Building and Using Tools to See.
UVA Architecture + Data Science. IT Director. Instructor.
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Apr 17, 2024
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Powerplants
data = FileAttachment("powerplants_allUS_2010.csv").csv()
data
Type Table, then Shift-Enter. Ctrl-space for more options.
mile = "5280" // a variable as a string, similar to how a csv file brings in data.
Number(mile) // change to a number
+mile // change to a number
mile2 = "5,280" // another string, this time with a comma in it.
mile2.replace(/,/g,'') // finds all (the g) instances of the text , between the //, and replaces it with nothing ''
toNum = (item) => {if (typeof(item)=="string") {return Number(item.replace(/,/g,''))} else {return item}}
toNum(mile2)
data.forEach(d => {
d["Plant annual net generation (MWh)"] = toNum(d["Plant annual net generation (MWh)"]);
d["Plant annual CO2 equivalent emissions (tons)"] = toNum(d["Plant annual CO2 equivalent emissions (tons)"]);
})
data[1]
data[1]["Plant annual net generation (MWh)"]
data[1]["Plant annual CO2 equivalent emissions (tons)"]
data.forEach(d => {
d["Plant CO2 factor"] = toNum(d["Plant annual CO2 emissions (tons)"]) * 1;
d["Plant CH4 factor"] = toNum(d["Plant annual CH4 emissions (lbs)"]) * 2000 * 25;
})
data[1]["Plant annual CO2 emissions (tons)"]
data[1]["Plant annual CH4 emissions (lbs)"]
data[1]["Plant CO2 factor"]
data[1]["Plant CH4 factor"]
data.length
data.filter(d => d["Plant state abbreviation"] == "VA")
data.filter(d => d["Plant annual net generation (MWh)"] > 300000)
data.filter(d => d["Plant primary fuel generation category"] == "GAS")
data.filter(d => d["Plant primary fuel generation category"] == "GAS" && d["Plant annual net generation (MWh)"] > 300000)
filteredData = data.filter(d => d["Plant primary fuel generation category"] == "GAS" && d["Plant annual net generation (MWh)"] > 300000)
filteredData
Type Table, then Shift-Enter. Ctrl-space for more options.
minNetGen = d3.min(data, d => d["Plant annual net generation (MWh)"])
maxNetGen = d3.max(data, d => d["Plant annual net generation (MWh)"])
extentsNetGen = d3.extent(data, d => d["Plant annual net generation (MWh)"]) // both min and max
sumNetGen = d3.sum(data, d => d["Plant annual net generation (MWh)"]) // in true Tableau fashion!
meanNetGen = d3.mean(data, d => d["Plant annual net generation (MWh)"])
medianNetGen = d3.median(data, d => d["Plant annual net generation (MWh)"])
plantsByState = d3.group(data, d => d["Plant state abbreviation"])
viewof plantsByStateTable = Inputs.table(plantsByState)
netGenByState = d3.rollup(data, v => d3.sum(v, d => d["Plant annual net generation (MWh)"]), d => d["Plant state abbreviation"]);
netGenByState.get("VA")
netGenByStateAndFuel = d3.rollup(data, v => d3.sum(v, d => d["Plant annual net generation (MWh)"]), d => d["Plant state abbreviation"], d => d["Plant primary fuel generation category"]);
netGenByStateAndFuel.get("VA")
netGenByStateAndFuel.get("VA").get("GAS")
stateSums = d3.rollup(data,
v => Object.fromEntries(["Plant annual net generation (MWh)","Plant annual CO2 equivalent emissions (tons)"].map(col => [col, d3.sum(v, d => toNum(d[col]))])),
d => d["Plant state abbreviation"])
stateAvgLongs = d3.rollup(data, v => d3.mean(v, d => d["Plant longitude"]), d => d["Plant state abbreviation"]);
stateAvgLongs.get("VA")
states = [...new Set(plantsByState.keys())]
//sortedData = d3.sort(data, d => d["Plant annual net generation (MWh)"])
//sortedData = d3.sort(data,(a,b) => d3.ascending(a["Plant annual net generation (MWh)"],b["Plant annual net generation (MWh)"]))
viewof sortedtable = Inputs.table(sortedData)
data.slice(0,1000)
data.slice(1000,5000)
shuffledData = d3.shuffle(data)
viewof shuffledtable = Inputs.table(shuffledData)
sample = d3.shuffle(data).slice(0,1000)
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.
