2b:null 2c:["$","title",null,{"children":"EITC para el año contributivo 2023 | José M. Zavala González | Observable"}] 31:T8fe, { let thedata = eitc_trapezoid_paths_2023; let thevariable = {variable: 'eitc'}; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'o más ' : ''}dependiente${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { filter: d => d.filing_status === 'married', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: 5, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata, { filter: d => d.filing_status === 'single', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(eitc_trapezoids_2023, { filter: d => d.filing_status === 'single', x: "plateau_middle", y: 'max_credit', text: d => d3.format('$,.0f')(d['max_credit']), dy: -8, lineAnchor: 'bottom', textAnchor: 'middle', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), ] }) return theplot }32:T9b8, grafica_trapezoides_eitc = { let thedata = eitc_trapezoid_paths_2023; let thevariable = {variable: 'eitc'}; let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos', width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'o más ' : ''}dependiente${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { filter: d => d.filing_status === 'married', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: 5, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.line(thedata, { filter: d => d.filing_status === 'single', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.text(eitc_trapezoids_2023, { filter: d => d.filing_status === 'single', x: "plateau_middle", y: 'max_credit', text: d => d3.format('$,.0f')(d['total_desembolsado']) + ` (${d3.format(',.0f')(d.credito_trabajo_planillas)} familias)`, dy: -8, lineAnchor: 'bottom', textAnchor: 'middle', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), ] }) return theplot }33:T9c0, grafica_trapezoides_eitc_en = { let thedata = eitc_trapezoid_paths_2023; let thevariable = {variable: 'eitc'}; let theplot = Plot.plot({ caption: 'Sources: Department of the Treasury and analysis by Espacios Abiertos', width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'or more ' : ''}dependent${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.axisX({ label: 'Gross earned income', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Earned Income Tax Credit', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { filter: d => d.filing_status === 'married', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: 5, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.line(thedata, { filter: d => d.filing_status === 'single', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.text(eitc_trapezoids_2023, { filter: d => d.filing_status === 'single', x: "plateau_middle", y: 'max_credit', text: d => d3.format('$,.0f')(d['total_desembolsado']) + ` (${d3.format(',.0f')(d.credito_trabajo_planillas)} families)`, dy: -8, lineAnchor: 'bottom', textAnchor: 'middle', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), ] }) return theplot }34:T446,eitc_trapezoids_2023 = { const filing_status_options = ['single', 'married']; const kids_options = ['0', '1', '2', '3']; let data = []; for (let filing_status of filing_status_options) { for (let kids of kids_options) { const d = structuredClone(eitc_params_2023[filing_status][kids]) d['filing_status'] = filing_status d['kids'] = +kids d['plateau_middle'] = 0.5*(d.phase_in + d.phase_out_start) const extra_info = credito_trabajo_promedio_by_num_deps.filter(f => f.ct_categoria_dependientes_num === +kids)[0] d['total_desembolsado'] = extra_info.credito_trabajo d['credito_trabajo_planillas'] = extra_info.credito_trabajo_planillas // d['total_familias'] = d['path'] = [ {income: 0, eitc: 0, desc: 'start', i: 0}, {income: d.phase_in, eitc: d.max_credit, desc: 'phase_in_end', i: 1}, {income: d.phase_out_start, eitc: d.max_credit, desc: 'phase_out_start', i: 2}, {income: d.phase_out_end, eitc: 0, desc: 'phase_out_end', i: 3}, ] data = [...data, d] } } return data }35:T67b,eitc_params_2023 = Object({ "single": { "0": { "phase_in_rate": 0.15, "max_credit": 1656, "phase_in": 11040.0, "phase_out_start": 17660, "phase_out_end": 28700, "phase_out_rate": -0.15 }, "1": { "phase_in_rate": 0.3398, "max_credit": 3864, "phase_in": 11371.39493819894, "phase_out_start": 19870, "phase_out_end": 34222, "phase_out_rate": 0.2692 }, "2": { "phase_in_rate": 0.4, "max_credit": 6072, "phase_in": 15180.0, "phase_out_start": 23180, "phase_out_end": 40841, "phase_out_rate": -0.3438 }, "3": { "phase_in_rate": 0.4483, "max_credit": 7173, "phase_in": 16000.44612982378, "phase_out_start": 23180, "phase_out_end": 44147, "phase_out_rate": -0.3421 } }, "married": { "0": { "phase_in_rate": 0.15, "max_credit": 1656, "phase_in": 11040.0, "phase_out_start": 19870, "phase_out_end": 30910, "phase_out_rate": -0.15 }, "1": { "phase_in_rate": 0.3398, "max_credit": 3864, "phase_in": 11371.39493819894, "phase_out_start": 24280, "phase_out_end": 38632, "phase_out_rate": -0.2692 }, "2": { "phase_in_rate": 0.4, "max_credit": 6072, "phase_in": 15180.0, "phase_out_start": 27590, "phase_out_end": 45251, "phase_out_rate": -0.3438 }, "3": { "phase_in_rate": 0.4483, "max_credit": 7173, "phase_in": 16000.44612982378, "phase_out_start": 27590, "phase_out_end": 48557, "phase_out_rate": -0.3421 } } })36:T8fe, { let thedata = eitc_trapezoid_paths_2024; let thevariable = {variable: 'eitc'}; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'o más ' : ''}dependiente${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { filter: d => d.filing_status === 'married', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: 5, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata, { filter: d => d.filing_status === 'single', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(eitc_trapezoids_2024, { filter: d => d.filing_status === 'single', x: "plateau_middle", y: 'max_credit', text: d => d3.format('$,.0f')(d['max_credit']), dy: -8, lineAnchor: 'bottom', textAnchor: 'middle', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), ] }) return theplot }37:T694,eitc_params_2024 = Object({ "single": { "0": { "phase_in_rate": 0.15, "max_credit": 1745, "phase_in": 11633.333333333334, "phase_out_start": 18610, "phase_out_end": 30240, "phase_out_rate": -0.15 }, "1": { "phase_in_rate": 0.3398, "max_credit": 4071, "phase_in": 11980.576809888169, "phase_out_start": 20940, "phase_out_end": 36062, "phase_out_rate": -0.2692 }, "2": { "phase_in_rate": 0.4, "max_credit": 6400, "phase_in": 16000.0, "phase_out_start": 24430, "phase_out_end": 43045, "phase_out_rate": -0.3438 }, "3": { "phase_in_rate": 0.4483, "max_credit": 7563, "phase_in": 16870.39928619228, "phase_out_start": 24430, "phase_out_end": 46537, "phase_out_rate": -0.3421 } }, "married": { "0": { "phase_in_rate": 0.15, "max_credit": 1745, "phase_in": 11633.333333333334, "phase_out_start": 20940, "phase_out_end": 32570, "phase_out_rate": -0.15 }, "1": { "phase_in_rate": 0.3398, "max_credit": 4071, "phase_in": 11980.576809888169, "phase_out_start": 25590, "phase_out_end": 40712, "phase_out_rate": -0.2692 }, "2": { "phase_in_rate": 0.4, "max_credit": 6400, "phase_in": 16000.0, "phase_out_start": 29080, "phase_out_end": 47695, "phase_out_rate": -0.3438 }, "3": { "phase_in_rate": 0.4483, "max_credit": 7563, "phase_in": 16870.39928619228, "phase_out_start": 29080, "phase_out_end": 51187, "phase_out_rate": -0.3421 } } })38:T92a, { let thedata = eitc_trapezoid_paths_2023 let thevariable = {variable: 'eitc'}; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'o más ' : ''}dependiente${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { filter: d => d.filing_status === 'married' & d.kids !== 0, x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: 5, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata, { filter: d => d.filing_status === 'single' & d.kids !== 0, x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(eitc_trapezoids_2023, { filter: d => d.filing_status === 'single' & d.kids !== 0, x: "plateau_middle", y: 'max_credit', text: d => d3.format('$,.0f')(d['max_credit']), dy: -8, lineAnchor: 'bottom', textAnchor: 'middle', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), ] }) return theplot }39:Te91,grafica_proyecciones_jsf = { let thedata = eitc_yearly_spending_by_share; thedata = thedata.filter(d => d.year <= 2024) thedata.forEach(d => { d.is_projected = d.year > 2023 }) let thedata_totals = aq.from(thedata) .groupby('year').rollup({ year_total: d => op.sum(d.amount) }) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totals), 'year') .derive({ year_pct: d => (d.amount / d.year_total) , }) .objects(); let normal_colors = {'Federal': EA_COLORES_MAP['INDIGO'], 'Local': EA_COLORES_MAP['GREEN']} let proyection_colors = {'Federal': chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), 'Local': chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()} let theplot = Plot.plot({ caption: 'Fuentes: Junta de Supervisión Fiscal y análisis de Espacios Abiertos', width: 650, marginLeft: 95, marginTop: 40, marginBottom: 50, x: { // domain: [2019, 2020, 2021, 2022, 2023], }, y: { // domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, color: { legend: true, // tickFormat: d => d.replace('Proyectado', 'Projected'), domain: ['Federal', 'Local', 'Federal - Proyectado JSF', 'Local - Proyectado JSF'], range: [EA_COLORES_MAP['INDIGO'], // chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() EA_COLORES_MAP['GREEN'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex(), ] }, marks: [ Plot.axisX({ tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: null, }), Plot.axisY({ tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Crédito por trabajo - total', }), Plot.barY(thedata, { x: "year", y: 'amount', // fill: d => d['Año contributivo'] === 2023 ? chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() : EA_COLORES_MAP['INDIGO'], z: 'share', fill: d => (d.year > 2023 ? proyection_colors : normal_colors)[d.share], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 2, // strokeDasharray: '10 3', // stroke: d => d['Año contributivo'] === 2023 ? 'black' : 'none', }), Plot.text(thedata, Plot.stackY2({ x: 'year', y: 'amount', z: 'share', text: d => d3.format('$,.0f')(d['amount']/1e6)+'M' + (d['year'] > 2020 ? `\n\n(${d3.format('.1%')(d.year_pct)})` : ''), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', // fontWeight: 'bold', fill: d => (d.share === 'Federal' && !d.is_projected) ? 'white' : 'black', })), Plot.text(thedata_totals, { x: "year", y: 'year_total', text: d => d3.format('$,.0f')(d['year_total']/1e6)+'M', dy: -10, lineAnchor: 'bottom', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) // add_estilos(theplot) return theplot }3a:T13cc,grafica_recorte_proyectado = { // let thedata = eitc_yearly_spending_by_share; // thedata = thedata.filter(d => d.year <= 2024) // thedata.forEach(d => { // d.is_projected = d.year > 2023 // }) let thedata = [ {year: 2023, share: "Federal", amount: 664000000, is_projected: false}, {year: 2023, share: "Local", amount: 638962058, is_projected: false}, {year: 2024, share: "Federal", amount: 700e6, is_projected: true}, {year: 2024, share: "Local", amount: 200e6, is_projected: true}, ] let thedata_totals = aq.from(thedata) .groupby('year').rollup({ year_total: d => op.sum(d.amount) }) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totals), 'year') .derive({ year_pct: d => (d.amount / d.year_total) , }) .objects(); let normal_colors = {'Federal': EA_COLORES_MAP['INDIGO'], 'Local': EA_COLORES_MAP['GREEN']} let proyection_colors = {'Federal': chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), 'Local': chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()} let theplot = Plot.plot({ style: 'overflow: visible', caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos', width: 350, marginLeft: 60, marginTop: 50, marginBottom: 50, x: { // domain: [2019, 2020, 2021, 2022, 2023], }, y: { // domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, color: { legend: true, // tickFormat: d => d.replace('Proyectado', 'Projected'), domain: ['Federal', 'Local', 'Federal - Proyectado EA', 'Local - Proyectado EA'], range: [EA_COLORES_MAP['INDIGO'], // chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() EA_COLORES_MAP['GREEN'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex(), ] }, marks: [ Plot.axisX({ // tickFormat: "", tickFormat: d => d === 2023 ? 'Últimos datos\nde Hacienda' : 'Proyectado\npor EA', interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: null, }), Plot.axisY({ tickSize: 0, tickFormat: d => d3.format("$,.0f")(d/1e6)+'M', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Crédito por trabajo - total', }), Plot.barY(thedata, { x: "year", y: 'amount', // fill: d => d['Año contributivo'] === 2023 ? chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() : EA_COLORES_MAP['INDIGO'], z: 'share', fill: d => (d.year > 2023 ? proyection_colors : normal_colors)[d.share], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 2, // strokeDasharray: '10 3', // stroke: d => d['Año contributivo'] === 2023 ? 'black' : 'none', }), Plot.text(thedata, Plot.stackY2({ x: 'year', y: 'amount', z: 'share', text: d => d3.format('$,.0f')(d['amount']/1e6)+'M' + (d['year'] > 2020 ? `\n\n(${d3.format('.1%')(d.year_pct)})` : ''), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', // fontWeight: 'bold', fill: d => (d.share === 'Federal' && !d.is_projected) ? 'white' : 'black', })), Plot.line([ {year: 2023, amount: 1303e6}, {year: 2024, amount: 900e6}, ], { x: 'year', y: 'amount', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-after', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.text(thedata_totals, { x: "year", y: 'year_total', text: d => d3.format('$,.0f')(d['year_total']/1e6)+'M', dy: -10, lineAnchor: 'bottom', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', stroke: 'white', strokeWidth: 5, }), Plot.text([0], { x: 2024, dx: 5, y: 1303e6, text: d => `Recorte de\nhasta $400M`, fill: '#d10074', textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernina-sans-comp', fontWeight: 'bold', fontSize: 30, }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) // add_estilos(theplot) return theplot }3b:T13c9,grafica_recorte_proyectado_en = { // let thedata = eitc_yearly_spending_by_share; // thedata = thedata.filter(d => d.year <= 2024) // thedata.forEach(d => { // d.is_projected = d.year > 2023 // }) let thedata = [ {year: 2023, share: "Federal", amount: 664000000, is_projected: false}, {year: 2023, share: "Local", amount: 638962058, is_projected: false}, {year: 2024, share: "Federal", amount: 700e6, is_projected: true}, {year: 2024, share: "Local", amount: 200e6, is_projected: true}, ] let thedata_totals = aq.from(thedata) .groupby('year').rollup({ year_total: d => op.sum(d.amount) }) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totals), 'year') .derive({ year_pct: d => (d.amount / d.year_total) , }) .objects(); let normal_colors = {'Federal': EA_COLORES_MAP['INDIGO'], 'Local': EA_COLORES_MAP['GREEN']} let proyection_colors = {'Federal': chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), 'Local': chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()} let theplot = Plot.plot({ style: 'overflow: visible', caption: 'Sources: Department of Treasury and analysis by Espacios Abiertos', width: 350, marginLeft: 60, marginTop: 50, marginBottom: 50, x: { // domain: [2019, 2020, 2021, 2022, 2023], }, y: { // domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, color: { legend: true, // tickFormat: d => d.replace('Proyectado', 'Projected'), domain: ['Federal', 'Local', 'Federal - EA Projection', 'Local - EA Projection'], range: [EA_COLORES_MAP['INDIGO'], // chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() EA_COLORES_MAP['GREEN'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex(), ] }, marks: [ Plot.axisX({ // tickFormat: "", tickFormat: d => d === 2023 ? 'Latest data\nfrom Hacienda' : 'Proyected\nby EA', interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: null, }), Plot.axisY({ tickSize: 0, tickFormat: d => d3.format("$,.0f")(d/1e6)+'M', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Earned Income Tax Credit - Total', }), Plot.barY(thedata, { x: "year", y: 'amount', // fill: d => d['Año contributivo'] === 2023 ? chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() : EA_COLORES_MAP['INDIGO'], z: 'share', fill: d => (d.year > 2023 ? proyection_colors : normal_colors)[d.share], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 2, // strokeDasharray: '10 3', // stroke: d => d['Año contributivo'] === 2023 ? 'black' : 'none', }), Plot.text(thedata, Plot.stackY2({ x: 'year', y: 'amount', z: 'share', text: d => d3.format('$,.0f')(d['amount']/1e6)+'M' + (d['year'] > 2020 ? `\n\n(${d3.format('.1%')(d.year_pct)})` : ''), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', // fontWeight: 'bold', fill: d => (d.share === 'Federal' && !d.is_projected) ? 'white' : 'black', })), Plot.line([ {year: 2023, amount: 1303e6}, {year: 2024, amount: 900e6}, ], { x: 'year', y: 'amount', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-after', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.text(thedata_totals, { x: "year", y: 'year_total', text: d => d3.format('$,.0f')(d['year_total']/1e6)+'M', dy: -10, lineAnchor: 'bottom', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', stroke: 'white', strokeWidth: 5, }), Plot.text([0], { x: 2024, dx: 5, y: 1303e6, text: d => `Cuts\nup to $400M`, fill: '#d10074', textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernina-sans-comp', fontWeight: 'bold', fontSize: 30, }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) // add_estilos(theplot) return theplot }3c:Te95,grafica_proyecciones_jsf_en = { let thedata = eitc_yearly_spending_by_share; thedata = thedata.filter(d => d.year <= 2024) thedata.forEach(d => { d.is_projected = d.year > 2023 }) let thedata_totals = aq.from(thedata) .groupby('year').rollup({ year_total: d => op.sum(d.amount) }) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totals), 'year') .derive({ year_pct: d => (d.amount / d.year_total) , }) .objects(); let normal_colors = {'Federal': EA_COLORES_MAP['INDIGO'], 'Local': EA_COLORES_MAP['GREEN']} let proyection_colors = {'Federal': chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), 'Local': chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()} let theplot = Plot.plot({ caption: 'Sources: Fiscal Oversight Board and analysis by Espacios Abiertos', width: 650, marginLeft: 95, marginTop: 40, marginBottom: 50, x: { // domain: [2019, 2020, 2021, 2022, 2023], }, y: { // domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, color: { legend: true, // tickFormat: d => d.replace('Proyectado', 'Projected'), domain: ['Federal', 'Local', 'Federal - FOMB Projection', 'Local - FOMB Projection'], range: [EA_COLORES_MAP['INDIGO'], // chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() EA_COLORES_MAP['GREEN'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex(), ] }, marks: [ Plot.axisX({ tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: null, }), Plot.axisY({ tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Earned Income Tax Credit - Total', }), Plot.barY(thedata, { x: "year", y: 'amount', // fill: d => d['Año contributivo'] === 2023 ? chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() : EA_COLORES_MAP['INDIGO'], z: 'share', fill: d => (d.year > 2023 ? proyection_colors : normal_colors)[d.share], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 2, // strokeDasharray: '10 3', // stroke: d => d['Año contributivo'] === 2023 ? 'black' : 'none', }), Plot.text(thedata, Plot.stackY2({ x: 'year', y: 'amount', z: 'share', text: d => d3.format('$,.0f')(d['amount']/1e6)+'M' + (d['year'] > 2020 ? `\n\n(${d3.format('.1%')(d.year_pct)})` : ''), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', // fontWeight: 'bold', fill: d => (d.share === 'Federal' && !d.is_projected) ? 'white' : 'black', })), Plot.text(thedata_totals, { x: "year", y: 'year_total', text: d => d3.format('$,.0f')(d['year_total']/1e6)+'M', dy: -10, lineAnchor: 'bottom', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) // add_estilos(theplot) return theplot }3d:T4b8,eitc_yearly_spending_by_share = { let thedata = [ {year: 2019, share: 'Local', amount: 117_494_800}, {year: 2020, share: 'Local', amount: 109_638_306}, {year: 2021, share: 'Federal', amount: 600e6}, {year: 2021, share: 'Local', amount: 461e6}, {year: 2022, share: 'Federal', amount: 618e6}, // {year: 2022, share: 'Local', amount: 499e6}, {year: 2022, share: 'Local', amount: 1_123e6 - 618e6}, {year: 2023, share: 'Federal', amount: 664e6}, {year: 2023, share: 'Local', amount: 1_302_962_058 - 664e6}, {year: 2024, share: 'Federal', amount: 700e6}, {year: 2024, share: 'Local', amount: 732e6}, {year: 2025, share: 'Federal', amount: 722e6}, {year: 2025, share: 'Local', amount: 755e6}, {year: 2026, share: 'Federal', amount: 740e6}, {year: 2026, share: 'Local', amount: 770e6}, {year: 2027, share: 'Federal', amount: 756e6}, {year: 2027, share: 'Local', amount: 785e6}, {year: 2028, share: 'Federal', amount: 773e6}, {year: 2028, share: 'Local', amount: 799e6}, {year: 2029, share: 'Federal', amount: 789e6}, {year: 2029, share: 'Local', amount: 812e6}, ] return thedata }3e:Tbec, { let latest_year_data = (await client.query(`select sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio, sum(credito_trabajo_planillas) as credito_trabajo_planillas from dependientes`))[0] let thedata = [ // ...eitc_isla_2019_2022, {'credito_trabajo_promedio': 442, 'year': 2019, 'credito_trabajo_planillas': 669000}, {'credito_trabajo_promedio': 420, 'year': 2020, 'credito_trabajo_planillas': 669000}, {'credito_trabajo_promedio': (881_077_755 + 180_020_220)/646_078, 'year': 2021, 'credito_trabajo_planillas': 646_078}, {'credito_trabajo_promedio': (946_781_454 + 176_458_573)/648_883, 'year': 2022, 'credito_trabajo_planillas': 648_883}, {'credito_trabajo_promedio': latest_year_data['credito_trabajo_promedio'], 'year': 2023, 'credito_trabajo_planillas': latest_year_data['credito_trabajo_planillas']}, ]; let theplot = Plot.plot({ marginLeft: 60, marginTop: 40, marginBottom: 50, x: { domain: [2019, 2020, 2021, 2022, 2023], }, y: { domain: [0, 2000], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, // color: { // legend: true, // domain: ['Realizado', 'Proyectado'], // range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()] // }, marks: [ Plot.axisX({ label: null, tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: 'Credito por trabajo - promedio', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.barY(thedata, { x: "year", y: 'credito_trabajo_promedio', fill: d => d['year'] === 2024 ? chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() : EA_COLORES_MAP['INDIGO'], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 2, // strokeDasharray: '4 2', // stroke: d => d['Año contributivo'] === 2023 ? 'black' : 'none', }), Plot.text(thedata, { x: "year", y: 'credito_trabajo_promedio', text: d => d['credito_trabajo_promedio'].toLocaleString(undefined,{maximumFractionDigits: 0, style: 'currency',currency: "USD",}), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: d => d['year'] === 2024 ? 'black' : 'white', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }3f:Tb1b,{ let thedata = [ {year: 2019, recibe_eitc: true, planillas: 265_964}, {year: 2019, recibe_eitc: false, planillas: 1_371_445 - 265_964}, {year: 2020, recibe_eitc: true, planillas: 261_608}, {year: 2020, recibe_eitc: false, planillas: 1_345_022 - 261_608}, {year: 2021, recibe_eitc: true, planillas: 646_078}, {year: 2021, recibe_eitc: false, planillas: 1_236_012 - 646_078}, {year: 2022, recibe_eitc: true, planillas: 648_883}, {year: 2022, recibe_eitc: false, planillas: 1_210_621 - 648_883}, {year: 2023, recibe_eitc: true, planillas: 695_689}, {year: 2023, recibe_eitc: false, planillas: 1_239_998 - 695_689}, ] let thedata_totales = aq.from(thedata) .groupby('year') .rollup({'total': op.sum('planillas')}) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totales)) .derive({pct: d => d.planillas/d.total}) .objects() let theplot = Plot.plot({ width: 600, marginLeft: 65, // marginTop: 30, // marginBottom: 50, x: { // domain: num_dependientes_options, interval: 1, }, color: { legend: true, domain: [true, false], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()], tickFormat: d => d ? 'Recibió crédito por trabajo' : 'No recibió crédito por trabajo', // range: }, marks: [ Plot.axisX({ label: null, tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), Plot.barY(thedata, { x: 'year', y: 'planillas', fill: 'recibe_eitc', // fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, Plot.stackY({ x: 'year', y: 'planillas', z: 'recibe_eitc', text: d => d3.format(',.0f')(d['planillas']) +'\n'+ `(${d3.format('.0%')(d.pct)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d['recibe_eitc'] ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), Plot.text(thedata_totales, ({ x: 'year', y: 'total', text: d => d3.format(',.0f')(d['total']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, // fill: d => d['recibe_eitc'] ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }40:Tfc5,grafica_34 = { let thedata = datos_by_year_estado_civil let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 30, marginBottom: 45, // x: {}, // fx: { // paddingInner: .2, // reverse: true, // }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {tickFormat: "$,.0f"}, color: { legend: true, range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), // tickFormat: d => capitalizeOnlyFirstLetter(d), // tickFormat: d => d === 'Casado' ? 'Casada/o' : 'Individual', reverse: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, label: null, }), Plot.axisY({ label: 'Crédito por trabajo - promedio', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), // Plot.axisFy({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // tickPadding: 25, // }), Plot.line(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', stroke: 'estado_civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', fill: 'estado_civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['estado_civil'] === 'Casado', x: 'year_contributivo', y: 'credito_trabajo_promedio', z: 'estado_civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), textAnchor: 'start', // dx: 9, dy: -10, fontSize: 12, fontWeight: 'bold', }), Plot.text(thedata, { filter: d => d['estado_civil'] === 'Contribuyente Individual', x: 'year_contributivo', y: 'credito_trabajo_promedio', z: 'estado_civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), dy: 12, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }41:T136b,grafica_23 = { let thedata = data_by_year_sexo let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 70, marginRight: 30, marginTop: 60, marginBottom: 45, // x: {}, fx: { paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {min: 0}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, // range: [0,.5, .85].map(color_interp), range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.ruleY([0], {stroke: null}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", label: null, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo - promedio', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', tickPadding: 30 }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), // Plot.axisFy({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.line(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], stroke: 'sexo', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], fill: 'sexo', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => (d['sexo'] === 'Femenino'), x: 'year_contributivo', y: 'credito_trabajo_promedio', //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), dy: -15, fontSize: 12, fontWeight: 'bold', z: 'sexo', strokeWidth: 3, }), // Plot.text(thedata, { // filter: d => (d['Sexo Contribuyente'] === 'Femenino') & (d['Estado Civil'] === 'Casado'), // x: 'Año contributivo', // y: thevariable.variable, // //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // dy: 5, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), // Plot.text(thedata, { // filter: d => (d['Sexo Contribuyente'] === 'Masculino') & (d['Estado Civil'] === 'Casado'), // x: 'Año contributivo', // y: thevariable.variable, // //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // dy: -10, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), Plot.text(thedata, { filter: d => (d['sexo'] === 'Masculino') & (d['year_contributivo'] > 2019), x: 'year_contributivo', y: 'credito_trabajo_promedio', //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), dy: 12, fontSize: 12, fontWeight: 'bold', z: 'sexo', strokeWidth: 3, }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }42:Tf93,{ let thedata = data_by_year_sexo_estado_civil let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 60, marginBottom: 45, // x: {}, // fx: { // paddingInner: .2, // reverse: true, // }, fy: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }, // y: {tickFormat: "$,.0f"}, color: { // tickFormat: d => capitalizeOnlyFirstLetter(d), // tickFormat: d => d === 'Casado' ? d : 'Individual', tickFormat: d => d === 'Casado' ? 'Casada/o' : 'Individual', legend: true, range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), reverse: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ label: null, // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo - promedio', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisFy({ label: 'Sexo', fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, tickPadding: 25, }), Plot.line(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', stroke: 'estado_civil', fy: 'sexo', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', fill: 'estado_civil', fy: 'sexo', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['estado_civil'] === 'Casado', x: 'year_contributivo', y: 'credito_trabajo_promedio', z: 'estado_civil', fy: 'sexo', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), dx: -9, dy: -10, fontSize: 12, fontWeight: 'bold', }), Plot.text(thedata, { filter: d => d['estado_civil'] === 'Contribuyente Individual', x: 'year_contributivo', y: 'credito_trabajo_promedio', z: 'estado_civil', fy: 'sexo', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), dy: 10, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }43:T828,{ let thedata = planillas_by_sexo_estado_civil let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 40, marginBottom: 60, x: { // domain: [0,1,2,3], reverse: true, }, marks: [ Plot.axisFx({ label: 'Sexo del contribuyente', // tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: 'Estado civil', tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Total de familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, // tickFormat: "$,.0f", fontSize: 12, }), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), fx: 'sexo', x: 'estado_civil', y: 'credito_trabajo_planillas', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: (d => d['credito_trabajo_planillas'] > 15_000), fx: 'sexo', x: 'estado_civil', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: (d => d['credito_trabajo_planillas'] <= 15_000), fx: 'sexo', x: 'estado_civil', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }44:T581,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Crédito por trabajo - promedio', tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_promedio', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_promedio, }), ] }) return theplot }45:T574,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Total de familias', tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_planillas', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_planillas, }), ] }) return theplot }46:T7ec,grafica_pensionados = { let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos', marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, color: { legend: true, domain: ['Pensionados', 'Resto'], range: [EA_COLORES_MAP['RED'], EA_COLORES_MAP['INDIGO']] }, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Total de familias', tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_planillas', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: d => d.edad > 65 ? EA_COLORES_MAP['RED'] : EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, // tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_planillas, }), Plot.tip(credito_trabajo_promedio_by_edad, Plot.pointerX({ x: d => d['edad'] + 0.5, y: 'credito_trabajo_planillas', fontFamily: 'jaf-bernino-sans', fontSize: 16, title: d => `Edad: ${d.edad}${d.edad === 85 ? '+' : ''}\nFamilias: ${d3.format(',.0f')(d.credito_trabajo_planillas)}`, })), ] }) return theplot }47:T7eb,grafica_pensionados_en = { let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ caption: 'Sources: Department of the Treasury and analysis by Espacios Abiertos', marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, color: { legend: true, domain: ['Retirees', 'Others'], range: [EA_COLORES_MAP['RED'], EA_COLORES_MAP['INDIGO']] }, marks: [ Plot.axisX({ label: `Age`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Total families', tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_planillas', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: d => d.edad > 65 ? EA_COLORES_MAP['RED'] : EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, // tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_planillas, }), Plot.tip(credito_trabajo_promedio_by_edad, Plot.pointerX({ x: d => d['edad'] + 0.5, y: 'credito_trabajo_planillas', fontFamily: 'jaf-bernino-sans', fontSize: 16, title: d => `Age: ${d.edad}${d.edad === 85 ? '+' : ''}\nFamilies: ${d3.format(',.0f')(d.credito_trabajo_planillas)}`, })), ] }) return theplot }48:T7f9,grafica_jovenes = { let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos', marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, color: { legend: true, domain: ['Jóvenes entre 19 y 26 años', 'Resto'], range: [EA_COLORES_MAP['RED'], EA_COLORES_MAP['INDIGO']] }, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Total de familias', tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_planillas', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: d => d.edad < 27 ? EA_COLORES_MAP['RED'] : EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, // tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_planillas, }), Plot.tip(credito_trabajo_promedio_by_edad, Plot.pointerX({ x: d => d['edad'] + 0.5, y: 'credito_trabajo_planillas', fontFamily: 'jaf-bernino-sans', fontSize: 16, title: d => `Edad: ${d.edad}${d.edad === 85 ? '+' : ''}\nFamilias: ${d3.format(',.0f')(d.credito_trabajo_planillas)}`, })), ] }) return theplot }49:T7f2,grafica_jovenes_en = { let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ caption: 'Sources: Department of the Treasury and analysis by Espacios Abiertos', marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, color: { legend: true, domain: ['Young aged 19 to 26', 'Others'], range: [EA_COLORES_MAP['RED'], EA_COLORES_MAP['INDIGO']] }, marks: [ Plot.axisX({ label: `Age`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Total families', tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_planillas', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: d => d.edad < 27 ? EA_COLORES_MAP['RED'] : EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, // tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_planillas, }), Plot.tip(credito_trabajo_promedio_by_edad, Plot.pointerX({ x: d => d['edad'] + 0.5, y: 'credito_trabajo_planillas', fontFamily: 'jaf-bernino-sans', fontSize: 16, title: d => `Age: ${d.edad}${d.edad === 85 ? '+' : ''}\nFamilies: ${d3.format(',.0f')(d.credito_trabajo_planillas)}`, })), ] }) return theplot }4a:T579,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ marginTop: 35, marginLeft: 80, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Crédito por trabajo - total desembolsado', tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo, }), ] }) return theplot }4b:T57f,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Ingreso bruto ganado - promedio', tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'ingreso_ganado_promedio', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.ingreso_ganado_promedio, }), ] }) return theplot }4c:T585,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Ingreso bruto ajustado - promedio', tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'ingreso_ajustado_promedio', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.ingreso_ajustado_promedio, }), ] }) return theplot }4d:T7a9,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] const global_average = aq.from(credito_trabajo_promedio_by_edad) .rollup({'global_average': d => op.sum(d['credito_trabajo']) / op.sum(d['ingreso_ganado'])}) .get('global_average', 0) let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Crédito por trabajo como porciento de ingreso ganado', tickFormat: ".0%", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_pct_by_ingreso_ganado', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, channels: {'Porciento': d => d3.format(".1%")(d['credito_trabajo_pct_by_ingreso_ganado']) }, tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_pct_by_ingreso_ganado, }), Plot.ruleY([global_average]), Plot.text([0], { y: global_average, dy: -5, x: d => 90, text: d => `Promedio\n${d3.format('.1%')(global_average)}`, textAnchor: 'end', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) return theplot }4e:T7e1,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] const global_average = aq.from(credito_trabajo_promedio_by_edad) // .filter(d => d.edad > 26) .rollup({'global_average': d => op.sum(d['credito_trabajo']) / op.sum(d['ingreso_ajustado'])}) .get('global_average', 0) let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Crédito por trabajo como porciento de ingreso ajustado', tickFormat: ".0%", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_pct_by_ingreso_ajustado', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), Plot.arrow([0], { x1: max_edad.edad, x2: max_edad.edad + 5, y: max_edad.credito_trabajo_pct_by_ingreso_ajustado, }), Plot.ruleY([global_average]), Plot.text([0], { y: global_average, dy: -5, x: d => 57, text: d => `Promedio\n${d3.format('.1%')(global_average)}`, textAnchor: 'middle', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) return theplot }4f:T592,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ marginTop: 35, marginLeft: 50, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Cantidad de dependientes cualificados', tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'ct_cantidad_dependientes', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, // channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), // Plot.arrow([0], { // x1: max_edad.edad, x2: max_edad.edad + 5, // y: max_edad.ct_cantidad_dependientes, // }), ] }) return theplot }50:T5a4,{ let max_edad = credito_trabajo_promedio_by_edad[credito_trabajo_promedio_by_edad.length - 1] let theplot = Plot.plot({ marginTop: 35, marginLeft: 50, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Porciento de familias con dependientes', tickFormat: ",.0%", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(credito_trabajo_promedio_by_edad, { // x: "edad", x1: d => d.edad === 20 ? 19 : d.edad, x2: d => d.edad + 1, y: 'credito_trabajo_planillas_con_dependientes_pct', // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, channels: {'Porciento': d => d3.format('.1%')(d.credito_trabajo_planillas_con_dependientes_pct), }, tip: true, }), // Plot.arrow([0], { // x1: max_edad.edad, x2: max_edad.edad + 5, // y: max_edad.ct_cantidad_dependientes, // }), ] }) return theplot }51:T594,{ let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [0,1,2,3], }, marks: [ Plot.axisX({ label: 'Dependientes cualificados', tickFormat: d => labels_dependientes_cualificados['es'][d], tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Crédito por trabajo - promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), Plot.barY(credito_trabajo_promedio_by_num_deps, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(credito_trabajo_promedio_by_num_deps, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_promedio', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), ] }) return theplot }52:T7ef,{ let thedata = credito_trabajo_promedio_by_num_deps let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [0,1,2,3], }, marks: [ Plot.axisX({ label: 'Dependientes cualificados', tickFormat: d => labels_dependientes_cualificados['es'][d], tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Total de familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] > 70_000, x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] <= 70_000, x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }53:T932,grafica_dependientes = { let thedata = credito_trabajo_promedio_by_num_deps let theplot = Plot.plot({ // width: 800, caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos', marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [0,1,2,3], }, color: { legend: true, domain: ['Sin dependientes', 'Resto'], range: [EA_COLORES_MAP['RED'], EA_COLORES_MAP['INDIGO']] }, marks: [ Plot.axisX({ label: 'Dependientes cualificados', tickFormat: d => labels_dependientes_cualificados['es'][d], tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Total de familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', fill: d => d.ct_categoria_dependientes_num === 0 ? EA_COLORES_MAP['RED'] : EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] > 70_000, x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] <= 70_000, x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }54:T974,grafica_dependientes_en = { let thedata = credito_trabajo_promedio_by_num_deps let theplot = Plot.plot({ caption: 'Sources: Department of the Treasury and analysis by Espacios Abiertos', // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [0,1,2,3], }, color: { legend: true, domain: ['No dependents', 'Others'], range: [EA_COLORES_MAP['RED'], EA_COLORES_MAP['INDIGO']] }, marks: [ Plot.axisX({ label: 'Eligible dependents', // tickFormat: d => labels_dependientes_cualificados['es'][d], tickFormat: d => labels_dependientes_cualificados['en'][d], tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Total families', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', fill: d => d.ct_categoria_dependientes_num === 0 ? EA_COLORES_MAP['RED'] : EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] > 70_000, x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] <= 70_000, x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }55:T97c,{ let thedata = credito_trabajo_promedio_by_estado_civil_num_deps let theplot = Plot.plot({ width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, fx: { reverse: true, }, x: { domain: [0,1,2,3], }, marks: [ Plot.axisFx({ label: 'Estado civil', // tickFormat: d => labels_dependientes_cualificados['es'][d], tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: 'Dependientes cualificados', // tickFormat: d => labels_dependientes_cualificados['es'][d], tickFormat: d => d === 3 ? '3+' : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Total de familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), fx: 'estado_civil', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] > 70_000, fx: 'estado_civil', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d['credito_trabajo_planillas'] <= 70_000, fx: 'estado_civil', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_num_deps'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }56:T90f,{ let theplot = Plot.plot({ width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [0,1,2,3], }, marks: [ Plot.axisFx({ label: 'Sexo del contribuyente', // tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: 'Dependientes cualificados', // tickFormat: d => labels_dependientes_cualificados['es'][d], tickFormat: d => d === 3 ? '3 o más' : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Total de familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), Plot.barY(data_by_num_deps_sexo, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), fx: 'sexo', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(data_by_num_deps_sexo, { filter: d => d.credito_trabajo_planillas > 30e3, fx: 'sexo', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_sexo'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(data_by_num_deps_sexo, { filter: d => d.credito_trabajo_planillas < 30e3, fx: 'sexo', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) + `\n(${d3.format('.1%')(d['credito_trabajo_planillas_pct_by_sexo'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }57:T8d3,{ let theplot = Plot.plot({ width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [0,1,2,3], }, marks: [ Plot.axisFx({ label: 'Sexo del contribuyente', // tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: 'Dependientes cualificados', // tickFormat: d => labels_dependientes_cualificados['es'][d], tickFormat: d => d === 3 ? '3 o más' : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Total de familias (%)', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ".0%", fontSize: 12, }), Plot.barY(data_by_num_deps_sexo, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), fx: 'sexo', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas_pct_by_sexo', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(data_by_num_deps_sexo, { filter: d => d.credito_trabajo_planillas_pct_by_sexo > .05, fx: 'sexo', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas_pct_by_sexo', text: d => d3.format('.1%')(d['credito_trabajo_planillas_pct_by_sexo']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(data_by_num_deps_sexo, { filter: d => d.credito_trabajo_planillas_pct_by_sexo <= .05, fx: 'sexo', x: 'ct_categoria_dependientes_num', y: 'credito_trabajo_planillas_pct_by_sexo', text: d => d3.format('.1%')(d['credito_trabajo_planillas_pct_by_sexo']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }58:T6ea,{ let theplot = Plot.plot({ width: 800, marginLeft: 60, marginTop: 45, marginBottom: 50, x: { // domain: [0,1,2,3], }, marks: [ Plot.axisX({ label: 'Sexo del contribuyente', // tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisFx({ label: 'Dependientes cualificados', // tickFormat: d => labels_dependientes_cualificados['es'][d], tickFormat: d => d ? 'Al menos un dependiente' : '0 dependientes', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Crédito por trabajo desembolsado', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: d => d3.format("$,.0f")(d/1e6)+'M', fontSize: 12, }), Plot.barY(data_by_has_deps_sexo, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'sexo', fx: 'ct_has_dependientes', y: 'credito_trabajo', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(data_by_has_deps_sexo, { // filter: d => d.credito_trabajo_planillas > 30e3, x: 'sexo', fx: 'ct_has_dependientes', y: 'credito_trabajo', text: d => d3.format("$,.0f")(d['credito_trabajo']/1e6)+'M' + `\n(${d3.format(',.0f')(d['credito_trabajo_planillas'])} familias)`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), ] }) return theplot }59:T541,with initial as ( select sexo, regexp_extract(ct_categoria_dependientes, '\d+').regexp_replace('^$', '0')::INT as ct_categoria_dependientes_num, sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio, sum(ct_cantidad_dependientes) as ct_cantidad_dependientes, -- sum(ct_cantidad_dependientes_planillas) as ct_cantidad_dependientes_planillas, -- NO usar porque usa 0 planillas para 0 deps sum(credito_trabajo) as credito_trabajo, sum(credito_trabajo_planillas) as credito_trabajo_planillas from dependientes_sexo where sexo != 'Otros' group by sexo, ct_categoria_dependientes_num order by sexo, ct_categoria_dependientes_num ), absolute_aggregates as ( select sexo, ct_categoria_dependientes_num > 0 as ct_has_dependientes, sum(ct_cantidad_dependientes) as ct_cantidad_dependientes, sum(credito_trabajo) as credito_trabajo, sum(credito_trabajo_planillas) as credito_trabajo_planillas, sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio, sum(ct_cantidad_dependientes) / sum(credito_trabajo_planillas) as ct_cantidad_dependientes_promedio from initial group by all ) select *, credito_trabajo_planillas / sum(credito_trabajo_planillas) over (partition by sexo) as credito_trabajo_planillas_pct_by_sexo, from absolute_aggregates order by sexo, ct_has_dependientes5a:T6cf,{ let thedata = d3.sort(credito_trabajo_promedio_by_ocupacion, d => -d['credito_trabajo_promedio']).slice(0,5) let xformats = { 'Estilista, Barbero y Cosmetólogo': 'Estilista, Barbero\ny Cosmetólogo', 'Técnico de Refrigeración': 'Técnico de\nRefrigeración', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['ocupacion']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => capitalizeOnlyFirstLetter(xformats[d] ?? d), label: 'Ocupación', }), Plot.axisY({ label: 'Crédito por trabajo - promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ocupacion', y: 'credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ocupacion', y: 'credito_trabajo_promedio', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), ] }) return theplot }5b:T785,{ let thedata = d3.sort(credito_trabajo_promedio_by_ocupacion, d => d['credito_trabajo_promedio']).slice(0,5) let xformats = { 'Estilista, Barbero y Cosmetólogo': 'Estilista, Barbero\ny Cosmetólogo', 'Técnico de Refrigeración': 'Técnico de\nRefrigeración', 'Propagandista Médico': 'Propagandista\nmédico', 'Agente y Corredor de Valores': 'Agente y corredor\nde valores', 'Artista, Actor, Bailarín, Cantante': 'Artista, actor,\nbailarín o cantante', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['ocupacion']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (xformats[d] ?? d), label: 'Ocupación', }), Plot.axisY({ label: 'Crédito por trabajo - promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ocupacion', y: 'credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'ocupacion', y: 'credito_trabajo_promedio', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), ] }) return theplot }5c:T800,grafica_40 = { let fuente_ingreso_newline_labels = Object({ 'Empleado del gobierno': 'Empleado del\ngobierno local', 'Empleado del gobierno federal': 'Empleado del\ngobierno federal', 'Trabajo por cuenta propia': 'Trabajo por\ncuenta propia', 'Retirado / Pensionado': 'Retirado\no pensionado', 'Empleado de empresa privada': 'Empleado de\nempresa privada', 'Otro': 'Otra', }) let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 50, marginBottom: 60, x: { domain: d3.sort(credito_trabajo_promedio_by_fuente_ingreso, d => d['categoria_ingreso'] == 'Otro', d => -d['credito_trabajo_promedio']).map(d => d['categoria_ingreso']), }, marks: [ Plot.axisY({ label: 'Crédito por trabajo - promedio', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisX({ label: 'Fuente de mayor ingreso', tickSize: 0, tickFormat: d => (fuente_ingreso_newline_labels[d] ?? d), fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.barY(credito_trabajo_promedio_by_fuente_ingreso, { x: 'categoria_ingreso', y: 'credito_trabajo_promedio', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(credito_trabajo_promedio_by_fuente_ingreso, { x: 'categoria_ingreso', y: 'credito_trabajo_promedio', text: d => d3.format('$,.0f')(d[ 'credito_trabajo_promedio' ]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 5, // textAnchor: 'end', dx: -5, }), ] }) return theplot }5d:Ta78, { let fuente_ingreso_newline_labels = Object({ 'Empleado del gobierno': 'Empleado del\ngobierno local', 'Empleado del gobierno federal': 'Empleado del\ngobierno federal', 'Trabajo por cuenta propia': 'Trabajo por\ncuenta propia', 'Retirado / Pensionado': 'Retirado\no pensionado', 'Empleado de empresa privada': 'Empleado de\nempresa privada', 'Otro': 'Otra', }) let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 60, x: { domain: d3.sort(credito_trabajo_promedio_by_fuente_ingreso, d => d['categoria_ingreso'] == 'Otro', d => -d['credito_trabajo_planillas']).map(d => d['categoria_ingreso']), }, marks: [ Plot.axisY({ label: 'Total de familias', tickSize: 0, tickFormat: ",.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisX({ label: 'Fuente de mayor ingreso', tickSize: 0, tickFormat: d => (fuente_ingreso_newline_labels[d] ?? d), fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.barY(credito_trabajo_promedio_by_fuente_ingreso, { x: 'categoria_ingreso', y: 'credito_trabajo_planillas', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(credito_trabajo_promedio_by_fuente_ingreso, { filter: d => d['credito_trabajo_planillas'] > 10e3, x: 'categoria_ingreso', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d[ 'credito_trabajo_planillas' ]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 5, // textAnchor: 'end', dx: -5, }), Plot.text(credito_trabajo_promedio_by_fuente_ingreso, { filter: d => d['credito_trabajo_planillas'] <= 10e3, x: 'categoria_ingreso', y: 'credito_trabajo_planillas', text: d => d3.format(',.0f')(d[ 'credito_trabajo_planillas' ]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: 'black', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -5, // textAnchor: 'end', dx: -5, }), ] }) return theplot }5e:Tc3b,{ // let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; // let thedata_initial = aq.from( // eitc_municipal_2019_2022 // .filter(d => d['Año contributivo'] === year_select) // ) // .derive({ // 'is_defined': d => d.Municipios !== 'OTROS*' // }) // .groupby('is_defined') // .rollup({ // 'Planillas Total - Crédito por Trabajo': op.sum('Planillas Total - Crédito por Trabajo') // }) // // .filter(d => d.Municipios !== 'OTROS*') // Excluir otros // .objects() // ; // let total = aq.from(thedata_initial).rollup({'total': op.sum(thevariable.variable)}).objects()[0]['total'] // let thedata = thedata_initial; let theplot = Plot.plot({ width: 250, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { axis: false, // domain: thedata.map(d => d['Municipios']), }, color: { legend: true, domain: [true, false], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()], tickFormat: d => d ? 'Municipio especificado' : 'Municipio no especificado', }, marks: [ // Plot.axisX({ // hide: false, // tickSize: 0, // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisY({ label: 'Familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(planillas_by_municipio_especificado, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: d => 1, y: 'credito_trabajo_planillas', fill: 'municipio_especificado', }), Plot.text(planillas_by_municipio_especificado, Plot.stackY({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: d => 1, y: 'credito_trabajo_planillas', z: 'municipio_especificado', // channels: {z: {reverse: true}}, text: d => d3.format(',.0f')(d['credito_trabajo_planillas']) +' '+ `(${d3.format('.1%')(d['porciento'])})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d.municipio_especificado ? 'white' : 'black', // stroke: 'black', // textAnchor: 'end', dx: -5, })), // Plot.text(thedata, Plot.selectLast({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), // x: 'Año contributivo', // y: thevariable.variable, // z: 'Anejo CT: Categoría Dependientes Cualificados', // text: d => (d['Anejo CT: Categoría Dependientes Cualificados']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // stroke: 'white', fill: 'black', // textAnchor: 'end', dx: -25, // })), ] }) return theplot }5f:T6ba,{ let thedata = d3.sort(data_by_municipio, d => -d['credito_trabajo_promedio']).slice(0,5) let xformats = { 'Estilista, Barbero y Cosmetólogo': 'Estilista, Barbero\ny Cosmetólogo', 'Técnico de Refrigeración': 'Técnico de\nRefrigeración', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['municipio']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => capitalizeOnlyFirstLetter(xformats[d] ?? d), label: 'Municipio', }), Plot.axisY({ label: 'Crédito por trabajo - promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'municipio', y: 'credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'municipio', y: 'credito_trabajo_promedio', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), ] }) return theplot }60:T6a0,{ let thedata = d3.sort(data_by_municipio, d => d['credito_trabajo_promedio']).slice(0,5) let xformats = { 'Estilista, Barbero y Cosmetólogo': 'Estilista, Barbero\ny Cosmetólogo', 'Técnico de Refrigeración': 'Técnico de\nRefrigeración', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['municipio']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (xformats[d] ?? d), label: 'Municipio', }), Plot.axisY({ label: 'Crédito por trabajo - promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'municipio', y: 'credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'municipio', y: 'credito_trabajo_promedio', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), ] }) return theplot }61:T94c,grafica_recortes_municipios = { let thedata = credito_recortes_municipios.filter(d => d.id_municipio !== null) thedata = d3.sort(thedata, (a, b) => d3.descending(a['Recorte superior'], b['Recorte superior'])) thedata = thedata.slice(0, num_municipios_show) let theplot = Plot.plot({ // width: 1100, caption: htl.html`Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos.`, // height: 300, height: 2000/78 * Math.min(78, num_municipios_show) + 30 + 50, marginLeft: 150, marginTop: 30, marginBottom: 50, y: { domain: thedata.map(d => d.Municipio), }, marks: [ Plot.axisY({ // label: 'Dependientes cualificados', label: null, // tickFormat: d => categoria_distribucion_labels[d], tickFormat: d => d === 'no_especificado' ? 'No especificado' : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: 'Recorte estimado', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, // tickFormat: "$,.0f", tickFormat: d => d3.format('$,.0f')(d/1e6) + 'M', fontSize: 12, }), Plot.barX(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), y: 'Municipio', x: 'Recorte superior', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d['Recorte superior'] > 6.5e6, y: 'Municipio', x: 'Recorte superior', text: d => d3.format('$,.0f')(d['Recorte superior']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'middle', dx: -7, textAnchor: 'end', fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d['Recorte superior'] <= 6.5e6, y: 'Municipio', x: 'Recorte superior', text: d => d3.format('$,.0f')(d['Recorte superior']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'middle', dx: 7, textAnchor: 'start', fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }62:T951,grafica_recortes_municipios_en = { let thedata = credito_recortes_municipios.filter(d => d.id_municipio !== null) thedata = d3.sort(thedata, (a, b) => d3.descending(a['Recorte superior'], b['Recorte superior'])) thedata = thedata.slice(0, num_municipios_show_en) let theplot = Plot.plot({ // width: 1100, caption: htl.html`Sources: Department of Treasury and analysis by Espacios Abiertos.`, // height: 300, height: 2000/78 * Math.min(78, num_municipios_show_en) + 30 + 50, marginLeft: 150, marginTop: 30, marginBottom: 50, y: { domain: thedata.map(d => d.Municipio), }, marks: [ Plot.axisY({ // label: 'Dependientes cualificados', label: null, // tickFormat: d => categoria_distribucion_labels[d], tickFormat: d => d === 'no_especificado' ? 'No especificado' : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: 'Estimated cut', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, // tickFormat: "$,.0f", tickFormat: d => d3.format('$,.0f')(d/1e6) + 'M', fontSize: 12, }), Plot.barX(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), y: 'Municipio', x: 'Recorte superior', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d['Recorte superior'] > 6.5e6, y: 'Municipio', x: 'Recorte superior', text: d => d3.format('$,.0f')(d['Recorte superior']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'middle', dx: -7, textAnchor: 'end', fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d['Recorte superior'] <= 6.5e6, y: 'Municipio', x: 'Recorte superior', text: d => d3.format('$,.0f')(d['Recorte superior']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'middle', dx: 7, textAnchor: 'start', fill: 'black', // textAnchor: 'end', dx: -5, }), ] }) return theplot }63:T5aa,{ let thedata = credito_trabajo_promedio_by_ingreso_ganado let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, y: { domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Crédito por trabajo - promedio", tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, // channels: { // 'Promedio': thevariable.variable, // 'Planillas': 'Planillas Total - Crédito por Trabajo', // 'limite_inferior': 'limite_inferior', // 'limite_superior': 'limite_superior', // }, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }64:T4ff,{ let thedata = credito_trabajo_promedio_by_ingreso_ganado let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, y: { // domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Total de familias", tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'credito_trabajo_planillas', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': 'escala_ingreso_ganado_limite_inferior', }, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }65:T589,{ let thedata = credito_trabajo_promedio_by_ingreso_ganado let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, y: { // domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Porciento de familias con dependientes", tickFormat: ",.0%", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'credito_trabajo_planillas_con_dependientes_pct', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': 'escala_ingreso_ganado_limite_inferior', 'Porciento': d => d3.format(',.1%')(d.credito_trabajo_planillas_con_dependientes_pct) }, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }66:T529,{ let thedata = credito_trabajo_promedio_by_ingreso_ganado let theplot = Plot.plot({ marginLeft: 40, marginBottom: 50, marginTop: 30, y: { // domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Crédito por trabajo - total desembolsado", tickFormat: d => d3.format("$,.0f")(d/1e6)+'M', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'credito_trabajo', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': 'escala_ingreso_ganado_limite_inferior', }, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }67:T512,{ let thedata = credito_trabajo_promedio_by_ingreso_ganado let theplot = Plot.plot({ marginLeft: 50, marginBottom: 50, marginTop: 30, y: { // domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Cantidad de dependientes cualificados", tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'ct_cantidad_dependientes', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': 'escala_ingreso_ganado_limite_inferior', }, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }68:T6ca,{ let thedata = datos_by_estado_civil_sexo_ingreso_ganado.filter(d => d.estado_civil === 'Contribuyente Individual') let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: { // domain: [0, 2600], }, marks: [ Plot.axisFx({ label: 'Sexo del contribuyente', // tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Total de familias", tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'credito_trabajo_planillas', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, // channels: { // 'Promedio': thevariable.variable, // 'Planillas': 'Planillas Total - Crédito por Trabajo', // 'limite_inferior': 'limite_inferior', // 'limite_superior': 'limite_superior', // }, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }69:T5a8,with edited_ingreso_ganado_sexo as ( select * replace ( escala_ingreso_ganado.replace('45500 ó más', '45000 <= x < Infinity') as escala_ingreso_ganado ) from ingreso_ganado_sexo where sexo != 'Otros' -- No hay escala de ingreso disponible para 'Otros' ), escalas_processed as ( select sexo, escala_ingreso_ganado, regexp_extract(escala_ingreso_ganado, '(\d+) <= x < (\d+)?', 1)::INT as escala_ingreso_ganado_limite_inferior, regexp_extract(escala_ingreso_ganado, '(\d+) <= x < (\d+)?', 2).nullif('')::INT as escala_ingreso_ganado_limite_superior, credito_trabajo_promedio_individual, credito_trabajo_individual_planillas, credito_trabajo_promedio_casados, credito_trabajo_casados_planillas, from edited_ingreso_ganado_sexo ), estado_civil_unpivot as ( unpivot escalas_processed ON (credito_trabajo_promedio_individual, credito_trabajo_individual_planillas) AS "Contribuyente Individual", (credito_trabajo_promedio_casados, credito_trabajo_casados_planillas) AS "Casado" INTO NAME estado_civil VALUE credito_trabajo_promedio, credito_trabajo_planillas ), estado_civil_filtered as ( -- Filter out escalas que caen fuera del programa (e.g. estado individual es ingreso < 44,500) from estado_civil_unpivot where not ((estado_civil = 'Contribuyente Individual') and (escala_ingreso_ganado_limite_inferior >= 44500)) -- Limite Individual es 44,147 ) from estado_civil_filtered6a:T7f6,{ let thedata = datos_by_estado_civil_ingreso_ganado_pivot_by_sexo.filter(d => d.estado_civil === 'Contribuyente Individual') let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: { domain: [-.0270, .0270], }, color: { legend: true, domain: ['Femenino','Masculino'], range: [EA_COLORES_MAP['INDIGO'], EA_COLORES_MAP['GREEN']] }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Representación intra-grupo (%)", tickFormat: d => d3.format(".2%")(d > 0 ? d : -d), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'femenino_credito_trabajo_planillas_pct_by_estado', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_inferior']), 'Femenino': d => d3.format('.2%')(d['femenino_credito_trabajo_planillas_pct_by_estado']), }, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: d => -d['masculino_credito_trabajo_planillas_pct_by_estado'], fill: EA_COLORES_MAP['GREEN'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_inferior']), 'Masculino': d => d3.format('.2%')(d['masculino_credito_trabajo_planillas_pct_by_estado']), }, }), ] }) return theplot }6b:T8a6,{ let thedata = datos_by_estado_civil_ingreso_ganado_pivot_by_sexo.filter(d => d.estado_civil === 'Contribuyente Individual') let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: { // domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Representación: % Femenino - % Masculino", tickFormat: ".2%", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'sexo_diferencia_credito_trabajo_planillas_pct_by_estado', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Superior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_superior']), 'Diferencia': d => d3.format('.2%')(d['sexo_diferencia_credito_trabajo_planillas_pct_by_estado']), }, }), // // Linea acumulado // Plot.line(thedata, { // // fx: 'sexo', // x: 'escala_ingreso_ganado_limite_superior', // y: 'cumu_sexo_diferencia_credito_trabajo_planillas_pct_by_estado', // // fill: EA_COLORES_MAP['INDIGO'], // // stroke: 'white', strokeWidth: .5, // tip: true, // marker: 'dot', // channels: { // 'Superior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_superior']), // 'Diferencia acumulada': d => d3.format('.2%')(d['cumu_sexo_diferencia_credito_trabajo_planillas_pct_by_estado']), // }, // }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }6c:T7bc,{ let thedata = datos_by_estado_civil_ingreso_ganado_pivot_by_sexo.filter(d => d.estado_civil === 'Contribuyente Individual') let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: { domain: [-3e3, 3e3], }, color: { legend: true, domain: ['Femenino','Masculino'], range: [EA_COLORES_MAP['INDIGO'], EA_COLORES_MAP['GREEN']] }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Crédito por trabajo - promedio", tickFormat: d => d3.format("$,.0f")(d > 0 ? d : -d), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'femenino_credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_inferior']), 'Femenino': d => d3.format('$,.0f')(d['femenino_credito_trabajo_promedio']), }, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: d => -d['masculino_credito_trabajo_promedio'], fill: EA_COLORES_MAP['GREEN'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_inferior']), 'Masculino': d => d3.format('$,.0f')(d['masculino_credito_trabajo_promedio']), }, }), ] }) return theplot }6d:T874,{ let thedata = datos_by_estado_civil_ingreso_ganado_pivot_by_sexo.filter(d => d.estado_civil === 'Contribuyente Individual') let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: { // domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Diferencia entre crédito trabajo promedio de cada sexo", tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'sexo_diferencia_credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Superior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_superior']), 'Diferencia': d => d3.format('.2%')(d['sexo_diferencia_credito_trabajo_promedio']), }, }), // Plot.line(thedata, { // // fx: 'sexo', // x: 'escala_ingreso_ganado_limite_superior', // y: 'cumu_sexo_diferencia_credito_trabajo_planillas_pct_by_estado', // // fill: EA_COLORES_MAP['INDIGO'], // // stroke: 'white', strokeWidth: .5, // tip: true, // marker: 'dot', // channels: { // 'Superior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_superior']), // 'Diferencia acumulada': d => d3.format('.2%')(d['cumu_sexo_diferencia_credito_trabajo_planillas_pct_by_estado']), // }, // }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }6e:T7ae,{ let thedata = datos_by_estado_civil_ingreso_ganado_pivot_by_sexo.filter(d => d.estado_civil === 'Casado') let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: { domain: [-3.8e3, 3.8e3], }, color: { legend: true, domain: ['Femenino','Masculino'], range: [EA_COLORES_MAP['INDIGO'], EA_COLORES_MAP['GREEN']] }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Crédito por trabajo - promedio", tickFormat: d => d3.format("$,.0f")(d > 0 ? d : -d), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'femenino_credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_inferior']), 'Femenino': d => d3.format('$,.0f')(d['femenino_credito_trabajo_promedio']), }, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: d => -d['masculino_credito_trabajo_promedio'], fill: EA_COLORES_MAP['GREEN'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_inferior']), 'Masculino': d => d3.format('$,.0f')(d['masculino_credito_trabajo_promedio']), }, }), ] }) return theplot }6f:T862,{ let thedata = datos_by_estado_civil_ingreso_ganado_pivot_by_sexo.filter(d => d.estado_civil === 'Casado') let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: { // domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Diferencia entre crédito trabajo promedio de cada sexo", tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { // fx: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'sexo_diferencia_credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Superior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_superior']), 'Diferencia': d => d3.format('.2%')(d['sexo_diferencia_credito_trabajo_promedio']), }, }), // Plot.line(thedata, { // // fx: 'sexo', // x: 'escala_ingreso_ganado_limite_superior', // y: 'cumu_sexo_diferencia_credito_trabajo_planillas_pct_by_estado', // // fill: EA_COLORES_MAP['INDIGO'], // // stroke: 'white', strokeWidth: .5, // tip: true, // marker: 'dot', // channels: { // 'Superior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_superior']), // 'Diferencia acumulada': d => d3.format('.2%')(d['cumu_sexo_diferencia_credito_trabajo_planillas_pct_by_estado']), // }, // }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable.variable, // }), ] }) return theplot }70:Tcc2,-- datos_by_estado_civil_ingreso_ganado_pivot_by_sexo with edited_ingreso_ganado_sexo as ( select * replace ( escala_ingreso_ganado.replace('45500 ó más', '45000 <= x < Infinity') as escala_ingreso_ganado ) from ingreso_ganado_sexo where sexo != 'Otros' -- No hay escala de ingreso disponible para 'Otros' ), escalas_processed as ( select sexo, escala_ingreso_ganado, regexp_extract(escala_ingreso_ganado, '(\d+) <= x < (\d+)?', 1)::INT as escala_ingreso_ganado_limite_inferior, regexp_extract(escala_ingreso_ganado, '(\d+) <= x < (\d+)?', 2).nullif('')::INT as escala_ingreso_ganado_limite_superior, credito_trabajo_promedio_individual, credito_trabajo_individual_planillas, credito_trabajo_promedio_casados, credito_trabajo_casados_planillas, from edited_ingreso_ganado_sexo ), estado_civil_unpivot as ( unpivot escalas_processed ON (credito_trabajo_promedio_individual, credito_trabajo_individual_planillas) AS "Contribuyente Individual", (credito_trabajo_promedio_casados, credito_trabajo_casados_planillas) AS "Casado" INTO NAME estado_civil VALUE credito_trabajo_promedio, credito_trabajo_planillas ), estado_civil_filtered as ( -- Filter out escalas que caen fuera del programa (e.g. estado individual es ingreso < 44,500) from estado_civil_unpivot where not ((estado_civil = 'Contribuyente Individual') and (escala_ingreso_ganado_limite_inferior >= 44500)) -- Limite Individual es 44,147 ), sexo_pivot as ( pivot (from estado_civil_filtered select * replace (sexo.lower() as sexo)) on sexo IN ('femenino', 'masculino') -- Need the IN clause con mi current version del Observable client. -- See https://github.com/observablehq/framework/issues/1016 para arreglarlo using sum(credito_trabajo_promedio) as credito_trabajo_promedio, sum(credito_trabajo_planillas) as credito_trabajo_planillas group by escala_ingreso_ganado, escala_ingreso_ganado_limite_inferior, escala_ingreso_ganado_limite_superior, estado_civil ), sexo_pct_windows as ( select *, femenino_credito_trabajo_planillas / sum(femenino_credito_trabajo_planillas) over (partition by estado_civil) as femenino_credito_trabajo_planillas_pct_by_estado, masculino_credito_trabajo_planillas / sum(masculino_credito_trabajo_planillas) over (partition by estado_civil) as masculino_credito_trabajo_planillas_pct_by_estado, femenino_credito_trabajo_planillas_pct_by_estado - masculino_credito_trabajo_planillas_pct_by_estado as sexo_diferencia_credito_trabajo_planillas_pct_by_estado, femenino_credito_trabajo_promedio - masculino_credito_trabajo_promedio as sexo_diferencia_credito_trabajo_promedio, from sexo_pivot ) select *, sum(sexo_diferencia_credito_trabajo_planillas_pct_by_estado) over (partition by estado_civil order by escala_ingreso_ganado_limite_inferior ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) as cumu_sexo_diferencia_credito_trabajo_planillas_pct_by_estado from sexo_pct_windows order by estado_civil, escala_ingreso_ganado_limite_inferior -- NOTE: El tope de las escalas para Casados de hombre son mayores a las escalas disponibles para mujeres -- Una solucion es merge them pero preferiby usando arquero luego cuando necesario71:T717,{ let thedata = planillas_sin_dependientes_by_sexo_estado_civil let theplot = Plot.plot({ style: 'overflow: visible', marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, x: { domain: [0, 30_910] }, y: { // domain: [0, 2600], }, fx: {reverse: true}, marks: [ Plot.axisFx({ label: 'Estado civil', // tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisFy({ label: 'Sexo', // tickFormat: d => d.replace(' ', '\n'), tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisX({ label: `Ingreso bruto ganado`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Total de familias", tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { fx: 'estado_civil', fy: 'sexo', x1: "escala_ingreso_ganado_limite_inferior", x2: 'escala_ingreso_ganado_limite_superior', y: 'ct_cantidad_sin_dependientes_planillas', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Inferior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_inferior']), 'Superior': d => d3.format('$,.0f')(d['escala_ingreso_ganado_limite_superior']), 'Familias': d => d3.format(',.0f')(d['ct_cantidad_sin_dependientes_planillas']), }, }), ] }) return theplot }72:T486,create or replace table simulacion_planillas_sin_dependientes_by_sexo_estado_civil as ( with planillas_sin_dependientes_by_sexo_estado_civil as ( from 'planillas_sin_dependientes_by_sexo_estado_civil.csv' ), parameters as ( select *, escala_ingreso_ganado_limite_inferior as lower_bound, escala_ingreso_ganado_limite_superior as upper_bound, ct_cantidad_sin_dependientes_planillas as "n", (escala_ingreso_ganado_limite_inferior > 0) as left_endpoint, false as right_endpoint, from planillas_sin_dependientes_by_sexo_estado_civil ), intermediates as ( select *, "upper_bound" - "lower_bound" as L, if(left_endpoint and right_endpoint, 2, if(left_endpoint or right_endpoint, 1, 0) ) as num_endpoints, L / ("n" + 1 - num_endpoints) as step, lower_bound + if(left_endpoint, 0, step) as "offset", "offset" as x1, "offset" + ("n" - 1)*step as xn from parameters ) select sexo, escala_ingreso_ganado, escala_ingreso_ganado_limite_inferior, escala_ingreso_ganado_limite_superior, estado_civil, unnest(generate_series(1, n).list_transform(i -> "offset" + (i-1)*step)) as ingreso_ganado_estimado from intermediates )73:T49a,with planillas_sin_dependientes_by_sexo_estado_civil as ( from 'planillas_sin_dependientes_by_sexo_estado_civil.csv' ), parameters as ( select *, escala_ingreso_ganado_limite_inferior as lower_bound, escala_ingreso_ganado_limite_superior as upper_bound, ct_cantidad_sin_dependientes_planillas as "n", (escala_ingreso_ganado_limite_inferior > 0) as left_endpoint, false as right_endpoint, from planillas_sin_dependientes_by_sexo_estado_civil ), intermediates as ( select *, "upper_bound" - "lower_bound" as L, if(left_endpoint and right_endpoint, 2, if(left_endpoint or right_endpoint, 1, 0) ) as num_endpoints, L / ("n" + 1 - num_endpoints) as step, lower_bound + if(left_endpoint, 0, step) as "offset", -- "offset" as x1, -- "offset" + ("n" - 1)*step as xn from parameters ), casicasi as ( select sexo, escala_ingreso_ganado, escala_ingreso_ganado_limite_inferior, escala_ingreso_ganado_limite_superior, estado_civil, -- *, (generate_series(1, n).list_transform(i -> "offset" + (i-1)*step)) as listado_ingreso_ganado_estimado from intermediates ) select *, listado_ingreso_ganado_estimado.list_aggregate('min') from casicasi74:Tc8b,-- datos_by_ingreso_ganado_sexo_sin_dependientes with edited_ingreso_ganado_sexo as ( select * replace ( escala_ingreso_ganado.replace('45500 ó más', '45000 <= x < Infinity') as escala_ingreso_ganado ) from ingreso_ganado_sexo where sexo != 'Otros' -- No hay escala de ingreso disponible para 'Otros' ), desagregacion_estado_civil as ( select sexo, escala_ingreso_ganado, regexp_extract(escala_ingreso_ganado, '(\d+) <= x < (\d+)?', 1)::INT as escala_ingreso_ganado_limite_inferior, regexp_extract(escala_ingreso_ganado, '(\d+) <= x < (\d+)?', 2).nullif('')::INT as escala_ingreso_ganado_limite_superior, ingreso_ganado_individual, ingreso_ganado_individual_planillas, ingreso_ganado_casados, ingreso_ganado_casados_planillas, credito_trabajo_promedio_individual, credito_trabajo_individual_planillas, credito_trabajo_promedio_casados, credito_trabajo_casados_planillas, ct_cantidad_dependientes, ct_cantidad_dependientes_planillas, 0 as ct_cantidad_sin_dependientes, -- Formalidad por convenciones lol (ingreso_ganado_planillas - ct_cantidad_dependientes_planillas) as ct_cantidad_sin_dependientes_planillas, -- ver documentación en repositorio 'Espacios-Abiertos/prtax' case when escala_ingreso_ganado_limite_inferior < 28_700 -- tranchas mixtas then ( case sexo when 'Femenino' then 222_559 / 229_292 when 'Masculino' then 223_460 / 261_317 when 'Otros' then 1_329 / 1_435 else null end ) when escala_ingreso_ganado_limite_inferior < 30_910 then 0 -- tranchas casados else 0 -- tranchas inelegibles end as multiplicador_ct_cantidad_sin_dependientes_individual_planillas, round(ct_cantidad_sin_dependientes_planillas * multiplicador_ct_cantidad_sin_dependientes_individual_planillas)::INT as ct_cantidad_sin_dependientes_individual_planillas, ct_cantidad_sin_dependientes_planillas - ct_cantidad_sin_dependientes_individual_planillas as ct_cantidad_sin_dependientes_casados_planillas from edited_ingreso_ganado_sexo ), -- Un tweak leve para que todo cuadre desagregacion_estado_civil_arreglo as ( from desagregacion_estado_civil select * replace ( -- Moviendo 4 planillas de individual a casados en la última trancha de hombres case when (sexo = 'Masculino' AND escala_ingreso_ganado_limite_inferior = 28_500) then ct_cantidad_sin_dependientes_individual_planillas - 4 else ct_cantidad_sin_dependientes_individual_planillas end as ct_cantidad_sin_dependientes_individual_planillas, case when (sexo = 'Masculino' AND escala_ingreso_ganado_limite_inferior = 28_500) then ct_cantidad_sin_dependientes_casados_planillas + 4 else ct_cantidad_sin_dependientes_casados_planillas end as ct_cantidad_sin_dependientes_casados_planillas, ) ) from desagregacion_estado_civil_arreglo -- CONFIRMAR QUE CADA VALOR DE... -- select sexo, -- sum(ct_cantidad_sin_dependientes_individual_planillas), -- F: 222,559 M: 223,460 -- sum(ct_cantidad_sin_dependientes_casados_planillas), -- F: 7,140 M: 39,864 -- sum(ct_cantidad_sin_dependientes_planillas), -- F: 229,699 M: 263,324 -- from desagregacion_estado_civil_arreglo -- group by sexo75:Tbeb,{ let thedata = [ {year: 2021, renglon: 'bajo', planillas: 352_680 - 62_147}, {year: 2021, renglon: 'rebasa', planillas: 62_147}, {year: 2021, renglon: 'sobre', planillas: 646_078 - 352_680}, {year: 2022, renglon: 'bajo', planillas: 362_668 - 64_336}, {year: 2022, renglon: 'rebasa', planillas: 64_336}, {year: 2022, renglon: 'sobre', planillas: 648_883 - 362_668}, {year: 2023, renglon: 'bajo', planillas: 370_836 - 69_173}, {year: 2023, renglon: 'rebasa', planillas: 69_173}, {year: 2023, renglon: 'sobre', planillas: 695_689 - 370_836}, ] let thedata_totales = aq.from(thedata) .groupby('year') .rollup({'total': op.sum('planillas')}) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totales)) .derive({pct: d => d.planillas/d.total}) .objects() let legend_labels = { 'bajo': 'Familias por debajo del umbral de pobreza', 'rebasa': 'Familias que rebasan el umbral de pobreza', 'sobre': 'Familias por encima del umbral de pobreza', } let theplot = Plot.plot({ width: 400, height: 450, marginLeft: 65, // marginTop: 30, // marginBottom: 50, x: { // domain: num_dependientes_options, interval: 1, }, color: { legend: true, tickFormat: d => legend_labels[d] ?? d, // domain: [true, false], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), EA_COLORES_MAP['GREEN']], // tickFormat: d => d ? 'Recibió Crédito por Trabajo' : 'No recibió Crédito por Trabajo', // range: }, marks: [ Plot.axisX({ label: null, tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Familias', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), Plot.barY(thedata, { x: 'year', y: 'planillas', fill: 'renglon', // fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, Plot.stackY({ x: 'year', y: 'planillas', z: 'recibe_eitc', text: d => d3.format(',.0f')(d['planillas']) +'\n'+ `(${d3.format('.1%')(d.pct)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d['renglon'] === 'bajo' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), Plot.text(thedata_totales, ({ x: 'year', y: 'total', text: d => d3.format(',.0f')(d['total']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, // fill: d => d['renglon'] === 'bajo' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }76:T85a,{ let thedata = [ {year: 2021, personas: 94959+28156, planillas: 62147}, {year: 2022, personas: 100002+26181, planillas: 64336}, {year: 2023, personas: 109569+26686, planillas: 69173}, ] let theplot = Plot.plot({ width: 400, marginLeft: 60, // marginTop: 30, // marginBottom: 50, x: { // domain: num_dependientes_options, interval: 1, }, marks: [ Plot.axisX({ label: null, tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Personas que rebasan el umbral federal de pobreza', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'year', y: 'personas', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'year', y: 'personas', text: d => d3.format(',.0f')(d['personas'])+'\npersonas\n\n'+`(${d3.format(',.0f')(d['planillas'])}\nfamilias)`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), // Plot.text(thedata, Plot.selectLast({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), // x: 'Año contributivo', // y: thevariable.variable, // z: 'Anejo CT: Categoría Dependientes Cualificados', // text: d => (d['Anejo CT: Categoría Dependientes Cualificados']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // stroke: 'white', fill: 'black', // textAnchor: 'end', dx: -25, // })), ] }) return theplot }77:T9df,{ let thedata = [ {year: 2023, renglon: 'poblacion', variable: 'ingreso', cantidad: 16841}, {year: 2023, renglon: 'poblacion', variable: 'eitc', cantidad: 1873}, {year: 2023, renglon: 'rebasando', variable: 'ingreso', cantidad: (1_049_170_091 + 206_309_698)/(60_336 + 8_837)}, {year: 2023, renglon: 'rebasando', variable: 'eitc', cantidad: (203_583_968 + 31_767_196)/(60_336 + 8_837)}, ] let thedata_totales = aq.from(thedata) .groupby('year', 'renglon') .rollup({'total': op.sum('cantidad')}) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totales)) .derive({pct: d => d.cantidad/d.total}) .objects() let renglon_labels = { 'rebasando': 'Familias que reclamaron el\ncrédito y rebasaron el umbral\nfederal de pobreza', 'poblacion': 'Total de familias que\nreclamaron el crédito\npor trabajo', 'sobre': 'Familias por encima\ndel umbral federal\nde pobreza', } let theplot = Plot.plot({ // width: 400, marginLeft: 60, // marginTop: 30, marginBottom: 70, x: { // domain: num_dependientes_options, // interval: 1, }, color: { legend: true, domain: ['ingreso', 'eitc'], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()], tickFormat: d => d === 'ingreso' ? 'Ingreso bruto ganado - promedio' : 'Crédito por trabajo - promedio', }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 16, tickFormat: d => renglon_labels[d] ?? d, }), Plot.axisY({ label: 'Cantidad', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), Plot.barY(thedata, { x: 'renglon', y: 'cantidad', fill: 'variable', // fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, Plot.stackY({ x: 'renglon', y: 'cantidad', z: 'variable', text: d => d3.format('$,.0f')(d['cantidad']) +' '+ `(${d3.format('.0%')(d.pct)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d['variable'] === 'ingreso' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }78:T8fe,{ let thedata = ingreso_nuevo_by_dependientes const num_deps_cutoff = 3; let thedata_totales = aq.from(thedata) .groupby('ct_categoria_dependientes_num') .rollup({'total': op.sum('cantidad')}) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totales)) .derive({pct: d => d.cantidad/d.total}) .objects() // let renglon_labels = { // 'bajo': 'No rebasó la pobreza', // 'poblacion': 'Población que reclamó\nel Crédito por Trabajo', // 'sobre': 'Fuera de pobreza', // } let theplot = Plot.plot({ width: 800, marginLeft: 60, // marginTop: 30, marginBottom: 50, x: { // domain: num_dependientes_options, // interval: 1, }, color: { legend: true, domain: ['ingreso_ganado_promedio', 'credito_trabajo_promedio',], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()], tickFormat: d => d === 'ingreso_ganado_promedio' ? 'Ingreso bruto ganado (promedio)' : 'Crédito por trabajo (promedio)', }, marks: [ Plot.axisX({ label: 'Dependientes', tickFormat: d => d === num_deps_cutoff ? `${num_deps_cutoff} o más` : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, // tickFormat: d => renglon_labels[d] ?? d, }), Plot.axisY({ label: 'Cantidad', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), Plot.barY(thedata, { x: 'ct_categoria_dependientes_num', y: 'cantidad', fill: 'variable_name', // fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, Plot.stackY({ x: 'ct_categoria_dependientes_num', y: 'cantidad', z: 'variable_name', text: d => d3.format('$,.0f')(d['cantidad']) +'\n'+ `(${d3.format('.0%')(d.pct)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d['variable_name'] === 'ingreso_ganado_promedio' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }79:Te55,aplot = { let thedata = poverty_puerto_rico let thevariable = {variable: 'below_poverty'} let thedata_extremes = aq.from(poverty_puerto_rico) .derive({ 'min_year': op.min('year'), 'max_year': op.max('year'), }) .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // .select(aq.not('min_year', 'max_year')) .objects(); // console.log(thedata_extremes) let thedata_changes = aq.from(thedata_extremes) // .orderby('year') .derive({ 'current_domain': d => d['year'], 'previous_domain': d => op.lag(d['year']), 'current_value': d => d['below_poverty'], 'previous_value': d => op.lag(d['below_poverty']), }) .filter(d => d.previous_value !== undefined) .derive({ 'mid_domain': d => (d.current_domain + d.previous_domain)/2, 'change_value': d => d.current_value - d.previous_value, }) .objects(); let theplot = Plot.plot({ width: 700, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 50, color: { legend: true, }, marks: [ Plot.ruleY([0], {stroke: null}), Plot.axisX({ label: null, tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, interval: 1, }), Plot.axisY({ tickFormat: '.0%', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 20, label: 'Tasa de pobreza', }), Plot.line(thedata, { x: 'year', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_extremes, { // filter: d => [2006, 2022].includes(d.year), x: 'year', y: thevariable.variable, text: d => d3.format('.1%')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 12, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), Plot.line(thedata_extremes, { x: 'year', y: thevariable.variable, markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-before', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'mid_domain', y: 'current_value', text: d => d3.format('.1%')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) // console.log('aplot plot', theplot) return theplot }7a:Te6e,aplot_discrete = { let thedata = [ {eitc_incluido: false, below_poverty: .396}, {eitc_incluido: true, below_poverty: .353}, ] let thedata_changes = aq.from(thedata) // .orderby('year') .derive({ 'current_domain': d => d['eitc_incluido'], 'previous_domain': d => op.lag(d['eitc_incluido']), 'current_value': d => d['below_poverty'], 'previous_value': d => op.lag(d['below_poverty']), }) .filter(d => d.previous_value !== undefined) .derive({ 'mid_domain': d => (d.current_domain + d.previous_domain)/2, 'change_value': d => d.current_value - d.previous_value, }) .objects(); let theplot = Plot.plot({ width: 400, marginLeft: 60, // marginTop: 30, marginBottom: 50, x: { // domain: num_dependientes_options, // interval: 1, }, marks: [ Plot.axisX({ label: null, tickFormat: d => d ? 'Tasa de pobreza\nestimada con el\nimpacto del Crédito por Trabajo' : 'Tasa de pobreza', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Tasa de pobreza', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0%", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'eitc_incluido', y: 'below_poverty', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'eitc_incluido', y: 'below_poverty', text: d => d3.format(',.1%')(d['below_poverty']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.line(thedata, { x: 'eitc_incluido', y: 'below_poverty', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-after', stroke: 'black', strokeWidth: 2, dy: -0, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'current_domain', y: 'previous_value', dy: -0, text: d => d3.format('.1%')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), // Plot.text(thedata, Plot.selectLast({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), // x: 'Año contributivo', // y: thevariable.variable, // z: 'Anejo CT: Categoría Dependientes Cualificados', // text: d => (d['Anejo CT: Categoría Dependientes Cualificados']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // stroke: 'white', fill: 'black', // textAnchor: 'end', dx: -25, // })), ] }) return theplot }7b:T1d98,{ let thedata = datos_pobreza_por_trapezoide const dot_size = 5 let theplot = Plot.plot({ style: 'overflow: visible', width: 1000, height: 700, marginTop: 80, marginBottom: 50, x: { domain: [5e3, 65e3] }, y: { reverse: true, domain: [0, 8.25], }, fx: { reverse: true, }, // color: { // legend: true, // domain: ['Ingreso ganado de familias que permanecen bajo pobreza',2,3,4], // range: ['black', 'green', 'red', 'blue'], // }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickFormat: '$,.0f', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 16, tickPadding: 15, tickSize: 0, }), Plot.axisY({ label: 'Dependientes cualificados', interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 16, }), // Plot.gridX({ // stroke: 'black', // strokeOpacity: 0.20, // interval: 10e3, // }), Plot.gridY({ stroke: 'black', strokeOpacity: 0.20, interval: 1, }), Plot.axisFx({ label: 'Estado civil', tickPadding: 40, fontFamily: 'jaf-bernino-sans', fontSize: 16, }), // Umbrales pobreza Plot.dot(thedata, { x: 'umbral_pobreza', y: 'dependientes_por_planilla', fx: 'estado_civil', symbol: 'plus', r: dot_size, }), // Sobrepasan pobreza Plot.ruleY(thedata, { filter: d => d['ingreso_ganado_rebasando_pobreza_despues_eitc'] !== null, x1: 'ingreso_ganado_promedio_rebasando_pobreza_despues_eitc', x2: d => d['ingreso_ganado_promedio_rebasando_pobreza_despues_eitc'] + d['eitc_promedio_rebasando_pobreza_despues_eitc'], y: 'dependientes_por_planilla', fx: 'estado_civil', }), Plot.dot(thedata, { filter: d => d['ingreso_ganado_rebasando_pobreza_despues_eitc'] !== null, x: 'ingreso_ganado_promedio_rebasando_pobreza_despues_eitc', // Ingreso bajo pobreza y: 'dependientes_por_planilla', fx: 'estado_civil', fill: 'red', r: dot_size, }), Plot.dot(thedata, { filter: d => d['ingreso_ganado_rebasando_pobreza_despues_eitc'] !== null, x: d => d['ingreso_ganado_promedio_rebasando_pobreza_despues_eitc'] + d['eitc_promedio_rebasando_pobreza_despues_eitc'], // Ingreso bajo pobreza y: 'dependientes_por_planilla', fx: 'estado_civil', fill: 'blue', r: dot_size, }), Plot.arrow(thedata, { filter: d => d['ingreso_ganado_rebasando_pobreza_despues_eitc'] !== null, x1: 'ingreso_ganado_promedio_rebasando_pobreza_despues_eitc', x2: d => d['ingreso_ganado_promedio_rebasando_pobreza_despues_eitc'] + d['eitc_promedio_rebasando_pobreza_despues_eitc'], y: 'dependientes_por_planilla', fx: 'estado_civil', insetStart: dot_size, insetEnd: dot_size, }), // Permanecen en pobreza Plot.ruleY(thedata, { x1: 'ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc', x2: d => d['ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc'] + d['eitc_promedio_permaneciendo_pobreza_despues_eitc'], y: 'dependientes_por_planilla', fx: 'estado_civil', }), Plot.dot(thedata, { // filter: d => d['ingreso_ganado_rebasando_pobreza_despues_eitc'] !== null, x: 'ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc', // Ingreso bajo pobreza y: 'dependientes_por_planilla', fx: 'estado_civil', fill: 'black', r: dot_size, }), Plot.dot(thedata, { // filter: d => d['ingreso_ganado_rebasando_pobreza_despues_eitc'] !== null, x: d => d['ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc'] + d['eitc_promedio_permaneciendo_pobreza_despues_eitc'], // Ingreso bajo pobreza y: 'dependientes_por_planilla', fx: 'estado_civil', fill: 'darkgreen', r: dot_size, }), Plot.arrow(thedata, { x1: 'ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc', x2: d => d['ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc'] + d['eitc_promedio_permaneciendo_pobreza_despues_eitc'], y: 'dependientes_por_planilla', fx: 'estado_civil', insetStart: dot_size, insetEnd: dot_size, }), // Texto adicional Plot.text(thedata, { // "33% (1/3) familias rebansan la pobreza" x: 65e3, y: 'dependientes_por_planilla', fx: 'estado_civil', text: d => { let formatted_pct = d3.format('.1%')(d['porciento_planillas_rebasando_pobreza_despues_eitc']) formatted_pct = (formatted_pct === '0.0%' ? '0%' : formatted_pct) let formatted_fraction = d3.format(',.0f')(d['numero_planillas_rebasando_pobreza_despues_eitc']) + '/' + d3.format(',.0f')(d['ingreso_ganado_planillas']) let formatted_umbral = d3.format('$,.0f')(d.umbral_pobreza) let show_explanation = (d.dependientes_por_planilla === 0) & (d.estado_civil === 'Contribuyente Individual') return `${formatted_pct} (${formatted_fraction})` + (show_explanation ? ' de familias rebasan la pobreza' : '') + (show_explanation ? `\nal cruzar ${formatted_umbral} usando el crédito por trabajo` : `\ncruzan ${formatted_umbral}`) }, textAnchor: 'end', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontSize: 12, dy: -7, }), Plot.text(thedata, { x: d => 0.5*( 2*d.ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc + d.eitc_promedio_permaneciendo_pobreza_despues_eitc ), y: 'dependientes_por_planilla', fx: 'estado_civil', text: d => d3.format('+$,.0f')(d.eitc_promedio_permaneciendo_pobreza_despues_eitc), textAnchor: 'middle', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontSize: 12, dy: -12, }), Plot.text(thedata, { x: d => 0.5*( 2*d.ingreso_ganado_promedio_rebasando_pobreza_despues_eitc + d.eitc_promedio_rebasando_pobreza_despues_eitc ), y: 'dependientes_por_planilla', fx: 'estado_civil', text: d => d3.format('+$,.0f')(d.eitc_promedio_rebasando_pobreza_despues_eitc), textAnchor: 'middle', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontSize: 12, dy: -12, }), Plot.text(thedata, { x: 'ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc', y: 'dependientes_por_planilla', fx: 'estado_civil', text: d => d3.format('$,.3s')(d.ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc), textAnchor: 'end', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontSize: 12, dy: 10, }), Plot.text(thedata, { x: 'ingreso_ganado_promedio_rebasando_pobreza_despues_eitc', y: 'dependientes_por_planilla', fx: 'estado_civil', text: d => d3.format('$,.3s')(d.ingreso_ganado_promedio_rebasando_pobreza_despues_eitc), textAnchor: 'end', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontSize: 12, dy: 10, }), ] }) return theplot }7c:T8fe,with trapezoide_group as ( select year_contributivo, estado_civil, dependientes_por_planilla, -- case when dependientes_por_planilla > 3 then 3 else dependientes_por_planilla end as dependientes_trapezoide, umbral_pobreza, sum(ingreso_ganado) as ingreso_ganado, sum(ingreso_ganado_planillas) as ingreso_ganado_planillas, sum(credito_trabajo) as credito_trabajo, sum(credito_trabajo_planillas) as credito_trabajo_planillas, sum(ct_cantidad_dependientes) as ct_cantidad_dependientes, sum(personas_por_planilla * ingreso_ganado_planillas) as personas, sum(numero_planillas_bajo_pobreza_antes_eitc) as numero_planillas_bajo_pobreza_antes_eitc, sum(ifnull(numero_planillas_rebasando_pobreza_despues_eitc, 0)) as numero_planillas_rebasando_pobreza_despues_eitc, sum(ifnull(eitc_rebasando_pobreza_despues_eitc, 0)) as eitc_rebasando_pobreza_despues_eitc, sum(ifnull(ingreso_ganado_rebasando_pobreza_despues_eitc, 0)) as ingreso_ganado_rebasando_pobreza_despues_eitc, from eitc_pobreza_merged -- where umbral_pobreza is not null group by all ) select *, -- Todos ingreso_ganado / ingreso_ganado_planillas as ingreso_ganado_promedio, credito_trabajo / credito_trabajo_planillas as credito_trabajo_promedio, -- Rebasando pobreza ingreso_ganado_rebasando_pobreza_despues_eitc / numero_planillas_rebasando_pobreza_despues_eitc as ingreso_ganado_promedio_rebasando_pobreza_despues_eitc, eitc_rebasando_pobreza_despues_eitc / numero_planillas_rebasando_pobreza_despues_eitc as eitc_promedio_rebasando_pobreza_despues_eitc, numero_planillas_rebasando_pobreza_despues_eitc / ingreso_ganado_planillas as porciento_planillas_rebasando_pobreza_despues_eitc, -- Permaneciendo pobreza (ingreso_ganado - ifnull(ingreso_ganado_rebasando_pobreza_despues_eitc, 0)) / (ingreso_ganado_planillas - ifnull(numero_planillas_rebasando_pobreza_despues_eitc, 0)) as ingreso_ganado_promedio_permaneciendo_pobreza_despues_eitc, credito_trabajo - ifnull(eitc_rebasando_pobreza_despues_eitc,0) as eitc_permaneciendo_pobreza_despues_eitc, eitc_permaneciendo_pobreza_despues_eitc / (credito_trabajo_planillas - ifnull(numero_planillas_rebasando_pobreza_despues_eitc,0)) as eitc_promedio_permaneciendo_pobreza_despues_eitc, from trapezoide_group7d:T1720, { let estado_civil_select_renamed = estado_civil_select === 'Contribuyente Individual' ? 'single' : 'married' let thedata = eitc_trapezoid_paths_2023 .filter(d => (d.filing_status === estado_civil_select_renamed) & (d.kids === dependientes_select)) ; let thedata_trapezoid = eitc_trapezoids_2023 .filter(d => (d.filing_status === estado_civil_select_renamed) & (d.kids === dependientes_select))[0] ; let thevariable = {variable: 'eitc'}; let thedata_pobreza = datos_pobreza_por_trapezoide .filter(d => (d.estado_civil === estado_civil_select) & (d.dependientes_por_planilla === dependientes_select))[0] let umbral_pobreza = thedata_pobreza.umbral_pobreza let trapezoid_linear_parameters = [thedata_trapezoid].map(d => [ {x0: 0, x1: d.phase_in, m: d.phase_in_rate, b: 0}, {x0: d.phase_in, x1: d.phase_out_start, m: 0, b: d.max_credit}, {x0: d.phase_out_start, x1: d.phase_out_end, m: d.phase_out_rate, b: d.max_credit - d.phase_out_start*d.phase_out_rate}, ])[0] trapezoid_linear_parameters.forEach(d => { d.xsol = (umbral_pobreza + 1 - d.b) / (d.m + 1) d.ysol = d.m * d.xsol + d.b d.valid = (d.x0 <= d.xsol) && (d.xsol <= d.x1) }) let trapezoid_umbral_solution = trapezoid_linear_parameters.filter(d => d.valid)[0] // console.log('trapezoid_linear_parameters', trapezoid_linear_parameters) let thedata_credito_requerido = [ // {income: 0, eitc: umbral_pobreza}, {income: trapezoid_umbral_solution.xsol, eitc: trapezoid_umbral_solution.ysol}, {income: umbral_pobreza, eitc: 0}, ] const cost_data = [ {cost: thedata_pobreza.eitc_permaneciendo_pobreza_despues_eitc, x0: 0, x1: trapezoid_umbral_solution.xsol}, {cost: thedata_pobreza.eitc_rebasando_pobreza_despues_eitc, x0: trapezoid_umbral_solution.xsol, x1: umbral_pobreza}, {cost: undefined, x0: umbral_pobreza, x1: thedata_trapezoid.phase_out_end}, ] cost_data.forEach(d => d.xmid = 0.5 * (d.x0 + d.x1)) // return cost_data const money_format = d3.format('$,.0f') let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'o más ' : ''}dependiente${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, y: { domain: [0, thedata_trapezoid.max_credit * 1.10] }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { filter: d => d.filing_status === 'married', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: 5, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata, { filter: d => d.filing_status === 'single', x: 'income', y: d => d.eitc * plateu_multiplicador, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata_credito_requerido, { x: 'income', y: 'eitc', strokeWidth: 3, tip: true, }), Plot.ruleX([trapezoid_umbral_solution.xsol, umbral_pobreza], { // x: 'xsol', // fill: 'transparent', stroke: 'black', strokeWidth: 3, strokeDasharray: "5,3" }), Plot.dot([trapezoid_umbral_solution], { x: 'xsol', y: 'ysol', fill: 'transparent', stroke: 'black', strokeWidth: 3, r: 5, }), Plot.text([trapezoid_umbral_solution], { x: 'xsol', y: 'ysol', text: d => money_format(d.xsol) + ' + ' + money_format(d.ysol) + ' = ' + money_format(umbral_pobreza) + ' + $1', lineAnchor: 'bottom', textAnchor: 'start', dx: 5, dy: -7, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fill: 'black', stroke: 'white', strokeWidth: 5, }), Plot.text(cost_data, { x: 'xmid', y: thedata_trapezoid.max_credit * 1.10, text: d => d.cost !== undefined ? money_format(d.cost) : '???', lineAnchor: 'bottom', textAnchor: 'middle', // dx: 5, dy: -7, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fill: 'black', stroke: 'white', strokeWidth: 5, }), // Plot.text(eitc_trapezoids_2023, { // filter: d => d.filing_status === 'single', // x: "plateau_middle", // y: 'max_credit', // text: d => d3.format('$,.0f')(d['total_desembolsado']) + ` (${d3.format(',.0f')(d.credito_trabajo_planillas)} familias)`, // dy: -8, lineAnchor: 'bottom', // textAnchor: 'middle', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fill: 'black', // }), ] }) return theplot }7e:T17a7, { let estado_civil_select_renamed = estado_civil_select === 'Contribuyente Individual' ? 'single' : 'married' let thedata = eitc_trapezoid_paths_2023 .filter(d => (d.filing_status === estado_civil_select_renamed) & (d.kids === dependientes_select)) ; let thedata_trapezoid = eitc_trapezoids_2023 .filter(d => (d.filing_status === estado_civil_select_renamed) & (d.kids === dependientes_select))[0] ; let thevariable = {variable: 'eitc'}; // let thedata_pobreza = datos_pobreza_por_trapezoide // .filter(d => (d.estado_civil === estado_civil_select) & (d.dependientes_por_planilla === dependientes_select))[0] // let umbral_pobreza = thedata_pobreza.umbral_pobreza let umbral_pobreza = umbral_slider; let trapezoid_linear_parameters = [thedata_trapezoid].map(d => [ {x0: 0, x1: d.phase_in, m: d.phase_in_rate, b: 0}, {x0: d.phase_in, x1: d.phase_out_start, m: 0, b: d.max_credit}, {x0: d.phase_out_start, x1: d.phase_out_end, m: d.phase_out_rate, b: d.max_credit - d.phase_out_start*d.phase_out_rate}, ])[0] trapezoid_linear_parameters.forEach(d => { d.xsol = (umbral_pobreza + 1 - d.b) / (d.m + 1) d.ysol = d.m * d.xsol + d.b d.valid = (d.x0 <= d.xsol) && (d.xsol <= d.x1) }) let trapezoid_umbral_solution = trapezoid_linear_parameters.filter(d => d.valid)[0] // console.log('trapezoid_linear_parameters', trapezoid_linear_parameters) let thedata_credito_requerido = [ // {income: 0, eitc: umbral_pobreza}, {income: trapezoid_umbral_solution.xsol, eitc: trapezoid_umbral_solution.ysol}, {income: umbral_pobreza, eitc: 0}, ] // const cost_data = [ // {cost: thedata_pobreza.eitc_permaneciendo_pobreza_despues_eitc, x0: 0, x1: trapezoid_umbral_solution.xsol}, // {cost: thedata_pobreza.eitc_rebasando_pobreza_despues_eitc, x0: trapezoid_umbral_solution.xsol, x1: umbral_pobreza}, // {cost: undefined, x0: umbral_pobreza, x1: thedata_trapezoid.phase_out_end}, // ] // cost_data.forEach(d => d.xmid = 0.5 * (d.x0 + d.x1)) // return cost_data const money_format = d3.format('$,.0f') let theplot = Plot.plot({ style: 'overflow: visible', width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'o más ' : ''}dependiente${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, y: { domain: [0, thedata_trapezoid.max_credit * 1.10] }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { filter: d => d.filing_status === 'married', x: 'income', y: thevariable.variable, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: 5, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata, { filter: d => d.filing_status === 'single', x: 'income', y: d => d.eitc * plateu_multiplicador, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata_credito_requerido, { x: 'income', y: 'eitc', strokeWidth: 3, tip: true, }), Plot.ruleX([trapezoid_umbral_solution.xsol, umbral_pobreza], { // x: 'xsol', // fill: 'transparent', stroke: 'black', strokeWidth: 3, strokeDasharray: "5,3" }), Plot.dot([trapezoid_umbral_solution], { x: 'xsol', y: 'ysol', fill: 'transparent', stroke: 'black', strokeWidth: 3, r: 5, }), Plot.text([trapezoid_umbral_solution], { x: 'xsol', y: 'ysol', text: d => money_format(d.xsol) + ' + ' + money_format(d.ysol) + ' = ' + money_format(umbral_pobreza) + ' + $1', lineAnchor: 'bottom', textAnchor: 'start', dx: 5, dy: -7, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fill: 'black', stroke: 'white', strokeWidth: 5, }), // Plot.text(cost_data, { // x: 'xmid', // y: thedata_trapezoid.max_credit * 1.10, // text: d => d.cost !== undefined ? money_format(d.cost) : '???', // lineAnchor: 'bottom', // textAnchor: 'middle', // // dx: 5, dy: -7, // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fill: 'black', stroke: 'white', strokeWidth: 5, // }), // Plot.text(eitc_trapezoids_2023, { // filter: d => d.filing_status === 'single', // x: "plateau_middle", // y: 'max_credit', // text: d => d3.format('$,.0f')(d['total_desembolsado']) + ` (${d3.format(',.0f')(d.credito_trabajo_planillas)} familias)`, // dy: -8, lineAnchor: 'bottom', // textAnchor: 'middle', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fill: 'black', // }), ] }) return theplot }7f:T16bb, { let estado_civil_select_renamed = estado_civil_select === 'Contribuyente Individual' ? 'single' : 'married' let thedata = eitc_trapezoid_paths_2023 .filter(d => (d.filing_status === estado_civil_select_renamed) & (d.kids === dependientes_select)) ; let thedata_trapezoid = eitc_trapezoids_2023 .filter(d => (d.filing_status === estado_civil_select_renamed) & (d.kids === dependientes_select))[0] ; let thevariable = {variable: 'eitc'}; let thedata_pobreza = datos_pobreza_por_trapezoide .filter(d => (d.estado_civil === estado_civil_select) & (d.dependientes_por_planilla === dependientes_select))[0] let umbral_pobreza = thedata_pobreza.umbral_pobreza let trapezoid_linear_parameters = [thedata_trapezoid].map(d => [ {x0: 0, x1: d.phase_in, m: d.phase_in_rate, b: 0}, {x0: d.phase_in, x1: d.phase_out_start, m: 0, b: d.max_credit}, {x0: d.phase_out_start, x1: d.phase_out_end, m: d.phase_out_rate, b: d.max_credit - d.phase_out_start*d.phase_out_rate}, ])[0] trapezoid_linear_parameters.forEach(d => { d.xsol = (umbral_pobreza - d.b) / (d.m + 1) d.ysol = d.m * d.xsol + d.b d.valid = (d.x0 <= d.xsol) && (d.xsol <= d.x1) }) let trapezoid_umbral_solution = trapezoid_linear_parameters.filter(d => d.valid)[0] // console.log('trapezoid_linear_parameters', trapezoid_linear_parameters) let thedata_credito_requerido = [ // {income: 0, eitc: umbral_pobreza}, {income: trapezoid_umbral_solution.xsol, eitc: trapezoid_umbral_solution.ysol}, {income: umbral_pobreza, eitc: 0}, ] let path_cut = structuredClone(thedata) for (let i = 0; i < path_cut.length; i++) { if (trapezoid_umbral_solution.xsol < path_cut[i].income) { path_cut.splice(i, 0, { income: trapezoid_umbral_solution.xsol, eitc: trapezoid_umbral_solution.ysol, filing_status: path_cut[i].filing_status, kids: path_cut[i].kids, }) break } } const money_format = d3.format('$,.0f') let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'o más ' : ''}dependiente${d === 1 ? '' : 's'}`, legend: false, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, y: { domain: [0, thedata_trapezoid.max_credit * 1.10] }, marks: [ Plot.axisX({ label: 'Ingreso bruto ganado', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo', tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), // Plot.line(thedata, { // filter: d => d.filing_status === 'married', // x: 'income', // y: thevariable.variable, // z: d => d.kids +'_'+ d.filing_status, // + marital_status // stroke: d => d.kids, // strokeWidth: 3, // strokeDasharray: 5, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), Plot.line(path_cut, { filter: d => d.income <= trapezoid_umbral_solution.xsol, x: 'income', y: d => d.eitc, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, // strokeDasharray: 4, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(path_cut, { filter: d => d.income >= trapezoid_umbral_solution.xsol, x: 'income', y: d => d.eitc, z: d => d.kids +'_'+ d.filing_status, // + marital_status stroke: d => d.kids, strokeWidth: 3, strokeDasharray: "0 10 0", // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.line(thedata_credito_requerido, { x: 'income', y: 'eitc', strokeWidth: 3, stroke: EA_COLORES_MAP['INDIGO'], tip: true, }), // Plot.dot([trapezoid_umbral_solution], { // x: 'xsol', // y: 'ysol', // fill: 'transparent', // stroke: 'black', // strokeWidth: 3, // r: 5, // }), // Plot.text([trapezoid_umbral_solution], { // x: 'xsol', // y: 'ysol', // text: d => money_format(d.xsol) + ' + ' + money_format(d.ysol) + ' = ' + money_format(umbral_pobreza), // lineAnchor: 'bottom', // textAnchor: 'start', // dx: 5, dy: -7, // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fill: 'black', stroke: 'white', strokeWidth: 5, // }), // Plot.text(eitc_trapezoids_2023, { // filter: d => d.filing_status === 'single', // x: "plateau_middle", // y: 'max_credit', // text: d => d3.format('$,.0f')(d['total_desembolsado']) + ` (${d3.format(',.0f')(d.credito_trabajo_planillas)} familias)`, // dy: -8, lineAnchor: 'bottom', // textAnchor: 'middle', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fill: 'black', // }), ] }) return theplot }80:T13b5,{ let thedata = eitc_simulacion_cambios_pobreza_costo let thevariable = {variable: 'planillas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos.', style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, }, marks: [ Plot.ruleY([0], {stroke: null}), Plot.ruleY([69404], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, // fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: 'Familias rebasando el umbral de la pobreza del gobierno federal', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }81:T13ae,{ let thedata = eitc_simulacion_cambios_pobreza_costo let thevariable = {variable: 'planillas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: "Sources: Department of the Treasury and analysis by Espacios Abiertos", style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, }, marks: [ Plot.ruleY([0], {stroke: null}), Plot.ruleY([69404], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Increase or decrease in investment in the program →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, // fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: "Families surpassing the federal government's poverty line", }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }82:T139d,{ let thedata = eitc_simulacion_cambios_pobreza_costo let thevariable = {variable: 'numero_personas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos.', style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, }, marks: [ Plot.ruleY([0], {stroke: null}), Plot.ruleY([136990], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: 'Personas rebasando el umbral de la pobreza del gobierno federal', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }83:T1394,{ let thedata = eitc_simulacion_cambios_pobreza_costo let thevariable = {variable: 'numero_personas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: "Sources: Department of the Treasury and analysis by Espacios Abiertos", style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, }, marks: [ Plot.ruleY([0], {stroke: null}), Plot.ruleY([136990], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Increase or decrease in investment in the program →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: "People surpassing the federal government's poverty line", }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }84:T1b31,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps let thevariable = {variable: 'planillas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos.', style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['es'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.ruleY([69404], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14 , tickPadding: 30, label: 'Familias rebasando el umbral de la pobreza del gobierno federal', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', stroke: 'num_dependientes', z: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), // Plot.text(thedata, { // // filter: d => [2006, 2022].includes(d.year), // x: 'cambio_costo', // y: thevariable.variable, // text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 14, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes !== 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes !== 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes === 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes === 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }85:T1b2a,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps let thevariable = {variable: 'planillas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: "Sources: Department of the Treasury and analysis by Espacios Abiertos", style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['en'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.ruleY([69404], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Increase or decrease in investment in the program →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14 , tickPadding: 30, label: "Families surpassing the federal government's poverty line", }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', stroke: 'num_dependientes', z: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), // Plot.text(thedata, { // // filter: d => [2006, 2022].includes(d.year), // x: 'cambio_costo', // y: thevariable.variable, // text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 14, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes !== 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes !== 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes === 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes === 2), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }86:T18d0,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps let thevariable = {variable: 'numero_personas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos.', style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['es'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.ruleY([136990], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: 'Personas rebasando el umbral de la pobreza del gobierno federal', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', stroke: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes !== 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes !== 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes === 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes === 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }87:T18c7,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps let thevariable = {variable: 'numero_personas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: "Sources: Department of the Treasury and analysis by Espacios Abiertos", style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['en'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.ruleY([136990], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Increase or decrease in investment in the program →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: "People surpassing the federal government's poverty line", }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', stroke: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes !== 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes !== 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo !== 0) & (d.num_dependientes === 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => (d.cambio_costo === 0) & (d.num_dependientes === 0), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 18, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 10, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }88:T10c3,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil let thevariable = {variable: 'planillas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, // domain: [0, 1,2,3] }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.ruleY([69404], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '$0M' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 30, label: 'Familias rebasando el umbral de la pobreza del gobierno federal', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', stroke: d => d['num_dependientes'] + ' - ' + d['estado_civil'], // z: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { // filter: d => [2006, 2022].includes(d.year), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }89:T1712,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil let thevariable = {variable: 'planillas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos.', style: 'overflow: visible', width: 1000, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 70, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['es'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, fx: { padding: .15, reverse: true, }, // facet: { // x: "Fx", // margin: 100 // wider gap between facet columns // }, marks: [ Plot.ruleY([0], {opacity: 0.5}), // Plot.ruleY([69404], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: 'Familias rebasando el umbral de la pobreza del gobierno federal', }), Plot.axisFx({ label: 'Estado Civil', tickPadding: 30, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 14, // label: 'Familias rebasando el umbral de la pobreza', }), Plot.axisFy({label: null, ticks: null, tickFormat: d => ''}), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', stroke: d => d['num_dependientes'], fx: 'estado_civil', fy: 'num_dependientes', // z: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, // filter: d => [2006, 2022].includes(d.year), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }8a:T170b,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil let thevariable = {variable: 'planillas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: "Sources: Department of the Treasury and analysis by Espacios Abiertos", style: 'overflow: visible', width: 1000, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 70, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['en'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, fx: { padding: .15, reverse: true, }, // facet: { // x: "Fx", // margin: 100 // wider gap between facet columns // }, marks: [ Plot.ruleY([0], {opacity: 0.5}), // Plot.ruleY([69404], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Increase or decrease in investment in the program →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: "Families surpassing the federal government's poverty line", }), Plot.axisFx({ label: 'Estado Civil', tickPadding: 30, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 14, // label: 'Familias rebasando el umbral de la pobreza', }), Plot.axisFy({label: null, ticks: null, tickFormat: d => ''}), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', stroke: d => d['num_dependientes'], fx: 'estado_civil', fy: 'num_dependientes', // z: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, // filter: d => [2006, 2022].includes(d.year), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }8b:T10ce,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil let thevariable = {variable: 'numero_personas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ style: 'overflow: visible', width: 800, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 50, color: { legend: true, // domain: [0,1,2,3] }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.ruleY([136990], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '$0M' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 30, label: 'Personas rebasando el umbral de la pobreza del gobierno federal', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'num_dependientes', stroke: d => d['num_dependientes'] + ' - ' + d['estado_civil'], strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { // filter: d => [2006, 2022].includes(d.year), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }8c:T16bb,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil let thevariable = {variable: 'numero_personas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos.', style: 'overflow: visible', width: 1000, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 70, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['es'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, fx: { padding: .15, reverse: true, }, fy: { tickFormat: d => '', label: null, }, marks: [ Plot.ruleY([0], {opacity: 0.5}), // Plot.ruleY([136990], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Aumento o reducción en la inversión en el programa →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: 'Personas rebasando el umbral de la pobreza del gobierno federal', }), Plot.axisFx({ label: 'Estado Civil', tickPadding: 30, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 14, // label: 'Familias rebasando el umbral de la pobreza', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'num_dependientes', stroke: d => d['num_dependientes'], fx: 'estado_civil', fy: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, // filter: d => [2006, 2022].includes(d.year), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }8d:T16b2,{ let thedata = eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil let thevariable = {variable: 'numero_personas_rebasando_pobreza'} // let thedata_extremes = aq.from(poverty_puerto_rico) // .derive({ // 'min_year': op.min('year'), // 'max_year': op.max('year'), // }) // .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // // .select(aq.not('min_year', 'max_year')) // .objects(); // console.log(thedata_extremes) // let thedata_changes = aq.from(thedata_extremes) // // .orderby('year') // .derive({ // 'current_domain': d => d['year'], // 'previous_domain': d => op.lag(d['year']), // 'current_value': d => d['below_poverty'], // 'previous_value': d => op.lag(d['below_poverty']), // }) // .filter(d => d.previous_value !== undefined) // .derive({ // 'mid_domain': d => (d.current_domain + d.previous_domain)/2, // 'change_value': d => d.current_value - d.previous_value, // }) // .objects(); let theplot = Plot.plot({ caption: "Sources: Department of the Treasury and analysis by Espacios Abiertos", style: 'overflow: visible', width: 1000, // height: 230, marginLeft: 80, marginBottom: 50, marginTop: 70, color: { legend: true, domain: [0, 1,2,3], tickFormat: d => labels_dependientes_cualificados['en'][d], range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, fx: { padding: .15, reverse: true, }, fy: { tickFormat: d => '', label: null, }, marks: [ Plot.ruleY([0], {opacity: 0.5}), // Plot.ruleY([136990], {opacity: 0.5}), Plot.ruleX([0], {opacity: 0.5}), Plot.axisX({ label: 'Increase or decrease in investment in the program →', labelAnchor: 'center', labelArrow: 'left', tickSize: 0, tickFormat: d => d === 0 ? '' : d3.format("+$.0s")(d), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 14, }), Plot.axisX({ label: null, tickSize: 0, tickFormat: d => d === 0 ? 'Actual' : '', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14, }), Plot.axisY({ tickFormat: ',.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 14, tickPadding: 30, label: "People surpassing the federal government's poverty line", }), Plot.axisFx({ label: 'Estado Civil', tickPadding: 30, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 14, // label: 'Familias rebasando el umbral de la pobreza', }), Plot.line(thedata, { x: 'cambio_costo', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'num_dependientes', stroke: d => d['num_dependientes'], fx: 'estado_civil', fy: 'num_dependientes', strokeWidth: 2, marker: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata, { filter: d => d.cambio_costo === 0, x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d.cambio_costo !== 0, // filter: d => [2006, 2022].includes(d.year), x: 'cambio_costo', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), stroke: 'white', strokeWidth: 3, fill: 'black', fx: 'estado_civil', fy: 'num_dependientes', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'normal', fontSize: 14, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), // Plot.line(thedata_extremes, { // x: 'year', // y: thevariable.variable, // markerEnd: 'arrow', // // z: 'Fuente de Mayor Ingreso', // // stroke: 'Fuente de Mayor Ingreso', // curve: 'step-before', // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: EA_COLORES_MAP['INDIGO'], // tip: true, // }), // Plot.text(thedata_changes, { // // filter: d => [2006, 2022].includes(d.year), // x: 'mid_domain', // y: 'current_value', // text: d => d3.format('.1%')(d['change_value']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // // fill: 'white', // fontWeight: 'bold', // fontSize: 16, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // stroke: 'white', strokeWidth: 8, // fill: 'black', // // lineAnchor: 'bottom', dy: -8, // // textAnchor: 'end', dx: -5, // }), ] }) // console.log('aplot plot', theplot) return theplot }8e:Tc94,{ let thedata = participacion_laboral let thevariable = {variable: 'rate'} let thedata_extremes = aq.from(thedata) .derive({ 'min_year': op.min('year'), 'max_year': op.max('year'), }) .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // .select(aq.not('min_year', 'max_year')) .objects(); // console.log(thedata_extremes) let thedata_changes = get_data_changes(thedata_extremes, 'year', 'rate') let theplot = Plot.plot({ width: 700, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 50, color: { legend: true, }, marks: [ Plot.ruleY([0], {stroke: null}), Plot.axisX({ label: null, tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, interval: 1, }), Plot.axisY({ tickFormat: '.0%', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 20, label: 'Tasa de participación laboral', }), Plot.line(thedata, { x: 'year', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_extremes, { // filter: d => [2006, 2022].includes(d.year), x: 'year', y: thevariable.variable, text: d => d3.format('.1%')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 12, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), Plot.line(thedata_extremes, { x: 'year', y: thevariable.variable, markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-after', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'mid_domain', y: 'previous_value', text: d => d3.format('.1%')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) // console.log('aplot plot', theplot) return theplot }8f:Td2c,grafica_participacion_laboral = { let thedata = participacion_laboral.filter(d => d.year >= 2018) let thevariable = {variable: 'rate'} let thedata_extremes = aq.from(thedata) .derive({ 'min_year': op.min('year'), 'max_year': op.max('year'), }) .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // .select(aq.not('min_year', 'max_year')) .objects(); // console.log(thedata_extremes) let thedata_changes = get_data_changes(thedata_extremes, 'year', 'rate') let theplot = Plot.plot({ caption: 'Fuentes: Departamento del Trabajo y análisis de Espacios Abiertos', width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 50, color: { legend: true, }, // x: {type: 'linear'}, marks: [ Plot.ruleY([0], {stroke: null}), Plot.axisX({ label: null, tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ tickFormat: '.0%', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 20, label: 'Tasa de participación laboral', }), Plot.barY(thedata, { x: 'year', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], // tip: true, }), Plot.text(thedata, { // filter: d => [2006, 2022].includes(d.year), x: 'year', y: thevariable.variable, text: d => d3.format('.1%')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 7, // textAnchor: 'end', dx: -5, }), Plot.line(thedata_extremes, { x: 'year', y: thevariable.variable, markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-before', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 2020, y: 'current_value', text: d => d3.format('+.1%')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) // console.log('aplot plot', theplot) return theplot }90:Td2a,grafica_participacion_laboral_en = { let thedata = participacion_laboral.filter(d => d.year >= 2018) let thevariable = {variable: 'rate'} let thedata_extremes = aq.from(thedata) .derive({ 'min_year': op.min('year'), 'max_year': op.max('year'), }) .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // .select(aq.not('min_year', 'max_year')) .objects(); // console.log(thedata_extremes) let thedata_changes = get_data_changes(thedata_extremes, 'year', 'rate') let theplot = Plot.plot({ caption: 'Sources: Department of Labor and analysis by Espacios Abiertos', width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 50, color: { legend: true, }, // x: {type: 'linear'}, marks: [ Plot.ruleY([0], {stroke: null}), Plot.axisX({ label: null, tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ tickFormat: '.0%', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 20, label: 'Labor Force Participation Rate', }), Plot.barY(thedata, { x: 'year', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], // tip: true, }), Plot.text(thedata, { // filter: d => [2006, 2022].includes(d.year), x: 'year', y: thevariable.variable, text: d => d3.format('.1%')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 7, // textAnchor: 'end', dx: -5, }), Plot.line(thedata_extremes, { x: 'year', y: thevariable.variable, markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-before', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 2020, y: 'current_value', text: d => d3.format('+.1%')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) // console.log('aplot plot', theplot) return theplot }91:Te26,grafica_gastos_ley22 = { let thedata = ley_22_tax_expenditures let thevariable = {variable: 'amount'} let thedata_extremes = aq.from(thedata) .derive({ 'min_year': op.min('year'), 'max_year': op.max('year'), }) .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // .select(aq.not('min_year', 'max_year')) .objects(); // console.log(thedata_extremes) let thedata_changes = get_data_changes(thedata_extremes, 'year', 'amount') let theplot = Plot.plot({ caption: 'Fuentes: Departamento de Hacienda y análisis de Espacios Abiertos', width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { legend: true, domain: ['Real', 'Proyección - Departamento de Hacienda'], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()] }, // x: {type: 'linear'}, marks: [ Plot.ruleY([0], {stroke: null}), Plot.axisX({ label: null, tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ tickFormat: '$.0s', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 20, label: 'Gastos tributarios de la Ley Núm. 22 de 2012', }), Plot.barY(thedata, { x: 'year', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: d => d.year < 2023 ? EA_COLORES_MAP['INDIGO'] : chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), // tip: true, }), Plot.text(thedata, { // filter: d => [2006, 2022].includes(d.year), x: 'year', y: thevariable.variable, text: d => d3.format('$.3s')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: d => d.year < 2023 ? 'white' : 'black', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 7, // textAnchor: 'end', dx: -5, }), Plot.line(thedata_extremes, { x: 'year', y: thevariable.variable, markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-before', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: false, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 2021, y: 'current_value', text: d => d3.format('+$.3s')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) // console.log('aplot plot', theplot) return theplot }92:Te1f,grafica_gastos_ley22_en = { let thedata = ley_22_tax_expenditures let thevariable = {variable: 'amount'} let thedata_extremes = aq.from(thedata) .derive({ 'min_year': op.min('year'), 'max_year': op.max('year'), }) .filter(d => (d.year === d.min_year) | (d.year === d.max_year)) // .select(aq.not('min_year', 'max_year')) .objects(); // console.log(thedata_extremes) let thedata_changes = get_data_changes(thedata_extremes, 'year', 'amount') let theplot = Plot.plot({ caption: 'Sources: Department of the Treasury and analysis by Espacios Abiertos', width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, color: { legend: true, domain: ['Actual', 'Projection - Departament of Treasury'], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()] }, // x: {type: 'linear'}, marks: [ Plot.ruleY([0], {stroke: null}), Plot.axisX({ label: null, tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, // interval: 1, }), Plot.axisY({ tickFormat: '$.0s', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickPadding: 20, label: 'Tax expenditures from Act 22-2012', }), Plot.barY(thedata, { x: 'year', y: thevariable.variable, // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: d => d.year < 2023 ? EA_COLORES_MAP['INDIGO'] : chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), // tip: true, }), Plot.text(thedata, { // filter: d => [2006, 2022].includes(d.year), x: 'year', y: thevariable.variable, text: d => d3.format('$.3s')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: d => d.year < 2023 ? 'white' : 'black', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', lineAnchor: 'top', dy: 7, // textAnchor: 'end', dx: -5, }), Plot.line(thedata_extremes, { x: 'year', y: thevariable.variable, markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-before', stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 2021, y: 'current_value', text: d => d3.format('+$.3s')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) // console.log('aplot plot', theplot) return theplot }93:Ta3c,{ // const cols_show = ['state', 'below_poverty'] const cols_show = Object.keys(poverty_table[0]).filter(d => d !== 'GEO_ID') let thedata = aq.from(poverty_table).orderby (aq.desc('percent_below_poverty')).objects() const formatters = { 'percent_below_poverty': '.1%', 'population_under_poverty': ',.0f', 'total_population': ',.0f', 'total_household': ',.0f', 'total_families': ',.0f', 'households_median_income': '$,.0f', 'families_median_income': '$,.0f', } const classes = { 'state': 'left-align first-col', 'percent_below_poverty': 'tabular-nums bold', 'population_under_poverty': 'tabular-nums', 'total_population': 'tabular-nums', 'total_household': 'tabular-nums', 'total_families': 'tabular-nums', 'households_median_income': 'tabular-nums', 'families_median_income': 'tabular-nums last-col', } const row_classes = { 'Puerto Rico': 'highlight-row', } const headers = { 'state': 'Estado o jurisdicción', 'percent_below_poverty': 'Población bajo nivel de pobreza (%)', 'population_under_poverty': 'Población bajo nivel de pobreza (#)', 'total_population': 'Población total (#)', 'total_household': 'Total de hogares (#)', 'total_families': 'Total de familias (#)', 'households_median_income': 'Mediana de ingresos por hogar ($)', 'families_median_income': 'Mediana de ingresos por familia ($)', } return htl.html` ${cols_show.map(c => htl.html``)} ${thedata.map((row) => htl.html` ${cols_show.map((col) => htl.html`` )} ` )}

${headers[c] ?? c}
${formatters[col] ? d3.format(formatters[col])(row[col]) : row[col]}

` }94:Tc5b,{ // const cols_show = ['state', 'below_poverty'] // const cols_show = Object.keys(federal_outflows[0]) const cols_show = ['state', 'obligations', 'collections', 'outflow', 'total_population', 'outflow_per_capita'] const thedata = aq.from(federal_outflows) .derive({ 'obligations': d => d.obligations * -1 }) .objects() const formatters = { 'below_poverty': '.1%', 'population_under_poverty': ',.0f', 'total_population': ',.0f', 'total_household': ',.0f', 'total_families': ',.0f', 'households_median_income': '$,.0f', 'families_median_income': '$,.0f', 'obligations': '+$,.0f', 'collections': '+$,.0f', 'outflow_per_capita': '+$,.0f', 'outflow': '+$,.0f', } const classes = { 'state': 'left-align first-col', 'below_poverty': 'tabular-nums bold', 'population_under_poverty': 'tabular-nums', 'total_population': 'tabular-nums', 'outflow': 'tabular-nums', 'collections': 'tabular-nums', 'obligations': 'tabular-nums', 'outflow_per_capita': 'tabular-nums last-col bold', } const row_classes = { 'Puerto Rico': 'highlight-row', } const headers = { 'state': 'Estado o jurisdicción', 'below_poverty': 'Población bajo nivel de pobreza (%)', 'population_under_poverty': 'Población bajo nivel de pobreza (#)', 'total_population': 'Población total (#)', 'total_household': 'Total de hogares (#)', 'total_families': 'Total de familias (#)', 'households_median_income': 'Mediana de ingresos por hogar ($)', 'families_median_income': 'Mediana de ingresos por familia ($)', 'obligations': 'Gastos del gobierno federal ($)', 'collections': 'Ingresos del gobierno federal ($)', 'outflow_per_capita': 'Contribución neta per cápita del gobierno federal ($)', 'outflow': 'Contribución neta del gobierno federal ($)', } return htl.html` ${cols_show.map(c => htl.html``)} ${thedata.map((row) => htl.html` ${cols_show.map((col) => htl.html`` )} ` )}

${headers[c] ?? c}
${formatters[col] ? d3.format(formatters[col])(row[col]) : row[col]}

` }95:T114d,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10236, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 70, marginRight: 70, marginBottom: 100, marginTop: 30, width: 1000, height: 500, caption: htl.html`Fuentes: (2023) Internal Revenue Service (IRS). “SOI Tax Stats – Gross Collections, by Type of Tax and State – IRS Data Book Table 5”; Recuperado el 10 de febrero de 2025 de (2023) USA Spending; U.S. Census. State Population Totals. Análisis por Espacios Abiertos.`, // x: {percent: true,}, marks: [ Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Contribución neta per cápita del gobierno federal (estados y territorios con déficit)', }), Plot.axisX({ label: null, fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, rotate: -90, // reverse: true, // tickSpacing: .3, // tickPadding: 30, textAnchor: 'end', lineAnchor: 'middle', tickFormat: d => state_labels[d] ?? d, }), Plot.ruleY([eitc_promedio]), Plot.barY(thedata, { y: thevariable.variable, x: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : ( d.below_pr ? chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex() : '#cdd1ec' ), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {x: "y", reverse: false}}), // Plot.ruleX([credito_promedio]), // Plot.text(thedata, { // filter: d => d[thevariable.variable] < 0, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fontSize: 14.5, // y: thevariable.variable, // x: 'state', // fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 30_000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, rotate: -90, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, textAnchor: 'end', dy: 8, sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] < 30_000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, rotate: -90, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', dy: -8, sort: {x: "y", reverse: true}}), Plot.text([0], { y: eitc_promedio, // dx: 5, dy: -15, x: d => 'Idaho', text: d => `Promedio\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }96:T111c,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10236, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 70, marginRight: 70, marginBottom: 100, marginTop: 30, width: 1000, height: 450, caption: htl.html`Sources: (2023) Internal Revenue Service (IRS). “SOI Tax Stats – Gross Collections, by Type of Tax and State – IRS Data Book Table 5”; Accessed on February 10, 2025 for (2023) USA Spending; U.S. Census. State Population Totals. Analysis by Espacios Abiertos.`, // x: {percent: true,}, marks: [ Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Deficits in Net Federal Expenditures per Capita', }), Plot.axisX({ label: null, fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, rotate: -90, // reverse: true, // tickSpacing: .3, // tickPadding: 30, textAnchor: 'end', lineAnchor: 'middle', tickFormat: d => state_labels[d] ?? d, }), Plot.ruleY([eitc_promedio]), Plot.barY(thedata, { y: thevariable.variable, x: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : ( d.below_pr ? chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex() : '#cdd1ec' ), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {x: "y", reverse: false}}), // Plot.ruleX([credito_promedio]), // Plot.text(thedata, { // filter: d => d[thevariable.variable] < 0, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fontSize: 14.5, // y: thevariable.variable, // x: 'state', // fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 30_000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, rotate: -90, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, textAnchor: 'end', dy: 8, sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] < 30_000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, rotate: -90, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', dy: -8, sort: {x: "y", reverse: true}}), Plot.text([0], { y: eitc_promedio, // dx: 5, dy: -15, x: d => 'Idaho', text: d => `Average\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }97:T11d8,grafica_net_expenditures = { // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10300, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ style: 'overflow: visible', marginLeft: 160, // marginRight: 70, marginBottom: 50, // marginTop: 30, // width: 1000, height: 700, caption: htl.html`Fuentes: (2023) Internal Revenue Service (IRS). “SOI Tax Stats – Gross Collections, by Type of Tax and State – IRS Data Book Table 5”; Recuperado el 10 de febrero de 2025 de (2023) USA Spending; U.S. Census. State Population Totals. Análisis por Espacios Abiertos.`, // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 16, tickFormat: '$,.0s', label: 'Contribución neta per cápita del gobierno federal (estados y territorios con déficit)', }), Plot.axisY({ reverse: true, label: null, fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 16, // rotate: -90, // reverse: true, // tickSpacing: .3, // tickPadding: 30, textAnchor: 'end', lineAnchor: 'middle', // tickFormat: d => state_labels[d] ?? d, }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : ( d.below_pr ? chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex() : '#cdd1ec' ), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}, }), // Plot.ruleX([credito_promedio]), // Plot.text(thedata, { // filter: d => d[thevariable.variable] < 0, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fontSize: 14.5, // y: thevariable.variable, // x: 'state', // fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 30000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, // rotate: -90, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', // dy: 8, // sort: {y: "x", reverse: false}, }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 30000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, // rotate: -90, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', // dy: -8, // sort: {y: "x", reverse: false}, }), Plot.text([0], { x: eitc_promedio, dx: 5, // dy: -8, y: d => 'Idaho', text: d => `Promedio\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) // add_estilos(theplot, 'somebar') return theplot }98:T11c9,grafica_net_expenditures_english = { // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10300, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 160, // marginRight: 70, marginBottom: 50, // marginTop: 30, // width: 1000, height: 700, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, caption: htl.html`Sources: (2023) Internal Revenue Service (IRS). “SOI Tax Stats – Gross Collections, by Type of Tax and State – IRS Data Book Table 5”; Accessed on February 10, 2025 for (2023) USA Spending; U.S. Census. State Population Totals. Analysis by Espacios Abiertos.`, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 16, tickFormat: '$,.0s', label: 'Deficits in Net Federal Expenditures per Capita', }), Plot.axisY({ reverse: true, label: null, fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 16, // rotate: -90, // reverse: true, // tickSpacing: .3, // tickPadding: 30, textAnchor: 'end', lineAnchor: 'middle', // tickFormat: d => state_labels[d] ?? d, }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : ( d.below_pr ? chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex() : '#cdd1ec' ), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}, }), // Plot.ruleX([credito_promedio]), // Plot.text(thedata, { // filter: d => d[thevariable.variable] < 0, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fontSize: 14.5, // y: thevariable.variable, // x: 'state', // fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 30000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, // rotate: -90, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', // dy: 8, // sort: {y: "x", reverse: false}, }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 30000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, // rotate: -90, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', // dy: -8, // sort: {y: "x", reverse: false}, }), Plot.text([0], { x: eitc_promedio, dx: 5, // dy: -8, y: d => 'Idaho', text: d => `Average\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) // add_estilos(theplot, 'somebar') return theplot }99:Te91,{ // let thevariable = variables_metadata.filter(d => (d.dato === 'monto_eitc') & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'average_eitc'} let estados_incluir = federal_outflows.filter(d => d.outflow_per_capita < 0).map(d => d.state) let estados_below_pr = federal_outflows .filter(d => estados_incluir.includes(d.state)) .filter(d => d.outflow_per_capita < -10236) .map(d => d.state); console.log('some estados_below_pr', estados_below_pr); let thedata = eitc_returns .filter(d => estados_incluir.includes(d.state)) .map(d => { return {...d, below_pr: estados_below_pr.includes(d.state)} }) ; // thedata = aq.from(thedata) // .derive({ // 'outflow_per_capita': d => d.outflow_per_capita*-1 // }) console.log('some thedata', thedata) let eitc_promedio = aq.from(thedata.filter(d => d.state !== 'Puerto Rico')) .rollup({ 'average_eitc': op.mean('average_eitc') }) .objects()[0]['average_eitc'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 70, marginRight: 70, marginBottom: 100, marginTop: 30, width: 1000, height: 500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Crédito por trabajo federal - promedio', }), Plot.axisX({ label: null, fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, rotate: -90, // tickSpacing: .3, // tickPadding: 30, textAnchor: 'end', lineAnchor: 'middle', // tickFormat: d => state_labels[d] ?? d, }), Plot.ruleY([eitc_promedio]), Plot.barY(thedata, { y: thevariable.variable, x: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : ( d.below_pr ? chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex() : '#cdd1ec' ), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {x: "y", reverse: true}}), // Plot.ruleX([credito_promedio]), // Plot.text(thedata, { // filter: d => d[thevariable.variable] < 0, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fontSize: 14.5, // y: thevariable.variable, // x: 'state', // fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { // filter: d => d[thevariable.variable] >= 14000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, rotate: -90, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, // stroke: 'white', strokeWidth: 5, textAnchor: 'end', dy: 8, sort: {x: "y", reverse: true}}), Plot.text([0], { y: eitc_promedio, // dx: 5, dy: -8, x: d => 'Vermont', text: d => `Promedio\n(sin Puerto Rico)\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }9a:Tbcb,{ let thevariable = {variable: 'share'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'total') .filter(d => d.jurisdiction === 'federal') // .filter(d => d['Año contributivo'] === year_select); thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us') .rollup({ share: op.mean('share'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata, 'is_us', 'share') let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { // domain: num_dependientes_options, // interval: 1, reverse: true, }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d ? 'Promedio 50 estados y DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Crédito por trabajo - aportación federal', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0%", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'is_us', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], // stroke: 'white', strokeWidth: 2, }), Plot.text(thedata, { x: 'is_us', y: thevariable.variable, text: d => d3.format(',.1%')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.line(thedata, { x: 'is_us', y: 'share', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-after', stroke: 'black', strokeWidth: 2, dy: -0, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'current_domain', y: 'previous_value', dy: -0, text: d => d3.format('+.1%')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) return theplot }9b:Tb14,{ let thedata = eitc_returns let thevariable = {variable: 'average_eitc'}; thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us') .rollup({ average_eitc: op.mean('average_eitc'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata, 'is_us', 'average_eitc'); let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [true, false], // interval: 1, // reverse: true, }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d ? 'Promedio 50 estados y DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Crédito por trabajo federal - promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'is_us', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { x: 'is_us', y: thevariable.variable, text: d => d3.format('$,.0f')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.line(thedata, { x: 'is_us', y: 'average_eitc', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-after', stroke: 'black', strokeWidth: 2, dy: -0, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'current_domain', y: 'previous_value', dy: -0, text: d => d3.format('+$,.0f')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) return theplot }9c:T980,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let estados_incluir = ['Puerto Rico', 'New Mexico', 'Louisiana', 'Mississippi',] let thevariable = {variable: 'average_eitc'} let thedata = eitc_returns .filter(d => d.state !== 'International') .filter(d => estados_incluir.includes(d.state)) // .filter(d => d['Año contributivo'] === year_select); thedata = aq.from(thedata).orderby(aq.desc('average_eitc')).objects() let eitc_promedio = aq.from(thedata) .filter(d => d.state !== 'Puerto Rico') .rollup({ 'average_eitc': op.mean('average_eitc') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, marginTop: 30, width: 640, // height: 1500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Crédito por trabajo federal - promedio', }), Plot.axisX({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: null, }), Plot.ruleY([eitc_promedio]), Plot.barY(thedata, { y: thevariable.variable, x: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {x: "y"}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dy: 7, lineAnchor: 'top', sort: {x: "y",}}), Plot.text([0], { y: eitc_promedio, dy: 5, x: d => 'Puerto Rico', text: d => `Promedio\n(sin Puerto Rico)\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'middle', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }9d:Tbc8,{ let thevariable = {variable: 'share'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'total') .filter(d => d.jurisdiction === 'local') // .filter(d => d['Año contributivo'] === year_select); thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us') .rollup({ share: op.mean('share'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata, 'is_us', 'share') let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { // domain: num_dependientes_options, // interval: 1, reverse: true, }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d ? 'Promedio 50 estados y DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Crédito por trabajo - aportación local', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0%", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'is_us', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], // stroke: 'white', strokeWidth: 2, }), Plot.text(thedata, { x: 'is_us', y: thevariable.variable, text: d => d3.format(',.1%')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.line(thedata, { x: 'is_us', y: 'share', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-before', stroke: 'black', strokeWidth: 2, dy: -0, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'previous_domain', y: 'current_value', dy: -0, text: d => d3.format('+.1%')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) return theplot }9e:Tb91,{ let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') .filter(d => d.jurisdiction === 'local') // .filter(d => d['Año contributivo'] === year_select); let thevariable = {variable: 'amount'}; thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us') .rollup({ amount: op.mean('amount'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata, 'is_us', 'amount'); let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [true, false], // interval: 1, // reverse: true, }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d ? 'Promedio 50 estados y DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Crédito por trabajo local - promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'is_us', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { x: 'is_us', y: thevariable.variable, text: d => d3.format('$,.0f')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), Plot.line(thedata, { x: 'is_us', y: 'amount', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-before', stroke: 'black', strokeWidth: 2, dy: -0, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'previous_domain', y: 'current_value', dy: -0, text: d => d3.format('+$.0f')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) return theplot }9f:Tf85,grafica_19 = { let thedata = data_by_municipio_with_geojson let theplot = Plot.plot({ axis: false, width: 1300, marginTop: 10, // width, color: { legend: true, interpolate: color_interp, type: 'quantile', label: 'Crédito por trabajo - promedio', // label: legend_label_hack[thevariable.variable] ?? thevariable.variable, tickFormat: '$,.0f', fontVariant: 'tabular-nums', width: 400, }, aspectRatio: 1, marks: [ Plot.geo(thedata, { fill: d => ( d.properties.eitc_municipal [0]['credito_trabajo_promedio']), stroke: 'white', strokeWidth: 1.15, }), Plot.text(thedata.features, Plot.centroid({ // text: d => d.properties.eitc_municipal.filter(d => d['Año contributivo'] === year_select)[0][thevariable.variable].toLocaleString(undefined,{maximumFractionDigits: 0, style: 'currency',currency: "USD", // }).slice(1), text: d => (municipios_label_hack[d.properties.municipio] ?? d.properties.municipio).replace(' ', '\n'), // dy: d => d.properties.municipio === 'Quebradillas' ? -10 : 0, fill: "black", fontSize: 12, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', pointerEvents: 'none', ariaDescription: 'number-label', })), Plot.geo(thedata, { title: d => d.properties.municipio, stroke: null, strokeWidth: 3, fill: null, pointerEvents: 'all', ariaDescription: 'outline-key', }), // on(Plot.geo(thedata, { // title: d => d.properties.municipio, // stroke: null, strokeWidth: 3, // fill: null, // pointerEvents: 'all', // ariaDescription: 'outline-key', // }), // { // click: function (event, { datum }) { // // Cuando se haga click sobre un municipio: // let muni = d3.select(event.target).selectChild('title').text(); // let sibs = getSiblings(event.target); // let prev_muni = mutable muni_mutable; // if (muni === prev_muni) { // mutable muni_mutable = null; // // d3.select(event.target).style("fill", null).style('fill-opacity', 1.0); // // sibs.forEach(s => d3.select(s).style("fill", null).style('fill-opacity', 1.0)) // return // } else { // mutable muni_mutable = muni; // } // // d3.select(event.target).style("fill", null).style('fill-opacity', 1.0); // // sibs.forEach(s => d3.select(s).style("fill", 'black').style('fill-opacity', 0.6)) // // console.log('a click event!', event) // // console.log('el muni es', muni) // event.stopPropagation() // }, // } // ), // Plot.text(thedata.features, Plot.centroid({ // filter: d => d.properties.municipio === muni_mutable, // basically para bring el label to top // // text: d => d.properties.eitc_municipal.filter(d => d['Año contributivo'] === year_select)[0][thevariable.variable].toLocaleString(undefined,{maximumFractionDigits: 0, style: 'currency',currency: "USD", // // }).slice(1), // text: d => d.properties.municipio.replace(' ', '\n'), // fill: "black", // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fontSize: 12, // pointerEvents: 'none', // ariaDescription: 'number-label', // })), ] }) d3.select(theplot).selectAll("svg g[aria-description='outline-key']") .classed('outline-key', true) // Click event para el mapa overall // d3.select(theplot).on("click", () => { // console.log('some random click there'); // d3.select(theplot).selectAll('svg path').style("fill", null) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").selectAll('path').style('stroke', 'none') // mutable muni_mutable = null; // }) add_estilos(theplot) return theplot }a0:T9fd,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; // let thedata = eitc_municipal_2019_2022.filter(d => d['Año contributivo'] === year_select) let thedata = data_by_municipio let credito_promedio = aq.from(thedata) .rollup({ 'elpromedio': op.mean('credito_trabajo_promedio'), // 'Planillas Total - Crédito por Trabajo': op.sum('Planillas Total - Crédito por Trabajo') }) // .derive({ // 'Crédito por Trabajo - Promedio': d => d['Crédito por Trabajo - Total'] / d['Planillas Total - Crédito por Trabajo'] // }) .objects()[0]['elpromedio'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Crédito por trabajo - promedio', // label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, tickFormat: d => d === 'OTROS*' ? 'Otros*' : d, }), Plot.barX(thedata, { x: 'credito_trabajo_promedio', y: "municipio", fill: d => d['credito_trabajo_promedio'] >= credito_promedio ? EA_COLORES_MAP['INDIGO'] : chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), Plot.ruleX([credito_promedio]), Plot.text(thedata, { fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: 'credito_trabajo_promedio', y: "municipio", fill: d => d['credito_trabajo_promedio'] >= credito_promedio ? 'white' : 'black', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), // Plot.ruleX([0]), Plot.text([0], { x: credito_promedio, dx: 5, y: d => 'Hormigueros', text: d => `Promedio\nPuerto\nRico\n${d3.format('$,.0f')(credito_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }a1:Tc67,grafica_27 = { let thedata = data_by_year_sexo_estado_civil // .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let theplot = Plot.plot({ width: 1000, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 60, marginBottom: 70, // x: {}, fy: { // paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {min: 0}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, range: [0,.5].map(color_interp) }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.ruleY([0], {stroke: null}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ label: 'Sexo', // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // tickFormat: '', // interval: 1, rotate: 15, textAnchor: 'start', }), Plot.axisY({ // label: legend_label_hack[thevariable.variable] ?? thevariable.variable, label: 'Crédito por trabajo - promedio', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', }), Plot.axisFy({ label: 'Estado Civil', fontFamily: 'jaf-bernino-sans', fontSize: 12, // tickFormat: d => d.replace(' ', '\n') tickFormat: d => d === 'Casado' ? 'Casada/o' : 'Individual', }), Plot.axisFx({ label: 'Año contributivo', fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '' }), Plot.barY(thedata, { x: 'sexo', fx: 'year_contributivo', y: 'credito_trabajo_promedio', fy: 'estado_civil', fill: 'sexo', tip: true, }), // Plot.text(thedata, { // filter: d => d['Año contributivo'] > 2020, // x: 'Año contributivo', // y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('.2f')(d['Promedio - Anejo CT Cantidad de dependientes cualificados']), // dy: -10, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), Plot.text(thedata, { x: 'sexo', fx: 'year_contributivo', y: 'credito_trabajo_promedio', fy: 'estado_civil', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }a2:Tcbb,grafica_29 = { let thedata = data_by_year_sexo_estado_civil .filter(d => d['year_contributivo'] > 2020) ; // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; // let thevariable = {variable: 'Promedio - Total Ingreso bruto ganado para la determinación del EITC'} let theplot = Plot.plot({ width: 800, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 60, marginBottom: 45, // x: {}, fy: { // paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {min: 0}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.ruleY([0], {stroke: null}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ label: 'Sexo', // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // tickFormat: '', // interval: 1, // rotate: 15, // textAnchor: 'start', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', label: 'Ingreso bruto ganado - promedio' }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, // tickFormat: d => d.replace(' ', '\n') tickFormat: d => d === 'Casado' ? 'Casada/o' : 'Individual', label: 'Estado civil', }), Plot.axisFx({ label: 'Año contributivo', fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '' }), Plot.barY(thedata, { x: 'sexo', fx: 'year_contributivo', y: 'ingreso_ganado_promedio', fy: 'estado_civil', fill: 'sexo', tip: true, }), // Plot.text(thedata, { // filter: d => d['Año contributivo'] > 2020, // x: 'Año contributivo', // y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('.2f')(d['Promedio - Anejo CT Cantidad de dependientes cualificados']), // dy: -10, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), Plot.text(thedata, { x: 'sexo', fx: 'year_contributivo', y: 'ingreso_ganado_promedio', fy: 'estado_civil', text: d => d3.format('$,.0f')(d['ingreso_ganado_promedio']), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }a3:T7e9,grafica_6 = { let thedata = credito_trabajo_promedio_by_ocupacion ; // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ width: 800, marginLeft: 250, marginBottom: 50, // caption: `La consolidación de "Profesiones con pocas observaciones" responde a la necesidad de preservar la confidencialidad del contribuyente. Esta categoría incluye las siguientes profesiones para el año ${year_select_6}: ${year_to_otras_profesiones[year_select_6].join(', ')}`, y: { domain: d3.sort(thedata, d => d['ocupacion'] == 'Otros', d => -d['credito_trabajo_promedio'] ).map(d => d['ocupacion']) }, marks: [ Plot.axisX({ label: 'Crédito por trabajo - promedio', // label: legend_label_hack[thevariable.variable] ?? thevariable.variable, tickFormat: '$,.0f', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: d => d === 'Otros listado abajo*' ? 'Profesiones con pocas observaciones' : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, label: 'Ocupación', }), Plot.barX(thedata, { x: 'credito_trabajo_promedio', y: 'ocupacion', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { x: 'credito_trabajo_promedio', y: 'ocupacion', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 12, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'end', dx: -5, }), ] }) return theplot }a4:Te93,grafica_32 = { let thedata = data_by_year_fuente_ingreso // .filter(d => d['Fuente de Mayor Ingreso'] !== null) ; // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 50, marginRight: 150, marginTop: 60, marginBottom: 45, // x: {}, // fx: { // paddingInner: .2, // reverse: true, // }, fy: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }, // y: {tickFormat: "$,.0f"}, color: { legend: true, // range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), // reverse: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ label: 'Año contributivo', // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, }), Plot.axisY({ label: 'Crédito por trabajo - promedio', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisFy({ label: 'Fuente de mayor ingreso', fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => fuente_ingreso_newline_labels[d] ?? d, tickPadding: 25, }), Plot.line(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', stroke: EA_COLORES_MAP['INDIGO'], fy: 'categoria_ingreso', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'year_contributivo', y: 'credito_trabajo_promedio', fill: EA_COLORES_MAP['INDIGO'], fy: 'categoria_ingreso', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { // filter: d => d['Estado Civil'] === 'Casado', x: 'year_contributivo', y: 'credito_trabajo_promedio', fy: 'categoria_ingreso', text: d => d3.format('$,.0f')(d['credito_trabajo_promedio']), // dx: -9, dy: 12, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }a5:T8a3,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'rate'} let thedata = eitc_participation_rates .filter(d => d.year == 2021) .filter(d => d.state !== 'National') // .filter(d => d['Año contributivo'] === year_select); let rate_promedio = aq.from(thedata) .rollup({ 'mean_rate': op.mean('rate') }) .objects()[0]['mean_rate'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1500, caption: 'TODO: Waheed debe actualizar el take-up rate de Puerto Rico', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '.0%', label: 'Tasa de participación en crédito por trabajo', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'Estado o jurisdicción', }), Plot.ruleX([rate_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('.1%')(d[thevariable.variable]), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), Plot.text([0], { x: rate_promedio, dx: 5, y: d => 'Washington', text: d => `Promedio\n${d3.format('.1%')(rate_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }a6:T87e,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'average_eitc'} let thedata = eitc_returns .filter(d => d.state !== 'International') // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata) .rollup({ 'average_eitc': op.mean('average_eitc') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Crédito por trabajo federal - promedio', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'Estado o jurisdicción', }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), Plot.text([0], { x: eitc_promedio, dx: 5, y: d => 'Washington', text: d => `Promedio\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }a7:Taa3,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'amount'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') .filter(d => d.jurisdiction === 'local') // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata) .rollup({ 'average_eitc': op.mean('amount') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 950, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Aportación local al crédito por trabajo - promedio', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'Estado o jurisdicción', }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { filter: d => d[thevariable.variable] > 100, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] < 100, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: 5, textAnchor: 'start', sort: {y: "x", reverse: true}}), Plot.text([0], { x: eitc_promedio, dx: 5, y: d => 'Louisiana', text: d => `Promedio\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }a8:Tffa,grafica_eitc_estados_aportaciones = { // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'amount'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata) .rollup({ 'average_eitc': op.mean('amount') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1200, caption: htl.html`Fuentes: (2023) Internal Revenue Service (IRS). "Statistics for tax returns with the Earned Income Tax Credit"; (2023) IRS. "States and local governments with Earned Income Tax Credit"; (2023) Departamento de Hacienda de Puerto Rico. Análisis por Espacios Abiertos.`, // x: {percent: true,}, color: { legend: true, domain: ['Federal', 'Local'], range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), tickFormat: d => d === 'Federal' ? 'Crédito por trabajo - aportación federal' : 'Crédito por trabajo - aportación local', }, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Aportación federal y local al crédito por trabajo - promedio' }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: "Estado o jurisdicción", }), // Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], // fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', z: 'jurisdiction', fill: d => d.state === 'Puerto Rico' ? (d.jurisdiction === 'federal' ? EA_COLORES_MAP['INDIGO'] : EA_COLORES_MAP['GREEN']) : (d.jurisdiction === 'federal' ? '#cdd1ec' : chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, Plot.stackX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', // fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', fill: d => d.jurisdiction === 'federal' ? (d.state === 'Puerto Rico' ? 'white' : 'black') : null, text: d => d.share > 0 ? d3.format('$,.0f')(d[thevariable.variable]) +` (${d3.format('.1%')(d.share)})` : '', dx: 0, textAnchor: 'middle', sort: {y: "x", reverse: true} })), Plot.text(thedata, Plot.stackX1({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.jurisdiction === 'local' ? (d.state === 'Puerto Rico' ? 'black' : 'black') : null, text: d => d.share > 0 ? d3.format('$,.0f')(d[thevariable.variable]) +` (${d3.format('.1%')(d.share)})` : '', dx: 5, textAnchor: 'start', sort: {y: "x", reverse: true} })), // Plot.text([0], { // x: eitc_promedio, // dx: 5, // y: d => 'Washington', // z: 'jurisdiction', // text: d => `Promedio\nEstados\nUnidos\n${d3.format('$,.0f')(eitc_promedio)}`, // textAnchor: 'start', // lineAnchor: 'top', // fontFamily: 'jaf-bernino-sans', // fontWeight: 'bold', // fontSize: 13, // }), ] }) // console.log('ugh', theplot) // add_estilos(theplot, 'somebar') return theplot }a9:Tc3c,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'average_eitc'} let thedata = eitc_returns .filter(d => d.state !== 'International') .filter(d => d.claims < 1e6) // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata) .rollup({ 'average_eitc': op.mean('average_eitc') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 110, // marginRight: 50, marginBottom: 50, height: 800, width: 1000, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {type: 'log', grid: true,}, // y: {type: 'log', grid: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickPadding: 20, // tickFormat: ',.0f' label: 'Familias', }), Plot.axisY({ label: 'Crédito por trabajo - total', fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', tickPadding: 20, }), // Plot.ruleX([eitc_promedio]), Plot.dot(thedata, { x: 'claims', y: 'total_eitc', stroke: d => d.state === 'Puerto Rico' ? 'red' : 'black', fill: d => d.state === 'Puerto Rico' ? 'red' : 'none', // fill: EA_COLORES_MAP['INDIGO'], // fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' // sort: {y: "x", reverse: true} channels: {'state' : 'state', 'ab': 'abbreviation'}, tip: true, }), Plot.text(thedata, { x: 'claims', y: 'total_eitc', text: d => dot_abbreviation_hack[d.abbreviation] ?? d.abbreviation, dy: -10, // stroke: d => d.state === 'Puerto Rico' ? 'red' : 'black', // fill: EA_COLORES_MAP['INDIGO'], // fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' // sort: {y: "x", reverse: true} // channels: {'state' : 'state'}, // tip: true, }), // Plot.ruleX([credito_promedio]), // Plot.text(thedata, { // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // fontWeight: 'bold', // fontSize: 14.5, // x: thevariable.variable, // y: 'state', // fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: -5, // textAnchor: 'end', // sort: {y: "x", reverse: true}}), // Plot.text([0], { // x: eitc_promedio, // dx: 5, // y: d => 'Washington', // text: d => `Promedio\nEstados\nUnidos\n${d3.format('$,.0f')(eitc_promedio)}`, // textAnchor: 'start', // lineAnchor: 'top', // fontFamily: 'jaf-bernino-sans', // fontWeight: 'bold', // fontSize: 13, // }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }aa:Tbf0,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = aq.from(federal_outflows) .derive({ 'below_pr': d => d.outflow_per_capita < -10300, }) .objects() let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 170, marginRight: 70, marginBottom: 50, width: 600, height: 1500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Contribución neta per cápita del gobierno federal', }), Plot.axisY({ label: 'Estado o jurisdicción', fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, // tickSpacing: .3, tickPadding: 80, tickFormat: d => state_labels[d] ?? d, }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], // fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : ( d.below_pr ? chroma(EA_COLORES_MAP['RED']).alpha(1.).hex() : '#cdd1ec' ), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { filter: d => d[thevariable.variable] < 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: 5, textAnchor: 'start', sort: {y: "x", reverse: true}}), Plot.text([0], { x: eitc_promedio, dx: -5, y: d => 'South Dakota', text: d => `Promedio\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'end', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }ab:T6dd,eitc_returns = { let data = await eitc_returns_2023_file.csv({typed: true}) // let state_replace = { // "California ": 'California', // 'District Of Columbia': 'District of Columbia', // } data = aq.from(data) .rename({'State': 'state', 'Number of EITC Claims': 'claims', 'Total EITC Amount': 'total_eitc', 'Average EITC Amount': 'average_eitc'}) // .fold(aq.not('state'), {'as': ['year', 'rate']}) .derive({ state: aq.escape(d => (state_replace[d.state] ?? d.state).trim()), year: d => 2022 }) .objects() data.forEach(d => { d.state = convertToTitleCase(d.state) d.state = state_replace[d.state] ?? d.state let scaler = 1; switch(d.claims.slice(-1)) { case 'K': scaler = 1e3 break; case 'M': scaler = 1e6 break; case 'B': scaler = 1e9 break; default: scaler = 1 // code block } // d.claims_scaler = scaler d.claims = (+d.claims.slice(0,-1))*scaler scaler = 1; switch(d.total_eitc.slice(-1)) { case 'K': scaler = 1e3 break; case 'M': scaler = 1e6 break; case 'B': scaler = 1e9 break; default: scaler = 1 // code block } d.total_eitc = (+d.total_eitc.slice(1,-1))*scaler // d.rate = (+d.rate.slice(0, -1))/100 d.average_eitc = +d.average_eitc.replace('$', '').replace(',','') }) data= [{ state: 'Puerto Rico', claims: 695_689, total_eitc: 664e6, average_eitc: Math.round(664e6 / 695_689), year: 2023, // claims_scaler: 1, }, ...data] data = aq.from(data).join_left(aq.from(state_abbreviations), 'state').objects() return data }ac:T458,eitc_returns_with_local = { let aq_eitc_local = aq.from(eitc_local_governments) return aq.from(eitc_returns) .join_left(aq_eitc_local, 'state') // .filter(d => d.percentage_of_federal_credit !== undefined) .impute({ percentage_of_federal_credit: () => 0 }) .rename({ 'total_eitc': 'total_eitc_federal', 'average_eitc': 'average_eitc_federal', }) .derive({ 'total_eitc_local': d => (d.total_eitc_federal * d.percentage_of_federal_credit), 'average_eitc_local': d => (d.total_eitc_federal * d.percentage_of_federal_credit)/d.claims, }) .derive({ 'total_eitc': d => d.total_eitc_federal + d.total_eitc_local, 'average_eitc': d => d.average_eitc_federal + d.average_eitc_local }) .fold(aq.not('state', 'claims', 'year', 'percentage_of_federal_credit', 'refundable', 'total_eitc', 'average_eitc'), {as: ['eitc_var', 'amount']}) .derive({ 'scale': d => op.split(d.eitc_var, '_')[0], 'jurisdiction': d => op.split(d.eitc_var, '_')[2], }) .derive({ 'share': d => d.amount / (d.scale === 'total' ? d.total_eitc : d.average_eitc), }) .objects() }ad:T43e,{ let aq_eitc_local = aq.from(eitc_local_governments) return aq.from(eitc_returns) .join_left(aq_eitc_local, 'state') // .filter(d => d.percentage_of_federal_credit !== undefined) .impute({ percentage_of_federal_credit: () => 0 }) .rename({ 'total_eitc': 'total_eitc_federal', 'average_eitc': 'average_eitc_federal', }) .derive({ 'total_eitc_local': d => (d.total_eitc_federal * d.percentage_of_federal_credit), 'average_eitc_local': d => (d.total_eitc_federal * d.percentage_of_federal_credit)/d.claims, }) .derive({ 'total_eitc': d => d.total_eitc_federal + d.total_eitc_local, 'average_eitc': d => d.average_eitc_federal + d.average_eitc_local }) .fold(aq.not('state', 'claims', 'year', 'percentage_of_federal_credit', 'refundable', 'total_eitc', 'average_eitc'), {as: ['eitc_var', 'amount']}) .derive({ 'scale': d => op.split(d.eitc_var, '_')[0], 'jurisdiction': d => op.split(d.eitc_var, '_')[2], }) .derive({ 'share': d => d.amount / (d.scale === 'total' ? d.total_eitc : d.average_eitc), }) .objects() }ae:T889,client = { const client_class = (duckdb_client === 'Compatibility') ? DuckDBClientCompat : DuckDBClient; const c = new client_class(db_instance); // Attach external db await c.query(`attach if not exists 'eitc_2023_database.db'`) // await c.query(`use eitc_2023_database`) // Create views in main db for convenience // For some reason el 'use eitc_2023_database' statement is not working // pero this workaround is good enough const tables_in_db = (await c.query(` select name from (show all tables) where database = 'eitc_2023_database' `)).map(d => d.name) for (let table_name of tables_in_db) { await c.query(` create or replace view ${table_name} as ( from eitc_2023_database.${table_name} ) `) } await c.query(` create or replace table eitc_t4_fuente_ingreso_2019_2022_file as ( from 'eitc_t4_fuente_ingreso_2019_2022.csv' ) `) await c.query(` create or replace table eitc_pobreza_merged as ( from read_xlsx('eitc_pobreza_merged.xlsx') ) `) await process_old_table(c, 'eitc_t4_fuente_ingreso_2019_2022_file', 'eitc_t4_fuente_ingreso_2019_2022') await process_old_table(c, 'eitc_t6_estado_sexo_2019_2022_file', 'eitc_t6_estado_sexo_2019_2022') await c.query(` create or replace macro generate_bounded_series(lower_bound, upper_bound, n, left_endpoint := true, right_endpoint := true) as ( with intermediates as ( select "upper_bound" - "lower_bound" as L, if(left_endpoint and right_endpoint, 2, if(left_endpoint or right_endpoint, 1, 0) ) as num_endpoints, L / (n + 1 - num_endpoints) as step, if(left_endpoint, 0, step) as "offset", "offset" as x1, "offset" + (n - 1)*step as xn ) select generate_series(1, n).list_transform(i -> "offset" + (i-1)*step) from intermediates ) `) // Create tables here // await c.query(` // create or replace table penguins as ( // from penguins_file // ) // `) // await c.query(` // create or replace table iris as ( // from 'iris.csv' -- Filename used as first arg to db.registerFileBuffer // ) // `) return c; }af:T57f,async function process_old_table(client, input_table, output_table) { // client // const input_table = 'eitc_t6_estado_sexo_2019_2022_file' // const output_table = 'eitc_t6_estado_sexo_2019_2022' // const rename_mapper = Object({ // 'Estado Civil': 'estado_civil', // 'Sexo Contribuyente': 'sexo', // 'Año contributivo': 'year_contributivo', // 'Casados - Total Ingreso bruto ganado para la determinación del EITC': 'ingreso_ganado_casados', // 'Planillas Casados - Total Ingreso bruto ganado para la determinación del EITC': 'ingreso_ganado_casados_planillas', // }) let rename_mapper = structuredClone(planillas_column_label_to_name) rename_mapper = {...rename_mapper, ...{ 'Sexo Contribuyente': 'sexo', }} const input_columns = (await client.query(`describe ${input_table}`)).map(d => d.column_name) const renameable_columns = input_columns.filter(d => d in rename_mapper) let rename_query = '' if (renameable_columns.length > 0) { rename_query = 'select\n' for (let col of input_columns) { rename_query += `\t"${col}"${renameable_columns.includes(col) ? ' as '+rename_mapper[col] : ''},\n` } } rename_query += `from ${input_table}` let final_query = ` create or replace table ${output_table} as ( with renamed as ( ${rename_query} ) from renamed ) ` await client.query(final_query) return final_query }b0:T627,class DuckDBClientCompat extends DuckDBClient { async queryStream(query, params) { const connection = await this._db.connect(); let reader, batch; try { if (params?.length > 0) { const statement = await connection.prepare(query); reader = await statement.send(...params); } else { reader = await connection.send(query); } batch = await reader.next(); if (batch.done) throw new Error("missing first batch"); } catch (error) { await connection.close(); throw error; } // Mosaic utility: convert Arrow value to Javascript value const converters = {} batch.value.schema.fields.forEach(d => { console.log('Type for ', d.name, d.type) converters[d.name] = convertArrowValue(d.type) }) return { schema: getArrowTableSchema(batch.value), async *readRows() { try { while (!batch.done) { let batch_array = batch.value.toArray(); // Convert all values to Javascript version let object_array = [] for (let i = 0; i < batch_array.length; i++) { const d_proxy = batch_array[i]; const d_obj = {} for (let k of Object.keys(converters)) { d_obj[k] = converters[k](d_proxy[k]) } object_array.push(d_obj) } yield object_array; batch = await reader.next(); } } finally { await connection.close(); } } }; } }b1:T6f4,async function insertFile(database, name, file, options) { const url = await file.url(); if (url.startsWith("blob:")) { const buffer = await file.arrayBuffer(); await database.registerFileBuffer(file.name, new Uint8Array(buffer)); } else { await database.registerFileURL(file.name, new URL(url, location).href, 4); // duckdb.DuckDBDataProtocol.HTTP } const connection = await database.connect(); try { switch (file.mimeType) { case "text/csv": case "text/tab-separated-values": { return await connection.insertCSVFromPath(file.name, { name, 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\n \n

\n Casados\n

\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":853,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":850,"value":"eitc_trapezoid_paths_2024 = {\n let data = structuredClone(eitc_trapezoids_2024)\n data.forEach(d => {\n const path = d.path\n path.forEach(p => {\n p['filing_status'] = d.filing_status\n p['kids'] = d.kids\n })\n \n })\n\n return data.map(d => d.path).flat()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":847,"value":"eitc_trapezoids_2024 = {\n const filing_status_options = ['single', 'married'];\n const kids_options = ['0', '1', '2', '3'];\n\n let data = [];\n for (let filing_status of filing_status_options) {\n for (let kids of kids_options) {\n const d = structuredClone(eitc_params_2024[filing_status][kids])\n d['filing_status'] = filing_status\n d['kids'] = +kids\n d['plateau_middle'] = 0.5*(d.phase_in + d.phase_out_start)\n\n d['path'] = [\n {income: 0, eitc: 0, desc: 'start', i: 0},\n {income: d.phase_in, eitc: d.max_credit, desc: 'phase_in_end', i: 1},\n {income: d.phase_out_start, eitc: d.max_credit, desc: 'phase_out_start', i: 2},\n {income: d.phase_out_end, eitc: 0, desc: 'phase_out_end', i: 3},\n ]\n\n data = [...data, d]\n }\n }\n\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":839,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2611,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":537,"value":"## Proyecciones contribucion local y federal","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":859,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2805,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2842,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2655,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":973,"value":"En 2023, la aportación federal se mantuvo mayor que la del gobierno local. Se proyecta que de 2024 en adelante el gobierno local aporte más que el federal. Sin embargo, la diferencia es mínima y las aportaciones son esencialmente idénticas.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":862,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":864,"value":"![image.png](${await FileAttachment(\"image.png\").url()})","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":540,"value":"## Credito por trabajo promedio según año","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1243,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1273,"value":"Los promedios de años anteriores no cambiaron desde la publicación del año anterior. La proyección de Hacienda para 2023 fue de $1,932 y en actualidad fue $1,873.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":542,"value":"## Credito por trabajo promedio según año y sexo\nAñadir una segunda barra para cada año.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":190,"value":"## Familias que reciben el crédito por trabajo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":192,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1516,"value":"## Crédito por trabajo promedio según estado civil","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1536,"value":"$40","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1518,"value":"with data_2023 as (\n select year_contributivo, estado_civil,\n credito_trabajo, credito_trabajo_planillas, credito_trabajo_promedio\n from estado_civil\n),\n\ndata_before as (\n select year_contributivo, estado_civil,\n sum(credito_trabajo_casados) + sum(credito_trabajo_individual) as credito_trabajo,\n sum(credito_trabajo_casados_planillas) + sum(credito_trabajo_individual_planillas) as credito_trabajo_planillas,\n credito_trabajo / credito_trabajo_planillas as credito_trabajo_promedio\n from eitc_t6_estado_sexo_2019_2022\n group by all\n)\n\nfrom data_2023\nunion all by name\nfrom data_before\n\n","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"datos_by_year_estado_civil","database":"var:client"},{"id":1544,"value":"## Crédito por trabajo promedio según sexo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1565,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1567,"value":"color_interp = d3.interpolateHcl(\n chroma.mix('white',EA_COLORES_MAP['INDIGO'], .90, 'lch').hex(),\n EA_COLORES_MAP['LIME'])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1546,"value":"with data_2023 as (\n select year_contributivo, sexo,\n sum(credito_trabajo) as credito_trabajo, sum(credito_trabajo_planillas) as credito_trabajo_planillas,\n sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio\n from estado_civil_sexo\n where sexo != 'Otros'\n group by all\n),\n\ndata_before as (\n select year_contributivo, sexo,\n sum(credito_trabajo_casados) + sum(credito_trabajo_individual) as credito_trabajo,\n sum(credito_trabajo_casados_planillas) + sum(credito_trabajo_individual_planillas) as credito_trabajo_planillas,\n credito_trabajo / credito_trabajo_planillas as credito_trabajo_promedio\n from eitc_t6_estado_sexo_2019_2022\n where sexo != 'No marcó o otro'\n group by all\n)\n\nfrom data_2023\nunion all by name\nfrom data_before","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"data_by_year_sexo","database":"var:client"},{"id":1575,"value":"## Crédito por trabajo promedio según sexo y estado civil","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1590,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1579,"value":"with data_2023 as (\n select year_contributivo, sexo, estado_civil,\n credito_trabajo, credito_trabajo_planillas, credito_trabajo_promedio,\n ingreso_ganado, ingreso_ganado_planillas, ingreso_ganado_promedio,\n from estado_civil_sexo\n where sexo != 'Otros'\n),\n \ndata_before as (\n select year_contributivo, sexo, estado_civil,\n credito_trabajo_casados + credito_trabajo_individual as credito_trabajo,\n credito_trabajo_casados_planillas + credito_trabajo_individual_planillas as credito_trabajo_planillas,\n credito_trabajo / credito_trabajo_planillas as credito_trabajo_promedio,\n\n ingreso_ganado_casados + ingreso_ganado_individual as ingreso_ganado,\n ingreso_ganado_casados_planillas + ingreso_ganado_individual_planillas as ingreso_ganado_planillas,\n ingreso_ganado / ingreso_ganado_planillas as ingreso_ganado_promedio,\n from eitc_t6_estado_sexo_2019_2022\n where sexo != 'No marcó o otro'\n -- group by all\n)\n\nfrom data_2023\nunion all by name\nfrom data_before","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"data_by_year_sexo_estado_civil","database":"var:client"},{"id":1838,"value":"select\n year_contributivo, sexo, estado_civil,\n ingreso_ganado_casados + ingreso_ganado_individual as ingreso_ganado,\n ingreso_ganado_casados_planillas + ingreso_ganado_individual_planillas as ingreso_ganado_planillas,\n ingreso_ganado / ingreso_ganado_planillas as ingreso_ganado_promedio,\nfrom eitc_t6_estado_sexo_2019_2022","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:client"},{"id":561,"value":"## Familias que reciben el crédito por trabajo según sexo y estado civil","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":582,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":604,"value":"- 88.4% (615,152 / 695,689) de planillas fueron radicadas con estado civil de contribuyente individual\n- 55.4% (340,998 / 615,152) de planillas con estado civil de contribuyente individual fueron radicadas por mujeres. Es casi 1:1 con los hombres\n- 14.9% (11,960 / 80,537) de planillas con estado civil de casado fueron radicadas por mujeres. Es más desbalanceado comparar.\n\nPor ende, hay dos tipos de comparaciones que se pueden realizar en estas facetas de sexo y estado civil: Sexo del contribuyente cuando es un contribuyente individual, y sexo del contribuyente que radica una planilla como pareja casada. El volumen de planillas de individuos sugiere que hay más tela que cortar en ese análisis comparado al de contribuyentes casados vs sexo.\n\nOtros datos utiles para referencia:\n- Hay solo 1,582 planillas bajo sexo de \"Otros\", o sea 694,107 planillas de masculino y femenino. Util para saber cuando totales parezcan no cuadrar; verificar si se excluyó a \"Otros\".","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":570,"value":"from estado_civil_sexo\nselect sexo, estado_civil, sum(credito_trabajo_planillas) as credito_trabajo_planillas\ngroup by all","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"planillas_by_sexo_estado_civil","database":"var:client"},{"id":209,"value":"## Crédito por trabajo promedio según edad","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":229,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1043,"value":"El mayor crédito suele desembolsarse a familias cuyo contribuyente tiene una edad entre los 35 y 40 años","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1039,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2634,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2664,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2643,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2667,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1062,"value":"La cantidad de familias va disminuyendo con la edad del contribuyente","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1064,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1073,"value":"El pico del total desembolsado ocurre alrededor de la edad de 35 años","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1075,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1083,"value":"No hay grupo de edad cuyo ingreso ganado promedio sobrepase los $19 mil. La JSF usa ingreso ajustado entonces miramos a ver:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1119,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1123,"value":"La mayor diferencia ocurre en una reducción masiva para jovenes de 26 años o menos.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1046,"value":"with initial as (\nselect * replace (regexp_extract(edad_table.edad, '\\d+')::INT as edad)\nfrom edad as edad_table\n ),\n\nis_joven_aggregates as (\nselect edad <= 26 as is_joven,\n sum(credito_trabajo_planillas) as credito_trabajo_planillas,\n sum(credito_trabajo) as credito_trabajo,\n sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio,\n sum(ingreso_ganado) / sum(ingreso_ganado_planillas) as ingreso_ganado_promedio,\nfrom initial\ngroup by all\n)\n\nselect *,\n credito_trabajo_planillas / sum(credito_trabajo_planillas) over () as credito_trabajo_planillas_pct,\n credito_trabajo / sum(credito_trabajo) over () as credito_trabajo_pct,\nfrom is_joven_aggregates","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:client"},{"id":1158,"value":"Jovenes de 26 años o menos cobran sobre $191 millones (14.7%) del crédito por trabajo desembolsado.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1088,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1125,"value":"Usando ingreso ganado, el promedio de EITC normalizado por ingreso ganado es 11.1%. La JSF usa ingreso ajustado para esto:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1127,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1135,"value":"Esta gráfica es bien distinta porque hay una cantidad menor de planillas que reportaron ingreso bruto ajustado comparado a las que recibieron el crédito por trabajo. En otras palabras, usar ingreso ajustado no es un análisis adecuado. Tener más planillas acumulando EITC que planillas acumulando ingreso ajustado significa que la cifra de porcentaje estará muy inflada. Habiendo dicho eso, el promedio de 14% cuadra con la cifra la JSF.\n\nResumen: El análisis con ingreso bruto ganado demuestra una cifra de 11% que resolvería los cuestionamientos de la JSF. Sin embargo, un análisis usando ingreso ajustado en vez no es posible ya que hay datos incompletos. En mi opinión, la JSF debe proveer metodología más detallada debido a estas debilidades en usar el ingreso ajustado por cual ellos optaron.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1375,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1429,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1434,"value":"Estas gráficas desglosan la distribución de dependientes dentro de cada escala de edad. Tanto en términos absolutos como porcentuales, los grupos de edad alrededor de 35-40 años presentan más dependientes. Cabe destacar que las escalas de jovenes de 26 años o menos tienen un máximo de 25.7% (para escala de 26-27 años) de sus familias con dependientes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1032,"value":"select regexp_extract(edad_table.edad, '\\d+')::INT as edad, credito_trabajo_promedio, credito_trabajo, \n credito_trabajo_planillas, ingreso_ganado_planillas, ingreso_ajustado_planillas,\n ingreso_ajustado, ingreso_ajustado_promedio,\n ingreso_ganado, ingreso_ganado_promedio,\n ct_cantidad_dependientes,\n credito_trabajo / ingreso_ganado as credito_trabajo_pct_by_ingreso_ganado,\n credito_trabajo / ingreso_ajustado as credito_trabajo_pct_by_ingreso_ajustado,\n ct_cantidad_dependientes / credito_trabajo_planillas as ct_cantidad_dependientes_promedio,\n credito_trabajo_planillas - ct_cantidad_dependientes_planillas as credito_trabajo_planillas_sin_dependientes,\n credito_trabajo_planillas_sin_dependientes / credito_trabajo_planillas as credito_trabajo_planillas_sin_dependientes_pct,\n 1 - credito_trabajo_planillas_sin_dependientes_pct as credito_trabajo_planillas_con_dependientes_pct\nfrom edad as edad_table","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"credito_trabajo_promedio_by_edad","database":"var:client"},{"id":544,"value":"## Crédito por trabajo promedio según edad y sexo\nIntentar una piramide, dale tiempo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":268,"value":"## Crédito por trabajo promedio según número de dependientes","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":293,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":987,"value":"El crédito promedio desembolsado al reportarse al menos un dependiente es más del doble comparado a familias sin dependientes.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":991,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2645,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2674,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1010,"value":"Sin embargo, sobre 7 de cada 10 familias que reciben el crédito no reportan dependiente alguno.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1339,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":976,"value":"with initial as (\nselect\n regexp_extract(ct_categoria_dependientes, '\\d+').regexp_replace('^$', '0')::INT as ct_categoria_dependientes_num,\n sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio,\n sum(credito_trabajo_planillas) as credito_trabajo_planillas,\n sum(credito_trabajo) as credito_trabajo,\nfrom dependientes\ngroup by ct_categoria_dependientes_num\n)\n\nselect *,\n credito_trabajo_planillas / sum(credito_trabajo_planillas) over () as credito_trabajo_planillas_pct_by_num_deps\nfrom initial","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"credito_trabajo_promedio_by_num_deps","database":"var:client"},{"id":1334,"value":"with initial as (\nselect\n estado_civil,\n regexp_extract(ct_categoria_dependientes, '\\d+').regexp_replace('^$', '0')::INT as ct_categoria_dependientes_num,\n sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio,\n sum(credito_trabajo_planillas) as credito_trabajo_planillas,\nfrom dependientes\ngroup by estado_civil, ct_categoria_dependientes_num\n)\n\nselect *,\n credito_trabajo_planillas / sum(credito_trabajo_planillas) over () as credito_trabajo_planillas_pct_by_num_deps\nfrom initial\norder by estado_civil desc, ct_categoria_dependientes_num","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"credito_trabajo_promedio_by_estado_civil_num_deps","database":"var:client"},{"id":297,"value":"labels_dependientes_cualificados = Object({\n 'es': {\n 0: 'No dependientes',\n 1: '1 dependiente',\n 2: '2 dependientes',\n 3: '3 o más dependientes',\n },\n 'en': {\n 0: 'No dependents',\n 1: '1 dependent',\n 2: '2 dependents',\n 3: '3 or more dependents',\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":547,"value":"## Crédito por trabajo promedio según número de dependientes y sexo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":916,"value":"$56","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":940,"value":"Para ambos estados civiles. Las planillas radicadas por mujeres reportan más dependientes en términos absolutos.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":927,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":943,"value":"En términos porcentuales, la proporción de planillas por mujeres que reporta al menos un dependiente (100% - 65.1% = 34.9%) es mayor al equivalente para los hombres (100% - 77.2% = 22.8%). Esta diferencia en dependientes reportados impacta el crédito promedio desembolsado por sexo.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1199,"value":"$58","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1225,"value":"Por diseño, las familias con dependientes reclaman mucho más crédito por trabajo que las familias sin dependientes. Las mujeres radican más planillas con dependientes que los hombres. Por tanto, el crédito promedio que reclaman las mujeres es mayor.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":905,"value":"with initial as (\nselect\n sexo,\n regexp_extract(ct_categoria_dependientes, '\\d+').regexp_replace('^$', '0')::INT as ct_categoria_dependientes_num,\n sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio,\n sum(credito_trabajo_planillas) as credito_trabajo_planillas\nfrom dependientes_sexo\nwhere sexo != 'Otros'\ngroup by sexo, ct_categoria_dependientes_num\norder by sexo, ct_categoria_dependientes_num\n)\n\nselect *,\n credito_trabajo_planillas / sum(credito_trabajo_planillas) over (partition by sexo) as credito_trabajo_planillas_pct_by_sexo\nfrom initial","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"data_by_num_deps_sexo","database":"var:client"},{"id":946,"value":"$59","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"data_by_has_deps_sexo","database":"var:client"},{"id":307,"value":"## Crédito por trabajo promedio según ocupación","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":318,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":327,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1030,"value":"Bajo ocupación de \"Retirado\" hay 73,872 familias reclamando $89,670,265. La familia promedio de retirado reclama $1,214 y tiene un ingreso ganado de $14,767.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1162,"value":"select ocupacion, credito_trabajo_promedio, credito_trabajo, credito_trabajo_planillas, ingreso_ganado_promedio\nfrom ocupacion","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"credito_trabajo_promedio_by_ocupacion","database":"var:client"},{"id":550,"value":"## Crédito por trabajo promedio según ocupación y sexo\nAdd segunda barra para sexo por cada ocupación","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":343,"value":"## Crédito por trabajo promedio según fuente de mayor ingreso","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":354,"value":"$5c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1180,"value":"$5d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1184,"value":"Bajo fuente de mayor de ingreso de \"Retirado o pensionado\" hay 86,735 familias reclamando un total de $104,783,593. Su crédito promedio es de $1,208 y tienen un ingreso ganado promedio de $14,656","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1173,"value":"select categoria_ingreso, credito_trabajo_promedio, credito_trabajo, credito_trabajo_planillas, ingreso_ganado_promedio\nfrom fuente_ingreso","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"credito_trabajo_promedio_by_fuente_ingreso","database":"var:client"},{"id":555,"value":"## Crédito por trabajo promedio según fuente de mayor ingreso y sexo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":387,"value":"## Familias según municipio especificado","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":390,"value":"$5e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":369,"value":"with data as (\n select\n municipio != 'OTROS' as municipio_especificado,\n sum(credito_trabajo_planillas) as credito_trabajo_planillas\n from municipio\n group by all\n)\n\nselect *, sum(credito_trabajo_planillas) over () as total_planillas,\n credito_trabajo_planillas / total_planillas as porciento\nfrom data","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"planillas_by_municipio_especificado","database":"var:client"},{"id":396,"value":"## Crédito por trabajo promedio según municipio","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1758,"value":"data_by_municipio_with_geojson.features.filter(d => d.properties.eitc_municipal === undefined).map(d => Object({'en_geojson': d.properties.municipio, 'en_hacienda': d.properties.eitc_municipal?.municipio}))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1785,"value":"data_by_municipio = {\n let thedata = structuredClone(data_by_municipio_unedited)\n const muni_replace = {\n 'Anasco': 'Añasco',\n 'Bayamon': 'Bayamón',\n 'Canovanas': 'Canóvanas',\n 'Catano': 'Cataño',\n 'Comerio': 'Comerío',\n 'Guanica': 'Guánica',\n 'Juana Diaz': 'Juana Díaz',\n 'Las Marias': 'Las Marías',\n 'Loiza': 'Loíza',\n 'Manati': 'Manatí',\n 'Mayaguez': 'Mayagüez',\n 'Penuelas': 'Peñuelas',\n 'Rincon': 'Rincón',\n 'Rio Grande': 'Río Grande',\n 'San German': 'San Germán',\n 'San Sebastian': 'San Sebastián',\n }\n thedata.forEach(d => {\n d.municipio = muni_replace[d.municipio] ?? d.municipio\n })\n return thedata\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1752,"value":"municipios_label_hack = Object({\n 'Quebradillas': '\\nQuebradillas',\n 'Camuy': '\\nCamuy',\n 'Guaynabo': '\\nGuaynabo',\n 'Canóvanas': '\\nCanóvanas',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":403,"value":"$5f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":411,"value":"$60","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":398,"value":"select municipio, credito_trabajo_promedio, credito_trabajo, credito_trabajo_planillas\nfrom municipio","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"data_by_municipio_unedited","database":"var:client"},{"id":1747,"value":"data_by_municipio_with_geojson = add_data_to_geojson(\n municipios_geojson,\n data_by_municipio,\n ['municipio', 'municipio'],\n 'eitc_municipal'\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2651,"value":"// d3.csvFormat(data_by_municipio)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2771,"value":"credito_recortes_municipios = await credito_recortes_municipios_file.csv({typed: true})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2788,"value":"viewof num_municipios_show = Inputs.select([10, 25, 50, 99999], {label: \"Municipios:\", value: 10, format: d => d > 100 ? 'Todos' : d})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2799,"value":"viewof num_municipios_show_en = Inputs.select([10, 25, 50, 99999], {label: \"Municipalities:\", value: 10, format: d => d > 100 ? 'All' : d})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2767,"value":"$61","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2801,"value":"$62","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":415,"value":"## Crédito por trabajo promedio según ingreso ganado","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":425,"value":"$63","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1312,"value":"$64","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1317,"value":"Hay una caida fuerte alrededor del ingreso de $28,700 que es cuando acaba el phase-out para contribuyentes individuales sin dependientes. Recordamos que el 88.4% de planillas fueron radicadas con estado civil de contribuyente individual. 7 de cada 10 familias no reportaron dependientes. Además, son 64.3% si miramos solamente aquellos contribuyentes individuales sin dependientes (el trapezoide bajo).\n\nSolo hay 36,796 familias (5.3%) con ingreso ganado de al menos $29 mil, y estas cobraron un total de $67,513,718 (5.2%) en crédito por trabajo. El crédito promedio de este grupo es $1,835 mientras que su ingreso ganado promedio es de $33,495.\n\nEn resumen, pese a que el programa desembolsa fondos a familias con ingresos mayores que el promedio, la mayoría de familias (94.7%) tienen ingresos ganados menores que el cierre del phase-out del trapezoide más restrictivo (contribuyente individual y cero dependientes)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1409,"value":"$65","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1421,"value":"Esta gráfica presenta la proporción de familias con dependientes dentro de cada escala de ingreso. El aumento súbito ocurre porque se alcanzan los umbrales donde familias deben reportar al menos un dependiente para reclamar el crédito. Estos umbrales son $28,700 para contribuyentes individuales y $30,910 para casados. Mayormente se mantiene entre 20% y 30% para ingresos menores que los umbrales.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1304,"value":"$66","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1390,"value":"El pico de total desembolsado ocurre justo antes de los $20 mil en ingreso ganado.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1393,"value":"$67","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1401,"value":"El total de dependientes cualificados parece ser bastante simétrico alrededor de los $20 mil en ingreso ganado.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":417,"value":"select escala_ingreso_ganado,\n regexp_extract(escala_ingreso_ganado, '(\\d+) <= x < (\\d+)', 1)::INT as escala_ingreso_ganado_limite_inferior,\n regexp_extract(escala_ingreso_ganado, '(\\d+) <= x < (\\d+)', 2)::INT as escala_ingreso_ganado_limite_superior,\n credito_trabajo, credito_trabajo_planillas, credito_trabajo_promedio,\n ct_cantidad_dependientes,\n ct_cantidad_dependientes_planillas,\n credito_trabajo_planillas - ct_cantidad_dependientes_planillas as credito_trabajo_planillas_sin_dependientes,\n credito_trabajo_planillas_sin_dependientes / credito_trabajo_planillas as credito_trabajo_planillas_sin_dependientes_pct,\n 1 - credito_trabajo_planillas_sin_dependientes_pct as credito_trabajo_planillas_con_dependientes_pct\nfrom ingreso_ganado","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"credito_trabajo_promedio_by_ingreso_ganado","database":"var:client"},{"id":1320,"value":"with initial as (\nselect escala_ingreso_ganado,\n regexp_extract(escala_ingreso_ganado, '(\\d+) <= x < (\\d+)', 1)::INT as escala_ingreso_ganado_limite_inferior,\n regexp_extract(escala_ingreso_ganado, '(\\d+) <= x < (\\d+)', 2)::INT as escala_ingreso_ganado_limite_superior,\n credito_trabajo, credito_trabajo_planillas, credito_trabajo_promedio, ingreso_ganado, ingreso_ganado_planillas,\nfrom ingreso_ganado\n )\n\nselect sum(credito_trabajo_planillas), sum(credito_trabajo), sum(credito_trabajo) / sum(credito_trabajo_planillas),\n sum(ingreso_ganado) / sum(ingreso_ganado_planillas)\nfrom initial\nwhere escala_ingreso_ganado_limite_inferior >= 29_000","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:client"},{"id":558,"value":"## Crédito por trabajo promedio según ingreso ganado y sexo\nPiramide","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":651,"value":"$68","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":614,"value":"$69","pinned":false,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"datos_by_estado_civil_sexo_ingreso_ganado","database":"var:client"},{"id":660,"value":"Para contribuyentes individuales. Es dificil comparar en números absolutos por el total de intervalos y la cantidad casi 1:1 de planillas radicadas por mujeres a hombres.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":754,"value":"$6a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":779,"value":"Para contribuyentes individuales. Cada color de barras suma a 100% de su respectivo sexo. Generalmente no parecen haber diferencias gigantes sobre como se distribuyen ingresos comparando sexo. El siguiente gráfico se utiliza para comparar las diferencias por escala y acumuladas.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":732,"value":"$6b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":788,"value":"Para contribuyentes individuales. Esta gráfica presenta la sobrerepresentación de uno u otro sexo en cada escala de ingreso. En terminos absolutos, no me parece que hay diferencias significantes. Donde sí se mueve es en las siguientes: Ingresos debajo de ~$15,000 hay una mayor proporción de mujeres, mientras que para escalas entre $15 a $29 mil suelen haber más hombres. Luego de 29 mil suelen haber más mujeres","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":797,"value":"$6c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":807,"value":"Para contribuyentes individuales. Parece haber un sesgo hacia mujeres que tienden recibir un poco más crédito en promedio. No obstante, vamos a examinar la diferencia:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":805,"value":"$6d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":815,"value":"Para contribuyentes individuales. Definitivamente hay un sesgo hasta los $28,500. Esto es donde acabe el phase-out del programa para contribuyentes individuales con ningún dependiente.\n\nUn siguiente análisis es necesario con los dependientes promedio, pero esta variable de dependientes no desglosa entre individual/casado en esta tabla de ingreso ganado y sexo. La tabla de estado civil y sexo sugiere que cada contribuyente individual tiene, en promedio, 1.5 dependientes sin importar el sexo. La tabla de dependientes y sexo ayudaría a resolver algunas dudas, pero sacrifica la información de ingreso.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":824,"value":"$6e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":822,"value":"$6f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":831,"value":"Ambos anteriores son para contribuyentes casados.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":747,"value":"datos_by_estado_civil_ingreso_ganado_pivot_by_sexo","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":638,"value":"$70","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"datos_by_estado_civil_ingreso_ganado_pivot_by_sexo","database":"var:client"},{"id":2412,"value":"## Distribución de ingresos de familias sin dependientes\n\nExperimental","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2440,"value":"$71","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2524,"value":"registered_planillas_sin_dependientes_distribucion = { // IMPORTANTE: For registering a file back into duckdb\n const encoder = new TextEncoder()\n \n // Variable encoded as a CSV in an arrayBuffer\n let filename = 'planillas_sin_dependientes_by_sexo_estado_civil.csv'\n let encoded_buffer = encoder.encode(d3.csvFormat(planillas_sin_dependientes_by_sexo_estado_civil))\n\n // Actually register the file\n await db_instance.registerFileBuffer(\n filename, // Choose filename to use inside db\n encoded_buffer\n );\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2537,"value":"client_with_registers = registered_planillas_sin_dependientes_distribucion, client","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2574,"value":"client_with_simulacion = create_simulacion_planillas_sin_dependientes_by_sexo_estado_civil, client","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2573,"value":"with initial as (\nfrom simulacion_planillas_sin_dependientes_by_sexo_estado_civil\n select *,\n case when ingreso_ganado_estimado < 11_040\n then 0.15 * ingreso_ganado_estimado\n when ingreso_ganado_estimado < 19_870\n then 1_656\n when ingreso_ganado_estimado < 30_910\n then 1_656 - 0.15 * (ingreso_ganado_estimado - 19_870)\n else 0 end as credito_estimado,\n-- where estado_civil = 'Casados'\n)\n\nselect sexo, escala_ingreso_ganado, count(), sum(credito_estimado)\nfrom initial\ngroup by all","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:client_with_simulacion"},{"id":2599,"value":"eitc_trapezoids_2023","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2529,"value":"$72","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"create_simulacion_planillas_sin_dependientes_by_sexo_estado_civil","database":"var:client_with_registers"},{"id":2561,"value":"$73","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:client_with_registers"},{"id":2519,"value":"aq.from(planillas_sin_dependientes_by_sexo_estado_civil).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2452,"value":"planillas_sin_dependientes_by_sexo_estado_civil = aq.from(datos_by_ingreso_ganado_sexo_sin_dependientes)\n .select('sexo', 'escala_ingreso_ganado',\n 'escala_ingreso_ganado_limite_inferior', 'escala_ingreso_ganado_limite_superior',\n 'ct_cantidad_sin_dependientes_individual_planillas', 'ct_cantidad_sin_dependientes_casados_planillas',\n )\n .fold(['ct_cantidad_sin_dependientes_individual_planillas', 'ct_cantidad_sin_dependientes_casados_planillas'], { as: ['estado_civil', 'ct_cantidad_sin_dependientes_planillas'] })\n .derive({\n 'estado_civil': aq.escape(d => d.estado_civil.includes('individual') ? 'Contribuyente Individual' : 'Casados'),\n })\n.objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2409,"value":"$74","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"datos_by_ingreso_ganado_sexo_sin_dependientes","database":"var:client"},{"id":2491,"value":"generate_bounded_series_macro","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2509,"value":"","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:client"},{"id":2416,"value":"-- create or replace macro generate_bounded_series(lower_bound, upper_bound, n, left_endpoint := true, right_endpoint := true) as (\n\n-- with intermediates as (\n-- select\n-- \"upper_bound\" - \"lower_bound\" as L,\n-- if(left_endpoint and right_endpoint, 2,\n-- if(left_endpoint or right_endpoint, 1, 0)\n-- ) as num_endpoints,\n-- L / (n + 1 - num_endpoints) as step,\n-- if(left_endpoint, 0, step) as \"offset\",\n-- \"offset\" as x1,\n-- \"offset\" + (n - 1)*step as xn\n-- )\n\n-- select generate_series(1, n).list_transform(i -> \"offset\" + (i-1)*step)\n-- from intermediates\n-- )\nselect generate_bounded_series(0, 100, 3)","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"generate_bounded_series_macro","database":"var:client"},{"id":534,"value":"## Total de familias según ingreso ganado\n\nTODO. Usar tabla de previous grafica","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":431,"value":"## Renglones de pobreza por año","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":433,"value":"$75","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":564,"value":"## Renglones de pobreza por año según sexo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":441,"value":"## Personas rebasando la pobreza por año","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":443,"value":"$76","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":448,"value":"## Nuevo ingreso de grupos recibiendo el crédito por trabajo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":449,"value":"$77","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":464,"value":"## Nuevo ingreso según número de dependientes","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":475,"value":"$78","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":466,"value":"with data as (\nselect\n regexp_extract(ct_categoria_dependientes, '\\d+').regexp_replace('^$', '0')::INT as ct_categoria_dependientes_num,\n sum(credito_trabajo) / sum(credito_trabajo_planillas) as credito_trabajo_promedio,\n sum(ingreso_ganado) / sum(ingreso_ganado_planillas) as ingreso_ganado_promedio\nfrom dependientes\ngroup by ct_categoria_dependientes_num\n)\n\nunpivot data\non credito_trabajo_promedio, ingreso_ganado_promedio\ninto\n name variable_name value cantidad\norder by ct_categoria_dependientes_num, variable_name desc","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"ingreso_nuevo_by_dependientes","database":"var:client"},{"id":470,"value":"describe dependientes","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:client"},{"id":566,"value":"## Nuevo ingreso según número de dependientes y sexo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":492,"value":"## Tasa de pobreza","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":497,"value":"$79","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":494,"value":"poverty_puerto_rico = {\n let data = await FileAttachment(\"PovertyRate_1YACS.csv\").csv({typed: true})\n data = aq.from(data)\n .rename({\n 'Año': 'year',\n 'Population': 'total_population',\n 'Population under poverty': 'population_under_poverty',\n 'Poverty Rate': 'below_poverty',\n })\n .derive({\n 'below_poverty': d => d.below_poverty / 100\n })\n .objects()\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":500,"value":"## Tasa de pobreza estimada con el impacto del crédito por trabajo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":502,"value":"$7a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":507,"value":"participacion_laboral = {\n let data = await FileAttachment(\"tasa_participacion_laboral@1.csv\").csv({typed: true})\n data = aq.from(data).derive({\n rate: d => d.rate /100\n })\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2189,"value":"## Familias sobrepasando o permaneciendo en pobreza luego de recibir el EITC","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2079,"value":"$7b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2057,"value":"$7c","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"datos_pobreza_por_trapezoide","database":"var:client"},{"id":2264,"value":"viewof dependientes_select = Inputs.select([0, 1, 2, 3], {label: \"Dependientes\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2267,"value":"viewof estado_civil_select = Inputs.select(['Contribuyente Individual', 'Casados'], {label: \"Estado civil\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2335,"value":"viewof plateu_multiplicador = Inputs.range([0, 5], {label: \"Plateau multiplicador\", step: 0.20, value: 1})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2394,"value":"{\n let thedata_pobreza = datos_pobreza_por_trapezoide\n .filter(d => (d.estado_civil === estado_civil_select) & (d.dependientes_por_planilla === dependientes_select))[0]\n\n return [{costo: thedata_pobreza.eitc_permaneciendo_pobreza_despues_eitc}]\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2260,"value":"$7d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2849,"value":"viewof umbral_slider = Inputs.range([0, 60_000], {label: \"Umbral pobreza\", step: 1})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2852,"value":"$7e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2388,"value":"

\n Original\n

\n Ajustado\n

\n \n \n

\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2344,"value":"$7f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2858,"value":"## Impacto del cambio del costo de programa","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2863,"value":"$80","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3064,"value":"$81","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2915,"value":"$82","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3069,"value":"$83","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2925,"value":"$84","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3073,"value":"$85","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2931,"value":"$86","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3077,"value":"$87","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2940,"value":"$88","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2948,"value":"$89","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3080,"value":"$8a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2943,"value":"$8b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2961,"value":"$8c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3087,"value":"$8d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2860,"value":"eitc_simulacion_cambios_pobreza_costo = {\n let thedata = await eitc_simulacion_cambios_pobreza_costo_file.csv({typed: true})\n return thedata\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2920,"value":"eitc_simulacion_cambios_pobreza_costo_by_num_deps = {\n let thedata = await eitc_simulacion_cambios_pobreza_costo_by_num_deps_file.csv({typed: true})\n return thedata\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2938,"value":"eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil = {\n let thedata = await eitc_simulacion_cambios_pobreza_costo_by_num_deps_estado_civil_file.csv({typed: true})\n return thedata\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":509,"value":"## Tasa de participación laboral\n\n2025-01-13: Revisar más tarde el dato en enero para incluir diciembre\n\n2025-01-29: Ya revisado.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":511,"value":"$8e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":519,"value":"$8f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2680,"value":"$90","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2686,"value":"$91","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2697,"value":"$92","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2684,"value":"ley_22_tax_expenditures = [\n {year: 2017, amount: 264.4e6},\n {year: 2018, amount: 310.7e6},\n {year: 2019, amount: 324.8e6},\n {year: 2020, amount: 545e6},\n {year: 2021, amount: 601e6},\n {year: 2022, amount: 637e6},\n {year: 2023, amount: 649e6},\n {year: 2024, amount: 656e6},\n {year: 2025, amount: 667e6},\n {year: 2026, amount: 683e6},\n]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":513,"value":"function get_data_changes(thedata, domain_var, value_var) {\n const ugh = domain_var;\n let thedata_changes = aq.from(\n thedata.map(d => {return {...d, domain_var: d[domain_var], value_var: d[value_var]}})\n )\n // console.log('some thedata_changes', thedata_changes.objects())\n\n thedata_changes = thedata_changes\n .derive({\n 'current_domain': d => d['domain_var'],\n 'previous_domain': d => op.lag(d['domain_var']),\n \n 'current_value': d => d['value_var'],\n 'previous_value': d => op.lag(d['value_var']),\n })\n .filter(d => d.previous_value !== undefined)\n .derive({\n 'mid_domain': d => (d.current_domain + d.previous_domain)/2,\n 'change_value': d => d.current_value - d.previous_value,\n })\n .objects();\n // console.log('finished thedata_changes', thedata_changes)\n return thedata_changes\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1622,"value":"# Comparación con estados y DC","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1696,"value":"$93","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1721,"value":"$94","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1726,"value":"$95","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2003,"value":"$96","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2019,"value":"$97","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2028,"value":"$98","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1735,"value":"$99","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1626,"value":"$9a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1660,"value":"La proporción del monto total desembolsado de crédito que viene del gobierno federal hacia Puerto Rico es 51% ($664M / $1,302,962,058). Esto es una reducción de la aportación de 56.6% el año pasado. La proporción de los estados es tan alta porque hay muchos estados que no ofrecen crédito local y su aportación federal equivaldría al 100%.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1666,"value":"$9b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1669,"value":"Los cambios en la aportación significan que el crédito promedio proveniente del gobierno federal disminuyó de $979 a $954 en el 2023. Mientras tanto, el monto de los 50 estados y DC aumentó de $2,433 a $2,628.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1673,"value":"$9c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1676,"value":"Comparar Puerto Rico con los estados más pobres también muestra un aumento en la brecha separándolos.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1679,"value":"$9d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1683,"value":"Es el mismo gráfico que el de la aportación federal pero el complemento para llegar al 100%.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1685,"value":"$9e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1740,"value":"# Apéndice de gráficas","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1809,"value":"$9f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1812,"value":"$a0","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1823,"value":"$a1","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1835,"value":"data_by_year_sexo_estado_civil","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1833,"value":"$a2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1851,"value":"$a3","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1866,"value":"credito_trabajo_promedio_by_fuente_ingreso","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1860,"value":"$a4","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1915,"value":"fuente_ingreso_newline_labels = Object({\n 'Empleado de empresa privada': 'Empleado de\\nempresa privada',\n 'Empleado del gobierno federal': 'Empleado del\\ngobierno federal',\n 'Empleado del gobierno': 'Empleado del\\ngobierno local',\n 'Otro': 'Otra',\n 'Retirado / Pensionado': 'Retirado\\no pensionado',\n 'Trabajo por cuenta propia': 'Trabajo por\\ncuenta propia',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1875,"value":"with data_2023 as (\n select categoria_ingreso, year_contributivo,\n credito_trabajo, credito_trabajo_planillas, credito_trabajo_promedio\n from fuente_ingreso\n),\n \ndata_before as (\n select\n ifnull((map_from_entries([\n ('Empleado del Gobierno, municipios o Corporaciones Públicas', 'Empleado del gobierno'),\n ('Empleado de Empresa Privada', 'Empleado de empresa privada'),\n ('Trabajo por Cuenta Propia', 'Trabajo por cuenta propia'),\n ('Empleado del Gobierno Federal', 'Empleado del gobierno federal')\n \n ]))[\"Fuente de Mayor Ingreso\"][1], \"Fuente de Mayor Ingreso\") as categoria_ingreso, year_contributivo, \n credito_trabajo_casados + credito_trabajo_individual as credito_trabajo,\n credito_trabajo_casados_planillas + credito_trabajo_individual_planillas as credito_trabajo_planillas,\n credito_trabajo / credito_trabajo_planillas as credito_trabajo_promedio,\n from eitc_t4_fuente_ingreso_2019_2022\n where categoria_ingreso is not null\n)\n\nfrom data_2023\nunion all by name\nfrom data_before","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"data_by_year_fuente_ingreso","database":"var:client"},{"id":1924,"value":"$a5","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1926,"value":"eitc_participation_rates = {\n let data = await eitc_participation_rates_file.csv({typed: true})\n data = aq.from(data)\n // .derive({'latest_rate': d => d['Tax Year 2020']})\n .rename({'Participation Rate by State': 'state'})\n .fold(aq.not('state'), {'as': ['year', 'rate']})\n .derive({\n // state: d => op.capital(d.state),\n year: d => +op.replace(d.year, 'Tax Year ', '')\n })\n .objects()\n data.forEach(d => {\n d.state = convertToTitleCase(d.state)\n d.state = state_replace[d.state] ?? d.state\n d.rate = (+d.rate?.slice(0, -1))/100\n })\n data= [{\n state: 'Puerto Rico',\n year: 2021,\n // rate: .872,\n rate: 1.234,\n }, ...data]\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1932,"value":"$a6","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1943,"value":"$a7","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1946,"value":"$a8","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1949,"value":"$a9","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1951,"value":"dot_abbreviation_hack = Object({\n 'AK': 'AK ',\n 'CO': '\\n\\n\\n CO',\n 'CT': ' CT',\n 'DC': '\\n\\n\\n DC',\n 'IA': '\\n\\n\\n IA',\n 'ID': '\\n\\n\\n\\n ID',\n 'IL': '\\n IL',\n 'KS': '\\nKS ',\n // 'MA': ' MA',\n 'ME': ' ME',\n 'MD': ' MD',\n 'MO': 'MO ',\n 'MN': 'MN ',\n 'MT': '\\n\\n\\n MT',\n // 'NC': 'NC ',\n 'ND': '\\n\\n\\n\\n ND',\n 'NH': '\\n\\n\\n\\n NH',\n 'NJ': ' NJ',\n // 'NM': 'NM ',\n 'OH': '\\nOH ',\n 'RI': '\\n\\nRI ',\n 'SD': 'SD ',\n 'UT': '\\n\\n\\n\\n UT',\n 'VT': '\\n\\n\\n\\nVT',\n 'WY': '\\n\\n\\nWY ',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1984,"value":"$aa","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":142,"value":"# Apéndice","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1744,"value":"// Para añadir datos a un objeto Geojson en una propiedad nueva\nfunction add_data_to_geojson(geojson, data, join_key, prop_name = 'data') {\n const gj = structuredClone(geojson);\n let lk, rk;\n if (Array.isArray(join_key)) {\n if (join_key.length === 2) {\n lk = join_key[0];\n rk = join_key[1];\n } else {\n throw new Error('join_key must be a 2-element array or a string')\n }\n } else if (typeof join_key === 'string') {\n lk = join_key; rk = join_key;\n } else {\n throw new Error('join_key must be a 2-element array or a string');\n }\n\n const data_map = {};\n data.forEach(d => {\n let dm = data_map[d[rk]];\n data_map[d[rk]] = dm ? [...dm, d] : [d];\n });\n\n for (let f of gj.features) {\n const p = f.properties;\n p[prop_name] = data_map[p[lk]]\n }\n\n return gj\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":330,"value":"function capitalizeOnlyFirstLetter(string) {\n return string.charAt(0).toUpperCase() + string.slice(1).toLowerCase();\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1490,"value":"planillas_column_label_to_name = {\n const column_info = await planillas_2023_metadata_workbook.sheet('column_info', {headers: true})\n const column_label_to_name = Object()\n column_info.forEach(d => {\n column_label_to_name[d.column_source_label] = d.column_name\n })\n return column_label_to_name\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1496,"value":"planillas_2023_metadata_workbook = await planillas_2023_metadata_file.xlsx()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1598,"value":"$ab","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1619,"value":"eitc_local_governments = {\n let data = await FileAttachment(\"eitc_local_governments.csv\").csv({typed: true})\n data = aq.from(data)\n .rename({\n 'State or Local Government': 'state',\n 'Percentage of Federal Credit': 'percentage_of_federal_credit',\n 'Is Credit Refundable?': 'refundable',\n })\n .objects()\n data.forEach(d => {\n // d.state = convertToTitleCase(d.state)\n d.percentage_of_federal_credit = (+d.percentage_of_federal_credit.slice(0, -(\" percent\".length)))/100\n\n if (d.refundable === 'Yes') d.refundable = true;\n if (d.refundable === 'No') d.refundable = false;\n\n \n })\n data= [{\n state: 'Puerto Rico',\n percentage_of_federal_credit: .962292256,\n refundable: true,\n }, ...data]\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1644,"value":"select state, total_eitc, claims, average_eitc, jurisdiction, amount, share\nfrom eitc_returns_with_local\nwhere scale = 'total' and jurisdiction = 'federal'\n","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:eitc_returns_with_local"},{"id":1629,"value":"$ac","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1633,"value":"$ad","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1600,"value":"state_replace = Object({\n \"California \": 'California',\n 'District Of Columbia': 'District of Columbia',\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1602,"value":"state_abbreviations = {\n let data = await state_abbreviations_file.csv({typed: true})\n data = aq.from(data)\n .rename({\n 'State': 'state',\n 'Abbreviation': 'abbreviation',\n \n })\n .objects()\n data.forEach(d => {\n if (d.state === 'District Of Columbia') d.state = 'District of Columbia'\n\n \n })\n // data= [{\n // state: 'Puerto Rico',\n // percentage_of_federal_credit: .767679,\n // refundable: true,\n // }, ...data]\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1606,"value":"function convertToTitleCase(str) {\n if (!str) {\n return \"\"\n }\n return str.toLowerCase().replace(/\\b\\w/g, s => s.toUpperCase());\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1690,"value":"poverty_table = {\n let data = await poverty_income_file.xlsx()\n data = data.sheet(0, {headers: true})\n data = aq.from(data)\n .rename({\n 'State': 'state',\n 'Below Poverty': 'percent_below_poverty',\n 'Population under Poverty': 'population_under_poverty',\n 'Total Population': 'total_population',\n 'Total Household': 'total_household',\n 'Total Families': 'total_families',\n 'Households Median Income': 'households_median_income',\n 'Families Median Income': 'families_median_income',\n })\n .objects()\n\n // const commacast = d => +(d.replaceAll(',', ''))\n // data.forEach(d => {\n // const vars_commacast = ['population_under_poverty', 'total_population', 'total_household',\n // 'total_families', 'households_median_income','families_median_income']\n // for (const v of vars_commacast) {\n // d[v] = commacast(d[v])\n // }\n \n // })\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1709,"value":"usaspending_obligations = {\n let data = await usaspending_obligations_file.csv({typed: true})\n data = aq.from(data)\n .rename({\n 'display_name': 'state',\n 'aggregated_amount': 'obligations',\n 'shape_code': 'abbreviation',\n })\n .derive({\n state: d => op.replace(d.state, 'District Of Columbia', 'District of Columbia')\n })\n .select(aq.not('population', 'per_capita')) // mejor usar las de Wilma\n .objects()\n\n return data\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1717,"value":"irs_collections = {\n let data = await irs_collections_file.csv({typed: true})\n // collections estan en thousands y las llevamos a ones\n data = aq.from(data)\n .derive({\n collections: d => d.collections*1000\n })\n .objects()\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1719,"value":"federal_outflows = aq.from(poverty_table)\n .select('state', 'total_population')\n .join_full(aq.from(usaspending_obligations))\n .join_full(aq.from(irs_collections))\n .derive({\n 'outflow': d => d.collections - d.obligations, // outflow means FROM the state TO los federales\n })\n .derive({\n 'outflow_per_capita': d => d.outflow / d.total_population,\n })\n .orderby(aq.desc('outflow_per_capita'))\n.objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"## Estilos","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":168,"value":"estilos = htl.html`\n\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2001,"value":"htl.html`\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":171,"value":"^ Estilos block","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":173,"value":"fonts_link = \"https://use.typekit.net/szl4srx.css\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":175,"value":"fonts_css_content = await fetch(fonts_link).then(r => r.text())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":183,"value":"fonts_css = htl.html`\n