Visualization, Prototypes, ML, Data wrangling and Art.
url = "https://vikusviewer.fh-potsdam.de/data/vangogh/data.csv"
data = d3.csv(url)
viewof table = Inputs.table(data)
data[0]
https://vikusviewer.fh-potsdam.de/data/vangogh/1024/${data[11].id}.jpg
dataParsed = data.map(d => {
const keywords = d.keywords.split(",")
const aspectRatio = (d._width / d._height).toFixed(1)
return { ...d, keywords, keywords, aspectRatio }
})
dataParsed.map( d => d.keywords.map(k => ({ keyword: k, ...d })))
dataParsed[0]
d3.group(data, d => d.year)
d3.group(data, d => d._material)
Plot.plot({
marks: [
Plot.barX(dataParsed, Plot.groupY({x: "count"}, {y: "year", sort: {y: "x", reverse: true, limit: 10}})),
Plot.ruleX([0])
]
})
Plot.plot({
marks: [
Plot.barX(dataParsed, Plot.groupY({x: "count"}, {y: "_material", sort: {y: "x", reverse: true, limit: 10}})),
Plot.ruleX([0])
]
})
Plot.plot({
marks: [
Plot.barX(dataParsed, Plot.groupY({x: "count"}, {y: "aspectRatio", sort: {y: "x", reverse: true, limit: 10}})),
Plot.ruleX([0])
]
})
Plot.plot({
marks: [
Plot.tickX(dataParsed, {x: "id", y: "year"})
]
})
Plot.plot({
marks: [
Plot.rectY(dataParsed, Plot.binX({y: "count"}, {x: "year"})),
Plot.ruleY([0])
]
})
Plot.plot({
height: 400,
marks: [
Plot.dot(data, Plot.dodgeY({x: "year"}))
]
})
Plot.plot({
marks: [
Plot.voronoi(dataParsed, {x: "year", y: "_material", fill: "year", stroke: "white"})
]
})
Plot.plot({
marginLeft: 200,
marks: [
Plot.density(data, { x: "year", y: "_material" })
]
})
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