2f:[] 2d:{"title":"Historical view of weekly apps, cpa & internal competition by site","cells":[{"id":0,"value":"md `# Historical view of weekly apps, cpa & internal competition by site`","pinned":false,"hidden":false,"mode":"ojs"},{"id":732,"value":"html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":472,"value":"viewof file = Object.assign(Inputs.file({label: \"Load Performance File\", required: true}), {style: '--label-width: 200px'})","pinned":false,"hidden":false,"mode":"ojs"},{"id":735,"value":"md `*(Date range for this data set: **${formatter(hist_range[0])}** to **${formatter(hist_range[1])}**)*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":486,"value":"viewof node_selector = Inputs.select(clean_node_list.map(d => d.node), {label: \"Select a Node\", unique: true, sort: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":493,"value":"viewof site_selector = Inputs.select(clean_site_list.map(d => d.sites), {label: \"Select site(s)\", unique: true, sort: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":700,"value":"html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":719,"value":"md `## Applies and CPA \n\n*by week for **${node_selector}** and site **${site_selector}***`","pinned":false,"hidden":false,"mode":"ojs"},{"id":829,"value":"md `*The max weekly applies total for site(s) **${site_selector}** during this time period was **${apps_max}** applies at a **${dollar_formatter(cpa_for_app_max[0].calc_cpa)}** CPA during **Week ${weekNum_formatter(cpa_for_app_max[0].week_date)}***. \n\n*That week, the site had **${cpa_for_app_max[0].total_export}** day(s) with jobs exporting, meaning it had a daily apply run rate of **${normal_formatter(daily_run_rate)}** applies per day.*\n\n*The average CPA for this time period is **${dollar_formatter(cpa_avg[0].calc_cpa)}** and the max CPA for this time period is **${dollar_formatter(d3.max(cleanSiteFL, d => d.calc_cpa))}***`","pinned":false,"hidden":false,"mode":"ojs"},{"id":728,"value":"html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2034,"value":"md `*Assuming all weekdays were to yield the average number of applies, this week's apply projection is ${textcolor(week_projection + ' applies', color)} (based on a ${textcolor(current_run_rate_day.toFixed(1) + ' apps/day', color)} run rate)*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2553,"value":"html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2548,"value":"md `**Applies by Week**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":583,"value":"Plot.plot({\n y: {\n label: 'Total applies ↑'\n },\n x: {\n label: 'Week →',\n tickFormat: 'WK' + '%U',\n //ticks: 9\n },\n insetTop: inset_top,\n insetLeft: 10,\n //insetRight: 25,\n width: width,\n height: height,\n grid: true,\n marks: [\n Plot.ruleY([0]),\n // Plot.areaY(area, {\n // x: 'week',\n // y: 'total_apps',\n // fill: 'grey',\n // fillOpacity: 0.2\n // }),\n Plot.lineY(cleanSiteFL, \n {\n x: 'week_date',\n y: \"total_apps\",\n marker: 'circle',\n stroke: 'steelBlue',\n title: d => `Total Weekly Applies: ${d.total_apps}`\n }\n ),\n Plot.areaY(cleanSiteFL, {\n x: 'week_date',\n y: 'total_apps',\n fill: 'steelBlue',\n fillOpacity: 0.4\n }),\n Plot.text(cleanSiteFL,\n {\n x: 'week_date',\n y: 'total_apps',\n dy: -8,\n lineAnchor: 'bottom',\n text: d => `${d.total_apps}`\n }),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":861,"value":"Plot.plot({\n x: {\n label: 'The blue numbers represent the number of days a campaign for ' + `${site_selector}` + ' was exporting that week',\n labelAnchor: 'center',\n labelOffset: 15,\n ticks: false\n },\n y: {\n label: 'Number of days exporting per week'\n },\n //insetTop: 40,\n insetLeft: 20,\n width: width,\n //height: 75,\n grid: false,\n // color: {\n // scheme: 'GnBu',\n // reverse: false\n // },\n marks: [\n Plot.text(cleanSiteFL, \n {\n x: 'week_date',\n fontSize: 14,\n text: d => `${d.total_export}`,\n fill: d => d.total_export < 1 ? 'white' : 'steelBlue'\n //fill: d => d.total_export > 0 ? 'orange' : '#d9d9d9'\n //title: d => `Total Weekly Applies: ${d.total_apps}`\n }\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2331,"value":"apps_weekday = htl.html `

\nApplies: by day of the week

\n${\n Plot.plot({\n height: 300,\n width: 500,\n insetBottom: 15,\n insetLeft: 25,\n marginRight: 50,\n marginTop: 50,\n insetTop: 25,\n x: {\n tickFormat: Plot.formatWeekday('en', 'long'),\n tickSize: 0\n },\n y: {\n label: 'Applies per Weekday ↑',\n grid: true,\n tickSize: 0\n },\n marks: [\n //Plot.ruleY([0], {strokeOpacity: 0.5}),\n Plot.dot(clean_by_day.filter(d => d.applies > 0), {\n x: d => d.date.getDay(), \n y: \"applies\",\n fill: 'steelBlue',\n fillOpacity: 0.2,\n r: 5,\n symbol: 'circle',\n title: d => `${d.applies}`\n }),\n ]\n })\n }\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2473,"value":"htl.html `

Applies: calendar view by week

\n ${app_heat}
\n ${app_heat_legend}\n

\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":655,"value":"cpa = Plot.plot({\n y: {\n label: 'CPA ↑'\n },\n x: {\n label: 'Week →',\n tickFormat: 'WK' + '%U'\n },\n insetTop: inset_top,\n insetLeft: 10,\n marginTop: 50,\n width: width,\n height: height,\n grid: true,\n marks: [\n Plot.ruleY([0]),\n Plot.lineY(cleanSiteFL, \n {\n x: 'week_date',\n y: \"calc_cpa\",\n marker: 'circle',\n stroke: 'pink'\n }\n ),\n Plot.text(cleanSiteFL, //.filter(d => op.max(d.calc_cpa)),\n {\n x: 'week_date',\n y: 'calc_cpa',\n dy: -8,\n lineAnchor: 'bottom',\n text: d => `${dollar_formatter(d.calc_cpa)}`,\n fontSize: 8\n }\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1279,"value":"html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1241,"value":"md `## Node's Internal Competition`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1456,"value":"Plot.plot({\n insetLeft: 20,\n //marginLeft: 100,\n insetTop: 30,\n width,\n height: 200,\n color: {\n scheme: 'ylorrd'\n },\n grid: true,\n y: {\n label: 'Number of sites active in ' + `${node_selector}` + ' ↑'\n },\n x: {\n domain: cpa.scale(\"x\").domain,\n tickFormat: 'WK' + '%U',\n label:'Week →'\n },\n marks: [\n Plot.ruleY([0]),\n Plot.areaY(clean_multi_site, \n {\n x: 'week_date',\n y: 'sites_active',\n fill: 'firebrick',\n fillOpacity: 0.3,\n clip: true\n }\n ),\n Plot.lineY(clean_multi_site, \n {\n x: 'week_date',\n y: 'sites_active',\n marker: true,\n stroke: 'firebrick',\n title: d => `${d.sites_active} active sites`,\n clip: true\n }\n ),\n Plot.text(clean_multi_site, {\n x: 'week_date',\n y: 'sites_active',\n text: d => `${d.sites_active}`,\n dy: -10\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2449,"value":"html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1065,"value":"html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1755,"value":"md `## Campaign Activity for ${site_selector}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1850,"value":"Inputs.bind( Inputs.select(clean.map(d => d.node), {label: \"Select a Node\", unique: true, sort: true}), viewof node_selector)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1853,"value":"Inputs.bind( Inputs.select(cleanNodeFL.map(d => d.sites), {label: \"Select site(s)\", unique: true, sort: true}), viewof site_selector)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1821,"value":"md `*There have been multiple campaigns active for most sites, below is a calendar view of the ones that have had spend > 0*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1831,"value":"html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1307,"value":"calendar = Plot.plot({\n marginLeft: 35,\n marginTop: 40,\n //width: 575,\n // height: 2000,\n color: {\n range: ['#8cd9b3']\n },\n facet: {\n data: stop_export,\n y: 'job_req_id',\n marginRight: 120,\n label: null\n },\n y: {\n tickFormat: Plot.formatWeekday(\"en\", \"short\"),\n tickSize: 0\n },\n x: {\n tickSize: 0,\n ticks: 5,\n axis: 'top',\n label: 'Week →',\n padding: 0.2,\n labelOffset: 35,\n //domain: cpa.scale('x').domain\n },\n fy: {\n reverse: true,\n padding: 0.2\n },\n marks: [\n Plot.cell(stop_export, {\n x: d => d3.utcWeek.count(d3.utcYear(d.date), d.date), // not sure how, but this reduces the time unit to weeks\n y: d => d.date.getUTCDay(), // reducing the time's unit to weekdays\n fill: 'spending',\n title: d => `${formatter(d.date)} (WK ${wk_format(d.date)})`,\n rx: 10\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"
\n
\n
\n
\n
\n

","pinned":false,"hidden":false,"mode":"html"},{"id":20,"value":"md `## Appendix\n\n_Implementation & data stuff (a.k.a. boring stuff)_`","pinned":false,"hidden":false,"mode":"ojs"},{"id":971,"value":"md `### *Control Panel*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":974,"value":"viewof width = Inputs.range([400, 1500], {label: \"Width\", step: 10, value: 900})","pinned":false,"hidden":false,"mode":"ojs"},{"id":983,"value":"viewof height = Inputs.range([50, 600], {label: \"Height\", step: 10, value: 200})","pinned":false,"hidden":false,"mode":"ojs"},{"id":988,"value":"viewof inset_top = Inputs.range([0, 400], {label: \"Inset Top\", step: 10, value: 20})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2128,"value":"viewof color = Inputs.color({label: \"Text Highlight color\", value: \"#4682b4\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":198,"value":"### *Data*","pinned":false,"hidden":false,"mode":"md"},{"id":474,"value":"data = file.csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2274,"value":"// testing the method where I group by week by a date (i.e. week of \"Oct-15-2022\" instead of \"week 41\"\n\nviewof cleanSiteFL2 = aq.from(data)\n .filter(aq.escape(d => d.sites == site_selector))\n .derive({week_date: aq.escape( d => d3.utcWeek(op.parse_date(d.date)) ) })\n .orderby('date')\n .groupby('week_date','node','sites')\n .rollup({\n total_apps: d => op.sum(d.applies),\n total_spend: d => op.sum(d.spend),\n //total_export: d => op.sum(d.exporting == true),\n //distinct_sites: d => op.distinct(d.sites) // trying to get how many sites were active\n })\n .view()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1912,"value":"clean_by_day = aq.from(data)\n .filter(aq.escape(d => d.node == node_selector))\n .filter(aq.escape(d => d.sites == site_selector))\n .derive({\n date: d => op.parse_date(d.date),\n applies: d => op.parse_int(d.applies)\n }) \n .derive({\n week: d => op.utcweek(d.date)\n }) \n .derive({\n exporting: d => d.exported_jobs > 0}, {after: 'exported_jobs'\n })\n .orderby('date')\n //.filter(d => op.max(d.week) == d.week)\n //.filter(d => d.spend > 0)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":476,"value":"clean = aq.from(data)\n //.filter(aq.escape(d => !req_no_spend_list.includes(d.job_req_id)))\n //.reify()\n .derive({week_date: aq.escape( d => d3.utcWeek(op.parse_date(d.date)) ) }) \n .derive({\n exporting: d => d.exported_jobs > 0}, {after: 'exported_jobs'\n})\n .groupby('week_date','node','sites')\n .rollup({\n total_apps: d => op.sum(d.applies),\n total_spend: d => op.sum(d.spend),\n total_export: d => op.sum(d.exporting == true),\n //distinct_sites: d => op.distinct(d.sites) // trying to get how many sites were active\n })\n .groupby('sites')\n .derive({\n week_max: d => op.max(d.total_apps)\n })\n .groupby('week_date')\n .derive({\n calc_cpa: d => d.total_spend/d.total_apps\n }, {after: 'total_spend'})\n .orderby('week_date')\n .select(aq.not('total_spend'))\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1736,"value":"req_no_spend_list = reqs_no_spend.map(d => d.job_req_id)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1087,"value":"reqs_no_spend = aq.from(data)\n .groupby('job_req_id')\n .rollup({\n total_spend: d => op.sum(d.spend)\n })\n .filter(d => d.total_spend == 0)\n .select('job_req_id')\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":743,"value":"data_dates = aq.from(data)\n .derive({\n date: d => op.parse_date(d.date)\n })\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":687,"value":"apps_max = d3.max(cleanSiteFL, d => d.total_apps) // couldn't figure out how to do this inside the plot, so I just calculated the max","pinned":true,"hidden":false,"mode":"ojs"},{"id":777,"value":"app_total_quantile = d3.quantile(cleanSiteFL.map(d => d.total_apps), 0.9)","pinned":true,"hidden":false,"mode":"ojs"},{"id":827,"value":"cpa_for_app_max = aq.from(cleanSiteFL)\n .filter(aq.escape( d => d.total_apps == apps_max ))\n //.select('calc_cpa')\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1008,"value":"daily_run_rate = apps_max/cpa_for_app_max[0].total_export","pinned":true,"hidden":false,"mode":"ojs"},{"id":2583,"value":"cpa_avg = aq.from(clean_by_day)\n .rollup({\n total_spend: d => op.sum(d.spend),\n total_apps: d => op.sum(d.applies)\n })\n .derive({\n calc_cpa: d => d.total_spend / d.total_apps\n })\n .objects()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1186,"value":"clean_multi_site = aq.from(data)\n .filter(aq.escape(d => d.node == node_selector))\n .derive({week_date: aq.escape( d => d3.utcWeek(op.parse_date(d.date)) ) }) \n .filter(d => d.spend > 0)\n .groupby('week_date', 'node')\n .derive({\n sites_active: d => op.distinct(d.sites)\n })\n .select('week_date','node','sites_active')\n .dedupe()\n // .select('week', 'sites_active')\n .orderby('week_date')\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1739,"value":"// stop_export.filter(d => req_no_spend_list.includes(d.job_req_id))\n\n// // clean_multi_site = clean.filter(d => checkboxes.includes(d.Site))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1289,"value":"stop_export = aq.from(data)\n //.filter(aq.escape(d => !req_no_spend_list.includes(d.job_req_id)))\n // .filter(d => d.spend > 0)\n // .reify()\n .filter(aq.escape(d => d.sites == site_selector))\n .filter(d => d.spend > 0)\n .derive({\n date: d => op.parse_date(d.date),\n exporting: d => d.exported_jobs > 0}, {after: 'exported_jobs'\n })\n .derive({\n week: d => op.utcweek(d.date), \n weekday_name: d => op.utcdayofweek(d.date)}, {after: 'date'\n })\n .derive({\n spending: d => d.spend > 0 }, {after: 'exporting'\n })\n .orderby('date')\n //.filter(d => d.exporting == true && d.spending == false)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1964,"value":"md `### *Projection*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2159,"value":"clean_by_day_current_week = aq.from(data)\n .filter(aq.escape(d => d.node == node_selector))\n .filter(aq.escape(d => d.sites == site_selector))\n .derive({\n date: d => op.parse_date(d.date),\n applies: d => op.parse_int(d.applies)\n }) \n .derive({\n week: d => op.utcweek(d.date)\n }) \n .derive({\n exporting: d => d.exported_jobs > 0}, {after: 'exported_jobs'\n })\n .orderby('date')\n .filter(d => op.max(d.week) == d.week)\n .filter(d => d.spend > 0)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1966,"value":"week = aq.from(clean_by_day)\n .filter(d => d.spend > 0)\n .filter(d => op.max(d.week) == d.week)\n .derive({weekday: d => op.dayofweek(d.date)})\n .select('date', 'node', 'job_req_id', 'sites', 'spend', 'applies', 'cr', 'week', 'weekday')\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2017,"value":"current_week = aq.from(week)\n .select('week')\n .dedupe()\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2021,"value":"c_week = current_week[0].week","pinned":true,"hidden":false,"mode":"ojs"},{"id":2007,"value":"week_to_date = aq.from(week)\n .rollup({ total_apps: d => op.sum(d.applies)})\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2012,"value":"wtd_apps = week_to_date[0].total_apps","pinned":true,"hidden":false,"mode":"ojs"},{"id":1988,"value":"days_current_week = aq.from(week)\n .count()\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1996,"value":"days_remaining = 7 - days_current_week[0].count ","pinned":true,"hidden":false,"mode":"ojs"},{"id":2005,"value":"week_projection = Math.floor(days_remaining * current_run_rate_day + wtd_apps)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2091,"value":"md `### *Projection Viz*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2023,"value":"//data object for chart\nweek_projection_ = ([{week : c_week, total_apps: week_projection}])","pinned":true,"hidden":false,"mode":"ojs"},{"id":2093,"value":"sliced = cleanSiteFL.slice(-2)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2096,"value":"area = aq.from(sliced)\n .filter(d => op.max(d.week - 1) == d.week)\n .concat(aq.from(week_projection_))\n .select('week', 'total_apps')\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1926,"value":"md `### *Run rate*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1928,"value":"past_7_days = aq.from(clean_by_day_current_week)\n .filter(d => d.spend > 0)\n .filter(d => op.max(d.week) == d.week)\n .rollup({\n run_rate: d => op.sum(d.applies)/op.count()\n })\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1951,"value":"current_run_rate_day = past_7_days[0].run_rate","pinned":true,"hidden":false,"mode":"ojs"},{"id":478,"value":"md `### *Filters*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":491,"value":"cleanNodeFL = clean.filter(d => d.node == node_selector)","pinned":true,"hidden":false,"mode":"ojs"},{"id":495,"value":"cleanSiteFL = aq.from(cleanNodeFL)\n .filter(aq.escape(d => d.sites == site_selector))\n .derive({\n daily_run_rate: d => d.total_apps/d.total_export\n })\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2607,"value":"// need to only display the nodes that have data\n\nclean_node_list = aq.from(clean)\n .groupby('node')\n .rollup({\n t_app: d => op.sum(d.total_apps),\n t_export: d => op.sum(d.total_export)\n })\n .filter(d => d.t_app > 0 && d.t_export > 0)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1117,"value":"// need to only display the sites that we have data\n\nclean_site_list = aq.from(clean.filter(d => d.node == node_selector))\n .groupby('sites')\n .rollup({\n t_app: d => op.sum(d.total_apps),\n t_export: d => op.sum(d.total_export)\n })\n .filter(d => d.t_app > 0 && d.t_export > 0)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1233,"value":"md `### *Additional charts*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1091,"value":"Plot.plot({\n height: 300,\n width: 500,\n grid: true,\n //insetTop: 20,\n insetBottom: 25,\n // x: {\n // nice: true,\n // zero: true\n // },\n // y: {\n // nice: true,\n // zero: false\n // },\n marks: [\n Plot.linearRegressionY(cleanSiteFL, { \n x: 'calc_cpa', \n y: 'daily_run_rate', \n stroke: 'orange',\n ci: 0.95\n }),\n \n Plot.dot(cleanSiteFL, { \n x: \"calc_cpa\", \n y: \"daily_run_rate\",\n //fill: 'week',\n title: d => `CPA $${normal_formatter(d.calc_cpa)}, Daily run-rate: ${normal_formatter(d.daily_run_rate)}`\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2604,"value":"md `### *Calendar heatmap source code*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2148,"value":"app_heat = Plot.plot({\n marginBottom: 0,\n marginTop: 50,\n marginLeft: 30,\n width: 950,\n //width: 200,\n height: 300,\n color: {\n scheme: 'blues'\n },\n y: {\n tickSize: 0,\n //padding: 0.25,\n tickFormat: Plot.formatWeekday(\"en\", \"short\")\n },\n x: {\n padding: 0.5,\n axis: 'top',\n tickSize: 0,\n label: null,\n domain: d3.range(\n d3.min(clean_by_day, d => d3.utcWeek.count(d3.utcYear(d.date), d.date)), d3.max(clean_by_day, d => d3.utcWeek.count(d3.utcYear(d.date), d.date)) + 1\n )\n },\n facet: {\n data: clean_by_day,\n y: d => d.date.getUTCFullYear()\n },\n fy: {\n tickFormat: ''\n },\n marks: [\n Plot.cell(clean_by_day, Plot.group({fill: 'sum', title: \"sum\"},{\n x: d => d3.utcWeek.count(d3.utcYear(d.date), d.date),\n y: d => d.date.getUTCDay(),\n fill: 'applies',\n //inset: 0.5,\n title: 'applies',\n //rx: 10\n }))\n ]\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":2260,"value":"app_heat_legend = app_heat.legend('color', {height: 45 ,tickSize: 5})","pinned":false,"hidden":false,"mode":"ojs"},{"id":187,"value":"### *Secondary data sources*","pinned":false,"hidden":false,"mode":"md"},{"id":69,"value":"// all vega data-sets\n\n// to 'extract' a dataset as an array (only for .csv and .json)\n// use this format: football = v_data['football.json']()\n// for some .csv, it's better to use the url format: zipcodes = v_data['zipcodes.csv'].url\n// this is due to the fact that parsing it as an array of objects results in zipcodes of the form \"00313\" turning into \"313\"\n\nv_data = require('vega-datasets@2')","pinned":false,"hidden":false,"mode":"ojs"},{"id":98,"value":"zips = v_data['zipcodes.csv'].url","pinned":false,"hidden":false,"mode":"ojs"},{"id":159,"value":"us_holidays_2021 = USholidays.filter(d => op.utcyear(date_parser(d.date)) == 2021)","pinned":false,"hidden":false,"mode":"ojs"},{"id":193,"value":"### *Libraries*","pinned":false,"hidden":false,"mode":"md"},{"id":4,"value":"import {vl} from '@vega/vega-lite-api-v5'","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"import {uniqueValid} from '@uwdata/data-utilities'","pinned":false,"hidden":false,"mode":"ojs"},{"id":59,"value":"import { aq, op } from '@uwdata/arquero'","pinned":false,"hidden":false,"mode":"ojs"},{"id":139,"value":"// object containing all of the FIPS us state codes\n\nimport {statemap} from \"@d3/choropleth\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":137,"value":"// object containing all of the FiPS us county codes\n\nimport {counties} from \"@d3/choropleth\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":391,"value":"// list of US Holidays\n\nimport {USholidays} from \"@observablehq/microsoft-reactor-hackathon\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":2068,"value":"import {textcolor} from \"@observablehq/text-color-annotations-in-markdown\" // import additional functionality","pinned":false,"hidden":false,"mode":"ojs"},{"id":456,"value":"md `### *Functions*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2316,"value":"weekNum_formatter = d3.timeFormat('%U')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1507,"value":"// I grabbed this function from one of mbostock's examples for the normalizeY transform\nformatChange = {\n const format = d3.format(\"+d\");\n return x => format((x - 1) * 100);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1013,"value":"normal_formatter = d3.format('.0f')","pinned":true,"hidden":false,"mode":"ojs"},{"id":737,"value":"hist_range = d3.extent(data_dates, d => d.date)","pinned":true,"hidden":false,"mode":"ojs"},{"id":741,"value":"formatter = d3.utcFormat('%B %d, %Y')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1775,"value":"wk_format = d3.utcFormat('%U')","pinned":true,"hidden":false,"mode":"ojs"},{"id":660,"value":"dollar_formatter = d3.format('$.0f')","pinned":true,"hidden":false,"mode":"ojs"},{"id":409,"value":"date_parser = d3.utcParse('%m/%d/%Y')","pinned":true,"hidden":false,"mode":"ojs"},{"id":208,"value":"### *Chart configuration*","pinned":false,"hidden":false,"mode":"md"},{"id":202,"value":"clean_config = ({\n view: { stroke: null },\n axis: {\n gridOpacity: 0.5,\n labelFontWeight: 75,\n domain: false,\n offset: 5,\n titleFontWeight: 350,\n titleFontSize: 10\n },\n axisX: {\n tickCount: 5,\n titlePadding: 10,\n labelPadding: 4\n },\n axisY: {\n ticks: false,\n labelLimit: 300,\n labelFontSize: 7\n },\n title: {\n fontWeight: 300,\n fontSize: 13,\n offset: 25\n }\n})","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W2f","stdlib":"1","titling":"auto"}