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Eric Field
Building and Using Tools to See. UVA Architecture + Data Science. IT Director. Instructor.
Public
Edited
Apr 17, 2024
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2 stars
2
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data = FileAttachment("powerplants_allUS_2010.csv").csv()
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data
Type Table, then Shift-Enter. Ctrl-space for more options.

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mile = "5280" // a variable as a string, similar to how a csv file brings in data.
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Number(mile) // change to a number
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+mile // change to a number
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mile2 = "5,280" // another string, this time with a comma in it.
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mile2.replace(/,/g,'') // finds all (the g) instances of the text , between the //, and replaces it with nothing ''
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toNum = (item) => {if (typeof(item)=="string") {return Number(item.replace(/,/g,''))} else {return item}}
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toNum(mile2)
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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)"]);
})
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data[1]
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data[1]["Plant annual net generation (MWh)"]
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data[1]["Plant annual CO2 equivalent emissions (tons)"]
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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;
})
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data[1]["Plant annual CO2 emissions (tons)"]
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data[1]["Plant annual CH4 emissions (lbs)"]
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data[1]["Plant CO2 factor"]
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data[1]["Plant CH4 factor"]
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data.length
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data.filter(d => d["Plant state abbreviation"] == "VA")
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data.filter(d => d["Plant annual net generation (MWh)"] > 300000)
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data.filter(d => d["Plant primary fuel generation category"] == "GAS")
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data.filter(d => d["Plant primary fuel generation category"] == "GAS" && d["Plant annual net generation (MWh)"] > 300000)
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filteredData = data.filter(d => d["Plant primary fuel generation category"] == "GAS" && d["Plant annual net generation (MWh)"] > 300000)
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filteredData
Type Table, then Shift-Enter. Ctrl-space for more options.

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minNetGen = d3.min(data, d => d["Plant annual net generation (MWh)"])
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maxNetGen = d3.max(data, d => d["Plant annual net generation (MWh)"])
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extentsNetGen = d3.extent(data, d => d["Plant annual net generation (MWh)"]) // both min and max
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sumNetGen = d3.sum(data, d => d["Plant annual net generation (MWh)"]) // in true Tableau fashion!
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meanNetGen = d3.mean(data, d => d["Plant annual net generation (MWh)"])
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medianNetGen = d3.median(data, d => d["Plant annual net generation (MWh)"])
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plantsByState = d3.group(data, d => d["Plant state abbreviation"])
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viewof plantsByStateTable = Inputs.table(plantsByState)
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netGenByState = d3.rollup(data, v => d3.sum(v, d => d["Plant annual net generation (MWh)"]), d => d["Plant state abbreviation"]);
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netGenByState.get("VA")
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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"]);
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netGenByStateAndFuel.get("VA")
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netGenByStateAndFuel.get("VA").get("GAS")
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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"])
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stateAvgLongs = d3.rollup(data, v => d3.mean(v, d => d["Plant longitude"]), d => d["Plant state abbreviation"]);
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stateAvgLongs.get("VA")
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states = [...new Set(plantsByState.keys())]
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//sortedData = d3.sort(data, d => d["Plant annual net generation (MWh)"])
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//sortedData = d3.sort(data,(a,b) => d3.ascending(a["Plant annual net generation (MWh)"],b["Plant annual net generation (MWh)"]))
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viewof sortedtable = Inputs.table(sortedData)
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data.slice(0,1000)
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data.slice(1000,5000)
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shuffledData = d3.shuffle(data)
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viewof shuffledtable = Inputs.table(shuffledData)
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sample = d3.shuffle(data).slice(0,1000)
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