33:Tfd5,html`

based on Johns Hopkins' data, as of ${us_stats['Date'].toDateString()}. * based on The COVID Tracking Project's data, as of ${supplement_us_stats['date'].toDateString()}.

Test Results *

NaN

Daily Increase *

+NaN (+NaN%)

Confirmed

${us_stats['Confirmed']}

Daily Increase

+${us_stats['Confirmed_inc']} (+${us_stats['Confirmed_inc_pec']}%)

Recovered *

NaN

Daily Increase *

+NaN (+${us_stats['Recovered_inc_pec']}%)

Deaths

${us_stats['Deaths']}

Daily Increase

+${us_stats['Deaths_inc']} (+${us_stats['Deaths_inc_pec']}%)

` //

Currently Active

${us_stats['Confirmed']-us_stats['Deaths']-us_stats['Recovered']}

Currently Hospitalized ⚠️

${us_stats['Currently Hospitalized']}

⚠️ not all states report this data.

34:T1563,v_us_trend = { const selection = vl.selectSingle('Select') .fields('Type') .bind('legend'); const dd = us_daily_data; const line = vl.markLine() .data(dd.filter(d=> ['Confirmed', 'Recovered', 'Deaths'].includes(d["Type"]))) // .select( // vl.selectInterval().bind('scales').encodings('x') // bind to x-axis scale only // ) .encode( vl.x().fieldT("Date").timeUnit("yearmonthdate"), vl.y().fieldQ("Count"), vl.color().fieldN("Type") .scale({ domain: ['Confirmed', 'Recovered', 'Deaths'], range: ['rgb(220, 68, 68, 1)','rgb(49, 200, 48, 1)', 'rgb(0,0,0,0.6)' ] }) .legend({orient: 'top'}) , vl.tooltip([vl.tooltip().fieldQ("Count"), vl.tooltip().fieldN("Daily Increase"), vl.tooltip().fieldT("Date")]) ) const line_active = vl.markLine() .data(dd.filter(d=> ['Active'].includes(d["Type"]))) .encode( vl.x().fieldT("Date").timeUnit("yearmonthdate"), vl.y().fieldQ("Count"), vl.color().fieldN("Type") .scale({ domain: ['Active'], range: ['rgb(180, 148, 60, 1)'] }) .legend({orient: 'top'}) , vl.tooltip([ vl.tooltip().fieldQ("Count"), vl.tooltip().fieldT("Date")]) ) const line_hos = vl.markLine() .data(dd.filter(d=> ['Currently Hospitalized'].includes(d["Type"]))) .encode( vl.x().fieldT("Date").timeUnit("yearmonthdate"), vl.y().fieldQ("Count"), vl.color().fieldN("Type") .scale({ domain: ['Currently Hospitalized'], range: ['rgb(100, 68, 180, 1)'] }) .legend({orient: 'top'}) , vl.tooltip([ vl.tooltip().fieldQ("Count"), vl.tooltip().fieldT("Date")]) ) const daily_bar = vl.markBar() .data(dd.filter(d=>['Confirmed', 'Test Results'].includes(d["Type"]))) .transform( vl.calculate("if(datum.Type === 'Confirmed', 1, if(datum.Type === 'Test Results', 0, 2))").as("Order") ) .encode( vl.x().fieldT("Date").timeUnit("yearmonthdate"), vl.y().fieldQ("Daily Increase").axis({title: "Daily Counts (Overlayed)"}).stack(null), vl.color().fieldN("Type") .scale({ domain: [ 'Confirmed', 'Test Results' ], range: [ 'rgb(220, 68, 68, 1)', 'rgb(220, 60, 220, 1)' ] }).legend({orient: 'top'}), vl.order().fieldN("Order"), vl.tooltip([vl.tooltip().fieldN("Type"), vl.tooltip().fieldQ("Daily Increase"), vl.tooltip().fieldT("Date") ]) ) const daily_confirmed = vl.markBar() .data(dd.filter(d=>['Confirmed'].includes(d["Type"]))) .transform( vl.calculate("if(datum.Type === 'Confirmed', 1, if(datum.Type === 'Test Results', 0, 2))").as("Order") ) .encode( vl.x().fieldT("Date").timeUnit("yearmonthdate"), vl.y().fieldQ("Daily Increase").axis({title: "Daily Counts (Overlayed)"}).stack(null), vl.color().fieldN("Type") .scale({ domain: ['Confirmed'], range: ['rgb(220, 68, 68, 1)'] }).legend({orient: 'top'}), vl.order().fieldN("Order"), vl.tooltip([vl.tooltip().fieldN("Type"), vl.tooltip().fieldQ("Daily Increase"), vl.tooltip().fieldT("Date") ]) ) if (select_view === 'Cumulative Counts') { return vl.layer(line, line.markCircle()).width(800).render() } else if (select_view === 'Hospitalized') { return vl.layer(line_hos, line_hos.markCircle()).width(800).render() } else if (select_view === 'Active') { return vl.layer(line_active, line_active.markCircle()).width(800).render() } else if (select_view === 'Daily Confirmed') { return vl.layer(daily_confirmed).width(800).render() } else { return vl.layer(daily_bar).width(800).render() } }35:T934,v_us_states ={ const map = vl.markGeoshape({fill: '#ccc', stroke: '#fff', strokeWidth: 1}) .data(vl.topojson(usa).feature('states')); const points = vl.markCircle({fill: 'rgb(230, 68, 68, 0.5)', stroke: 'rgb(230, 68, 68, 0.8)', strokeWidth: 1}) .data(us_data) .transform( vl.calculate('datum.Confirmed - datum.Deaths - datum.Recovered').as('Active') ) .encode( vl.latitude().fieldQ('Latitude'), vl.longitude().fieldQ('Longitude'), vl.tooltip([vl.fieldN('Province/State'), vl.fieldN('County'), vl.fieldQ('Confirmed'), // vl.fieldQ('Daily Increase'), // vl.fieldQ('Active'), // vl.fieldQ('Recovered'), vl.fieldQ('Deaths') ]), vl.size().fieldQ('Confirmed').scale({range: [0, max_confirmed_state/30]}).legend(null), vl.order().fieldQ('Confirmed').sort('descending'), // place smaller circles on top ); return vl.layer(map, points) .project(vl.projection('albersUsa')) .width(900).height(500) .config({view: {stroke: null}}) .render(); } // v_world_states ={ // const map = vl.markGeoshape({fill: '#bbb', stroke: '#fff', strokeWidth: 1}) // .data(vl.topojson(world).feature('countries')); // // const points = vl.markCircle({fill: 'rgb(220, 68, 68, 1)'}) // // .data(us_data) // // .transform( // // vl.calculate('datum.Confirmed - datum.Deaths - datum.Recovered').as('Active') // // ) // // .encode( // // vl.latitude().fieldQ('Latitude'), // // vl.longitude().fieldQ('Longitude'), // // vl.tooltip([vl.fieldN('Province/State'), // // vl.fieldQ('Confirmed'), // // vl.fieldQ('Daily Increase'), // // vl.fieldQ('Active'), // // vl.fieldQ('Recovered'), // // vl.fieldQ('Deaths') // // ]), // // vl.size().fieldQ('Confirmed').scale({range: [0, 2000]}).legend(null), // // vl.order().fieldQ('Confirmed').sort('descending'), // place smaller circles on top // // ); // return vl.layer(map) // .project(vl.projection('mercator').translate([347, 836]).scale(600)) // .width(900).height(500) // .config({view: {stroke: null}}) // .render(); // }36:T989,{ const selection = vl.selectMulti('Select') .fields('State/Region') .bind('legend'); const line = vl.markLine() .data(daily_states_data.filter(d=> (d.Date>new Date(2020, 1, 21)) & d.Confirmed>0)) .encode( vl.x().fieldT("Date").timeUnit("yearmonthdate"), // date parsing! vl.y().fieldQ("Confirmed").scale({type: y_scale_state}), vl.color().fieldN('State/Region').legend({orient: 'top', columns: 10}).sort({"op": "max", "field": "Confirmed", "order": "descending"}).scale( { range: ["#FF4A46", "#FF34FF", "#008941", "#006FA6", "#A30059", "#7A4900", "#0000A6", "#63FFAC", "#B79762", "#004D43", //10 "#8FB0FF", "#997D87", "#5A0007", "#809693", "#1B4400", "#4FC601", "#3B5DFF", "#4A3B53", "#FF2F80", "#61615A", //20 "#BA0900", "#6B7900", "#00C2A0", "#FFAA92", "#FF90C9", "#B903AA", "#D16100", "#7B4F4B", "#A1C299", "#0AA6D8", //30 "#013349", "#00846F", "#372101", "#FFB500", "#C2FFED", "#A079BF", "#CC0744", "#C0B9B2", "#C2FF99", "#001E09", "#00489C", "#6F0062", "#0CBD66", "#EEC3FF", "#456D75", "#B77B68", "#7A87A1", "#788D66", "#885578", "#FAD09F", "#FF8A9A", "#D157A0", "#BEC459", "#456648", "#0086ED", "#886F4C", "#34362D", "#B4A8BD", "#00A6AA", "#452C2C", "#636375", "#A3C8C9", "#FF913F", "#938A81", "#575329", "#00FECF", "#B05B6F", "#8CD0FF", "#3B9700", "#04F757", "#C8A1A1", "#1E6E00", "#7900D7", "#A77500", "#6367A9", "#A05837", "#6B002C", "#772600", "#D790FF", "#9B9700", "#549E79", "#FFF69F", "#201625", "#72418F", "#BC23FF", "#99ADC0", "#3A2465", "#922329", "#5B4534", "#FDE8DC", "#404E55", "#0089A3", "#CB7E98", "#A4E804", "#324E72", "#6A3A4C", "#1CE6FF" ] } ), vl.opacity().if(selection, vl.value(0.75)).value(0.05), vl.tooltip([vl.tooltip().fieldQ("Confirmed"), vl.tooltip().fieldQ("Deaths"), vl.tooltip().fieldN('State/Region'), vl.tooltip().fieldT("Date")]) ) return vl.layer(line.select(selection), line.markCircle()).width(860).height(350) // .title("Number of Cases in Different States") .render() }37:T6d6,v_countries_trend = { const which = {"no normalization": "Confirmed", "normalized by population": "Confirmed (per million)" }; const which_scale = {"linear": {"no normalization": "linear", "normalized by population": "linear" }, "log": {"no normalization": "log", "normalized by population": "symlog" }}; const selection = vl.selectSingle('Select') .fields('Country/Region') .bind('legend'); const line = vl.markLine() .data(daily_data.filter(d => (d.Date>new Date(2020, 1, 11)) & (['Chile', 'US', 'Brazil', 'Russia', 'Iran', 'Spain', 'India', 'Peru', 'Italy', 'UK'].includes(d['Country/Region']) )) ) .encode( vl.x().fieldT("Date").timeUnit("yearmonthdate"), // date parsing! vl.y().fieldQ(which[normalization]).scale({type: which_scale[y_scale][normalization]}), vl.color().fieldN('Country/Region').sort({"op": "max", "field": "Confirmed", "order": "descending"}) .scale({ range: ['rgb(220, 68, 68)', 'rgb(194,185,105)', 'rgb(94,185,105)', 'rgb(76,120,198)', 'rgb(94,185,205)', 'rgb(176,120,198)', 'rgb(194,135,85)', 'rgb(76,220,198)', 'rgb(158,87,86)', 'rgb(258,87,186)', ] }).legend({orient:"top"}), vl.opacity().if(selection, vl.value(0.75)).value(0.05), vl.tooltip([vl.tooltip().fieldQ(which[normalization]), vl.tooltip().fieldN('Country/Region'), vl.tooltip().fieldT("Date")]) ) return vl.layer(line.select(selection), line.markCircle()).width(860).height(350).render() }38:Td05,md ` # State dashboards For much more detailed local county and state dashboards, click on one of the following links: | | | | | | |:----------|:-------------|:------|:------|:------| |[Alabama](https://alpublichealth.maps.arcgis.com/apps/opsdashboard/index.html#/6d2771faa9da4a2786a509d82c8cf0f7) | [Alaska](https://coronavirus-response-alaska-dhss.hub.arcgis.com/) | [Arizona](https://azdhs.gov/preparedness/epidemiology-disease-control/infectious-disease-epidemiology/index.php#novel-coronavirus-home) | [Arkansas](https://adem.maps.arcgis.com/apps/opsdashboard/index.html#/f533ac8a8b6040e5896b05b47b17a647) | [California](https://public.tableau.com/views/COVID-19PublicDashboard/Covid-19Public?:embed=y&:display_count=no&:showVizHome=no) | | [Colorado](https://covid19.colorado.gov/case-data) | [Delaware](https://coronavirus.delaware.gov/) |[Florida](https://experience.arcgis.com/experience/96dd742462124fa0b38ddedb9b25e429) | [Idaho](https://public.tableau.com/profile/idaho.division.of.public.health#!/vizhome/DPHIdahoCOVID-19Dashboard_V2/DPHCOVID19Dashboard2) | [Indiana](https://coronavirus.in.gov/) | | [Iowa](https://coronavirus.iowa.gov/) | [Kansas](https://kdhe.maps.arcgis.com/apps/opsdashboard/index.html#/05f4169dc6394aa98895072b94734134) | [Louisiana](http://ldh.la.gov/Coronavirus/) | [Maryland](https://coronavirus.maryland.gov/) | [Michigan](https://www.michigan.gov/coronavirus/0,9753,7-406-98163_98173---,00.html) | | [Minnesota](https://mndps.maps.arcgis.com/apps/opsdashboard/index.html#/f28f84968c1148129932c3bebb1d3a1a) |[Missouri](https://health.mo.gov/living/healthcondiseases/communicable/novel-coronavirus/results.php) | [Montana](https://montana.maps.arcgis.com/apps/MapSeries/index.html?appid=7c34f3412536439491adcc2103421d4b) | [Nebraska](https://nebraska.maps.arcgis.com/apps/opsdashboard/index.html#/4213f719a45647bc873ffb58783ffef3) | [Nevada](https://app.powerbigov.us/view?r=eyJrIjoiMjA2ZThiOWUtM2FlNS00MGY5LWFmYjUtNmQwNTQ3Nzg5N2I2IiwidCI6ImU0YTM0MGU2LWI4OWUtNGU2OC04ZWFhLTE1NDRkMjcwMzk4MCJ9) | | [New Jersey](https://covid19.nj.gov/#live-updates) | [North Carolina](https://www.ncdhhs.gov/divisions/public-health/covid19/covid-19-nc-case-count) | [North Dakota](https://www.health.nd.gov/diseases-conditions/coronavirus/north-dakota-coronavirus-cases) | [Ohio](https://coronavirus.ohio.gov/wps/portal/gov/covid-19/home/dashboard) | [Oregon](https://public.tableau.com/profile/oregon.health.authority.covid.19#!/vizhome/OregonHealthAuthorityCOVID-19DataDashboard/COVID-19EPIConfirmed?:display_count=y&:toolbar=n&:origin=viz_share_link&:showShareOptions=false) | | [South Carolina](https://www.scdhec.gov/infectious-diseases/viruses/coronavirus-disease-2019-covid-19/monitoring-testing-covid-19) | [Texas](https://txdshs.maps.arcgis.com/apps/opsdashboard/index.html#/ed483ecd702b4298ab01e8b9cafc8b83) | [Utah](https://coronavirus.utah.gov/case-counts/) | [Virginia](http://www.vdh.virginia.gov/coronavirus/) | [Washington](https://www.doh.wa.gov/Emergencies/Coronavirus) | | [West Virginia](https://dhhr.wv.gov/COVID-19/Pages/default.aspx) | [Wyoming](https://health.wyo.gov/publichealth/infectious-disease-epidemiology-unit/disease/novel-coronavirus/covid-19-map-and-statistics/) --- `39:T757,{ const selection = vl.selectSingle('Select') .fields('Country/Region') .bind('legend'); const line = vl.markLine() .data(daily_data.filter(d => ((d['Date']<=scrubber_date) & d['Daily Increase (7-day moving average)']>=1) & (['Mainland China', 'US', 'Italy', 'Iran', 'South Korea', 'Spain', 'Germany', 'France', 'Switzerland', 'UK', 'Chile', 'Brazil', 'Russia', 'India', 'Peru'].includes(d['Country/Region']) )) ) .encode( vl.x().fieldQ("Confirmed") .scale({ domain: [1, max_confirmed], type: 'log'}), vl.y().fieldQ('Daily Increase (7-day moving average)') .scale({ domain: [1, max_daily_inc], type: 'log'}), vl.color().fieldN('Country/Region').sort({"op": "max", "field": "Confirmed", "order": "descending"}) .scale({ domain: ['US', 'Brazil', 'Russia', 'India', 'Spain', 'Peru', 'Italy', 'Chile', 'France', 'Germany', 'Mainland China', 'Iran', 'UK', 'Switzerland', 'South Korea', ], range: ['rgb(220, 68, 68)', 'rgb(194,185,105)', 'rgb(94,185,105)', 'rgb(76,120,198)', 'rgb(94,185,205)', 'rgb(176,120,198)', 'rgb(194,135,85)', 'rgb(76,220,198)', 'rgb(158,87,86)', 'rgb(258,87,186)', 'rgb(16,120,198)', 'rgb(194,15,85)', 'rgb(76,20,198)', 'rgb(158,87,186)', 'rgb(258,187,186)' ] }).legend({orient:"top"}), vl.opacity().if(selection, vl.value(0.75)).value(0.05), vl.tooltip([vl.tooltip().fieldQ('Daily Increase (7-day moving average)'), vl.tooltip().fieldQ('Confirmed'), vl.tooltip().fieldN('Country/Region'), vl.tooltip().fieldT("Date")]) ) return vl.layer(line.select(selection), line.markCircle()).width(860).height(350).render(); }3a:T4cb,md `# Data Sources and Caveats The visualizations above are MOSTLY based on [Johns Hopkins' data](https://github.com/CSSEGISandData/COVID-19), and focused on the US. The ones with "*****" are pulled from [the COVID Tracking Project](https://covidtracking.com/api). - Johns Hopkins' data: You can find out more information about world-wide cases on their dashboard: - [Johns Hopkins' COVID-19 Global Cases Dashboard - Desktop](https://www.arcgis.com/apps/opsdashboard/index.html#/bda7594740fd40299423467b48e9ecf6) - [Johns Hopkins' COVID-19 Global Cases Dashboard - Mobile](http://www.arcgis.com/apps/opsdashboard/index.html#/85320e2ea5424dfaaa75ae62e5c06e61) The Johns Hopkins' data may not be most up-to-date, nor most accurate. From our visualization, one can easily spot that the data for some countries were not updated on certain days. Here's an alternative source of data and dashboard for comparison: [Coronavarius Cases Tracking by Worldometer](https://www.worldometers.info/coronavirus/) - The COVID Tracking Project: Unfortunately, they have ended their data project on March 7th, 2021. So before we find alternative data source for these numbers, we will leave these numbers as “NaN”. `3b:T400,{ Date.prototype.addDays = function(days) { var date = new Date(this.valueOf()); date.setDate(date.getDate() + days); return date; }; Date.prototype.mmddyyyy = function() { var mm = this.getMonth() + 1; // getMonth() is zero-based var dd = this.getDate(); return [ (mm>9 ? '' : '0') + mm, (dd>9 ? '' : '0') + dd, this.getFullYear() ].join('-'); }; Date.prototype.yyyymmdd_int = function() { var mm = this.getMonth() + 1; // getMonth() is zero-based var dd = this.getDate(); return parseInt([ this.getFullYear(), (mm>9 ? '' : '0') + mm, (dd>9 ? '' : '0') + dd ].join('')); }; Date.prototype.mdyy = function() { var mm = this.getMonth() + 1; // getMonth() is zero-based var dd = this.getDate(); var yyyy = this.getFullYear(); return [ mm.toString(), dd.toString(), yyyy.toString().slice(-2) ].join('/'); }; }3c:Td5f,daily_data = {const dates = getDates(new Date(2020, 2, 15), new Date()); var output = []; var ma_confirmed_inc = {}; var pre_confirmed = {}; for (var date of dates) { var date_string = mmddyyyy(date); var p = daily_report_path + date_string + '.csv'; try { var dat = (await d3.csv(p, d3.autoType)); // fix some data dat.forEach((d, index, arr) => { arr[index] = {"Country/Region": fix_data(d["Country/Region"] || d["Country_Region"]), "Date": date, "Confirmed": d["Confirmed"], "Deaths": d["Deaths"], "Recovered": d["Recovered"], "Active": d["Active"] } } ); var grouped_data = d3.nest() //set the key .key(function(d) {return d["Country/Region"];}) //rollup and sum values by country/region .rollup((function(d) { return { "Date": date, "Confirmed": d3.sum(d, function(e) { return e["Confirmed"]; }), "Confirmed (per million)": d3.sum(d, function(e) { return e["Confirmed"]*1000000 / pop_dict[0][e["Country/Region"] || e["Country_Region"]]; }), "Deaths": d3.sum(d, function(e) { return e["Deaths"]; }), "Recovered": d3.sum(d, function(e) { return e["Recovered"]; }), "Active": d3.sum(d, function(e) { return e["Active"]; }) }; })) .entries(dat); var daily_data = flattern_nested_data(grouped_data, 'Country/Region'); var new_daily_data = []; for (var d of daily_data) { var country = d["Country/Region"]; // calculate daily increase of confirmed if (!(country in pre_confirmed)) { var daily_inc = d['Confirmed']; } else { // fix confirmed if lower than previous one if (d['Confirmed'] < pre_confirmed[country]) { d['Confirmed'] = pre_confirmed[country]; } var daily_inc = d['Confirmed'] - pre_confirmed[country]; } pre_confirmed[country] = d['Confirmed']; d['Confirmed_Inc'] = daily_inc; // create country key to start storing 7 days of daily increases if (!(country in ma_confirmed_inc)) { ma_confirmed_inc[country] = []; d['7-day daily increase'] = null; d['Daily Increase (7-day moving average)'] = null; } else if (ma_confirmed_inc[country].length<6) { var ma = [...ma_confirmed_inc[country]]; ma.push(daily_inc); ma_confirmed_inc[country] = ma; } else { // calculate moving averages var ma = [...ma_confirmed_inc[country]]; if (ma.length>=7) { ma.shift(); }; ma.push(daily_inc); ma_confirmed_inc[country] = ma; d['7-day daily increase'] = ma; d['Daily Increase (7-day moving average)'] = ma.reduce((a, b) => a + b) / ma.length; }; // append new_daily_data.push(d); } output = output.concat(daily_data); } catch {}; }; return output }3d:T6fb,function get_daily_data(country) { const d = daily_data.filter(d => (d.Date>new Date(2020, 0, 19)) & ((d['Country/Region']===country ) | (d["Country_Region"]===country) )); var output = []; var previous_day_confirmed = 0; var previous_day_deaths = 0; var previous_day_recovered = 0; var previous_day_active = 0; for (var elem of d) { for (var key of ['Confirmed', 'Deaths', 'Recovered', 'Active']) { var rec = { "Country/Region": country , "Date": elem["Date"] }; rec["Count"] = elem[key]; rec["Type"] = key; if (key === 'Confirmed') { if (elem[key]>=previous_day_confirmed) { rec["Daily Increase"] = elem[key] - previous_day_confirmed; previous_day_confirmed = elem[key]; } else { rec["Daily Increase"] = 0; } } else if (key === "Deaths") { if (elem[key]>=previous_day_deaths) { rec["Daily Increase"] = elem[key] - previous_day_deaths; previous_day_deaths = elem[key]; } else { rec["Daily Increase"] = 0; } } else if (key === "Recovered") { if (elem[key]>=previous_day_recovered) { rec["Daily Increase"] = elem[key] - previous_day_recovered; previous_day_recovered = elem[key]; } else { rec["Daily Increase"] = 0; } } else if (key === "Active") { if (elem[key]>=previous_day_active) { rec["Daily Increase"] = elem[key] - previous_day_active; previous_day_active = elem[key]; } else { rec["Daily Increase"] = 0; } } output.push(rec) }; } return output }3e:Ta3e,us_daily_data = { var output = get_daily_data('US'); var dd = get_daily_data('US'); for (var d of daily_supplement_data.filter(d=> d.date.yyyymmdd_int() <=max_date.yyyymmdd_int())) { var ds = {}; ds["Country/Region"]= 'US'; ds["Date"]= d["date"]; ds['Count'] = d['hospitalizedCurrently']; ds['Daily Increase'] = d['hospitalizedIncrease']; ds['Type'] = 'Currently Hospitalized'; output.push(ds); var dt = {}; dt["Country/Region"]= 'US'; dt["Date"]= d["date"]; dt['Count'] = d['totalTestResults']; dt['Daily Increase'] = d['totalTestResultsIncrease']; dt['Type'] = 'Test Results'; output.push(dt); // var dn = {}; // var dc = dd.filter(ddd => (ddd["Date"].yyyymmdd_int() === d['date'].yyyymmdd_int()) // & (ddd["Type"]==="Confirmed"))[0]; // dn["Country/Region"]= 'US'; // dn["Date"]= d["date"]; // dn['Count'] = d['totalTestResults'] - dc['Count'] > 0 ? d['totalTestResults'] - dc['Count']: 0; // dn['Daily Increase'] = d['totalTestResultsIncrease'] - dc['Daily Increase'] > 0 ? d['totalTestResultsIncrease'] - dc['Daily Increase']: 0; // dn['Type'] = 'Test Results'; // output.push(dn); // var da = {}; // try { // var d_c = dd.filter(ddd => (ddd["Date"].yyyymmdd_int() === d['date'].yyyymmdd_int()) // & (ddd["Type"]==="Confirmed"))[0]['Count']; // var d_r = dd.filter(ddd => (ddd["Date"].yyyymmdd_int() === d['date'].yyyymmdd_int()) // & (ddd["Type"]==="Recovered"))[0]['Count']; // var d_d = dd.filter(ddd => (ddd["Date"].yyyymmdd_int() === d['date'].yyyymmdd_int()) // & (ddd["Type"]==="Deaths"))[0]['Count']; // var d_ci = dd.filter(ddd => (ddd["Date"].yyyymmdd_int() === d['date'].yyyymmdd_int()) // & (ddd["Type"]==="Confirmed"))[0]['Daily Increase']; // var d_ri = dd.filter(ddd => (ddd["Date"].yyyymmdd_int() === d['date'].yyyymmdd_int()) // & (ddd["Type"]==="Recovered"))[0]['Daily Increase']; // var d_di = dd.filter(ddd => (ddd["Date"].yyyymmdd_int() === d['date'].yyyymmdd_int()) // & (ddd["Type"]==="Deaths"))[0]['Daily Increase']; // da["Country/Region"]= 'US'; // da["Date"]= d["date"]; // da['Count'] = d_c - d_r - d_d > 0 ? d_c - d_r - d_d : 0; // da['Daily Increase'] = d_ci - d_ri - d_di > 0 ? d_ci - d_ri - d_di : 0; // da['Type'] = 'Active'; // output.push(da); // } // catch { // }; } return output }3f:T612,us_stats = { var dd = daily_data.filter(d=> (d.Date===max_date) & (d['Country/Region']==='US')); dd = dd[0] var ddy = daily_data.filter(d=> (d.Date>=previous_day) & (d.Datedd['Confirmed']) {dd['Confirmed']=dd['Confirmed_pre']}; if (dd['Recovered_pre']>dd['Recovered']) {dd['Recovered']=dd['Recovered_pre']}; if (dd['Deaths_pre']>dd['Deaths']) {dd['Deaths']=dd['Deaths_pre']}; dd['Confirmed_inc'] = dd['Confirmed'] - ddy['Confirmed']; dd['Recovered_inc'] = dd['Recovered'] - ddy['Recovered']; dd['Deaths_inc'] = dd['Deaths'] - ddy['Deaths']; dd['Confirmed_inc_pec'] = parseFloat(((dd['Confirmed_inc']/ dd['Confirmed_pre'])*100).toFixed(2)); dd['Confirmed_inc_sign'] = dd['Confirmed_inc_pec']>0 ? '+' : '-'; dd['Confirmed_inc_pec'] = Math.abs(dd['Confirmed_inc_pec']); dd['Recovered_inc_pec'] = (dd['Recovered_inc'] / dd['Recovered_pre']*100).toFixed(2); dd['Deaths_inc_pec'] = (dd['Deaths_inc'] / dd['Deaths_pre']*100).toFixed(2); //supplement data dd['Tested'] = supplement_us_stats['totalTestResults']; dd['Tested_inc'] = supplement_us_stats['totalTestResultsIncrease']; dd['Tested_inc_pec'] = (dd['Tested_inc']/dd['Tested']*100).toFixed(2); dd['Currently Hospitalized'] = supplement_us_stats['hospitalizedCurrently']; dd['Hospitalized'] = daily_report_us_data['Hospitalized'] return dd }40:T49c,function get_stats(country) { var dd = daily_data.filter(d=> (d.Date===max_date) & (d['Country/Region']===country)); dd = dd[0] var ddy = daily_data.filter(d=> (d.Date>=previous_day) & (d.Datedd['Confirmed']) {dd['Confirmed']=dd['Confirmed_pre']}; if (dd['Recovered_pre']>dd['Recovered']) {dd['Recovered']=dd['Recovered_pre']}; if (dd['Deaths_pre']>dd['Deaths']) {dd['Deaths']=dd['Deaths_pre']}; dd['Confirmed_inc'] = dd['Confirmed'] - ddy['Confirmed']; dd['Recovered_inc'] = dd['Recovered'] - ddy['Recovered']; dd['Deaths_inc'] = dd['Deaths'] - ddy['Deaths']; dd['Confirmed_inc_pec'] = parseFloat(((dd['Confirmed_inc']/ dd['Confirmed_pre'])*100).toFixed(2)); dd['Confirmed_inc_sign'] = dd['Confirmed_inc_pec']>0 ? '+' : '-'; dd['Confirmed_inc_pec'] = Math.abs(dd['Confirmed_inc_pec']); dd['Recovered_inc_pec'] = dd['Recovered_inc'] / dd['Recovered_pre']; dd['Deaths_inc_pec'] = dd['Deaths_inc'] / dd['Deaths_pre']; return dd }41:T532,daily_states_confirmed = { const dates = getDates(new Date(2020, 0, 11), new Date()); var output = []; var dat = (await d3.csv(time_series_us_confirmed)); var grouped_data = d3.nest() //set the key .key(function(d) {return d["Province_State"];}) //rollup and sum values .rollup((function(d) { var date_counts = {}; for (var date of dates) { try { date_counts[date.mdyy()] = d3.sum(d, function(e) { return parseInt(e[date.mdyy()]); }) } catch {}; }; return date_counts })) .entries(dat); var flat_dat = flattern_nested_data(grouped_data, 'State/Region'); // create Date columns var output = [] for (var d of flat_dat) { var expanded = [] for (var date of dates) { if (date.mdyy() in d) { expanded.push({"State/Region": d["State/Region"], "Date": date, "Confirmed": d[date.mdyy()] }) } }; output = output.concat(expanded) } return output };42:T529,daily_states_deaths = { const dates = getDates(new Date(2020, 0, 11), new Date()); var output = []; var dat = (await d3.csv(time_series_us_deaths)); var grouped_data = d3.nest() //set the key .key(function(d) {return d["Province_State"];}) //rollup and sum values .rollup((function(d) { var date_counts = {}; for (var date of dates) { try { date_counts[date.mdyy()] = d3.sum(d, function(e) { return parseInt(e[date.mdyy()]); }) } catch {}; }; return date_counts })) .entries(dat); var flat_dat = flattern_nested_data(grouped_data, 'State/Region'); // create Date columns var output = [] for (var d of flat_dat) { var expanded = [] for (var date of dates) { if (date.mdyy() in d) { expanded.push({"State/Region": d["State/Region"], "Date": date, "Deaths": d[date.mdyy()] }) } }; output = output.concat(expanded) } return output };43:T4ab,us_data = { var ds = max_date.mmddyyyy(); var p = daily_report_path + ds + '.csv'; var dat = (await d3.csv(p, d3.autoType)); var dd = dat.filter(d => (d['Country_Region']==='US')|(d['Country/Region']==='US')) var ds2 = previous_day.mmddyyyy(); var p2 = daily_report_path + ds2 + '.csv'; var dat2 = (await d3.csv(p2, d3.autoType)); var ddy = dat2.filter(d => (d['Country_Region']==='US')|(d['Country/Region']==='US')) var output = []; for (var d of dd) { var state = d['Province_State']; var dy_arr = ddy.filter(d => (d['Province_State']===state)|(d['Province/State']===state)); var dy = dy_arr.length!=0 ? dy_arr[0] : {"Confirmed": 0}; var pre_confirmed = dy != 0 ? dy["Confirmed"]: 0; var rec = { "Province/State": d['Province_State'], "Country/Region": d['Country_Region'], "County": d['Admin2'], "Confirmed": d["Confirmed"], "Deaths": d["Deaths"], "Recovered": d["Recovered"], "Latitude": d["Lat"], "Longitude": d["Long_"], "Confirmed (Previous Day)": pre_confirmed, "Daily Increase": d["Confirmed"] - pre_confirmed }; output.push(rec); } return output }44:T75f,other_countries_backup = {// html` //

Italy

Confirmed

${stats_italy['Confirmed']}

Daily Increase

${stats_italy['Confirmed_inc']} (${stats_italy['Confirmed_inc_sign']}${stats_italy['Confirmed_inc_pec']}%)

Recovered

${stats_italy['Recovered']}

Deaths

${stats_italy['Deaths']}

// ` }45:[] 31:{"title":"Tracking COVID-19 Outbreaks in the United States","cells":[{"id":240,"value":"md `# Tracking COVID-19 Outbreaks in the United States`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2635,"value":"md `It will take a few minutes for the data on this page to be processed. Please be patient.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":676,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":943,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1120,"value":"viewof select_view = select({\n title: \"Select View\",\n options: [\"Daily Confirmed\",\n \"Cumulative Counts\", \n \"Daily Counts\", \n // \"Hospitalized\",\n // \"Active\"\n ],\n value: \"US\"\n})\n// viewof select_country = select({\n// title: \"Select Country\",\n// options: [\"Italy\", \"Iran\", \"Spain\", \"Germany\", \"US\", \"France\", \"UK\", \"Switzerland\", \"South Korea\"],\n// value: \"US\"\n// })","pinned":false,"hidden":false,"mode":"ojs"},{"id":859,"value":"html`\n

\n${v_us_trend}\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":482,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1073,"value":"html`\n

\n${v_us_states}\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":953,"value":"md ``","pinned":false,"hidden":false,"mode":"ojs"},{"id":1702,"value":"md `## Comparison Between States`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1606,"value":"viewof y_scale_state = select({\n title: \"Scale for Y Axis\",\n options: [\"linear\", \"log\"],\n value: \"linear\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1594,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1506,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":1204,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"},{"id":572,"value":"md `# Comparison With Other Countries`","pinned":false,"hidden":false,"mode":"ojs"},{"id":591,"value":"viewof y_scale = select({\n title: \"Scale for Y Axis\",\n options: [\"linear\", \"log\"],\n value: \"linear\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1404,"value":"viewof normalization = select({\n title: \"Normalization\",\n options: [\"no normalization\", \"normalized by population\"],\n value: \"no normalization\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1079,"value":"html`\n

\n${v_countries_trend}\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1721,"value":"md `## How to Tell when the end is near?`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2284,"value":"viewof scrubber_date = Scrubber(dates, {\n autoplay: true,\n format: date => date.toLocaleString(\"en\", { year: \"numeric\", month: \"long\", day: \"numeric\" }),\n delay: 100\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1720,"value":"$39","pinned":false,"hidden":false,"mode":"ojs"},{"id":1884,"value":"html `

When plotted this way, a turn downward of the curve indicates that the cases are starting to get under control.

\n
\n

\nFor more detailed customization and exploration, checkout the original author's version: https://aatishb.com/covidtrends.\nFor an explanation of this plot, watch this video: How to Tell If We're Beating COVID-19.\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":989,"value":"html `\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":968,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs"},{"id":956,"value":"html `\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":963,"value":"md `# Calculations`","pinned":false,"hidden":false,"mode":"ojs"},{"id":0,"value":"md`## Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":293,"value":"daily_report_path = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports/'","pinned":false,"hidden":false,"mode":"ojs"},{"id":2397,"value":"daily_report_us_path = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_daily_reports_us/\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1558,"value":"time_series_us_confirmed=\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_US.csv\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1627,"value":"time_series_us_deaths = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_US.csv\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1729,"value":"track_covid_proj_path = 'https://api.covidtracking.com/v1/us/daily.csv'","pinned":false,"hidden":false,"mode":"ojs"},{"id":2497,"value":"function mmddyyyy(date) {\n var mm = date.getMonth() + 1; // getMonth() is zero-based\n var dd = date.getDate();\n\n return [\n (mm>9 ? '' : '0') + mm,\n (dd>9 ? '' : '0') + dd,\n date.getFullYear()\n ].join('-');\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2507,"value":"function addDays(date, days) {\n var date = new Date(date.valueOf());\n date.setDate(date.getDate() + days);\n return date;\n} ","pinned":false,"hidden":false,"mode":"ojs"},{"id":2509,"value":"function yyyymmdd_int(date) {\n var mm = date.getMonth() + 1; // getMonth() is zero-based\n var dd = date.getDate();\n\n return parseInt([\n this.getFullYear(),\n (mm>9 ? '' : '0') + mm,\n (dd>9 ? '' : '0') + dd\n ].join(''));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2511,"value":"function mdyy(date) {\n var mm = date.getMonth() + 1; // getMonth() is zero-based\n var dd = date.getDate();\n var yyyy = date.getFullYear();\n\n return [\n mm.toString(),\n dd.toString(),\n yyyy.toString().slice(-2)\n ].join('/');\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":278,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs"},{"id":322,"value":"function getDates(startDate, stopDate) {\n var dateArray = new Array();\n var currentDate = startDate;\n while (currentDate <= stopDate) {\n dateArray.push(new Date (currentDate));\n currentDate = currentDate.addDays(1);\n }\n return dateArray;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":288,"value":"function flattern_nested_data(data, group_column) {\n var output = []\n for (var d of data) {\n var flat_d = {};\n flat_d[group_column] = d['key'];\n var values = d['value'];\n \n for (var vk in values) {\n flat_d[vk] = values[vk];\n };\n output.push(flat_d);\n };\n return output\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":461,"value":"function flattern_nested_data_2(data, column1, column2) {\n var output = []\n for (var d of data) {\n var values = d['values'];\n for (var v of values) {\n var flat_d = {};\n flat_d[column1] = d['key'];\n flat_d[column2] = v['key'];\n var value = v['value'];\n for (var vk in value) {\n flat_d[vk] = value[vk];\n };\n output.push(flat_d);\n };\n \n };\n return output\n};","pinned":false,"hidden":false,"mode":"ojs"},{"id":606,"value":"function fix_data(d) {\n if (d==='China') {\n return 'Mainland China'\n } else if (d==='Korea, South') {\n return 'South Korea'\n } else if (d==='United Kingdom') {\n return 'UK'\n } else {\n return d\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":525,"value":"max_date = {\n var md = null;\n for (var d of daily_data) {\n if (md == null) {\n md = d.Date;\n } else if (md < d.Date) {\n md = d.Date\n }\n };\n return md\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2288,"value":"min_date = new Date(2020, 0, 28)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2291,"value":"dates = getDates(min_date, max_date)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1022,"value":"previous_day = {\n var previous_day = new Date(max_date.getTime())\n previous_day.setDate(max_date.getDate() - 1);\n return previous_day\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1369,"value":"pop_dict = [{\"US\": 331002651, \n \"Italy\": 60461826,\n \"Spain\": 46754778,\n \"France\": 65273511,\n \"Germany\": 83783942,\n \"Iran\": 83992949,\n \"UK\": 67886011,\n \"South Korea\": 51269185,\n \"Mainland China\": 1439323776\n }]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1864,"value":"function yyyymmdd_to_date(s) {\n var year = Math.floor(s/10000);\n var month = Math.floor(s%10000/100);\n var day = s%100;\n var date = [\n year.toString(),\n (month>9 ? '' : '0') + month,\n (day>9 ? '' : '0') + day\n ].join('-');\n \n return new Date(date+'T10:20:30Z')\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2132,"value":"daily_supplement_data = {\n var dat = (await d3.csv(track_covid_proj_path, d3.autoType));\n var output = [];\n for (var d of dat) {\n d['date'] = yyyymmdd_to_date(d['date']);\n output.push(d)\n };\n return output\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2402,"value":"daily_report_us_data = {\n var date_string = max_date.mmddyyyy();\n var p = daily_report_us_path + date_string + '.csv';\n var dat = (await d3.csv(p, d3.autoType));\n var grouped_data = d3.nest()\n //set the key\n .key(function(d) {return d[\"Country_Region\"];}) \n //rollup and sum values by country/region\n .rollup((function(d) {\n return {\n \"Date\": max_date,\n \"Hospitalized\": d3.sum(d, function(e) { return e[\"People_Hospitalized\"]; })\n };\n }))\n .entries(dat);\n var dd = flattern_nested_data(grouped_data, 'Country_Region')[0];\n return dd\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1732,"value":"supplement_us_stats = {\n var dat = daily_supplement_data;\n try {\n var output = dat.filter(d => d.date.yyyymmdd_int() <= max_date.yyyymmdd_int())[0];\n } catch {\n var output = dat[0];\n }\n return output\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":312,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs"},{"id":1095,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs"},{"id":2602,"value":"tt = daily_data.filter(d=> (d.Date.yyyymmdd_int()>20210222) & (d[\"Country/Region\"]==='US'))","pinned":false,"hidden":false,"mode":"ojs"},{"id":2148,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs"},{"id":680,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1171,"value":"$40","pinned":false,"hidden":false,"mode":"ojs"},{"id":1182,"value":"stats_italy = get_stats(\"Italy\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1218,"value":"stats_iran = get_stats(\"Iran\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1220,"value":"stats_spain = get_stats(\"Spain\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1222,"value":"stats_germany = get_stats(\"Germany\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1224,"value":"stats_france = get_stats(\"France\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1226,"value":"stats_uk = get_stats(\"UK\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1228,"value":"stats_switzerland = get_stats(\"Switzerland\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1230,"value":"stats_south_korea = get_stats(\"South Korea\");","pinned":false,"hidden":false,"mode":"ojs"},{"id":1557,"value":"$41","pinned":false,"hidden":false,"mode":"ojs"},{"id":1626,"value":"$42","pinned":false,"hidden":false,"mode":"ojs"},{"id":1635,"value":"daily_states_data = {\n var output = []\n for (var dd of daily_states_confirmed) {\n var state = dd['State/Region'];\n var date = dd['Date'].mmddyyyy();\n var sd = daily_states_deaths.filter(d=> (d['State/Region']===state & d['Date'].mmddyyyy()===date));\n if (\"Deaths\" in sd[0]) {\n dd['Deaths'] = sd[0]['Deaths'];\n } else {dd['Deaths'] = 0}\n output.push(dd);\n }\n return output\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1004,"value":"$43","pinned":false,"hidden":false,"mode":"ojs"},{"id":1311,"value":"max_confirmed = {\n var num = 0;\n for (var d of daily_data) {\n if (d['Confirmed'] > num) {\n num = d['Confirmed']\n }\n }\n return num\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2313,"value":"max_daily_inc = {\n var num = 0;\n for (var d of daily_data) {\n if (d['Confirmed_Inc'] > num) {\n num = d['Confirmed_Inc']\n }\n }\n return num\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2327,"value":"max_confirmed_state = {\n var num = 0;\n for (var d of us_data) {\n if (d['Confirmed'] > num) {\n num = d['Confirmed']\n }\n }\n return num\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1958,"value":"googleAnalytics = UA => {\n const script = document.createElement('script');\n script.src = 'https://www.googletagmanager.com/gtag/js?id=${UA}';\n script.type = 'text/javascript';\n script.async = true;\n\n script.onload = () => {\n window.dataLayer = window.dataLayer || [];\n function gtag() {\n var dataLayer = window.dataLayer;\n dataLayer.push(arguments);\n }\n gtag('js', new Date());\n\n gtag('config', UA);\n };\n // document.body.appendChild(script);\n document.getElementsByTagName('head')[0].appendChild(script);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1962,"value":"googleAnalytics('UA-162806362-1')","pinned":false,"hidden":false,"mode":"ojs"},{"id":1997,"value":"$44","pinned":false,"hidden":false,"mode":"ojs"},{"id":224,"value":"md `## Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":9,"value":"d3 = require(\"d3-dsv@1\", \"d3-fetch@1\", \"d3@5\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"import {table} from \"@tmcw/tables@513\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":121,"value":"import { vl } from \"@vega/vega-lite-api\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":597,"value":"import {select} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":474,"value":"datasets = require('vega-datasets@1.25')","pinned":false,"hidden":false,"mode":"ojs"},{"id":480,"value":"usa = datasets['us-10m.json']()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1281,"value":"world = datasets['world-110m.json']()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2281,"value":"import { Scrubber } from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W45","stdlib":"1","titling":"auto"}