data = FileAttachment("powerplants_allUS_2010.csv").csv()
viewof table = Inputs.table(data)
//randomize a slice -> shuffle then slice
data = data_full.slice(500,2000) // shows records 500 through 2000
data = d3.shuffle(data_full) // shuffles specified records (data_full)
data = data_full.filter(d => d["Plant state abbreviation"] => "CO" && d["Plant primary fuel category"] => "OIL") // filters by observations in Colorado that have plant primary fuel category
// When you filter the data, you also filter the visual.
data.length
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 = (text) => Number(text.replace(/,/g,''))
toNum(mile2)
data.forEach(d => {
d["Plant annual net generation (MWh)"] = toNum(d["Plant annual net generation (MWh)"]); //data cleaning
d["Plant annual CO2 equivalent emissions (tons)"] = toNum(d["Plant annual CO2 equivalent emissions (tons)"]);
d["Plant annual CO2 emissions (lbs)"]
})
data[1]
data[1]["Plant annual net generation (MWh)"] // 1st arg is index; 2nd arg is column name
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[5]["Plant CO2 factor"]
data[5]["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)
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"])
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")
stateAvgLongs = d3.rollup(data, v => d3.mean(v, d => d["Plant longitude"]), d => d["Plant state abbreviation"]);
stateAvgLongs.get("VA")
//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.
