2f:[{"id":"1e8d777fe930f685af47b44d7267a4850b6a21f557e50f9f4e0845c3ed995bd2537da2056c891be1b14258448c8b1a024a44b789d94498bbfda8f214890a09ef","href":"https://static.observableusercontent.com/files/1e8d777fe930f685af47b44d7267a4850b6a21f557e50f9f4e0845c3ed995bd2537da2056c891be1b14258448c8b1a024a44b789d94498bbfda8f214890a09ef","name":"Screenshot 2022-11-25 at 09.37.13.png","type":"image/png","lastModified":1669399147153.938,"size":253549},{"id":"daee6e93825c662a200618927349fc28f11633ca3c39c68646d4c652e36d29c2b44407483ba2abe3e1996af2e3b2580422755694338db20c3c0a17b0f56ca81a","href":"https://static.observableusercontent.com/files/daee6e93825c662a200618927349fc28f11633ca3c39c68646d4c652e36d29c2b44407483ba2abe3e1996af2e3b2580422755694338db20c3c0a17b0f56ca81a","name":"catalonia_vaccined_2020_01.csv","type":"text/csv","lastModified":1669841321333.602,"size":776190},{"id":"9d701c9171b6a957a267a57c09bd8ae85306702ad11f0ebbe40364cd40f61d476606de3f6b8fb643064ec47df0a30803ee65acfb26415788d984fc391a374036","href":"https://static.observableusercontent.com/files/9d701c9171b6a957a267a57c09bd8ae85306702ad11f0ebbe40364cd40f61d476606de3f6b8fb643064ec47df0a30803ee65acfb26415788d984fc391a374036","name":"plot-7-1.umd.js","type":"application/javascript","lastModified":1669827894724.252,"size":134029}] 2d:{"title":"Exercise 1: COVID waves","cells":[{"id":0,"value":"# Exercise 1: COVID waves\nWhich countries were most affected by the second wave of the pandemic? Data from *Our World In Data* can be found [here](https://raw.githubusercontent.com/xaquingv/data-for-mds/main/owid-covid-data.json). The [NYTimes had a story](https://www.nytimes.com/interactive/2020/12/04/world/europe/europe-covid-deaths.html) that visualized how loosening precautions led to thousands of deaths just months before vaccines arrived. (Their way is just one way to visualize this.)\n\n**You'll need to:**\n* ~~Load the data.~~\n* Parse it, filter it, transform it.\n* Plot it.\n\n**Extra credit:**\n* Pick a different dataset.\n* Plot it as a:\n * Facets —like the NYT's example,\n * and as some other visual device.","pinned":false,"hidden":false,"mode":"md"},{"id":39,"value":"FileAttachment(\"Screenshot 2022-11-25 at 09.37.13.png\").image()","pinned":false,"hidden":false,"mode":"ojs"},{"id":365,"value":"---\n## Loading the data","pinned":false,"hidden":false,"mode":"md"},{"id":517,"value":"First of all we load the data","pinned":false,"hidden":false,"mode":"md"},{"id":9,"value":"_data = (\n await fetch(\n \"https://raw.githubusercontent.com/xaquingv/data-for-mds/main/owid-covid-data.json\"\n )\n).json()","pinned":true,"hidden":false,"mode":"ojs"},{"id":892,"value":"## Parsing, filtering and transforming the data\n","pinned":false,"hidden":false,"mode":"md"},{"id":1427,"value":"We take all attributes we want and the COVID data itself.","pinned":false,"hidden":false,"mode":"md"},{"id":900,"value":"data_ALL_FLAT = Object.entries(_data)\n .map(([key, value]) => (\n [...value.data.map(d => ({id: key, continent: value.continent, population: value.population , name: value.location, parsedDate: new Date(d.date), ...d}))]\n )).flat().filter( obj => obj.continent)","pinned":true,"hidden":false,"mode":"ojs"},{"id":967,"value":"Inputs.table(data_ALL_FLAT)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1431,"value":"We filter our data to take only the records occurred between the first and second wave.","pinned":false,"hidden":false,"mode":"md"},{"id":911,"value":"data_bt_may_dec = data_ALL_FLAT.filter(d => d.parsedDate > new Date(\"2020-03-07\") && d.parsedDate < new Date(\"2021-01-16\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1437,"value":"To smooth our data, we group records by week, taking the mean of deaths.","pinned":false,"hidden":false,"mode":"md"},{"id":1659,"value":"function get2020Week(date) {\n return Math.ceil((((new Date(date).getTime() - new Date(2020, 0, 1).getTime()) / 86400000) + new Date(2020, 0, 1).getDay() + 1) / 7)\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":921,"value":"data_grouped_by_weeks_and_country = data_bt_may_dec.map( //obtaining week\n obj => (\n {...obj,\n week_num: get2020Week(obj.parsedDate)\n })\n).reduce((acc,obj) => { //grouping by week and country\n if (!acc[[obj.id,obj.week_num]]) {\n acc[[obj.id,obj.week_num]] = [];\n }\n acc[[obj.id,obj.week_num]].push(obj);\n return acc;\n},{})","pinned":true,"hidden":false,"mode":"ojs"},{"id":984,"value":"weekly_deaths = Object.entries(data_grouped_by_weeks_and_country)\n.reduce( (acc,obj) => {\n const week_mean = (obj[1].reduce(\n (acc,obj) => { \n return acc + obj.new_deaths \n }, 0) / obj.length) / 1000\n \n acc.push(\n {\n id: obj[1][0].id, \n name: obj[1][0].name, \n continent: obj[1][0].continent, \n date: obj[1][0].parsedDate, \n week: obj[1][0].week_num, deaths_mean: ! week_mean || week_mean < 0 ? 0 : week_mean\n })\n \n return acc\n},[])","pinned":true,"hidden":false,"mode":"ojs"},{"id":1441,"value":"We need to add the attribute type_of_wave to the data depending on 3 situations:\n\n- The second wave is much bigger than the first\n\n- The second wave is slightly bigger than the first\n\n- The second wave is smaller than the first\n\nOnce we have that, we can colour every graf depending on the COVID wave type.","pinned":false,"hidden":false,"mode":"md"},{"id":1482,"value":"waves_max_values = weekly_deaths.reduce((dic,obj) => {\n if(!dic.hasOwnProperty(obj.id)){\n dic[obj.id] = {};\n dic[obj.id][\"w1\"] = 0;\n dic[obj.id][\"w2\"] = 0;\n }\n \n if (obj.week < 27){ //more or less where the first wave end\n if (dic[obj.id][\"w1\"] < obj.deaths_mean){\n dic[obj.id][\"w1\"] = obj.deaths_mean;\n }\n } else if (obj.week > 37) { //more or less where the second wave starts\n if (dic[obj.id][\"w2\"] < obj.deaths_mean){\n dic[obj.id][\"w2\"] = obj.deaths_mean;\n }\n }\n\n return dic\n}, {})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1567,"value":"We also check if the results are \"good enough\" to be shown, if are not, we remove the country. ","pinned":false,"hidden":false,"mode":"md"},{"id":1513,"value":"color_wave_data = Object.assign({},...Object.entries(waves_max_values)\n .map(([k, v]) => {\n var wave_type = 0\n if (v[\"w1\"] === 0 && v[\"w2\"] === 0 || v[\"w1\"] + v[\"w2\"] < 0.25 ) { // no data or data not good enough\n wave_type = -1\n }\n else if (v[\"w1\"] > v[\"w2\"]){ //first wave biger that second wave\n wave_type = 2\n }\n else if (v[\"w1\"] < v[\"w2\"] && (v[\"w1\"]+0.1)*3 < v[\"w2\"]) { //first wave 3 times smaller than the second\n wave_type = 1\n }\n //else: first wave more than 3 times smaller than the second\n return {id: k, wave_type: wave_type}\n}).map((x) => ({[x.id]: x.wave_type})))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1519,"value":"data_to_plot = weekly_deaths.filter( obj => color_wave_data[obj.id] !== -1 )\n.map( obj => (\n { ...obj, \n wave_type: color_wave_data[obj.id]\n }))","pinned":true,"hidden":false,"mode":"ojs"},{"id":360,"value":"---\n## The plot","pinned":false,"hidden":false,"mode":"md"},{"id":1445,"value":"Using a js file to use new plot features.","pinned":false,"hidden":false,"mode":"md"},{"id":1170,"value":"myPlot = require(await FileAttachment(\"plot-7-1.umd.js\").url())","pinned":false,"hidden":false,"mode":"ojs"},{"id":1449,"value":"## Weekly COVID deaths mean by each Country \n### Deaths per 1000 people\n\n - The second wave is much bigger than the first\n\n - The second wave is slightly bigger than the first\n\n - The second wave is smaller than the first","pinned":false,"hidden":false,"mode":"md"},{"id":884,"value":"myPlot.plot({\n width: width,\n height: width,\n facet: {\n data: data_to_plot ,\n y: \"id\",\n columns: 6,\n marginRight: 100,\n },\n color: {\n type: \"categorical\",\n range: [\"#ebae34\", \"#e63d2e\", \"#77c4ed\"]\n },\n y: {\n label: \"Weekly deaths mean\",\n },\n x: {\n label: \"Month\"\n },\n marks: [\n myPlot.frame(),\n myPlot.line(data_to_plot , {\n x: \"date\",\n y: \"deaths_mean\",\n stroke: \"wave_type\"\n }),\n myPlot.text(data_to_plot , myPlot.selectFirst({ text: \"name\", y : 10 }))\n ]\n}) ","pinned":true,"hidden":false,"mode":"ojs"},{"id":3,"value":"---\n# Extracredit\n## Pick a different dataset.\nWe are using the csv \"catalonia_vaccined_2020_01\". Has the vaccine records of all Catalan states during the first month of 2020.\n## Loading data","pinned":false,"hidden":false,"mode":"md"},{"id":1238,"value":"function csvJSON(text){\n var lines= text.toString().split(\"\\r\");\n var result = [];\n const headers = ['SEXE_CODI','SEXE','COMARCA_CODI','COMARCA','DATA','RECOMPTE']\n for(var i=1;i {\n if (!acc[[obj.DATA,obj.COMARCA]]) {\n acc[[obj.DATA,obj.COMARCA]] = [];\n }\n \n acc[[obj.DATA,obj.COMARCA]].push(obj);\n \n return acc;\n},{})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1309,"value":"recount_v2 = Object.entries(data_grouped_by_date_v2).reduce( \n (acc,obj) => {\n const full_recount = (obj[1].reduce(\n (acc,obj) => { \n return acc + parseInt(obj.RECOMPTE) \n }, 0))\n acc.push({comarca: obj[1][0].COMARCA, data: new Date(obj[1][0].DATA), recompte: full_recount})\n return acc\n},[]).filter(obj => obj.comarca && obj.comarca !== \"No classificat\") //removing wrong data","pinned":true,"hidden":false,"mode":"ojs"},{"id":1679,"value":"We seach for each state where the vaccines are increasing or decreasing","pinned":false,"hidden":false,"mode":"md"},{"id":1615,"value":"recount_incressing_v2 = recount_v2.reduce((dic,obj) => {\n if(!dic.hasOwnProperty(obj.comarca)){\n dic[obj.comarca] = {};\n dic[obj.comarca][\"max_value_15first_days\"] = 0;\n dic[obj.comarca][\"last_value\"] = 0;\n }\n \n if (obj.data.getTime() < new Date(2021,6,1).getTime()){\n if (dic[obj.comarca][\"max_value_15first_days\"] < obj.recompte){\n dic[obj.comarca][\"max_value_15first_days\"] = obj.recompte\n }\n }\n\n if (obj.data.getTime() > new Date(2021,1,31).getTime()){\n dic[obj.comarca][\"last_value\"] = obj.recompte\n }\n \n return dic\n},{})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1681,"value":"We assign each state if vaccines are increasing.","pinned":false,"hidden":false,"mode":"md"},{"id":1613,"value":"data_to_plot_v2 = recount_v2.map( obj => {\n var is_incressing = 1\n if ( recount_incressing_v2[obj.comarca][\"max_value_15first_days\"] > recount_incressing_v2[obj.comarca][\"last_value\"] ){\n is_incressing = 0\n }\n return {...obj, incresing: is_incressing}\n \n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1460,"value":"## The plot\n### Number of new vaccinated against COVID-19 in each month by each state of Catalonia durning 2020 \n\n - Vaccines are decreasing\n\n - Vaccines are increasing ","pinned":false,"hidden":false,"mode":"md"},{"id":1313,"value":"myPlot.plot({\n width: width,\n height: width * 2,\n facet: {\n data: data_to_plot_v2,\n y: \"comarca\",\n columns: 3,\n marginRight: 100,\n },\n color: {\n type: \"categorical\",\n range: [\"#e63d2e\", \"#77c4ed\"]\n },\n y: {\n label: \"Vaccine recount\",\n },\n x: {\n label: \"Date\"\n },\n marks: [\n myPlot.frame(),\n myPlot.line(data_to_plot_v2, {\n x: \"data\",\n y: \"recompte\",\n stroke: \"incresing\"\n }),\n myPlot.text(data_to_plot_v2, myPlot.selectFirst({ text: \"comarca\", y : 5000 }))\n ]\n}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":1471,"value":"## The other plot\n### Number of new vaccinated against COVID-19 in each month by each state of Catalonia durning 2020 \n","pinned":false,"hidden":false,"mode":"md"},{"id":1325,"value":"Plot.plot({\n color: {\n legend: true\n },\n marks: [\n Plot.ruleY([0]),\n Plot.line(data_to_plot_v2, {x: \"data\", y: \"recompte\", stroke: \"comarca\"}),\n\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W2f","stdlib":"1","titling":"auto"}