2f:T584,{ const myDiv = html`

`; //const loc = dataWithISO.map(i => i.iso_code); //const cSizeScale = d3.scaleSqrt().domain(d3.extent(YearSelect, d => Number(d[totalCh]))).range([5, 50]); //const cSize = YearSelect.map(d=>cSizeScale(Number(d[totalCh]))); const hoverText = airportData.map(d => d.airport); var data = [ // Plotly Trace data { type: 'scattergeo', locationmode: 'USA-states', lon: airportData.map(a => Number(a.long)), lat: airportData.map(a => Number(a.lat)), hoverinfo: 'text', text: hoverText, //marker: { //size: cSize, //Size specified the diameter //line: { //color: "lightgrey", //width: 1 //} //} } ]; var layout = { title: 'Most Trafficked US airports', colorbar: true, geo: { scope: 'usa', projection: { type: 'albers usa' }, showland: true, landcolor: 'rgb(250,250,250)', subunitcolor: 'rgb(217,217,217)', countrycolor: 'rgb(217,217,217)', countrywidth: 0.5, subunitwidth: 0.5 } }; var layout = { title: 'Most Trafficked US airports', showlegend: false, geo: { scope: 'usa', showland: true } }; Plotly.newPlot(myDiv, data, {...layout, ...mapLayout }); return myDiv; }30:T5e5,{ const myDiv = html`

`; //const loc = dataWithISO.map(i => i.iso_code); const cSizeScale = d3.scaleSqrt().domain(d3.extent(FlightYear, d => Number(d[totalCh]))).range([5, 50]); const cSize = FlightYear.map(d=>cSizeScale(Number(d[totalCh]))); //const hoverText = airportData.map(d => d.airport); const hoverText = FlightYear.map(d => d.Name + " " + totalCh + "Flights:" + d[totalCh]); var data = [ // Plotly Trace data { type: 'scattergeo', locationmode: 'USA-states', lon: airportData.map(a => Number(a.long)), lat: airportData.map(a => Number(a.lat)), hoverinfo: 'text', text: hoverText, marker: { size: cSize, //Size specified the diameter line: { color: "lightgrey", width: 1 } } } ]; var layout = { title: 'Most Trafficked US airports', colorbar: true, geo: { scope: 'usa', projection: { type: 'albers usa' }, showland: true, landcolor: 'rgb(250,250,250)', subunitcolor: 'rgb(217,217,217)', countrycolor: 'rgb(217,217,217)', countrywidth: 0.5, subunitwidth: 0.5 } }; var layout = { title: '10 Trafficked US airports Flights Data Bubble Map', showlegend: false, geo: { scope: 'usa', showland: true } }; Plotly.newPlot(myDiv, data, {...layout, ...mapLayout }); return myDiv; }31:T5e0,{ const myDiv = html`

`; //const loc = dataWithISO.map(i => i.iso_code); const cSizeScale = d3.scaleSqrt().domain(d3.extent(PS_Year, d => Number(d[totalCh]))).range([5, 50]); const cSize = PS_Year.map(d=>cSizeScale(Number(d[totalCh]))); //const hoverText = airportData.map(d => d.airport); const hoverText = PS_Year.map(d => d.Name + " " + totalCh +"Passengers:"+ d[totalCh]); var data = [ // Plotly Trace data { type: 'scattergeo', locationmode: 'USA-states', lon: airportData.map(a => Number(a.long)), lat: airportData.map(a => Number(a.lat)), hoverinfo: 'text', text: hoverText, marker: { size: cSize, //Size specified the diameter line: { color: "lightgrey", width: 1 } } } ]; var layout = { title: 'Most Trafficked US airports', colorbar: true, geo: { scope: 'usa', projection: { type: 'albers usa' }, showland: true, landcolor: 'rgb(250,250,250)', subunitcolor: 'rgb(217,217,217)', countrycolor: 'rgb(217,217,217)', countrywidth: 0.5, subunitwidth: 0.5 } }; var layout = { title: '10 Trafficked US airports Passengers Data Bubble Map', showlegend: false, geo: { scope: 'usa', showland: true } }; Plotly.newPlot(myDiv, data, {...layout, ...mapLayout }); return myDiv; }32:T411,CleanedTabledata = TableData.map(d => { const regex = /
|

|\n|%/gi; const all = d; const reg = all['REgion'].replace('', ''); const Dem2020 = all['2020
Demand
vs 2019'].replace('%', ''); const Cap2020 = all['2020 Capacity vs 2019'].replace('%', ''); const Prof2020 = all['2020 profits'].replace('b', '').replaceAll('$', ''); const Dem2021 = all['2021
Demand
vs 2020
(vs 2019)'].replaceAll('

',''); const Cap2021 = all['2021
Capacity
vs 2020
(vs 2019)'].replace(regex,''); const Prof2021 = all['2021 Profits'].replace('b', '').replaceAll('$', ''); return {'Region': reg.replace('', ''), 'Demand Percentage 2020 vs 2019': Dem2020, 'Capacity Percentage 2020 vs 2019': Cap2020, '2020 Profit in Billions': Prof2020, 'Demand Percentage 2021 vs 2020 vs (2019) ': Dem2021.replace(regex, ''), 'Capacity Percentage 2021 vs 2020 vs (2019) ': Cap2021.replace('0',')'), '2021 Profit in Billions': Prof2021 } 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2d:{"title":"Impact of Covid on US Aviation","cells":[{"id":0,"value":"md`# Impact of Covid on US Aviation`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1054,"value":"md `The coronavirus pandemic paralyzed the aviation industry throughout 2020. Revenue streams of air carriers were immensely low such that revenue could not cover the costs of operating.The damage to the airline industry from the coronavirus pandemic last year was devastating.The financial hardship has continued into 2021 as airlines try to cope with a Covid 19 situation.The pandemic began to impact air travel in late January 2020, but by the end of March the air transport system virtually came to a halt.Moreover, with strict travel restrictions and quarantine requirements make pasengers are less interested in air travel.But as time passed domestic travel has fared better because people are worried about traveling too far from home and fewer health measures are involved than when crossing borders.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1056,"value":"md`Project overview:
a)US Domestic Airlies Revenue Observation
b)Regional Profit Comparison
c) US Busiest Airports Performance`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1062,"value":"md `## a)US Domestic Airlies Revenue Observation`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1078,"value":"md `\n1)Domestic airline revenue plot from year 2011 to 2020. In 2020, the revenue was -25M which is the worst of last 10 years records.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1066,"value":"{\n const myDiv = html`

`;\n\n const data = [\n {\n x: years,\n y: revByYear,\n type: 'scatter',\n mode:'lines+markers',\n line: {\n color: '#4a666e',\n width: 4\n },\n marker: {\n color: '#a7521f',\n size: 10\n }\n }\n ];\n \n var layout = {\n title: 'Domestic airlines revenue by year',\n xaxis:{autorange: true, title: 'Years', showticklabels: true},\n yaxis:{autorange: true, title: 'Net Income'},\n width:800,\n height:500\n }\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1070,"value":"md `\n2)Interactively choose one domestic airline by using the selection widget and compare the net income and profit loss from 2011 to 2020.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1086,"value":"viewof airlines = select({\n title: \"Select\",\n options: Object.values(names),\n value: \"Spirit Air Lines\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1088,"value":"viewof dd = radio({\n title: \"Select\",\n description: \"Please select one\",\n options: [{value: \"NET_INCOME\", label: \"NET INCOME\"}, {value:\"OP_PROFIT_LOSS\", label : \"PROFIT LOSS\"}],\n value: \"NET_INCOME\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1092,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n x: airyears,\n y: revORprofitByYear,\n type: 'scatter'\n \n }\n ];\n var layout = {\n title: `${airlines} ${dd} by year`,\n xaxis:{autorange: true, title: 'Years'},\n //yaxis:{autorange: true, title: 'Net Income'}\n }\n \n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1094,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n x: airyears,\n y: revORprofitByYear,\n type:\n 'bar', \n transforms: [\n {\n type: 'sort',\n target: 'y',\n order: 'descending'\n }\n ],\n }\n ];\n const layout = {\n title: `${airlines} ${dd} by year`,\n yaxis: {\n //title: 'Number of Case'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1096,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n labels: airyears,\n values: revORprofitByYear,\n type:'pie',\n //sort: false,\n direction: \"clockwise\",\n //insidetextorientation: \"radial\"\n }\n ];\n const layout = {\n title: `${airlines} ${dd} by year`\n };\n\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"md `\n3) Compare upto three domestic airlines performace in terms of net income and profit loss.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1101,"value":"viewof ch = limitCheckboxes(3, checkbox({\n options: Object.values(names),\n value: [\"Spirit Air Lines\", \"American Airlines Inc.\", \"Continental Air Lines Inc.\"],\n title: \"Select upto three states from the list\",\n multiple: true\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1103,"value":"viewof dd1 = radio({\n title: \"Select\",\n description: \"Please select one\",\n options: [{value: \"NET_INCOME\", label: \"NET INCOME\"}, {value:\"OP_PROFIT_LOSS\", label : \"PROFIT LOSS\"}],\n value: \"NET_INCOME\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1107,"value":"{\n const myDiv = html`

`;\n //const date = sortedStateData.map(d => d.submission_date);\n const data_new = [];\n const color = [\"blue\", \"green\", \"red\"];\n for (let i = 0; i < ch.length; i++) {\n //extract[i] = attribute=>sortedStateData[i].map(d=>d[attribute]),\n data_new[i] = {\n type: \"scatter\",\n mode: \"lines\",\n name: ch[i],\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[i]),\n line: { color: color[i] }\n };\n};\n \n \n Plotly.newPlot(myDiv, data_new);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1109,"value":"{\n const myDiv = html`

`;\n const trace1 = {\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[0]),\n type:'bar',\n name: ch[0]\n };\n const trace2 = {\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[1]),\n type:'bar',\n name: ch[1]\n };\n \n const trace3 = {\n x: airyears3,\n y: ThreeRevORprofitByYear.map(d => d[2]),\n type:'bar',\n name: ch[2]\n };\n var data = [trace1, trace2, trace3];\n const layout = {\n barmode: 'group',\n title: `${dd} comparisons by year`\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1116,"value":"md `## b)Regional Profit Comparison`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1119,"value":"md `1)Compareing the regional (North America, Asia Pacific, Latin America, Europe, Africa and Middle East) air travel capacity and demand by percentage of year 2020 vs 2019.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1121,"value":"viewof dvc = radio({\n title: \"Select\",\n description: \"Please select one\",\n options: [{value: \"Demand Percentage 2020 vs 2019\", label: \"Demand\"}, {value:\"Capacity Percentage 2020 vs 2019\", label : \"Capacity\"}],\n value: \"Demand Percentage 2020 vs 2019\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1124,"value":"{\n const myDiv = html`

`;\n const data = [\n {\n x: Regions,\n y: NewDataTable.map(i => i[dvc]),\n type:\n 'bar', \n transforms: [\n {\n type: 'sort',\n target: 'y',\n order: 'descending'\n }\n ],\n }\n ];\n const layout = {\n title: `${dvc} `,\n yaxis: {\n title: 'Percentage'\n }\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1130,"value":"md `2)Comparing the regional profit in billions for year 2020 and expected profit for year 2021.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1126,"value":"{\n const myDiv = html`

`;\n const y1 = NewDataTable.map(a => a['2020 Profit in Billions']*1000000000);\n const y2 = NewDataTable.map(a => a['2021 Profit in Billions']*1000000000);\n \n \n const trace1 = {\n x: Regions,\n y: y1,\n type:'bar',\n name: 2020\n };\n \n const trace2 = {\n x: Regions,\n y: y2,\n type:'bar',\n name: 2021\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'Profit comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1141,"value":"md `## c) US Busiest Airports Performance`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1148,"value":"md `1) Orlando International Airport (MCO) psssengers, flights and revenue passenger miles comparision from year 2010 to 2020 for both domestic and international.`","pinned":true,"hidden":false,"mode":"ojs"},{"id":1001,"value":"{\n const myDiv = html`

`;\n const y1 = mco1_Total.map(a => a['DOMESTIC']);\n const y2 = mco1_Total.map(a => a['INTERNATIONAL']);\n \n \n const trace1 = {\n x: MCOyear,\n y: y1,\n type:'bar',\n name: 'DOMESTIC'\n };\n \n const trace2 = {\n x: MCOyear,\n y: y2,\n type:'bar',\n name: 'INTERNATIONAL'\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'MCO Passengers comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1004,"value":"{\n const myDiv = html`

`;\n const y1 = mco_Total.map(a => a['DOMESTIC']);\n const y2 = mco_Total.map(a => a['INTERNATIONAL']);\n \n \n const trace1 = {\n x: MCOyear,\n y: y1,\n type:'bar',\n name: 'DOMESTIC'\n };\n \n const trace2 = {\n x: MCOyear,\n y: y2,\n type:'bar',\n name: 'INTERNATIONAL'\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'MCO Flights comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1018,"value":"{\n const myDiv = html`

`;\n const y1 = mco2_Total.map(a => a['DOMESTIC']);\n const y2 = mco2_Total.map(a => a['INTERNATIONAL']);\n \n \n const trace1 = {\n x: MCOyear,\n y: y1,\n type:'bar',\n name: 'DOMESTIC'\n };\n \n const trace2 = {\n x: MCOyear,\n y: y2,\n type:'bar',\n name: 'INTERNATIONAL'\n };\n \n var data = [trace1, trace2];\n const layout = {\n barmode: 'group',\n title: 'MCO RPM comparisons by year'\n };\n Plotly.newPlot(myDiv, data, layout);\n return myDiv;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1151,"value":"md `2) 221 US busiest Airports has been spotted in the map. Then comparing performance based on domestic and internationl passengers and flights number of 10 busiest US airports interactively choosing the years from 2010 to 2020. `","pinned":false,"hidden":false,"mode":"ojs"},{"id":897,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":857,"value":"viewof yearCh = radio({\n description: 'Choose any year',\n options: ['2010', '2011','2012', '2013','2014','2015','2016','2017','2018','2019','2020'],\n value: '2010'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":860,"value":"viewof totalCh = radio({\n description: 'Choose one',\n options: ['DOMESTIC', 'INTERNATIONAL', 'TOTAL'],\n value: 'TOTAL'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":851,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":895,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1160,"value":"md `## Data Sources`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1158,"value":"md`a)US Domestic Airlies Revenue Observation: https://www.transtats.bts.gov/Data_Elements_Financial.aspx?Qn6n=J
b)Regional Profit Comparison: https://www.iata.org/en/pressroom/pr/2020-11-24-01/
c)US Busiest Airports Performance: https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1173,"value":"md `## Data Processing: final week`","pinned":true,"hidden":false,"mode":"ojs"},{"id":867,"value":"airportData = d3.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1014,"value":"mco2_Total = data_rpm.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1012,"value":"data_rpm = FileAttachment(\"RPM_MCO.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":725,"value":"Flight_Total.filter(a => a.Year == yearCh).map(d=> Number(d[totalCh]))","pinned":true,"hidden":false,"mode":"ojs"},{"id":838,"value":"FlightYear = Flight_Total.filter(a => a.Year == yearCh)","pinned":true,"hidden":false,"mode":"ojs"},{"id":926,"value":"FlightbyYear = Flight_Total.filter(a=> a.Year)","pinned":true,"hidden":false,"mode":"ojs"},{"id":836,"value":"Flight_Total = FlightData.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":827,"value":"FlightData = Promise.all([data_Chi,data_Atl,data_Dfw,data_Phx,data_Den,data_Iah,data_Sfo,data_Lax,data_Mco,data_Clt]).then((big_datas) => d3.merge(big_datas))","pinned":true,"hidden":false,"mode":"ojs"},{"id":824,"value":"data_Chi = FileAttachment(\"Flights_chicago@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":822,"value":"data_Den = FileAttachment(\"Flights_Den@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":820,"value":"data_Dfw = FileAttachment(\"Flights_DFW@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":816,"value":"data_Phx = FileAttachment(\"Flights_PHX@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":700,"value":"data_Atl = FileAttachment(\"FlightsATL@2.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":965,"value":"data_Sfo = FileAttachment(\"Flights_SFO.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1008,"value":"mco_Total = data_Mco.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":967,"value":"data_Mco = FileAttachment(\"Flights_MCO.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":969,"value":"data_Lax = FileAttachment(\"Flights_IAH.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":973,"value":"data_Clt = FileAttachment(\"Flights_CLT.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":971,"value":"data_Iah = FileAttachment(\"Flights_CLT.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":892,"value":"PS_Year = PS_Total.filter(a => a.Year == yearCh)","pinned":true,"hidden":false,"mode":"ojs"},{"id":890,"value":"PS_Total = PSData.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":978,"value":"PSgroupByName = d3.group(PSData, d=>d.Name)","pinned":true,"hidden":false,"mode":"ojs"},{"id":888,"value":"PSData = Promise.all([data_Chi1,data_Atl1,data_Dfw1,data_Phx1,data_Den1,data_Iah1,data_Sfo1,data_Lax1,data_Mco1,data_Clt1]).then((big_datas) => d3.merge(big_datas))","pinned":true,"hidden":false,"mode":"ojs"},{"id":873,"value":"data_Atl1 = FileAttachment(\"Passengers_ATL@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":876,"value":"data_Phx1 = FileAttachment(\"Passengers_PHX@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":879,"value":"data_Den1 = FileAttachment(\"Passengers_Denxls@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":881,"value":"data_Chi1 = FileAttachment(\"Passengers_chicago@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":884,"value":"data_Dfw1 = FileAttachment(\"Passengers_DFW@1.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":962,"value":"data_Lax1 = FileAttachment(\"Passengers_LAX.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":960,"value":"data_Clt1 = FileAttachment(\"Passengers_CLT.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":958,"value":"data_Iah1 = FileAttachment(\"Passengers_IAH.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":956,"value":"data_Sfo1 = FileAttachment(\"Passengers_LAX.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":954,"value":"data_Mco1 = FileAttachment(\"Passengers_MCOxls.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":987,"value":"mco1_Total = data_Mco1.filter(a=> a.Month == 'TOTAL')","pinned":true,"hidden":false,"mode":"ojs"},{"id":992,"value":"MCOyear = [...data_Mco1byyear.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":982,"value":"data_Mco1byyear = d3.group(data_Mco1, d=>d.Year)","pinned":true,"hidden":false,"mode":"ojs"},{"id":750,"value":"usa = datasets['us-10m.json']()","pinned":true,"hidden":false,"mode":"ojs"},{"id":752,"value":"datasets = require('vega-datasets@1.25')","pinned":true,"hidden":false,"mode":"ojs"},{"id":755,"value":"import {vl} from '@vega/vega-lite-api'","pinned":true,"hidden":false,"mode":"ojs"},{"id":362,"value":"md `## Data Procesing: week 3`","pinned":true,"hidden":false,"mode":"ojs"},{"id":656,"value":"Table(CleanedTabledata)","pinned":true,"hidden":false,"mode":"ojs"},{"id":550,"value":"Regions = [...groupByRegion.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":548,"value":"groupByRegion = d3.group(NewDataTable, d=>d.Region)","pinned":true,"hidden":false,"mode":"ojs"},{"id":534,"value":"NewDataTable = CleanedTabledata.filter(a => a.Region != 'World')","pinned":true,"hidden":false,"mode":"ojs"},{"id":460,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":527,"value":"Table(TableData)","pinned":true,"hidden":false,"mode":"ojs"},{"id":376,"value":"TableData= getTableData(TableFromWeb[0], 0)","pinned":true,"hidden":false,"mode":"ojs"},{"id":364,"value":"TableFromWeb = scrapeTableData(\n \"https://www.iata.org/en/pressroom/pr/2020-11-24-01/\"\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":365,"value":"function tableToJson(table,start=1) {\n //Code from : https://stackoverflow.com/questions/9927126/how-to-convert-the-following-table-to-json-with-javascript\n var data = [];\n for (var i = start; i < table.rows.length; i++) {\n var tableRow = table.rows[i];\n var rowData = [];\n for (var j = 0; j < tableRow.cells.length; j++) {\n rowData.push(tableRow.cells[j].innerHTML);\n }\n data.push(rowData);\n }\n return data;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":368,"value":"getTableData = (data, start = 1) => {\n const d = tableToJson(data, start);\n const t = d.slice(1);\n const columns = d[0];\n return t.map(d =>\n columns.reduce((acc, e, i) => {\n acc[e] = d[i];\n return acc;\n }, {})\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":370,"value":"async function scrapeTableData(url) {\n const response = await soFetch(url);\n const text = await response.text();\n //const domparser = new DOMParser();\n //const dom = domparser.parseFromString(text, \"text/html\");\n const dom = html`${text}`;\n const tables = await dom.querySelectorAll('table');\n if (tables.length <= 0) {\n throw new Error('Could not find any tables.');\n }\n const tablesLen = tables.length;\n\n const tablesData = [];\n for (let i = 0; i < tablesLen; i++) {\n let tableEl = tables[i];\n\n tablesData.push(tableEl);\n }\n return tablesData;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":554,"value":"mapLayout = ({\n autosize: false,\n width: 900,\n height: 500,\n margin: {\n l: 20,\n r: 50,\n b: 20,\n t: 50,\n pad: 4\n },\n paper_bgcolor: '#AAAAAA',\n plot_bgcolor: '#8888c7'\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":422,"value":"import { toView } from \"@uwdata/arquero\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":372,"value":"import { soFetch } from '@alecglassford/so-fetch'","pinned":false,"hidden":false,"mode":"ojs"},{"id":96,"value":"md `## Data Procesing: week 2`","pinned":false,"hidden":false,"mode":"ojs"},{"id":344,"value":"md `\n1)Interactively choose one domestic airline by using the selection widget and compare the net income and profit loss from 2011 to 2020.
\n2) Compare upto three domestic airlines performace in terms of net income and profit loss.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":235,"value":"Table(AirData)","pinned":true,"hidden":false,"mode":"ojs"},{"id":252,"value":"import {checkbox} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":249,"value":"function limitCheckboxes(limit, widget) {\n widget.addEventListener('change', function onchange() {\n const checked = widget.querySelectorAll('input[type=\"checkbox\"]:checked');\n const unchecked = widget.querySelectorAll('input[type=\"checkbox\"]:not(:checked)');\n for(const n of unchecked) n.disabled = checked.length >= limit;\n });\n return widget;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":100,"value":"names = [...groupBynames.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":98,"value":"groupBynames = d3.group(combinedData, d=>d.CARRIER_NAME)","pinned":true,"hidden":false,"mode":"ojs"},{"id":140,"value":"AirByYears = d3.group(AirData, d=>d.YEAR)","pinned":true,"hidden":false,"mode":"ojs"},{"id":138,"value":"revORprofitByYear = airyears.map(year =>{\n const all = AirByYears.get(year)\n const rev = all.map(d =>d[dd])\n return d3.sum(rev)\n }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":158,"value":"airyears = [...AirByYears.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":108,"value":"AirData = combinedData.filter(d => d.CARRIER_NAME == airlines)","pinned":true,"hidden":false,"mode":"ojs"},{"id":276,"value":"AirData3 = ch.flatMap(airline => combinedData.filter(d => d.CARRIER_NAME == airline))","pinned":true,"hidden":false,"mode":"ojs"},{"id":289,"value":"AirByYears3 = d3.group(AirData3, d=>d.YEAR)","pinned":true,"hidden":false,"mode":"ojs"},{"id":295,"value":"ThreeRevORprofitByYear = airyears3.map(year =>{\n const all = AirByYears3.get(year)\n const air0 = all.filter(a => a.CARRIER_NAME == ch[0])\n const rev0 = air0.map(d =>d[dd1])\n const air1 = all.filter(a => a.CARRIER_NAME == ch[1])\n const rev1 = air1.map(d =>d[dd1])\n const air2 = all.filter(a => a.CARRIER_NAME == ch[2])\n const rev2 = air2.map(d =>d[dd1])\n return {0:d3.sum(rev0), 1:d3.sum(rev1), 2:d3.sum(rev2)}\n }\n )","pinned":false,"hidden":false,"mode":"ojs"},{"id":304,"value":"ThreeRevORprofitByYear.map(d => d['1'])","pinned":true,"hidden":false,"mode":"ojs"},{"id":292,"value":"airyears3 = [...AirByYears3.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":122,"value":"import { Table } from \"@observablehq/table\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":243,"value":"md `## Data Procesing: week 1`","pinned":true,"hidden":false,"mode":"ojs"},{"id":93,"value":"import {radio} from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":90,"value":"import { select } from \"@jashkenas/inputs\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":18,"value":"rev2020GroupbyAirlines = d3.group(data19and20, d=> d.CARRIER_NAME)","pinned":true,"hidden":false,"mode":"ojs"},{"id":4,"value":"data2020= FileAttachment(\"2020.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":20,"value":"data19and20 = FileAttachment(\"Rev20192020.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":31,"value":"data2019 = FileAttachment(\"2019.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":46,"value":"data2018 = FileAttachment(\"2018.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":49,"value":"data2017 = FileAttachment(\"2017.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":51,"value":"data2016 = FileAttachment(\"2016.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":53,"value":"data2015 = FileAttachment(\"2015.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":55,"value":"data2014 = FileAttachment(\"2014.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":57,"value":"data2013 = FileAttachment(\"2013.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":59,"value":"data2012 = FileAttachment(\"2012.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":61,"value":"data2011 = FileAttachment(\"2011.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":33,"value":"combinedData = Promise.all([data2011,data2012,data2013,data2014,data2015,data2016,data2017,data2018,data2019, data2020]).then((big_datas) => d3.merge(big_datas))","pinned":true,"hidden":false,"mode":"ojs"},{"id":65,"value":"years = [...groupByYears.keys()]","pinned":true,"hidden":false,"mode":"ojs"},{"id":68,"value":"groupByYears = d3.group(combinedData, d=>d.YEAR)","pinned":true,"hidden":false,"mode":"ojs"},{"id":71,"value":"revByYear = years.map(year =>{\n const all = groupByYears.get(year)\n const rev = all.map(d =>d.NET_INCOME)\n return d3.sum(rev)\n }\n )","pinned":true,"hidden":false,"mode":"ojs"},{"id":43,"value":"groupCombine = d3.group(combinedData, 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