25:["$","title",null,{"children":"Exercise 1: COVID waves - OLD | Vogelmann | Observable"}] 49:T55f,viewof graphs = { let num_columns = 2; let height = 30 + parseInt(data_selection.length / num_columns) ; if (data_selection.length == 0){ const context = DOM.context2d(width, height+50); context.clearRect(0, 0, width, height); context.font = "20px Segoe UI"; //context.fontSize = 10; context.fillText(" ↑↑ Select at least one country from the list above to show its COVID evolution ↑↑" , 20, height ); return context.canvas; } return Plot.plot({ grid: true, width: width, height: height, facet: { data: data_selection, y: "id", columns: num_columns }, y: { grid: true, label: "↑ New cases per million (smoothed)", //domain : [0, maxofmax] }, x:{ label: "Time →", }, color: { scheme: "dark2" }, marks: [ Plot.ruleY([0]), Plot.ruleX([new Date("2020-12-08")], {stroke: "red", strokeDasharray : 10}), Plot.line(data_selection, { x: "parsedDate", y: "new_cases_smoothed_per_million", //"new_deaths_smoothed_per_million" z: "id", curve: "natural", stroke: "id" }), Plot.areaY(data_selection, {x: "parsedDate", y: "new_cases_smoothed_per_million", z: "id", fill: "id", fillOpacity: 0.5}), Plot.text(data_selection, Plot.selectFirst({fontSize:20, text: "full_name", x: new Date('2020-04-25'), y : 29, fill : "id"})) ] }) }4a:[{"id":"1e8d777fe930f685af47b44d7267a4850b6a21f557e50f9f4e0845c3ed995bd2537da2056c891be1b14258448c8b1a024a44b789d94498bbfda8f214890a09ef","href":"https://static.observableusercontent.com/files/1e8d777fe930f685af47b44d7267a4850b6a21f557e50f9f4e0845c3ed995bd2537da2056c891be1b14258448c8b1a024a44b789d94498bbfda8f214890a09ef","name":"Screenshot 2022-11-25 at 09.37.13.png","type":"image/png","lastModified":1669399146207.753,"size":253549}] 29:{"id":"f05d7be6a462af84","slug":null,"title":"Exercise 1: COVID waves - OLD","thumbnail":"c0e04d55c4605c46c07538d9460c461e35fc045f963554aaf07221a114223e65","access_level":"public","create_time":"$D2022-11-25T17:59:06.207Z","update_time":"$D2022-11-30T11:40:33.475Z","view_time":null,"first_public_time":"$D2022-11-25T17:59:06.207Z","has_unpublished_edits":null,"fork":{"id":"3521a02aed63341d","slug":"mds-exercise-01","title":"Exercise 1: COVID waves","owner":{"id":"f69d350b474cbc9b","login":"xaquingv","name":""}},"stars":0,"starred":false,"owner":{"type":"team","id":"cf0cffacb5e3b6d7","login":"vogelmann","name":"Vogelmann","avatar_url":"https://avatars-next.observableusercontent.com/14e783e7de3ed861fe13f88f792dbecaf952c64d82da1fcd4d9583136876450d","tier":"starter_2024","bio":"🐦⚙️ Level 28 Aarakocra Tinkerer ||\n🔢♾️ Mathematician & Data Scientist ||\n🚀🤖 PhD (AI + Astrodynamics)\n☕💀 Fueled by caffeine and spite.","followed_by_you":false,"follows_you":false},"version":1452,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"ddb96b3865d46af6","login":"vogelmann","name":"Vogelmann","avatar_url":"https://avatars-next.observableusercontent.com/14e783e7de3ed861fe13f88f792dbecaf952c64d82da1fcd4d9583136876450d","tier":null,"bio":"🐦⚙️ Level 28 Aarakocra Tinkerer ||\n🔢♾️ Mathematician & Data Scientist ||\n🚀🤖 PhD (AI + Astrodynamics)\n☕💀 Fueled by caffeine and spite.","followed_by_you":false,"follows_you":false}],"members":[{"type":"individual","id":"ddb96b3865d46af6","login":"vogelmann","name":"Vogelmann","avatar_url":"https://avatars-next.observableusercontent.com/14e783e7de3ed861fe13f88f792dbecaf952c64d82da1fcd4d9583136876450d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":0,"collection":{"id":"c23cbb7ed4d69966","slug":"wips-and-tests","title":"WIPs and Tests","owner":{"id":"cf0cffacb5e3b6d7","login":"vogelmann","name":"Vogelmann"}},"type":"notebook","body":{"titling":"auto","title":"Exercise 1: COVID waves - OLD","cells":[{"id":0,"value":"# Exercise 1: COVID waves - OLD\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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":39,"value":"FileAttachment(\"Screenshot 2022-11-25 at 09.37.13.png\").image()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":961,"value":"---\n
\n# PART 1 : The COVID dataset","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":972,"value":"Notes:
\n(per borrar en versió definitiva)

\n-La 2a onada va començar aprox. Agost 2020 i va acabar aprox Maig 2021:\nhttps://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/articles/coronaviruscovid19infectionsurveytechnicalarticle/wavesandlagsofcovid19inenglandjune2021\n

\n-La gràfica NYT pinta de vermell els paisos que el seu \"pico\" va ser en primavera, i de groc els que eren abans (i de blau els que eren molt abans).

","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":365,"value":"## The data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":517,"value":"Let's begin by loading the CSV and flattening the data:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":9,"value":"rawdata = (\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","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":890,"value":"data = Object.entries(rawdata)\n .map(([key, value]) => (\n [...value.data.map(d => ({id: key, full_name: value.location, parsedDate: new Date(d.date), ...d}))]\n )).flat()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1445,"value":"Nota: usar 'new cases'; total cases és acomulatiu.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":904,"value":"Inputs.table(data)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1420,"value":"max_data = d3.rollup(\n data.filter(d => (d.parsedDate >= new Date(\"2020-03-01\")) & (d.parsedDate < new Date(\"2021-08-30\") )),\n v => d3.max(v, d => d.new_cases_smoothed_per_million), \n d => d.full_name // group by id\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1424,"value":"Inputs.table(max_data)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1429,"value":"maxofmax = d3.rollup(\n data.filter(d => (d.parsedDate >= new Date(\"2020-03-01\")) & (d.parsedDate < new Date(\"2021-08-30\") )),\n v => d3.max(v, d => d.new_cases_smoothed_per_million)\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1069,"value":"----","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":360,"value":"---\n## The plot (interactive version)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":878,"value":"This is an interactive plot. Select one or multiple countries from the list below to see their COVID evolution during the second wave. The red vertical line marks the deployment date of the Astrazeneca vaccine in the UK (which was the *first* functional vaccine made against the virus).

\n*Known bugs: the selection is reset every time a search is made.*","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1081,"value":"viewof search = Inputs.search(full_data_for_search, {placeholder: \"Type to filter countries/regions\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1025,"value":"viewof items = Inputs.table(search, {header : {name : \"Select/Unselect ALL\"},width: width, maxHeight: 250, required: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":884,"value":"$49","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3,"value":"---\n#### Addendum: data, libraries, other ...","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":924,"value":"import {Plot} from \"@fil/facet-wrap-332\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1351,"value":"Custom function definitions:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1215,"value":"function data_dictionary_to_country (dict) {\n let array_to_return = [];\n\n let value_blacklist = ['High income', 'Low income', 'Lower middle income', 'Upper middle income'];\n\n for (const [key, value] of Object.entries(dict)) {\n if(!value_blacklist.includes(value)) {\n array_to_return.push({\"name\": String(value)});\n }\n }\n\n return array_to_return;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1234,"value":"function convert_item_selection_to_array(item_selection) {\n let array_to_return = [];\n\n for (const [key, value] of Object.entries(item_selection)) { \n array_to_return.push(value.name);\n }\n \n return array_to_return\n \n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1346,"value":"
\n\n#### Additional data processing for the interactive covid graph:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1342,"value":"Country dictionary to feed to the search functions of the interactive graph:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1054,"value":"country_dictionary = Object.entries(rawdata).map(([key, value]) => ({id: key, full_name : value.location}))\n.reduce((acc, x) => ({...acc, [x.id]: x.full_name}), {})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1175,"value":"full_data_for_search = data_dictionary_to_country(country_dictionary) //Object.values(country_dictionary)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1329,"value":"

Final data selection array for the *Interactive Plot*:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":996,"value":"data_selection = data.filter(d => (d.parsedDate >= new Date(\"2020-03-01\")) & (d.parsedDate < new Date(\"2021-08-30\") ))\n.filter(d => convert_item_selection_to_array(items).includes(d.full_name))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4a","stdlib":"1"}} 24:null