2f:Td2e,top_salary_comparison = vl.hconcat( // Chart 1 vl.layer( vl.data(rp_top).title( eu_date_formatter(second_half_month[0].sh_start) + ' to ' + eu_date_formatter(second_half_month[0].sh_end)) .encode( vl.y().fieldN('company').sort(vl.max('t_mk').order('descending')).title(null) ) .layer( vl.markBar({tooltip: true}) //{color: appc2_color_scheme[0]}) .encode( vl.x().median('pay_hourly').title('Median Salary').axis({titleFontWeight: 200}), vl.color({format: '.1%'}).max('t_mk').scale({range: ['#ebebeb',_color]}).legend({orient: 'bottom', format: '%', gradientThickness: 7, titleFontWeight: 200, labelFontWeight: 100}).title('Market Share') ), vl.markText({color: 'white', dx: -20, size: 10}) .encode( vl.x().median('pay_hourly'), vl.text({format: '$.1f'}).median('pay_hourly') //vl.text({format: euro_format}).median('pay_hourly') ) ), vl.layer( // this is the average line for current week vl.markRule({color: appcast_color_palette[1], strokeWidth: 2}) .data(rp_top) //.data(recent_period) //.data(rp_top) // -- rp_top is the data for the whole market? .encode( vl.x().median('pay_hourly') ), vl.markText({fontWeight: 400, dx: 0, angle: 0, color: appcast_color_palette[1], size: 17, dy: -15}) //dy: -175 .data(rp_top) //.data(recent_period) //.data(rp_top) .encode( vl.x().median('pay_hourly'), vl.y().value(-'height'), vl.text({format: '$.2f'}).median('pay_hourly') //vl.text({format: euro_format}).median('pay_hourly') ) ) ).width(chart_width), // Chart 2 vl.layer( vl.data(pp_top).title( eu_date_formatter(first_half_month[0].fh_start) + ' to ' + eu_date_formatter(first_half_month[0].fh_end) ) .encode( vl.y().fieldN('company').sort(vl.max('t_mk').order('descending')).title(null) ) .layer( vl.markBar({tooltip: true}) .encode( vl.x().median('pay_hourly').title('Median Salary').axis({titleFontWeight: 200}), vl.color({format: '.1%'}).max('t_mk').scale({range: ['#616161','#ebebeb'], reverse: true}).legend({orient: 'bottom', format: '%', gradientThickness: 7, titleFontWeight: 200, labelFontWeight: 100}).title('Market Share') ), vl.markText({color: 'white', dx: -20, size: 10}) .encode( vl.x().median('pay_hourly'), vl.text({format: '$.1f'}).median('pay_hourly') ) ), vl.layer( // this is the average line for previous week vl.markRule({color: appcast_color_palette[1], strokeWidth: 2}) .data(pp_top) //.data(previous_period) //.data(pp_top) .encode( vl.x().median('pay_hourly') ), vl.markText({fontWeight: 400, dx: 0, angle: 0, color: appcast_color_palette[1], size: 17, dy: -15}) //dy: -175 .data(pp_top) //.data(previous_period) //.data(pp_top) .encode( vl.x().median('pay_hourly'), vl.y().value(-'height'), vl.text({format: '$.2f'}).median('pay_hourly') ) ) ).width(chart_width) ) .resolve({scale: {color: 'independent'} }) .spacing(chart_spacing) .config(clean_config) .padding({left: 70, top: 50, bottom: 50}) .render()30:T5d3,// this chart uses "recent_period_all_salaries" instead of "recent_period". The latter is a summary built for the top market share holders' comparison, whereas the former is all data with only the 2 week filter market_avg_2 = vl.hconcat( vl.markBar({tooltip: true, opacity: 0.9, color: appc2_color_scheme[0]}) .data(recent_period_all_salaries) .encode( vl.x().fieldQ('pay_hourly').bin({minstep: 2.5}) .axis({ format: '$.1d', labelOpacity: 0.5, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 15}) .title('Salary Range'), vl.y().count().title('Number of posts').axis({labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 20}) ).width(chart_width - 10), vl.markText({size: 55, fontWeight: 100, opacity: 0.7}) .data(recent_period_all_salaries) .title({ text: 'Market Average (Median)', fontSize: 17, subtitle: eu_date_formatter(second_half_month[0].sh_start) + ' to ' + eu_date_formatter(second_half_month[0].sh_end), subtitleFontWeight: 100, subtitleFontSize: 10, subtitlePadding: 7.5}) .encode( vl.text({format: '$.2f'}).median('pay_hourly')) ) .spacing(chart_spacing + 15) //.transform(vl.filter('datum.Company != "Amazon"')) .config(clean_config) .padding({top: 75, bottom: 75, left: 70, right: 30}) .render()31:Tcad,//.data(clean_by_multi_site.filter((d) => wh_final_Msite_list.includes(d.Company))) Msite_top_salary_comparison = vl.hconcat( // Chart 1 vl.layer( vl.data(Msite_rp_top).title( eu_date_formatter(second_half_month[0].sh_start) + ' to ' + eu_date_formatter(second_half_month[0].sh_end)) .encode( vl.y().fieldN('company').sort(vl.max('t_mk').order('descending')).title(null) ) .layer( vl.markBar({tooltip: true}) //{color: appc2_color_scheme[0]}) .encode( vl.x().median('pay_hourly').title('Median Salary').axis({titleFontWeight: 200}), vl.color({format: '.1%'}).max('t_mk').scale({range: ['#ebebeb',_color]}).legend({orient: 'bottom', format: '%', gradientThickness: 7, titleFontWeight: 200, labelFontWeight: 100}).title('Market Share') ), vl.markText({color: 'white', dx: -20, size: 10}) .encode( vl.x().median('pay_hourly'), vl.text({format: '$.1f'}).median('pay_hourly') ) ), vl.layer( // this is the average line for current week vl.markRule({color: appcast_color_palette[1], strokeWidth: 2}) .data(Msite_rp_top) .encode( vl.x().median('pay_hourly') ), vl.markText({fontWeight: 400, dx: 0, angle: 0, color: appcast_color_palette[1], size: 17, dy: -15}) //dy: -175 .data(Msite_rp_top) .encode( vl.x().median('pay_hourly'), vl.y().value(-'height'), vl.text({format: '$.2f'}).median('pay_hourly') ) ) ).width(chart_width), // Chart 2 vl.layer( vl.data(Msite_pp_top).title( eu_date_formatter(first_half_month[0].fh_start) + ' to ' + eu_date_formatter(first_half_month[0].fh_end) ) .encode( vl.y().fieldN('company').sort(vl.max('t_mk').order('descending')).title(null) ) .layer( vl.markBar({tooltip: true}) .encode( vl.x().median('pay_hourly').title('Median Salary').axis({titleFontWeight: 200}), vl.color({format: '.1%'}).max('t_mk').scale({range: ['#616161','#ebebeb'], reverse: true}).legend({orient: 'bottom', format: '%', gradientThickness: 7, titleFontWeight: 200, labelFontWeight: 100}).title('Market Share') ), vl.markText({color: 'white', dx: -20, size: 10}) .encode( vl.x().median('pay_hourly'), vl.text({format: '$.1f'}).median('pay_hourly') ) ), vl.layer( // this is the average line for previous week vl.markRule({color: appcast_color_palette[1], strokeWidth: 2}) .data(Msite_pp_top) //.data(previous_period) //.data(pp_top) .encode( vl.x().median('pay_hourly') ), vl.markText({fontWeight: 400, dx: 0, angle: 0, color: appcast_color_palette[1], size: 17, dy: -15}) //dy: -175 .data(Msite_pp_top) //.data(previous_period) //.data(pp_top) .encode( vl.x().median('pay_hourly'), vl.y().value(-'height'), vl.text({format: '$.2f'}).median('pay_hourly') ) ) ).width(chart_width) ) .resolve({scale: {color: 'independent'} }) .spacing(chart_spacing) .config(clean_config) .padding({left: 70, top: 50, bottom: 50}) .render()32:T6fe,// this chart uses "Msite_recent_period_all_salaries" instead of "Msite_recent_period". The latter is a summary built for the top market share holders' comparison, whereas the former is all data with only the 2 week filter Msite_market_avg2 = vl.hconcat( vl.layer( vl.markBar({tooltip: true, opacity: 0.9, color: appc2_color_scheme[0]}) //.title({text: mult_site_selector, fontSize: 8, limit: 400}) .encode( vl.x().fieldQ('pay_hourly').bin({minstep: 2.5}) .axis({ format: '$.1d', labelOpacity: 0.5, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 15}) .title('Salary Range'), vl.y().count().title('Number of posts').axis({labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 20}) ).width(chart_width - 50), // Tested adding a line for the median // vl.markRule({color: 'red'}) // .encode( // vl.x().median('pay_hourly') // ) ), vl.markText({size: 55, fontWeight: 100, opacity: 0.7}) .title({ text: 'Market Average (Median)', fontSize: 17, subtitle: eu_date_formatter(second_half_month[0].sh_start) + ' to ' + eu_date_formatter(second_half_month[0].sh_end), subtitleFontWeight: 100, subtitleFontSize: 10, subtitlePadding: 7.5}) .encode(vl.text({format: '$.2f'}).median('pay_hourly')) ) //.facet({column: {field: 'Segment', type: 'nominal', title: null} }) .spacing(chart_spacing + 15) .data(Msite_recent_period_all_salaries) .transform(vl.filter('datum.Company != "Amazon Workforce Staffing"')) .config(clean_config) .padding({top: 75, bottom: 75, left: 70, right: 30}) .render()33:T42f,over_time = vl.layer( // vl.markCircle({size: 7, color: '#cccccc'}) // .encode( // vl.x().fieldT('date'), // vl.y().fieldQ('pay_hourly'), // vl.opacity().value(0.5) // ), vl.markLine({tooltip: true, strokeWidth: 1.5, point: { color: appcast_color_palette[4], size: 75, stroke: 'white' }, stroke: appcast_color_palette[4] }) .encode( vl.x().fieldT('date').axis({grid: true, tickCount: 10}).title(null), vl.y().median('pay_hourly') .scale({padding: 20, zero: true}) .title('Median Salary') .axis({ format: '$.2f', labelOpacity: 0.5, titleFontWeight: 200, titlePadding: 15}), // vl.color().fieldN('amazon') // .scale({range: [appcast_color_palette[4], '#cccccc'], domain: ['true', 'false']}) // .legend({title: 'company'}) ) ) .data(clean_week_split) .width(width - 300) .config(clean_config) .padding({top: 50, left: 70, bottom: 20}) .render()34:T593,// I defined this custom instance of vl in order to adjust the locale for the various currencies vl = { const [vega, vegalite, api, tooltip] = await Promise.all( [ 'vega@5.17.0', 'vega-lite@4.14.1', 'vega-lite-api@0.14.0', 'vega-tooltip@0.24.2' ].map(module => require(module)) ); const options = { config: { // vega-lite default configuration config: { view: { continuousWidth: 400, continuousHeight: 300 }, mark: { tooltip: null } } }, init: view => { // initialize tooltip handler view.tooltip(new tooltip.Handler().call); // enable horizontal scrolling for large plots if (view.container()) view.container().style['overflow-x'] = 'auto'; }, view: { // view constructor options loader: vega.loader({ baseURL: 'https://vega.github.io/vega-datasets/' }), renderer: 'canvas' } }; if (country_selector == 'UK') { // I added this statement to use the pound sign when UK is selected vega.formatLocale(uk_locale) } else if (country_selector == 'TR') { vega.formatLocale(turkish_locale) } else if (country_selector == 'MX') { vega.formatLocale(spanish_mx_locale) } else { vega.formatLocale(eu_locale) // otherwise use the "eu" locale }; return api.register(vega, vegalite, options); }35:T4e3,market_avg = vl.hconcat( vl.markBar({tooltip: true, opacity: 0.9, color: appc2_color_scheme[0]}) .data(recent_period) .encode( vl.x().fieldQ('pay_hourly').bin({minstep: 2.5}) .axis({ format: '$.1d', labelOpacity: 0.5, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 15}) .title('Salary Range'), vl.y().count().title('Number of posts').axis({labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 20}) ).width(chart_width - 10), vl.markText({size: 55, fontWeight: 100, opacity: 0.7}) .data(recent_period) .title({ text: 'Market Average (Median)', fontSize: 17, subtitle: eu_date_formatter(second_half_month[0].sh_start) + ' to ' + eu_date_formatter(second_half_month[0].sh_end), subtitleFontWeight: 100, subtitleFontSize: 10, subtitlePadding: 7.5}) .encode( vl.text({format: '$.2f'}).median('pay_hourly')) ) .spacing(chart_spacing + 15) //.transform(vl.filter('datum.Company != "Amazon"')) .config(clean_config) .padding({top: 75, bottom: 75, left: 70, right: 30}) .render()36:T610,Msite_market_avg = vl.hconcat( vl.layer( vl.markBar({tooltip: true, opacity: 0.9, color: appc2_color_scheme[0]}) //.title({text: mult_site_selector, fontSize: 8, limit: 400}) .encode( vl.x().fieldQ('pay_hourly').bin({minstep: 2.5}) .axis({ format: '$.1d', labelOpacity: 0.5, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 15}) .title('Salary Range'), vl.y().count().title('Number of posts').axis({labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200, titlePadding: 20}) ).width(chart_width - 50), // Tested adding a line for the median // vl.markRule({color: 'red'}) // .encode( // vl.x().median('pay_hourly') // ) ), vl.markText({size: 55, fontWeight: 100, opacity: 0.7}) .title({ text: 'Market Average (Median)', fontSize: 17, subtitle: eu_date_formatter(second_half_month[0].sh_start) + ' to ' + eu_date_formatter(second_half_month[0].sh_end), subtitleFontWeight: 100, subtitleFontSize: 10, subtitlePadding: 7.5}) .encode(vl.text({format: '$.2f'}).median('pay_hourly')) ) //.facet({column: {field: 'Segment', type: 'nominal', title: null} }) .spacing(chart_spacing + 15) .data(Msite_recent_period) .transform(vl.filter('datum.Company != "Amazon Workforce Staffing"')) .config(clean_config) .padding({top: 75, bottom: 75, left: 70, right: 30}) .render()37:T583,sc1_avg = vl.hconcat( vl.markBar({width: {band: 0.8}, opacity: 0.9, color: appc2_color_scheme[0]}) .encode( vl.x().fieldQ('pay_hourly').bin({minstep: 1}) .axis({ format: '$', labelOpacity: 0.5, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200}) .scale({padding: 50}).title('Salary Range'), vl.y().count().title('Number of posts').axis({titleFontWeight: 200, labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200}) //.axis({format: '%', titlePadding: 40, titleFontWeight: 200, titleFontSize: 13, labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200}) ) .width(chart_width - 50), vl.markText({size: 55, fontWeight: 100, opacity: 0.7}) .title({ text: 'Amazon WFS Average (Median)', fontSize: 17, subtitle: eu_date_formatter(second_half_month[0].sh_start) + ' to ' + eu_date_formatter(second_half_month[0].sh_end), subtitleFontWeight: 100, subtitleFontSize: 10, subtitlePadding: 7.5}) .encode(vl.text({format: '$.2f'}).median('pay_hourly')) ) .spacing(50) //.facet({column: {field: 'Segment', type: 'nominal', title: null} }) .spacing(chart_spacing + 15) .data(recent_period) //.data(clean_week_split) .transform(vl.filter('datum.Company == "Amazon Workforce Staffing"')) .config(clean_config) .padding({top: 75, bottom: 30, left: 70, right: 30}) .render()38:T567,Msite_sc1_avg = vl.hconcat( vl.markBar({width: {band: 0.8}, opacity: 0.9, color: appc2_color_scheme[0]}) .encode( vl.x().fieldQ('pay_hourly').bin({minstep: 1}) .axis({ format: '$', labelOpacity: 0.5, tickOpacity: 0.3, labelFontWeight: 200, titleFontWeight: 200}) .scale({padding: 50}).title('Salary Range'), vl.y().count().title('Number of posts').axis({titleFontWeight: 200, labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200}) //.axis({format: '%', titlePadding: 40, titleFontWeight: 200, titleFontSize: 13, labelOpacity: 0.3, tickOpacity: 0.3, labelFontWeight: 200}) ) .width(chart_width - 50), vl.markText({size: 55, fontWeight: 100, opacity: 0.7}) .title({ text: 'Amazon WFS Average (Median)', fontSize: 17, subtitle: formatter2(second_half_month[0].sh_start) + ' to ' + formatter2(second_half_month[0].sh_end), subtitleFontWeight: 100, subtitleFontSize: 10, subtitlePadding: 7.5}) .encode(vl.text({format: '$.2f'}).median('pay_hourly')) ) .spacing(50) //.facet({column: {field: 'Segment', type: 'nominal', title: null} }) .spacing(chart_spacing + 15) .data(Msite_recent_period) .transform(vl.filter('datum.Company == "Amazon Workforce Staffing"')) .config(clean_config) .padding({top: 75, bottom: 30, left: 70, right: 30}) .render()39:[] 2d:{"title":"Europe Competitor Salary","cells":[{"id":0,"value":"title6 = md `# Europe Competitor Salary`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1213,"value":"break4 = html `
`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1216,"value":"title1 = md `### Competitor Intel Master File`","pinned":false,"hidden":false,"mode":"ojs"},{"id":781,"value":"viewof file = Inputs.file({label: \"Load data\", required: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1351,"value":"data_requirement = md `*The tool assumes the file contains dates corresponding to a 4-week window*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":796,"value":"req_date_range_text = md `### Date range\n\nThe file you have loaded: **${formatter(date_range[0])}** to **${formatter(date_range[1])}**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":785,"value":"break1 = html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2384,"value":"md `## Country`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1867,"value":"viewof country_selector = Inputs.select(data.map(d => d.country), {label: \"Select a Country\", unique: true, sort: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2274,"value":"md `### Mini data QA \n\n(at the country level)`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2265,"value":"md `***Date range***: ${formatter(country_date_range[0])} to ${formatter(country_date_range[1])}`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2352,"value":"md `***Calendar view:***`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2310,"value":"Plot.plot({\n //marginLeft: 75,\n width: 225,\n height: 150,\n y: {\n tickFormat: Plot.formatWeekday('en', 'short'),\n tickSize: 0\n },\n x: {\n tickSize: 0,\n label: 'Week'\n },\n marks: [\n Plot.cell(data_countryFL,{\n x: d => d3.utcWeek.count(d3.utcYear(d.date), d.date),\n y: d => d.date.getUTCDay(),\n fill: appcast_color_palette[0],\n //opacity: d => d3.count(d.date)\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":2307,"value":"md `***Hourly pay distribution:***`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2277,"value":"vl.markBar({tooltip: true, width: 20, color: appcast_color_palette[0]})\n .data(data_countryFL)\n .encode(\n vl.x().fieldQ('pay_hourly').bin(true).title('Hourly pay field')\n .axis({\n format: '$.1f',\n domain: true,\n offset: 0,\n tickOpacity: 1,\n tickminStep: 1\n }),\n vl.y().count().axis({format: ',.0f', tickMinStep: 1})\n )\n .height(75)//.width(600)\n .config(clean_config)\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2340,"value":"html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":954,"value":"break2 = html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1840,"value":"title9 = md `# By Node`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2367,"value":"Inputs.bind(Inputs.select(data.map(d => d.country), {label: \"Select a Country\", unique: true, sort: true}), viewof country_selector)","pinned":false,"hidden":false,"mode":"ojs"},{"id":792,"value":"viewof node_selector = Inputs.select(data_countryFL.map(d => d.search_market), {label: \"Select a Node\", unique: true, sort: true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":788,"value":"title2 = md `## Median Salary for Top Market Share Holders`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1157,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1196,"value":"break3 = html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1199,"value":"title3 = md `## Node Salary Distribution and Full Market Average`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2432,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1363,"value":"break6 = html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1535,"value":"break7 = html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1836,"value":"title8 = md `# Multi Site`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2121,"value":"Inputs.bind(Inputs.select(data.map(d => d.country), {label: \"Select a Country\", unique: true, sort: true}), viewof country_selector)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1805,"value":"Inputs.bind(Inputs.select(data.map(d => d.search_market), {label: \"Select a Node\", unique: true, sort: true}), viewof node_selector)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1734,"value":"viewof mult_site_selector = Object.assign(\n Inputs.checkbox(\n data_nodeFL.map((d) => d.search_submarket),\n {\n label: \"Select a Site or Sites\",\n unique: true,\n sort: true,\n value: multi_site_unique_list\n }\n ),\n { style: \"--label-width: 175px\" }\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1739,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":2442,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1312,"value":"title5 = md `**_Control Panel for Salary Charts_**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1305,"value":"viewof chart_spacing = Inputs.range([0, 300], {label: \"chart spacing\", step: 5, value: 120})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1296,"value":"viewof chart_width = Inputs.range([0, 600], {label: \"chart width\", step: 5})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1843,"value":"break8 = html `

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1496,"value":"title7 = md `## Market Median Salary over time`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1411,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":88,"value":"break5 = html `
\n
\n
\n

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":20,"value":"md `## Appendix\n\n_Implementation & data stuff_`","pinned":false,"hidden":false,"mode":"ojs"},{"id":198,"value":"### *Data*","pinned":false,"hidden":false,"mode":"md"},{"id":1229,"value":"// instead of this week vs previous week, the split is at first vs second half of a month back\n// this code looks at the first half\n\nfirst_half_month = aq.from(clean_week_split)\n .select('date', 'Segment')\n .orderby(0)\n .dedupe()\n .filter(d => d.Segment == 'first_half')\n .derive({\n fh_start: d => op.min(d.date),\n fh_end: d => op.max(d.date)\n })\n .select('fh_start', 'fh_end')\n .dedupe()\n .objects()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1269,"value":"// instead of this week vs previous week, the split is at first vs second half of a month back\n// this code looks at the second half\n\nsecond_half_month = aq.from(clean_week_split)\n .select('date', 'Segment')\n .orderby(0)\n .dedupe()\n .filter(d => d.Segment == 'second_half')\n .derive({\n sh_start: d => op.min(d.date),\n sh_end: d => op.max(d.date)\n })\n .select('sh_start', 'sh_end')\n .dedupe()\n .objects()","pinned":false,"hidden":false,"mode":"ojs"},{"id":2111,"value":"aq.from(clean_week_split)\n .view()","pinned":true,"hidden":false,"mode":"ojs"},{"id":482,"value":"//cleaned up for salary manipulation by week\n\nclean_week_split = aq.from(data_nodeFL)\n .derive({date: d => op.parse_date(d.date)}, {before: 'rank'})\n // .derive({site_length: aq.escape( d => d.search_submarket.length )})\n // .filter(d => d.site_length < 50)\n // .derive({\n // pay_hourly: d => op.slice(d.pay_hourly, 1) // this piece of code was used to trim out the currency symbol\n // })\n .derive({\n first_half: aq.escape(d => d.date <= two_weeks_back)\n })\n .derive({\n second_half: aq.escape(d => d.date > two_weeks_back)\n })\n .fold(['first_half','second_half'], {as: ['Segment', 'Value']})\n .filter(d => d.Value === true)\n .filter(d => d.pay_hourly > 0)\n .derive({\n amazon: d => op.includes(d.company, 'Amazon') //? 'Amazon' : 'Market'})\n })\n .filter(d => d.amazon == false)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2261,"value":"md `## Filters`","pinned":false,"hidden":false,"mode":"ojs"},{"id":2235,"value":"// this conditional ensures I don't filter out salaries in Turkish Lira since for the rest of the countries I am excluding values larger than 100 (i.e. it's unrealistic to have a 100 /hr salary in, say, UK)\n\ndata_countryFL = {\n \n if (country_selector == 'TR') {\n return aq.from(data_countryParsing)\n .derive({pay_hourly: d => op.parse_float(d.pay_hourly)})\n .objects()\n }\n \n else {\n return aq.from(data_countryParsing)\n .derive({pay_hourly: d => op.parse_float(d.pay_hourly)})\n .filter(d => d.pay_hourly < 100 )\n .objects()\n }\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1865,"value":"data_countryParsing = aq.from(data)\n .filter(aq.escape(d => d.country == country_selector))\n .derive({date: d => op.parse_date(d.date)})\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2346,"value":"data_nodeFL = data_countryFL.filter(d => d.search_market == node_selector)","pinned":true,"hidden":false,"mode":"ojs"},{"id":394,"value":"data = file.csv()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1746,"value":"### Multi site main data","pinned":false,"hidden":false,"mode":"md"},{"id":1750,"value":"Msite_clean = clean_week_split.filter(d => mult_site_selector.includes(d.search_submarket))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1797,"value":"multi_site_unique_list = uniqueValid(data_nodeFL, d => d.search_submarket)","pinned":true,"hidden":false,"mode":"ojs"},{"id":916,"value":"### By 2 week period","pinned":false,"hidden":false,"mode":"md"},{"id":990,"value":"*Recent 2 Weeks*","pinned":false,"hidden":false,"mode":"md"},{"id":1756,"value":"#### Node level","pinned":false,"hidden":false,"mode":"md"},{"id":2429,"value":"viewof recent_period_all_salaries = Inputs.table(aq.from(clean_week_split) \n .filter(d => d.Segment == 'second_half') \n .objects())","pinned":true,"hidden":false,"mode":"ojs"},{"id":2452,"value":"// Work in Progress: trying to merge the two tables below. The \"recent_period\" becomes redundant now that I've created the \"all_salaries\" table above. The main challenge I'm facing is to get the \"pay_hourly\" column after grouping by nominal variables.\n\nconsolidated_rp = aq.from(clean_week_split)\n .filter(d => d.Segment == 'second_half')\n .groupby('Segment','company')\n .count()\n .derive({market_share: d => d.count/op.sum(d.count)})\n .rollup({\n // median_salary: d => op.median(d.pay_hourly),\n t_ct: d => op.sum(d.count),\n t_mk: d => op.sum(d.market_share)\n })\n // .orderby(aq.desc('t_mk'))\n // .filter(d => op.rank() <= 20)\n .view()","pinned":true,"hidden":false,"mode":"ojs"},{"id":920,"value":"viewof recent_period = Inputs.table(aq.from(clean_week_split) \n .filter(d => d.Segment == 'second_half') \n //.filter(d => d.pay_hourly < 100) // added salary filter \n .groupby('date', 'company','Segment', 'pay_hourly')\n .count()\n .groupby('Segment')\n .derive({market_share: d => d.count/op.sum(d.count)})\n .orderby(aq.desc('market_share'))\n .objects())","pinned":true,"hidden":false,"mode":"ojs"},{"id":979,"value":"rp_top = aq.from(recent_period)\n .groupby('company', 'pay_hourly')\n .rollup({\n median_salary: d => op.median(d.pay_hourly),\n t_ct: d => op.sum(d.count),\n t_mk: d => op.sum(d.market_share)\n })\n .orderby(aq.desc('t_mk'))\n .filter(d => op.rank() <= 20)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1013,"value":"cw_list3 = rp_top.map(d => d.company)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1760,"value":"#### Multi site level","pinned":false,"hidden":false,"mode":"md"},{"id":2440,"value":"viewof Msite_recent_period_all_salaries = Inputs.table(aq.from(Msite_clean) \n .filter(d => d.Segment == 'second_half') \n .objects())","pinned":true,"hidden":false,"mode":"ojs"},{"id":1764,"value":"viewof Msite_recent_period = Inputs.table(aq.from(Msite_clean) \n .filter(d => d.Segment == 'second_half') \n .filter(d => d.pay_hourly < 100) // added salary filter\n .groupby('date', 'company','Segment', 'pay_hourly')\n .count()\n .groupby('Segment')\n .derive({market_share: d => d.count/op.sum(d.count)})\n .orderby(aq.desc('market_share')) \n .objects())","pinned":true,"hidden":false,"mode":"ojs"},{"id":1767,"value":"viewof Msite_rp_top = Inputs.table(aq.from(Msite_recent_period)\n .groupby('company', 'pay_hourly')\n .rollup({\n median_salary: d => op.median(d.pay_hourly),\n t_ct: d => op.sum(d.count),\n t_mk: d => op.sum(d.market_share)\n })\n .orderby(aq.desc('t_mk'))\n .filter(d => op.rank() <= 20)\n .objects())","pinned":true,"hidden":false,"mode":"ojs"},{"id":1773,"value":"Msite_rp_list3 = Msite_rp_top.map(d => d.company)","pinned":true,"hidden":false,"mode":"ojs"},{"id":994,"value":"*Previous 2 Weeks*","pinned":false,"hidden":false,"mode":"md"},{"id":1776,"value":"#### Node level","pinned":false,"hidden":false,"mode":"md"},{"id":932,"value":"viewof previous_period = Inputs.table(aq.from(clean_week_split) \n .filter(d => d.Segment == 'first_half')\n //.filter(d => d.pay_hourly < 100) // added salary filter\n .groupby('date', 'company','Segment', 'pay_hourly')\n .count()\n .groupby('Segment')\n .derive({market_share: d => d.count/op.sum(d.count)})\n .orderby(aq.desc('market_share')) \n .objects())","pinned":true,"hidden":false,"mode":"ojs"},{"id":1015,"value":"pp_top = aq.from(previous_period)\n .groupby('company', 'pay_hourly')\n .rollup({\n t_ct: d => op.sum(d.count),\n t_mk: d => op.sum(d.market_share)\n })\n .orderby(aq.desc('t_mk'))\n .filter(d => op.rank() <= 20)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1024,"value":"pw_list3 = pp_top.map(d => d.company)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1742,"value":"#### Multi site level","pinned":false,"hidden":false,"mode":"md"},{"id":1782,"value":"Msite_previous_period = aq.from(Msite_clean) \n .filter(d => d.Segment == 'first_half')\n .groupby('date', 'company','Segment', 'pay_hourly')\n .count()\n .groupby('Segment')\n .derive({market_share: d => d.count/op.sum(d.count)})\n .orderby(aq.desc('market_share')) \n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1784,"value":"Msite_pp_top = aq.from(Msite_previous_period)\n .filter(d => d.pay_hourly < 100) // added salary filter \n .groupby('company', 'pay_hourly')\n .rollup({\n t_ct: d => op.sum(d.count),\n t_mk: d => op.sum(d.market_share)\n })\n .orderby(aq.desc('t_mk'))\n .filter(d => op.rank() <= 20)\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":1792,"value":"Msite_pw_list3 = Msite_previous_period.map(d => d.company)","pinned":true,"hidden":false,"mode":"ojs"},{"id":402,"value":"#### Date","pinned":false,"hidden":false,"mode":"md"},{"id":573,"value":"formatter = d3.timeFormat('%d %B %Y')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1282,"value":"formatter2 = d3.timeFormat('%A %b %d, %Y')","pinned":true,"hidden":false,"mode":"ojs"},{"id":2017,"value":"eu_date_formatter = d3.timeFormat('%A %d %b, %Y')","pinned":true,"hidden":false,"mode":"ojs"},{"id":2021,"value":"eu_date_formatter(mydate)","pinned":true,"hidden":false,"mode":"ojs"},{"id":2019,"value":"mydate = new Date(2022,8,9)","pinned":true,"hidden":false,"mode":"ojs"},{"id":424,"value":"data_date_range = aq.from(data)\n .derive({date: d => op.parse_date(d.date)}, {before: 'Rank'})\n .objects()","pinned":true,"hidden":false,"mode":"ojs"},{"id":2267,"value":"country_date_range = d3.extent(data_countryFL, d => d.date)","pinned":true,"hidden":false,"mode":"ojs"},{"id":412,"value":"date_range = d3.extent(data_date_range, d => d.date)","pinned":true,"hidden":false,"mode":"ojs"},{"id":415,"value":"two_weeks_back = d3.timeDay.offset(date_range[1], -14)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1911,"value":"md `### *Locale*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1913,"value":"euro_locale = d3.formatLocale({\n decimal: \",\",\n thousands: \"\\u00a0\",\n grouping: [3],\n currency: [\"\", \"€\"],\n minus: \"\\u2212\",\n percent: \"\\u202f%\"\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":1917,"value":"euro_format = euro_locale.format(\"$,\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1927,"value":"eu_locale = {\n return {\n \"decimal\": \",\",\n \"thousands\": \".\",\n \"grouping\": [3],\n \"currency\": [\"\", \"\\u00a0€\"],\n // \"dateTime\": \"%a %b %e %X %Y\",\n // \"date\": \"%d.%m.%Y\",\n // \"time\": \"%H:%M:%S\",\n // \"periods\": [\"AM\", \"PM\"],\n // \"days\": [\"Sonntag\", \"Montag\", \"Dienstag\", \"Mittwoch\", \"Donnerstag\", \"Freitag\", \"Samstag\"],\n // \"shortDays\": [\"So\", \"Mo\", \"Di\", \"Mi\", \"Do\", \"Fr\", \"Sa\"],\n // \"months\": [\"Januar\", \"Februar\", \"März\", \"April\", \"Mai\", \"Juni\", \"Juli\", \"August\", \"September\", \"Oktober\", \"November\", \"Dezember\"],\n // \"shortMonths\": [\"Jan\", \"Feb\", \"Mär\", \"Apr\", \"Mai\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Okt\", \"Nov\", \"Dez\"]\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2005,"value":"uk_locale = {\n return {\n \"decimal\": \".\",\n \"thousands\": \",\",\n \"grouping\": [3],\n \"currency\": [\"£\", \"\"],\n \"dateTime\": \"%a %b %e %X %Y\",\n \"date\": \"%d.%m.%Y\",\n \"time\": \"%H:%M:%S\",\n \"periods\": [\"AM\", \"PM\"]\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2215,"value":"turkish_locale = {\n return {\n \"decimal\": \",\",\n \"thousands\": \".\",\n \"grouping\": [3],\n \"currency\": [\"₺\", \"\"],\n // \"dateTime\": \"%a %b %e %X %Y\",\n // \"date\": \"%d.%m.%Y\",\n // \"time\": \"%H:%M:%S\",\n // \"periods\": [\"AM\", \"PM\"]\n };\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":2415,"value":"spanish_mx_locale = {\n return {\n \"decimal\": \",\",\n \"thousands\": \".\",\n \"grouping\": [3],\n \"currency\": [\"$\", \"\"],\n // \"dateTime\": \"%a %b %e %X %Y\",\n \"date\": \"%d-%m-%Y\",\n // \"time\": \"%H:%M:%S\",\n // \"periods\": [\"AM\", \"PM\"]\n };\n}","pinned":true,"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":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":1969,"value":"$34","pinned":true,"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":151,"value":"import {countyShapes} from \"@enjalot/us-county-datasets\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":1005,"value":"companies = propArray('Company')","pinned":true,"hidden":false,"mode":"ojs"},{"id":1007,"value":"propArray = curry(\n (prop, list) =>\n ({\n [prop]: map(k => k[prop], list)\n })\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":987,"value":"import {R, map, curry, merge, reduce, length, append, filter, sortWith, descend, ascend, take, pipe, compose, sum} from \"@martien/ramda\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":159,"value":"//example to use the file above\ncountyShapes.features[0].properties.name","pinned":true,"hidden":false,"mode":"ojs"},{"id":208,"value":"### *Chart configuration*","pinned":false,"hidden":false,"mode":"md"},{"id":1154,"value":"viewof _color = Inputs.select(appcast_color_palette, {label: \"Select a color\", value: appcast_color_palette[4]})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1088,"value":"appcast_color_palette = ['#3138BB', '#FF7D6F', '#AF9BFC', '#C7FFE2', '#127FFC', '#FF9B77', '#FAFAFA']","pinned":true,"hidden":false,"mode":"ojs"},{"id":457,"value":"appc2_color_scheme = ['#127ffc', '#d3d3d3']","pinned":true,"hidden":false,"mode":"ojs"},{"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 //labelOpacity: 0.3,\n tickOpacity: 0.3\n },\n axisX: {\n grid: false,\n tickCount: 5,\n titlePadding: 10,\n labelPadding: 4,\n labelOpacity: 0.5\n },\n axisY: {\n ticks: false,\n labelLimit: 300,\n labelFontSize: 9,\n titlePadding: 10,\n },\n title: {\n fontWeight: 300,\n fontSize: 13,\n offset: 25\n }\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":811,"value":"### *Visualization Iterations*","pinned":false,"hidden":false,"mode":"md"},{"id":688,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":1809,"value":"$36","pinned":false,"hidden":false,"mode":"ojs"},{"id":1206,"value":"title4 = md `## Amazon WFS Salary Distribution and Average`","pinned":false,"hidden":false,"mode":"ojs"},{"id":755,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":614,"value":"vl.layer(\n \n vl.markSquare({color: appc2_color_scheme[0], size: 350})\n .encode(\n vl.x().fieldT('date').scale({padding: 20}),\n vl.y().fieldQ('pay_hourly').scale({padding: 20}).bin(true),\n vl.opacity().count().scale({range: [0.0005,0.999]})\n ),\n\n // vl.markTick({color: 'red', thickness: 2.5, width: 30})\n // .encode(\n // vl.x().fieldT('date'),\n // vl.y().median('pay_hourly')\n // ),\n\n // vl.markText({dx: 27, dy: 15})\n // .encode(\n // vl.x().fieldT('date'),\n // vl.text().median('pay_hourly')\n // ),\n\n // vl.markLine({color: 'red', strokeDash: [15,5], point: {color: 'red', size: 50}, opacity: 0.3})\n // .encode(\n // vl.x().fieldT('date'),\n // vl.y().median('pay_hourly')\n // ),\n\n \n )\n .data(clean_week_split)\n .config(clean_config)\n .width(width - 200)\n .render()","pinned":false,"hidden":false,"mode":"ojs"},{"id":1830,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W39","stdlib":"1","titling":"auto"}