31:T87f,top_entries = table.filter(d => d["port of entry"] != "") .derive({"port of entry": d => op.lower(d["port of entry"])}) .derive({"port of entry": d => op.replace(d["port of entry"], " (bridge)", "")}) .derive({"port of entry": d => op.replace(d["port of entry"], " (nd)", "")}) .derive({"port of entry": d => op.replace(d["port of entry"], " (ma)", "")}) .derive({"port of entry": d => op.replace(d["port of entry"], " colorado", " co")}) .derive({"port of entry": d => op.replace(d["port of entry"], " new mexico", " nm")}) .derive({"port of entry": d => op.replace(d["port of entry"], " montana", " mt")}) .derive({"port of entry": d => op.replace(d["port of entry"], " texas", " tx")}) .derive({"port of entry": d => op.replace(d["port of entry"], " pennsylvania", " pa")}) .derive({"port of entry": d => op.replace(d["port of entry"], " washington", " wa")}) .derive({"port of entry": d => op.replace(d["port of entry"], " virginia", " va")}) .derive({"port of entry": d => op.replace(d["port of entry"], " connecticut", " ct")}) .derive({"port of entry": d => op.replace(d["port of entry"], " arizona", " az")}) .derive({"port of entry": d => op.replace(d["port of entry"], " montana", " mt")}) .derive({"port of entry": d => op.replace(d["port of entry"], " michigan", " mi")}) .derive({"port of entry": d => op.replace(d["port of entry"], " north dakota", " nd")}) .derive({"port of entry": d => op.replace(d["port of entry"], " california", " ca")}) .derive({"port of entry": d => op.replace(d["port of entry"], " (ia)", "")}) .derive({"port of entry": d => op.replace(d["port of entry"], " florida", " fl")}) .derive({"port of entry": d => op.replace(d["port of entry"], " rhode island", " ri")}) .derive({"port of entry": d => op.replace(d["port of entry"], " ohio", " ri")}) .derive({"port of entry": d => op.replace(d["port of entry"], " maine", " me")}) .derive({"port of entry": d => op.replace(d["port of entry"], ", new york", ", ny")}) .derive({"port of entry": d => op.replace(d["port of entry"], " poe", "")}) .groupby("port of entry").count().orderby(aq.desc("count")) .slice(0, 30)32:[] 2f:{"title":"Alien files in the National Archives","cells":[{"id":0,"value":"md`# Alien files in the National Archives\n\nThis is a notebook primarily intended for the Spring 2021 NYU class \"Asylum in Crisis.\"\n\nThe core dataset here is a table with 1.1 million rows pulled from the\nNational Archives catalog using their API.\nI tried to get all A-files in the system, but I may have missed some.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":33,"value":"intro = {\n const rate = seen / (now - load_time) * 1000\n const time_to_see_hours = table.size / rate / 60 / 60 ;\n const time_to_see_days = Math.floor(time_to_see_hours/24 * 10)/10\n const time_to_see = `${time_to_see_days} days` \n return md`## First: look at five random items\n\nBelow you can see five random people who have A-Files in the national archives.\n\nThis includes their name, birth date, home country, and information about their immigration and naturalization history. There are 1.1 million entries in your browser right now; at your current rate, it would take ${time_to_see} to see them all. You can click on the links here to\nview the original record in the National Archives catalog.\n\n\n`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":206,"value":"viewof update = html``","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"{\n update\n mutable seen += 5\n let output = \"

Random Sample

${repr(row)}`\n }\n return html`${output}

`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":722,"value":"d = table.sample(1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":727,"value":"","pinned":true,"hidden":false,"mode":"ojs"},{"id":720,"value":"function repr(row) {\n let output = \"\"\n output += `${row.name}, born ${row['birth date'].toString().slice(4, 15)}. Arrived from ${row.country ? row.country : \"unknown country\"} ${row['date of entry'] ? row['date of entry'] .toString().slice(4, 15) + \".\" : \".\"} ${row['port of entry'] ? \"Port of entry \" + row['port of entry'] + \". \" : \"\"}`\n if (row['naturalization date']) {\n output += `Naturalization date ${new Date(row['date of entry']).toString().slice(4, 15)}`\n }\n if (row['sex']) {output += `Sex: ${row.sex}.`}\n if (row['alias']) {output += `Alias: ${row.alias}.`}\n \n \n return output\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":198,"value":"md`# Chart for February 23\n\nFor the pre-class assignment, you should choose a country and look at all six possible combinations of the two buttons below.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":188,"value":"viewof date_field = radio({title: \"Date field (note--this will take a few seconds to recalculate when you change it.)\", options: [\"date of entry\", \"birth date\", \"naturalization date\"], description: \"There are three different types of dates in this dataset. Date of entry is when they came into the United States; birth date is when they were born; and naturalization is when they gained citizenship (if they did).\", value: \"date of entry\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":242,"value":"viewof y_axis = radio({title: \"Y axis\", options: [\"count\", \"percentage of all records from year\"], value: \"count\", description: \"Just because a lot fewer people came in a given year from a country may not mean much if all others *also* dropped in that same year. Conversely, making up a large percentage of claims may not matter much if there are only a few people in any given year. To understand patterns, you'll need to think about both of these variables at once.\" })","pinned":false,"hidden":false,"mode":"ojs"},{"id":167,"value":"viewof country = select({title: \"Origin country\", options: [{\"label\": \"ALL COUNTRIES\", value:\"all\"}].concat(country_counts.filter(d => d.count > 1000)), value: \"all\", description: \"Country names may be different from those today or at the time of the immigrant's birth. (e.g., Ukraine may be USSR, \"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":551,"value":"chart = DOM.element(\"div\", {id: \"chart1\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":559,"value":"mutable year = undefined","pinned":false,"hidden":false,"mode":"ojs"},{"id":563,"value":"data = {\n if (year !== undefined) {\n let output = \"

Random Sample

${repr(row)}

`\n }\n\n return html`

Click on a point above to see five random entries

`\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":42,"value":" chart_object_with_listener = embed(chart, {\n $schema: 'https://vega.github.io/schema/vega-lite/v4.json',\n \"usermeta\": {\"embedOptions\": {\"renderer\": \"svg\"}},\n data: {\n values: yearly_counts.join(overall_yearly_totals).derive({\"share\": d => d.count/d.overall_count}).filter(d => d.year >= 1845).objects()\n },\n width: width * .75,\n mark: 'circle',\n title: `A-Files in the national archives from ${country}`,\n encoding: {\n x: {field: \"year\", type: \"quantitative\", scale: {zero: false}},\n y: {field: y_axis == \"count\" ? \"count\" : \"share\", type: \"quantitative\", \"axis\": y_axis == \"count\" ? undefined : {\"format\": \".1%\"} },\n tooltip: [{field: \"year\"}, {field: y_axis == \"count\" ? \"count\" : \"share\"}]\n }\n}).then(result => {\n result.view.addEventListener('click', function(event, item) {\n mutable year = item.datum.year\n console.log(item)\n });\n return result\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":195,"value":"md`# Random entries from that country\n\nThe table below shows random entries from that country.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":490,"value":"viewof refresh2 = html`