31:Tee6,citibike_stations_leaflet = { let container = DOM.element('div', { style: `width:${width}px;height:${width/2.5}px` }); yield container; //let map = L.map(container).setView([53.2527, -123.1207], 5).fitBounds(L.geoJson(file).getBounds()); let map = L.map(container).setView([40.747, -73.988], 12) map.scrollWheelZoom.disable(); let osmLayer = L.tileLayer('https://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}@2x.png', { attribution: '© OpenStreetMap contributors' }).addTo(map); //initialize svg to add to map L.svg().addTo(map) const overlay = d3.select(map.getPanes().overlayPane) const svg = overlay.select('svg') let stations = svg.append('g') .attr('class', 'station-set') let station = stations.selectAll("station") .data(citibikeStationsHistory.set, d => d['station_id']) .join('g') .attr('class', d => `station station-${d['station_id']}`) .attr('transform', d => { let point = map.latLngToLayerPoint([d.lat,d.lon]) return `translate(${point.x},${point.y})` }) .attr("pointer-events","visible") .on('mouseover', stationMouseOver) .on('mouseout', stationMouseOut ); station.append('circle') .attr('class', 'bubble') //.attr('pointer-events', 'none') .attr('r', d => { //d.r = r(d[selectR]) return 6 }) .attr('fill-opacity', 0) .attr('fill', '#FFF') station.append('circle') .attr('class', 'point') //.attr('pointer-events', 'none') .attr('r', 3) .attr('fill', d => (d.last_year < 2020) ? '#F2C85B' : "#555") const update = () => station .attr('transform', d => { let point = map.latLngToLayerPoint([d.lat,d.lon]) return `translate(${point.x},${point.y})` }) map.on("zoomend", update) function stationMouseOver(event, d){ d3.select(event.target).style("cursor", "pointer"); d3.select(`.station-set .station-${d['station_id']} .bubble`) .transition() .attr('stroke', 'orange') .attr('stroke-width', 2); let stationLookup = citibikeStationsHistory.map.get(d['station_id'])[0] let tipContent = ``; tipContent = `(${d['station_id']}) ${stationLookup.name}
`; // tipContent += ` // ` // Start Table // tipContent += `` // tipContent += `` // tipContent += `` // tipContent += `` // tipContent += `` // tipContent += `` // tipContent += `` // tipContent += `` // tipContent += `` // tipContent += `

First Day	${format.abs(d.val)}	${format.percent1d(d.chgMoAvg)}
7-Day Avg	${format.abs(d.mavg7)}	${format.percent1d(d.chgMov7MoAvg)}
${d.prev} Avg	${format.abs(d.prevMoAvg)}

`; // Close Table chartTip.style("left", () => { if (innerWidth - d.x < 120) { return event.pageX - (24+240) + "px"; } else { return event.pageX + 24 + "px"; } }) .style("top", event.pageY + 24 + "px") .style("display", "inline-block") .html(`${tipContent}`); } function stationMouseOut(event, d){ d3.select(event.target).style("cursor", "default"); chartTip.style("display", "none"); // Hide toolTip d3.select(`.station-set .station-${d['station_id']} .bubble`) .transition() .attr('stroke', '#FFF') .attr('stroke-width', 0); } }32:T5d6,citibikeSystemDaily = { let result = {} let formatMonth = d3.timeFormat("%b"); result.set = [] cbParsedSystemDaily.map(d => result.set.push(d)) let lookupSet = d3.group(result.set, d => d.date.toISOString()) // Group data by lines let lookupMonthAvg = d3.rollup(result.set, v => d3.mean(v, d => d.val), d => d.year, d => d.month) let lookupMonthAvgDT = d3.rollup(result.set, v => d3.mean(v, d => d.val), d => d.year, d => d.month, d => d.daytype) let movingAvg7 = movingAverage(result.set.map(e => e.val), 7) result.set.map((d, i) => { d.mavg7 = movingAvg7[i] let prevYr = lookupMonthAvg.get(d.year - 1); let prevYrDT = lookupMonthAvgDT.get(d.year - 1); d.chgMoAvg = null; d.chgMoAvgDT = null; d.chgMoAvgDT = null; d.chgMov7MoAvg = null; d.prev = null; // check for previous year if (prevYr !== undefined) { let prevMoAvg = prevYr.get(d.month) // check for previous year month if (prevMoAvg !== undefined) { let prevMoAvgDT = prevYrDT.get(d.month).get(d.daytype); // calculate the change over prev d.prev = `${formatMonth(d.date)} '${(d.year - 1).toString().substring(2)}`; d.prevMoAvg = prevMoAvg; d.chgMoAvg = (d.val - prevMoAvg)/prevMoAvg; d.chgMoAvgDT = (d.val - prevMoAvgDT)/prevMoAvgDT; d.chgMov7MoAvg = (d.mavg7 - d.prevMoAvg)/d.prevMoAvg; console.log(d.chgMoAvgDT) } } return d }) return result.set }33:T4ac,citibikeSystemAnnual = { let prevTrips = 0; let prevAvg = 0; let set = Array.from(d3.rollup( citibikeSystemDaily, v => ({ days: v.length, trips: d3.sum(v, d => d.val), tripsAvg: d3.mean(v, d => d.val), tripsMax: d3.max(v, d => d.val), }), d => d.year) ).map(d => { let item = {}; item.id = 'citibike'; item.year = d[0]; item.days = d[1].days; item.trips = d[1].trips; item.tripsAvg = d[1].tripsAvg; item.tripsMax = d[1].tripsMax; item.chgTrips = (prevTrips) ? (item.trips - prevTrips)/prevTrips : 0; item.chgAvg = (prevAvg) ? (item.tripsAvg - prevAvg)/prevAvg : 0; prevTrips = item.trips prevAvg = item.tripsAvg return item }) return set } // Calculate summary statistics by permit class (using .rollups() to return an array instead of a map) // summary_by_class_array = d3.rollups( // data, // v => ({ // count: v.length, // mean_cost: d3.mean(v, d => +d.EstProjectCost), // get the average estimated project cost! // max_cost: d3.max(v, d => +d.EstProjectCost) // get the max estimated project cost! // }), // d => d.PermitClass // )34:Tac8,function summarizeByStation(dataset, compare) { let result = {} result.summary = {} result.summary.startMax = d3.max(dataset, d => d.startcount); result.summary.endMax = d3.max(dataset, d => d.endcount); result.summary.max = Math.max(result.summary.startMax, result.summary.endMax) result.summary.startAvg = d3.mean(dataset, d => d.startcount); result.summary.endAvg = d3.mean(dataset, d => d.endcount); result.set = d3.groups(dataset, d => d.station_id).map(d => { let station_id = d[0]; let data = d[1].sort((a, b) => d3.ascending(a.date, b.date)); let firstday = data[0].date; let days = data.length; let startTotal = d3.sum(data, d => d.startcount); let startAvg = d3.mean(data, d => d.startcount); let startMax = d3.max(data, d => d.startcount); let endTotal = d3.sum(data, d => d.endcount); let endAvg = d3.mean(data, d => d.endcount); let endMax = d3.max(data, d => d.endcount); //result.data.push({station_id, data}) let startAvgMo = d3.rollup(data, v => d3.mean(v, d => d.startcount), d => d.month) let endAvgMo = d3.rollup(data, v => d3.mean(v, d => d.endcount), d => d.month) let startAvgMoDT = d3.rollup(data, v => d3.mean(v, d => d.val), d => d.month, d => d.daytype) let change = {}; if(compare) { let lookup = compare.map.get(station_id) change.prev = (lookup) ? true : false; if (lookup) { data.forEach(g => { let prevStartMoAvg = lookup[0]['startAvgMo'].get(g.month) // check for previous year month if (prevStartMoAvg !== undefined) { let prevStartMoAvgDT = lookup[0]['startAvgMoDT'].get(g.month).get(g.daytype); // calculate the change over prev change.startMoAvg = (g.startcount - prevStartMoAvg)/prevStartMoAvg; change.startMoAvgDT = (g.startcount - prevStartMoAvgDT)/prevStartMoAvgDT; } else { change.startMoAvg = null; //change.startMoAvgDT = null; } }) change.firstday = lookup[0]['firstday']; change.days = lookup[0]['days']; change.startTotal = lookup[0]['startTotal']; change.startAvg = lookup[0]['startAvg']; change.startAvgChg = (startAvg - change.startAvg)/change.startAvg; change.endTotal = d3.sum(data, d => d.endTotal); change.endAvg = lookup[0]['endAvg']; change.endAvgChg = (startAvg - change.endAvg)/change.endAvg; } } return {station_id, data, firstday, days, startTotal, startAvg, startMax, endMax, endTotal, endTotal, endAvg, startAvgMo, endAvgMo, startAvgMoDT, change}; }) result.map = d3.group(result.set, d => d.station_id); return result 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2f:{"title":"NYC Citi Bike Trip Data","cells":[{"id":0,"value":"md`# NYC Citi Bike Trip Data\nThis notebook collects historical Citi Bike trips in New York City and Jersey City based on the [process and R code](https://medium.com/qri-io/building-a-daily-aggregate-dataset-from-raw-citibike-trip-data-2b6502009871) shared by [Chris Whong](https://twitter.com/chris_whong) for aggregating the 2020 raw trip data published by [CitiBike](https://www.citibikenyc.com/system-data). I applied the same method over the full trips dataset to produce a daily count of trips by station that can be further summarized. \n\nCiti Bike publishes new trip data on monthly basis. This summary doesn't represent official adjusted ridership, but Citi Bike doesn't (to my knowledge) currently publish any aggregate data.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2002,"value":"md`### Visualizations\nI created this notebook to have a single source of the data for use in other observable visualization. Also this page is a bit resource intensive when all the data is fully loaded in. My visualizations are done in separate :\n\n+ Overview / Systemwide Summary (Coming Soon)\n+ Change in Trips by Station, 2019-2020 (Coming Soon)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2041,"value":"md`### In Development ////////////\nThis is still a work and progress and will I be continuing to refine, organize, and optimize. I hope it can be a resource others can reference. I was always interested exploring Citi Bike data more but the raw trip files can be challenging to work with. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1234,"value":"md`## Station Locations\nThis station collections is specificly for mapping historical trips data and looking up station information. It collects all unique station IDs between 2013-2020. In the map below, station that had\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"citibikeStationsHistory = {\n let result = {}\n \n //result.data = d3.csvParse(await FileAttachment(\"citibike-stations-20201210@1.csv\").text(), d3.autoType)\n // result.data = d3.csvParse(await FileAttachment(\"citibike-stations-data.csv\").text(), d3.autoType)\n \n result.set = await FileAttachment(\"citibike-stations-data.csv\").csv().then(\n function(data) {\n data.forEach(function(d) {\n d.lon = +d.lon;\n d.lat = +d.lat;\n //d.first_date = parseDate(d.first_date);\n d.first_year = +d.first_year;\n //d.last_date = parseDate(d.last_date);\n d.last_year = +d.last_year;\n d.data_days = +d.data_days;\n d.data_starts = +d.data_starts;\n d.data_ends = +d.data_ends;\n d.indata = true; \n });\n return data;\n })\n \n result.map = d3.group(result.set, d => d.station_id)\n \n return result\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1763,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1046,"value":"md`## Citi Bike Trip Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1126,"value":"md`### System Daily Counts\nBased on daily systemwide trips starts for both New York City and Jersey City by date\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1413,"value":"md`#### Parsed Daily Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1137,"value":"function parseSystemDaily(dataset) {\n let parseDate = d3.timeParse(\"%Y-%m-%d\");\n\n let formatYear = d3.timeFormat(\"%Y\");\n let formatMonth = d3.timeFormat(\"%m\");\n let formatDay = d3.timeFormat(\"%w\"); \n \n function dayType(num) {\n if (num === 0 || num === 6) {\n return \"weekend\";\n } else {\n return \"weekday\" \n }\n }\n \n let result = dataset.map(d => {\n let item = {}\n item.id = 'citibike';\n item.date = parseDate(d['date']);\n // item.dateiso = item.date.toISOString();\n item.year = +formatYear(item.date);\n item.month = +formatMonth(item.date);\n let day = +formatDay(item.date);\n item.daytype = dayType(day);\n item.stations = +d.stations;\n item.val = +d.startcount;\n \n return item\n })\n\n return result\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1140,"value":"// cbParsedSystemDaily = await FileAttachment(\"citibike-daily-total-latest@1.csv\").csv().then(d => parseSystemDaily(d))\ncbParsedSystemDaily = await FileAttachment(\"citibike-daily-system-latest-.csv\").csv().then(d => parseSystemDaily(d))\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":1415,"value":"md`#### Summarized Daily Data`","pinned":false,"hidden":false,"mode":"ojs"},{"id":620,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1643,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":987,"value":"// Applies a moving average if using an input to the citibikeSystemDaily \nfunction citibikeSystemDailyApplyMA(dataset, movAvgDays) {\n let result = {};\n let movingAvgSet = movingAverage(citibikeSystemDaily.set.map(e => e.val), movAvgDays)\n \n result.set = dataset.map((d, i) => {\n d.mavg = movingAvgSet[i]\n d.chgMovMoAvg = (d.mavg - d.prevMoAvg)/d.prevMoAvg; // prevMoAvg\n return d\n })\n return result;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1115,"value":"md`### Station Trip Counts\nDaily trip starts and ends by for each stations in both New York City and Jersey City by date and grouped annually.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1422,"value":"md`#### Parsed Station Data\nThis is the parsing function and parsed raw station data for each year.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":896,"value":"function parseStationDaily(dataset) {\n let parseDate = d3.timeParse(\"%-m/%-d/%y\"); // Parses 3/1/20\n //let parseDate = d3.timeParse(\"%Y-%m-%d\");\n\n let formatYear = d3.timeFormat(\"%Y\");\n let formatMonth = d3.timeFormat(\"%m\");\n let formatDay = d3.timeFormat(\"%w\"); \n \n function dayType(num) {\n if (num === 0 || num === 6) {\n return \"weekend\";\n } else {\n return \"weekday\" \n }\n }\n \n dataset.forEach(function(d, i) {\n d.station_id = d.station_id;\n d.startcount = +d.startcount; \n d.endcount = +d.endcount;\n d.date = parseDate(d.date);\n d.year = +formatYear(d.date);\n d.month = +formatMonth(d.date);\n let day = +formatDay(d.date);\n d.daytype = dayType(day);\n });\n \n return dataset.sort((a, b) => d3.ascending(a.date, b.date))\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2422,"value":"cbParsed2021 = await FileAttachment(\"citibike-daily-station-counts-2021-cs@2.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":937,"value":"cbParsed2020 = await FileAttachment(\"citibike-daily-station-counts-2020-cs@3.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":922,"value":"cbParsed2019 = await FileAttachment(\"citibike-daily-station-counts-2019-cs@3.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1840,"value":"cbParsed2018 = await FileAttachment(\"citibike-daily-station-counts-2018-cs@3.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1848,"value":"cbParsed2017 = await FileAttachment(\"citibike-daily-station-counts-2017-cs@2.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1851,"value":"cbParsed2016 = await FileAttachment(\"citibike-daily-station-counts-2016-cs@2.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1854,"value":"cbParsed2015 = await FileAttachment(\"citibike-daily-station-counts-2015-cs@3.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1855,"value":"cbParsed2014 = await FileAttachment(\"citibike-daily-station-counts-2014-cs@2.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1864,"value":"cbParsed2013 = await FileAttachment(\"citibike-daily-station-counts-2013-cs@3.csv\").csv().then(d => parseStationDaily(d))","pinned":false,"hidden":false,"mode":"ojs"},{"id":1512,"value":"md`#### Summarized Station Data (in process)\nThe summarizeByStation function will create a summary dataset of the annual count, and can optionally generate change data based on a previous year that has already been processes.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":915,"value":"$34","pinned":false,"hidden":false,"mode":"ojs"},{"id":946,"value":"citibikeByStation2020 = summarizeByStation(cbParsed2020, citibikeByStation2019)","pinned":false,"hidden":false,"mode":"ojs"},{"id":926,"value":"citibikeByStation2019 = summarizeByStation(cbParsed2019)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1981,"value":"md`### Higher level aggregates (in process)\nWhen the full set of annual station level data is uncommented above this can merge it all together into one array, but it isn't active by default due to the size. Currently using to explore unique stations over time..\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1867,"value":"cbParsedMerge = [...cbParsed2013,...cbParsed2014,...cbParsed2015,...cbParsed2016,...cbParsed2017,...cbParsed2018,...cbParsed2019,...cbParsed2020,...cbParsed2021]","pinned":false,"hidden":false,"mode":"ojs"},{"id":1887,"value":"function dailyFromStationsCombined (dataset) {\n let result = {}\n let formatDate = d3.timeFormat(\"%Y-%m-%d\")\n \n let set = d3.groups(dataset, d => d.date.toISOString()).map(d => {\n let item = {}\n let date = d[1][0].date;\n item.date = formatDate(date);\n item.startcount = d3.sum(d[1], d => d.startcount)\n item.stations = d[1].length\n return item;\n })\n return set \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1971,"value":"daily_trip_stations = dailyFromStationsCombined(cbParsedMerge)","pinned":false,"hidden":false,"mode":"ojs"},{"id":2298,"value":"function parseBoroughDaily(dataset) {\n let parseDate = d3.timeParse(\"%Y-%m-%d\");\n\n let formatYear = d3.timeFormat(\"%Y\");\n let formatMonth = d3.timeFormat(\"%m\");\n let formatDay = d3.timeFormat(\"%w\"); \n \n function dayType(num) {\n if (num === 0 || num === 6) {\n return \"weekend\";\n } else {\n return \"weekday\" \n }\n }\n \n let result = dataset.map(d => {\n let item = {}\n item.id = 'citibike';\n item.group = d.group\n item.date = parseDate(d['date']);\n // item.dateiso = item.date.toISOString();\n item.year = +formatYear(item.date);\n item.month = +formatMonth(item.date);\n let day = +formatDay(item.date);\n item.daytype = dayType(day);\n item.stations = +d.stations;\n item.startcount = +d.startcount;\n item.endcount = +d.startcount;\n \n return item\n })\n\n return result\n \n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":2303,"value":"\ncbParsedBoroughDaily = await FileAttachment(\"citibike-daily-by-borough@2.csv\").csv().then(d => parseBoroughDaily(d))\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2307,"value":"citibikeBoroughAnnual = {\n let boroset = Array.from(d3.group(cbParsedBoroughDaily, d => d.group)).map(d => {\n \n let prevStarts = 0;\n let prevStartsAvg = 0;\n \n let set = Array.from(d3.rollup(\n d[1], \n v => ({\n days: v.length,\n starts: d3.sum(v, g => g.startcount),\n startsAvg: d3.mean(v, g => g.startcount),\n startsMax: d3.max(v, g => g.startcount),\n }),\n g => g.year)\n ).map(g => {\n \n let item = {};\n item.id = d[0];\n item.mode = 'citibike'\n item.year = g[0];\n item.days = g[1].days;\n item.starts = g[1].starts;\n item.startsAvg = g[1].startsAvg;\n item.startsMax = g[1].startsMax;\n item.chgStarts = (prevStarts) ? (item.starts - prevStarts)/prevStarts : 0;\n item.chgStartsAvg = (prevStartsAvg) ? (item.startsAvg - prevStartsAvg)/prevStartsAvg : 0;\n \n prevStarts = item.starts\n prevStartsAvg = item.startsAvg\n \n return item\n })\n \n return set\n })\n \n return boroset.flat()\n}\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":2082,"value":"md`### Joins`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2099,"value":"cbGeoJoins = {\n let result = {}\n\n result.data = d3.tsvParse(await FileAttachment(\"citibike-stations-all-join-geo.tsv\").text()).map(d => {\n d.geoid_near = d.geoid_near.split(\",\");\n return d\n })\n \n result.links = []\n result.data.map(d => {\n d.geoid_near.map(g => {\n result.links.push({id: d.id, geoid: g})\n })\n })\n\n result.station = d3.group(result.data, d => d.id)\n result.geo = d3.groups(result.data, d => d.geoid)\n result.geoNear = d3.groups(result.links, d => d.geoid)\n return result\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1502,"value":"md`### Data Dictionary\nFor labels and colors of mode. (this may seem simple but fits in with a structure where multiple transit modes are visualized on the same chart)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"cbDictionary = ({\n citibike: {\n name: \"Citi Bike\",\n color: '#0098E4', // 'rgb(0, 152, 228)'\n coloralt: '#8DA6B9',\n },\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1085,"value":"md`## Utilities`","pinned":false,"hidden":false,"mode":"ojs"},{"id":77,"value":"function movingAverage(values, N) {\n let i = 0;\n let sum = 0;\n const means = new Float64Array(values.length).fill(NaN);\n for (let n = Math.min(N - 1, values.length); i < n; ++i) {\n sum += values[i];\n }\n for (let n = values.length; i < n; ++i) {\n sum += values[i];\n means[i] = sum / N;\n sum -= values[i - N + 1];\n }\n return means;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":521,"value":"function isNum(n) {\n return !isNaN(parseFloat(n)) && isFinite(n) && n !== \"\";\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":661,"value":"format = ({\n date1: d3.timeFormat(\"%a, %b %e %Y\"), // Timeline Tooltip Date Format\n date2: d3.timeFormat(\"%b %e %Y\"), // Timeline Tooltip Date Format\n percent1d: d3.format(\".1%\"),\n pct: d3.format(\".0%\"),\n abs: d3.format(\",.0f\"),\n avg: d3.format(\"(,.1f\"),\n chg: d3.format(\".1%\")\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1059,"value":"md`## References`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2111,"value":"import {tsGeoInfo} from '@benoldenburg/nyc-transit-turnstile-data'","pinned":true,"hidden":false,"mode":"ojs"},{"id":89,"value":"import {inputsGroup, chartTip} from '@benoldenburg/mta-daily-ridership-recovery'","pinned":false,"hidden":false,"mode":"ojs"},{"id":116,"value":"html`\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"import {select, slider, checkbox} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1485,"value":"import {rangeSlider} from '@mootari/range-slider'","pinned":true,"hidden":false,"mode":"ojs"},{"id":277,"value":"L = {\n const L = await require(\"leaflet@1/dist/leaflet.js\");\n if (!L._style) {\n const href = await require.resolve(\"leaflet@1/dist/leaflet.css\");\n document.head.appendChild(L._style = html``);\n }\n return L;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"d3 = require(\"d3@6\")","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}