uniq(data.map(d => d.Recipient_Name))
timeInterval[1]
data
Object.keys(data[0])
data.forEach(d => {
d.year = dateToYear(d.Date)
})
dispatchLocations = _.uniq(data.map(d => d.Dispatch_Location))
timeParser = d3.timeParse("%Y")
dateToYear(new Date)
dateToYear = d3.timeFormat("%Y")
earliest = d3.min(data.map(d => d.year))
latest = d3.max(data.map(d => d.year))
import {interval} from "@mootari/range-slider@1781"
import {jaune as yellow} from "@artistes-femmes-mac/charte-de-couleurs"
import {noir as black} from "@artistes-femmes-mac/charte-de-couleurs"
import {bordeau as burgundy} from "@artistes-femmes-mac/charte-de-couleurs"
import {gris as grey} from "@artistes-femmes-mac/charte-de-couleurs"
import {turquoise as blue} from "@maison-mona/preparation-reconciliation"
data
X
—
Y
territory
Color
gender
Size
—
Facet X
—
Facet Y
—
Mark
bar
Type Chart, then Shift-Enter. Ctrl-space for more options.
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.
