31:T716,Plot.plot({ width: 650, marginTop: 40, marginLeft: 50, marginRight: 50, grid: true, x: { tickFormat: timeformat_long, nice: true }, y: { zero: true, tickFormat: "%", domain: [0, 1.0], tickSize: 8, tickPadding: 4, labelAnchor: "top", label: ` ↑ Seats Relative to Jan / 20` }, color: { type: "categorical", range: Aplot_countries.colors, domain: Aplot_countries.isoalpha2 }, marks: [ Plot.ruleY([0], { stroke: "#666" }), Plot.line( wan_tc .filter( aq.escape((d) => _.includes(Aplot_countries.isoalpha2, d.isoalpha2)) ) .filter((d) => d.key == "seats_all") .derive({ order: aq.escape((d) => Aplot_countries.order.get(d.isoalpha2)) }) .orderby("order"), { x: "date", y: "value_reljan", stroke: "isoalpha2", strokeWidth: 2.5, strokeOpacity: 1 } ), Plot.dot( wan_tc .filter( aq.escape((d) => _.includes(Aplot_countries.isoalpha2, d.isoalpha2)) ) .filter((d) => d.key == "seats_all") .derive({ order: aq.escape((d) => Aplot_countries.order.get(d.isoalpha2)) }) .orderby("order"), { x: "date", y: "value_reljan", r: 4, fill: "isoalpha2" } ), Plot.text( wan_tc .filter( aq.escape((d) => _.includes(Aplot_countries.isoalpha2, d.isoalpha2)) ) .filter((d) => d.key == "seats_all") .join_left(countries), Plot.selectLast({ x: "date", y: "value_reljan", z: "isoalpha2", text: "country_name", textAnchor: "start", fontSize: 13, fill: "isoalpha2", dx: 5 }) ) ] })32:T447,Plot.plot({ width: 648, height: 400, marginBottom: 60, marginTop: 26, marginLeft: 66, y: { tickSize: 10, tickPadding: 6, tickFormat: (d) => d3.formatPrefix(".0", 1e3)(d), label: `↑ Seats`, labelAnchor: "top", labelOffset: 58, domain: [0, 1.2e6] }, x: { axis: false }, color: { domain: ["Europe", "Americas", "Asia"], range: ["#bd1550", "dodgerblue", "seagreen"], unknown: "#ddd" }, facet: { data: Dplot_data, x: "date", text: "date_label", marginLeft: 0, marginRight: 0, label: null }, fx: { inset: 0, tickFormat: (d) => (d.getMonth(d) % 3 == 0 ? timeformat_long(d) : null), tickSize: 8, tickPadding: 6, label: null }, marks: [ beeswarmY(Dplot_data, { y: "value", x: () => 0, r: 12, fill: "continent_name", mixBlendMode: "multiply" }), beeswarmY_text(Dplot_data, { y: "value", x: () => 0, text: "isoalpha2", fill: "white", color: "white", mixBlendMode: null, fontSize: "12px" }) ] })33:T453,_This plot shows the Top 5 destinations of German air-traffic each month. Most are in ${color_highlight('Europe:', "#bd1550")} Spain (***ES***), Great Britain (***GB***), Italy (***IT***), Austria (***AT***), Rumania (***RO***), Poland (***PL***), Greece (***GR***), Portugal (***PT***) and the Netherlands (***NL***). Two are in ${color_highlight('Asia:', "seagreen")} Turkey (***TR***) and United Arab Emirates (***AE***) and one is in the ${color_highlight('Americas:', "dodgerblue")} the USA (***US***). Note that very close circles are stacked to increase legibility_ We can see that the top destinations were in constand flux during the Covid epidemic, driven by changing travel restrictions and COVID incidences. In the first lockdown, non-European countries disappeared from the Top 5 destinations completely. In the second lockdown we see some unusual entries like Portugal and the United Arab Emirates, that were open to tourism at the time. Summer months were dominated by Mediterranean destinations like, Spain, Greece and Turkey. We can see the shift to the Mediterranean on these maps: 34:Tbf7,{ const w = 200; const h = 180; const proj = vl.projection("mercator").scale(300).rotate([24, -46, -29]); const world = (await require("vega-datasets"))["world-110m.json"].url; const data = wan_tc_germany .filter((d) => d.key === "seats_in") .filter((d) => d.isoalpha2 !== "DE"); const fist_facet = vl.layer( vl .markGeoshape({ fill: "lightgrey", stroke: "whitesmoke" }) .data(vl.topojson(world).feature("countries")) .project(proj), vl .markGeoshape({ stroke: "whitesmoke", strokeWidth: 0.5 }) .data(world_shape) .transform( vl.lookup("properties.iso_alpha2").from( vl .data( data.filter( (d) => op.datetime(2019, 11, 2) < d.date && d.date <= op.datetime(2020, 0, 2) ) ) .key("isoalpha2") .fields(["value"]) ) ) .project(proj) .encode( vl .color() .fieldQ("value") .scale({ domain: [0, 2e5, 5e5, 1e6], range: ["cornsilk", "#e97f02", "#bd1550", "#490a3d"] //["#490a3d", "#bd1550", "#e97f02", "cornsilk"] }) .legend({ title: "Seats", format: "~s", gradientLength: 80 }) ) .width(w) .height(h) .title("January 2020") ); const second_facet = vl.layer( vl .markGeoshape({ fill: "lightgrey", stroke: "whitesmoke" }) .data(vl.topojson(world).feature("countries")) .project(proj), vl .markGeoshape({ stroke: "whitesmoke", strokeWidth: 0.5 }) .data(world_shape) .transform( vl.lookup("properties.iso_alpha2").from( vl .data( data.filter( (d) => op.datetime(2020, 2, 2) < d.date && d.date <= op.datetime(2020, 3, 2) ) ) .key("isoalpha2") .fields(["value"]) ) ) .project(proj) .encode(vl.color().fieldQ("value").scale({ scheme: "goldred" })) .width(w) .height(h) .title("April 2020") ); const third_facet = vl.layer( vl .markGeoshape({ fill: "lightgrey", stroke: "whitesmoke" }) .data(vl.topojson(world).feature("countries")) .project(proj), vl .markGeoshape({ stroke: "whitesmoke", strokeWidth: 0.5 }) .data(world_shape) .transform( vl.lookup("properties.iso_alpha2").from( vl .data( data.filter( (d) => op.datetime(2021, 6, 1) < d.date && d.date <= op.datetime(2021, 11, 1) ) ) .key("isoalpha2") .fields(["value"]) ) ) .project(proj) .encode(vl.color().fieldQ("value").scale({ scheme: "goldred" })) .width(w) .height(h) .title("September 2021") ); return vl .concat([fist_facet, second_facet, third_facet]) .render({ renderer: "svg" }); }35:T49e,## Conclusion While local ground traffic in Germany has recovered in recent months (see our [monitor](https://www.covid-19-mobility.org/mobility-monitor)), air traffic remains severely affected by the pandemic. While recovery was driven by rebounding domestic travel in some countries, there is persistent reduction of domestic flights in Germany. Has the pandemic disruption caused a permanent change in how Germans travel domestically? In light of the political debate over short-distance air travel this seems a distinct possibility. This finding will also have consequences for scientists and how they should evaluate mobility data during a pandemic. _The commercial data source we use in this analysis, the OAG, provides us with seat-capacity data calculated as: number-of-flights × seats-per-airplane. Before the pandemic, flights were almost always booked close to full capacity. During the pandemic, restrictions have sometimes limited the capacity of flights and rapidly falling demand forced airlines to fly with only a handful passengers in some cases. We expect this data to slightly overestimate the actual number of passengers, especially during the first wave. _36:T44b,function beeswarmY( data, { gap = -4, ticks = 50, dynamic, direction, ...options } ) { const dots = Plot.dot(data, options); const { render } = dots; dots.render = function () { const g = render.apply(this, arguments); const circles = d3.select(g).selectAll("circle"); const nodes = []; const [cx, cy, x, y, forceX, forceY] = [ "cy", "cx", "y", "x", d3.forceY, d3.forceX ]; for (const c of circles) { const node = { x: +c.getAttribute(cx), y: +c.getAttribute(cy), r: 10 }; nodes.push(node); } const force = d3 .forceSimulation(nodes) .force("x", forceX((d) => d[x]).strength(0.8)) .force( "collide", d3 .forceCollide() .radius((d) => d.r + gap) .iterations(3) ) .tick(ticks) .stop(); update(); if (dynamic) force.on("tick", update).restart(); return g; function update() { circles.attr(cx, (_, i) => nodes[i].x).attr(cy, (_, i) => nodes[i].y); } }; return dots; }37:T44d,function beeswarmY_text( data, { gap = -4, ticks = 50, dynamic, direction, ...options } ) { const dots = Plot.text(data, options); const { render } = dots; dots.render = function () { const g = render.apply(this, arguments); const circles = d3.select(g).selectAll("text"); const nodes = []; const [cx, cy, x, y, forceX, forceY] = [ "y", "x", "y", "x", d3.forceY, d3.forceX ]; for (const c of circles) { const node = { x: +c.getAttribute(cx), y: +c.getAttribute(cy), r: 10 }; nodes.push(node); } const force = d3 .forceSimulation(nodes) .force("x", forceX((d) => d[x]).strength(0.8)) .force( "collide", d3 .forceCollide() .radius((d) => d.r + gap) .iterations(3) ) .tick(ticks) .stop(); update(); if (dynamic) force.on("tick", update).restart(); return g; function update() { circles.attr(cx, (_, i) => nodes[i].x).attr(cy, (_, i) => nodes[i].y); } }; return dots; }38:[{"id":"7e80a230610ed84f98bcb383bc053dbb67773ee4fcae00d932d426c7c94a918746473c74d3b214b37283b5050d8ac283aa5499f97e65b34ea8ecc7c08071744c","href":"https://static.observableusercontent.com/files/7e80a230610ed84f98bcb383bc053dbb67773ee4fcae00d932d426c7c94a918746473c74d3b214b37283b5050d8ac283aa5499f97e65b34ea8ecc7c08071744c","name":"isoalpha2countries_2021005.arrow","type":"application/octet-stream","lastModified":1633521791311.15,"size":8432},{"id":"f1900cbbeb855415ca7a35ae0f9b2f66c496093265d7e948e3e96ca2dc3317039464151214e0b1e635bc3e050d968633c0c96f5180cbf22c6bcdbadfdda25ba6","href":"https://static.observableusercontent.com/files/f1900cbbeb855415ca7a35ae0f9b2f66c496093265d7e948e3e96ca2dc3317039464151214e0b1e635bc3e050d968633c0c96f5180cbf22c6bcdbadfdda25ba6","name":"wan2019_countries_20210916.arrow","type":"application/octet-stream","lastModified":1633521791311.15,"size":3816},{"id":"bcd7267d223bb54ad20aec132889073f684c55da121d66496e4d93d880cb3716716c25ae194f02ddfcfa9c0015876cc3f3543b149a7d6a54e07fcad68ca779f0","href":"https://static.observableusercontent.com/files/bcd7267d223bb54ad20aec132889073f684c55da121d66496e4d93d880cb3716716c25ae194f02ddfcfa9c0015876cc3f3543b149a7d6a54e07fcad68ca779f0","name":"timecourse_countries_20210916@1.arrow","type":"application/octet-stream","lastModified":1633521791311.15,"size":111384},{"id":"0aa8cc383fd223375b643ed5bdc1ed916331111f84737949006022502a108a8641b824cf611f7656bf7f695c26bc8aee772d7a5ab6208b33effc6e0fe349b1b6","href":"https://static.observableusercontent.com/files/0aa8cc383fd223375b643ed5bdc1ed916331111f84737949006022502a108a8641b824cf611f7656bf7f695c26bc8aee772d7a5ab6208b33effc6e0fe349b1b6","name":"timecourse_germany_20211026@1.arrow","type":"application/octet-stream","lastModified":1635243841074.37,"size":32128},{"id":"85d4f3b0f2470d08f97f145f2de1f8257c25f64facb2234146b9dddbb2075b7bc43a678bba565870514dd8888b110ab561b648fdd599e43aa6719912c2625eec","href":"https://static.observableusercontent.com/files/85d4f3b0f2470d08f97f145f2de1f8257c25f64facb2234146b9dddbb2075b7bc43a678bba565870514dd8888b110ab561b648fdd599e43aa6719912c2625eec","name":"timecourse_countries_20211026@1.arrow","type":"application/octet-stream","lastModified":1635243978670.875,"size":3576},{"id":"706b02b743937f79f7aff7008e18c12c6a4e3ab4ee3b68614b790b2f7e6929c18e14cb76ad745963c388ea79bb6eb76436f8ef894be2269ccc10a5779351e02b","href":"https://static.observableusercontent.com/files/706b02b743937f79f7aff7008e18c12c6a4e3ab4ee3b68614b790b2f7e6929c18e14cb76ad745963c388ea79bb6eb76436f8ef894be2269ccc10a5779351e02b","name":"timecourse_germany_20211005.arrow","type":"application/octet-stream","lastModified":1633529853543.925,"size":53504},{"id":"5d463109f2b1d70eaa5f1486c2a1ad86b6aa5ac50cf748dc501bc85e0d1b973211379fa8365968f1038119c1bdfc847b43a0c4a881deefbe673f1683faccd7ed","href":"https://static.observableusercontent.com/files/5d463109f2b1d70eaa5f1486c2a1ad86b6aa5ac50cf748dc501bc85e0d1b973211379fa8365968f1038119c1bdfc847b43a0c4a881deefbe673f1683faccd7ed","name":"_world_countries_alpha2.json","type":"application/json","lastModified":1633600916812.622,"size":1673389}] 2f:{"title":"COVID-19s Impact on Air Travel","cells":[{"id":0,"value":"# COVID-19s Impact on Air Travel\n\nIn early 2020, COVID travel restrictions and high uncertainty led to an unprecedented disruption of air travel. Over a year later, daily mobility in Germany has largely returned to pre-pandemic levels, but air travel has remained deeply affected. In the blogpost, we take a look at how the pandemic has reshaped air travel in and around Germany. \n\n## Germany’s Air-Traffic Volume in Comparison to Other Countries\n\nThe pandemic has had varying effects on national air-traffic networks. China’s air-traffic network recovered quickly after the first wave. The US traffic recovered slowly but steadily. The air traffic of most European countries, including Germany, initially recovered slightly in Mid 2020 but then had a strong reduction of traffic in responses to the second wave in Winter 2020/2021. \n","pinned":false,"hidden":false,"mode":"md"},{"id":369,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":608,"value":"_This plot shows the aggregated air-traffic of ${color_highlight('Germany',\"#bd1550\")} in comparison to the ${color_highlight('USA',\"#555\")}, ${color_highlight('China',\"#BBB\")} and the ${color_highlight('UK',\"#888\")}. The traffic is shown in relation to the (mostly) pre-pandemic level of January 2020._\n\n\nThe differences in network recovery are surprising in some cases. China is still largely isolated from the world, so how can it be recovering so well? Like the U.S., China is a large country with plenty of domestic air traffic. Conversely, small countries have mostly international air traffic connections. To get a more detailed picture of the pandemic’s effect, it’s helpful to separate recovery into two categories: ${color_highlight('domestic', '#490a3d')} and ${color_highlight('international', '#bd1550')}. ","pinned":false,"hidden":false,"mode":"md"},{"id":659,"value":"Plot.plot({\n width: 648,\n height: 260,\n marginTop: 30,\n marginLeft: 40,\n x: { tickFormat: timeformat_long, tickSize: 6, tickPadding: 4 },\n y: {\n zero: true,\n tickFormat: (d) => (d != 0 ? d3.format(\"~s\")(d) : null),\n tickSize: 0,\n tickPadding: 8,\n labelAnchor: \"top\",\n label: ` ↑ Seats`\n },\n color: { domain: [\"seats_inner\", \"seats_in\"], range: [\"#490a3d\", \"#bd1550\"] },\n facet: {\n data: Bplot_data,\n x: \"country_name\",\n label: ``\n },\n marks: [\n //Plot.frame({ stroke: \"#BBB\" }),\n Plot.areaY(\n Bplot_data,\n Plot.stackY({\n x: \"date\",\n y: \"value\",\n fill: \"key\",\n stroke: \"key\"\n }),\n { strokeLinejoin: \"round\" }\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":648,"value":"_This plot shows the ${color_highlight('domestic', '#490a3d')} and ${color_highlight('international', '#bd1550')} travel volume of China and the USA. The volumes are stacked, so yes, there is very little international travel with China._\n\nIn China, domestic traffic quickly while international traffic ceased almost completely. In the U.S., both international and domestic travel recovered slowly but steadily, but international travel dominates. \n\nFor Germany this chart looks very different. The majority of traffic is always international and domestic travel has actually recovered less than international travel.","pinned":false,"hidden":false,"mode":"md"},{"id":825,"value":"Plot.plot({\n width: 648,\n height: 360,\n marginTop: 30,\n marginLeft: 40,\n x: { tickFormat: timeformat_long, tickSize: 6, tickPadding: 4 },\n y: {\n zero: true,\n tickFormat: (d) => (d != 0 ? d3.format(\"~s\")(d) : null),\n tickSize: 0,\n tickPadding: 8,\n labelAnchor: \"top\",\n label: ` ↑ Seats`\n },\n color: { domain: [\"seats_inner\", \"seats_in\"], range: [\"#490a3d\", \"#bd1550\"] },\n marks: [\n //Plot.frame({ stroke: \"#BBB\" }),\n Plot.areaY(\n Cplot_data,\n Plot.stackY({\n x: \"date\",\n y: \"value\",\n fill: \"key\",\n stroke: \"key\"\n }),\n { strokeLinejoin: \"round\" }\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":869,"value":"_This plot shows the ${color_highlight('domestic', '#490a3d')} and ${color_highlight('international', '#bd1550')} travel volume of Germany._\n\nIt is possible that domestic business travel, which contributed much to domestic air travel in Germany, has been replaced by conference calls. But that is only speculation. \nIt is unclear if domestic air-traffic in Germany will ever return to pre-pandemic levels. ","pinned":false,"hidden":false,"mode":"md"},{"id":845,"value":"----\n## Primary Destinations","pinned":false,"hidden":false,"mode":"md"},{"id":860,"value":"Now we know that international travel is dominating German air-travel. Let's look at the top destinations in more detail. ","pinned":false,"hidden":false,"mode":"md"},{"id":1012,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1298,"value":"$33","pinned":false,"hidden":false,"mode":"md"},{"id":1439,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":1764,"value":"_This plot shows the fight volume to European destinations. A darker shade indicates a higher volume of traffic. Grey countries received no international flights from Germany._\n\nFlight volume is low for all Northern and Eastern European countries, but up above pre-pandemic levels in Southern Europe. Part of this will be due to normal seasonal travel patterns and tourism. Travel to Turkey is likely boosted by family travel after long separation. We also see evidence of the complete travel ban to Belarus in 2021, which is not pandemic related. \n","pinned":false,"hidden":false,"mode":"md"},{"id":1803,"value":"$35","pinned":false,"hidden":false,"mode":"md"},{"id":207,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":1411,"value":"### Styling","pinned":false,"hidden":false,"mode":"md"},{"id":418,"value":"Aplot_countries = ({\n isoalpha2: [\"DE\", \"US\", \"CN\", \"GB\"],\n colors: [\"#bd1550\", \"#555\", \"#BBB\", \"#888\"],\n order: new Map([\n [\"DE\", 10],\n [\"US\", 8],\n [\"CN\", 7],\n [\"GB\", 9]\n ])\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":661,"value":"Bplot_data = wan_tc\n .filter(aq.escape((d) => _.includes([\"US\", \"CN\"], d.isoalpha2)))\n .filter(aq.escape((d) => _.includes([\"seats_inner\", \"seats_in\"], d.key)))\n .derive({ value_reljan: (d) => d.value_reljan / 2 })\n .join_left(countries)","pinned":true,"hidden":false,"mode":"ojs"},{"id":828,"value":"Cplot_data = wan_tc\n .filter(aq.escape((d) => \"DE\" === d.isoalpha2))\n .filter(aq.escape((d) => _.includes([\"seats_inner\", \"seats_in\"], d.key)))\n .derive({ value_reljan: (d) => d.value_reljan / 2 })","pinned":true,"hidden":false,"mode":"ojs"},{"id":975,"value":"Dplot_data = wan_tc_germany\n .filter((d) => d.key === \"seats_in\")\n .filter((d) => d.isoalpha2 !== \"DE\")\n .groupby(\"date\")\n .orderby(aq.desc(\"value\"))\n .slice(0, 5)\n .derive({ date_label: aq.escape((d) => timeformat_long(d.date)) })\n .join(countries)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1722,"value":"linear_color = {\n const colors = [\"#490a3d\", \"#bd1550\", \"lightgrey\"]; //[\"#490a3d\", \"#bd1550\", \"#e97f02\"]\n var domain = [1, 0.6, 0];\n return d3.scaleLinear().domain(domain).range(colors);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1094,"value":"$36","pinned":true,"hidden":false,"mode":"ojs"},{"id":1120,"value":"$37","pinned":true,"hidden":false,"mode":"ojs"},{"id":189,"value":"","pinned":true,"hidden":false,"mode":"html"},{"id":192,"value":"timeformat_long = {\n const isfirstofyear = (d) => (d.getDate() == 1) & (d.getMonth() == 0);\n return (d) =>\n isfirstofyear(d) ? d3.timeFormat(\"%b '%y\")(d) : d3.timeFormat(\"%b\")(d);\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":936,"value":"color_highlight = (text, color) => {\n const { r, g, b } = d3.rgb(color);\n let text_color;\n if ((r * 299 + g * 587 + b * 114) / 1000 < 160) {\n text_color = \"White\";\n } else {\n text_color = \"#111\";\n }\n\n return `${text}`;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":145,"value":"### Data","pinned":false,"hidden":false,"mode":"md"},{"id":210,"value":"wan_tc.view(3)","pinned":true,"hidden":false,"mode":"ojs"},{"id":977,"value":"wan_tc = {\n let raw = await aq.loadArrow(\n await FileAttachment(\"timecourse_countries_20211026@1.arrow\").url()\n );\n raw = raw.derive({\n date: (d) => op.parse_date(d.date),\n seats_all: (d) => d.seats_in + d.seats_out + d.seats_inner\n });\n\n let df = raw.fold([\"seats_inner\", \"seats_out\", \"seats_in\", \"seats_all\"]);\n\n df = df\n .join_left(\n df\n .filter((d) => d.date < op.datetime(2020, 0, 2))\n .derive({ value_base: (d) => d.value })\n .select(\"isoalpha2\", \"key\", \"value_base\")\n )\n .derive({\n value_reljan: (d) => d.value / d.value_base\n })\n .select(aq.not([\"value_base\", \"value_2019\"]));\n\n return df;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":987,"value":"_.max(wan_tc_germany.columnArray(\"date\"))","pinned":true,"hidden":false,"mode":"ojs"},{"id":18,"value":"wan_tc_germany = {\n let raw = await aq.loadArrow(\n await FileAttachment(\"timecourse_germany_20211026@1.arrow\").url()\n );\n raw = raw.derive({\n date: (d) => op.parse_date(d.date)\n });\n\n let df = raw.fold([\"seats_in\"]);\n\n df = df\n .join_left(\n df\n .filter((d) => d.date < op.datetime(2020, 0, 2))\n .derive({ value_base: (d) => d.value })\n .select(\"isoalpha2\", \"key\", \"value_base\")\n )\n .derive({\n value_reljan: (d) => d.value / d.value_base\n })\n .select(aq.not([\"value_base\", \"value_2019\"]));\n\n return df;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":121,"value":"countries = aq.loadArrow(\n await FileAttachment(\"isoalpha2countries_2021005.arrow\").url()\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1428,"value":"world_shape = (\n await FileAttachment(\"_world_countries_alpha2.json\").json()\n).features.filter((d) => d.id !== \"5\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":27,"value":"import { aq, op } from \"@uwdata/arquero\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":1964,"value":"d3 = require(\"d3@6\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1966,"value":"Plot = require(\"@observablehq/plot@0.2\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W38","stdlib":"1","titling":"auto"}