2b:null 2c:["$","title",null,{"children":"Graphs about the Earned Income Tax Credit in Puerto Rico from 2019 to 2022 | José M. Zavala González | Observable"}] 31:Tde4,grafica_01 = { let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === dato_reducers[dato_input]))[0]; const fmt = dato_formatters[dato_input] let theplot = Plot.plot({ axis: false, width: 1300, marginTop: 10, color: { legend: true, interpolate: color_interp, // label: dato_labels[thevariable.dato], label: legend_label_hack[thevariable.variable] ?? thevariable.variable, // label: 'Earned income tax credit - average', tickFormat: fmt, fontVariant: 'tabular-nums', className: 'large-font', width: 400, }, aspectRatio: 1, marks: [ Plot.geo(municipios_with_data, { fill: d => ( d.properties.eitc_municipal .filter(d => d['Año contributivo'] === year_mutable)[0][thevariable.variable]), stroke: 'white', strokeWidth: 1.15, }), Plot.text(municipios_with_data.features, Plot.centroid({ text: d => d3.format(fmt)(d.properties.eitc_municipal.filter(d => d['Año contributivo'] === year_select)[0][thevariable.variable]), fill: "black", fontSize: 12, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', pointerEvents: 'none', ariaDescription: 'number-label', })), on(Plot.geo(municipios_with_data, { 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(municipios_with_data.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), 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 }32:Tf42,grafica_19 = { let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === dato_reducers[dato_input]))[0]; const fmt = dato_formatters[dato_input] let theplot = Plot.plot({ axis: false, width: 1300, marginTop: 10, // width, color: { legend: true, interpolate: color_interp, type: 'quantile', // label: dato_labels[thevariable.dato], label: legend_label_hack[thevariable.variable] ?? thevariable.variable, // label: 'Earned income tax credit - average', tickFormat: fmt, fontVariant: 'tabular-nums', width: 400, }, aspectRatio: 1, marks: [ Plot.geo(municipios_with_data, { fill: d => ( d.properties.eitc_municipal .filter(d => d['Año contributivo'] === year_mutable)[0][thevariable.variable]), stroke: 'white', strokeWidth: 1.15, }), Plot.text(municipios_with_data.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', })), on(Plot.geo(municipios_with_data, { 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(municipios_with_data.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 }33:T856,TODO: 1. DONE: Grafico 2 de Tax Exp pero cada item es un municipio para monto promedio EITC y la raya de promedio es el promedio para PR 2. DONE: Version discreta con intervalos del mapa principal, y en texto incluir nombre municipios en vez de numeros 3. DONE: Grafico de barras que acompaña mapa hacer una version estatica (solo gris) para el escrito 4. DONE: Grafica de barras que acompaña mapa hacer una versión para las otras 2 variables 5. DONE: Grafica de lineas de EITC x Estado civil y sexo donde sea X-axis el año, y-axis es la variable, y facet by la combinacion de sexo y estado civil. Realizar esta para cada una de las 3 variables. 6. DONE: Grafica lineas EITC x estado x sexo donde x-axis el año, yaxis variable, y facet por estado civil, y un stroke por cada sexo. So serian 2 facets con 3 lineas per grafica en "una gráfica". Text mark con el promedio de dependientes que aplican. 7. DONE: Cambiar graficas barras que son solo grises al indigo como en tax expenditures 8. CASI: (Hay que fix it en los graficos que faltan crear) Fix typo de "No marco o otro" por "No marco u otro" 9. DONE: Añadir lineas stroke white a histogramas 10. DONE: (Check if ok) Graficos de edad que sean cada 5 años excepto el primer numero que sea el primer valor/edad del primer bin 11. DONE: Poner tip: true en todos los histogramas 12. DONE: Grafica de lineas de EITC x Sexo x Estado Civil igual que el punto 5, pero añadirle en text mark la cantidad de dependientes promedio (total dependientes / num planillas). Repetir para todas las 3 variables. So deberian ser 6 facetas (3 sexos x 2 estados) y 3 graficas (una por variable) 13. DONE: Line chart de fuente mayor ingreso. X-axis es año, y-axis es la variable, z es la fuente de mayor ingreso. So deberian ser 3 graficas (una por variable) con 6 lineas (una por fuente) 14. Grid 2D eye de Y es municipio x-axis es año, text mark adentro es el ranking del credito promedio en tal año (1 al 78, 1 es más credito) 15. DONE: Check porque el numero de planillas suma a 598k en vez de 646k, ej. primer bar chart de numero de planillas. Note: checkear para cada año. Ya fixed.34:Tcfe,// main_map1 = { // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; // let fun = (d => d['Crédito por Trabajo - Contribuyente Individual'] / d['Planillas Contribuyente Individual - Crédito por Trabajo']); // let theplot = Plot.plot({ // axis: false, // width, // color: { // legend: true, // scheme: 'GnBu', // // label: dato_labels[thevariable.dato], // label: thevariable.variable, // // tickFormat: "$,.0f", // }, // aspectRatio: 1, // marks: [ // Plot.geo(municipios_with_data, { // fill: d => ( // fun(d.properties.eitc_municipal // .filter(d => d['Año contributivo'] === year_mutable)[0])), // stroke: 'black', // }), // Plot.text(municipios_with_data.features, Plot.centroid({ // text: d => fun(d.properties.eitc_municipal.filter(d => d['Año contributivo'] === year_select)[0]).toLocaleString(undefined, // { // // maximumFractionDigits: 0, style: 'currency',currency: "USD", // } // ), // fill: "black", stroke: "white", // fontSize: 11, // })), // on(Plot.geo(municipios_with_data, { // 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() // }, // } // ), // ] // }) // 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) // mutable muni_mutable = null; // }) // return theplot // } null35:T85f,grafica_01_bar = { let thedata = ( muni_mutable) ? eitc_municipal_2019_2022.filter(d => d.Municipios === muni_mutable) : eitc_isla_2019_2022; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginTop: 40, marginBottom: 50, x: { domain: [2019, 2020, 2021, 2022], }, y: { domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, marks: [ Plot.axisX({ tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, fill: d => d['Año contributivo'] === year_mutable ? 'var(--primary-color)' : 'var(--black-color)', stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 5, }), { click: function (event, { datum }) { console.log('bar clicked!', datum) mutable year_mutable = datum['Año contributivo'] }, }), Plot.text(thedata, { x: "Año contributivo", y: thevariable.variable, text: d => d[thevariable.variable].toLocaleString(undefined,{maximumFractionDigits: 0, style: 'currency',currency: "USD",}), dy: -9, lineAnchor: 'bottom', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', }), Plot.ruleY([0]) ] }) d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }36:T841,grafica_01_bar1 = { let thedata = ( muni_mutable) ? eitc_municipal_2019_2022.filter(d => d.Municipios === muni_mutable) : eitc_isla_2019_2022; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginTop: 40, marginBottom: 50, x: { domain: [2019, 2020, 2021, 2022], }, y: { domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, marks: [ Plot.axisX({ tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 5, }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, { x: "Año contributivo", y: thevariable.variable, text: d => d[thevariable.variable].toLocaleString(undefined,{maximumFractionDigits: 0, style: 'currency',currency: "USD",}), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'white', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }37:T84f,grafica_01_bar2 = { let thedata = ( muni_mutable) ? eitc_municipal_2019_2022.filter(d => d.Municipios === muni_mutable) : eitc_isla_2019_2022; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginTop: 40, marginBottom: 50, x: { domain: [2019, 2020, 2021, 2022], }, y: { // domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, marks: [ Plot.axisX({ tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickSize: 0, // tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 5, }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, { x: "Año contributivo", y: thevariable.variable, text: d => d[thevariable.variable],//.toLocaleString(undefined,{maximumFractionDigits: 0, style: 'currency',currency: "USD",}), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'white', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }38:T84d,grafica_01_bar3 = { let thedata = ( muni_mutable) ? eitc_municipal_2019_2022.filter(d => d.Municipios === muni_mutable) : eitc_isla_2019_2022; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginTop: 40, marginBottom: 50, x: { domain: [2019, 2020, 2021, 2022], }, y: { // domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // tickFormat: d => d.toLocaleString(undefined, {maximumFractionDigits: 0, style: 'currency',currency: "USD"}) }, marks: [ Plot.axisX({ tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickSize: 0, // tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 5, }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, { x: "Año contributivo", y: thevariable.variable, text: d => d[thevariable.variable],//.toLocaleString(undefined,{maximumFractionDigits: 0, style: 'currency',currency: "USD",}), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'white', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }39:Ta91,grafica_01_bar4 = { let thedata = [...eitc_isla_2019_2022, {'Crédito por Trabajo - Promedio': 1932, 'Año contributivo': 2023, 'Planillas Total - Crédito por Trabajo': 669000} ]; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginLeft: 60, 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, 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: 'Crédito por trabajo - promedio', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, fill: d => d['Año contributivo'] === 2023 ? 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', }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, { x: "Año contributivo", y: thevariable.variable, text: d => d[thevariable.variable].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['Año contributivo'] === 2023 ? 'black' : 'white', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }3a:Ta9b,grafica_01_bar8 = { let thedata = [...eitc_isla_2019_2022, {'Crédito por Trabajo - Promedio': 1932, 'Año contributivo': 2023, 'Planillas Total - Crédito por Trabajo': 669000} ]; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginLeft: 60, 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, domain: ['Carried out', 'Projected'], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()] }, marks: [ Plot.axisX({ tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Tax year', }), Plot.axisY({ tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Earned income tax credit - Average', }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, fill: d => d['Año contributivo'] === 2023 ? 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', }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, { x: "Año contributivo", y: thevariable.variable, text: d => d[thevariable.variable].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['Año contributivo'] === 2023 ? 'black' : 'white', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }3b:Tb3a,grafica_01_bar6 = { let thedata = [...eitc_isla_2019_2022, {'Crédito por Trabajo - Promedio': 1932, 'Año contributivo': 2023, 'Planillas Total - Crédito por Trabajo': 669e3} ]; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginTop: 40, marginBottom: 50, x: { // label: 'Tax year', label: null, domain: [2019, 2020, 2021, 2022, 2023], }, y: { // label: 'Tax year', // domain: [0, (thevariable.dato === 'monto_eitc') ? 2000 : 17e3], // 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()], tickFormat: d => d === 'Realizado' ? 'Carried out' : 'Projected', }, marks: [ Plot.axisX({ label: 'Tax year', tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: legend_label_hack[thevariable.variable] ?? thevariable.variable, tickSize: 0, tickFormat: ",.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, fill: d => d['Año contributivo'] === 2023 ? 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', }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, { x: "Año contributivo", y: thevariable.variable, text: d => d[thevariable.variable],//.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['Año contributivo'] === 2023 ? 'black' : 'white', }), Plot.ruleY([0]) ] }) // d3.select(theplot).selectAll("svg g[aria-description='outline-key']").classed('outline-key', true) add_estilos(theplot) return theplot }3c:T1152,grafica_01_bar5 = { let thedata_original = [...eitc_isla_2019_2022, {'Crédito por Trabajo - Total': 1293e6, 'Año contributivo': 2023} ]; let fed_share = { 2019: 0, 2020: 0, 2021: 0.56571353396, 2022: 0.56571353396, 2023: 0.517246539056, } let thedata = [] thedata_original.forEach(d => { thedata.push({...d, share: 'Federal', 'Crédito por Trabajo - Total': d['Crédito por Trabajo - Total']*fed_share[d['Año contributivo']], share_pct: fed_share[d['Año contributivo']] }) thedata.push({...d, share: 'Local', 'Crédito por Trabajo - Total': d['Crédito por Trabajo - Total']*(1-fed_share[d['Año contributivo']]), share_pct: 1-fed_share[d['Año contributivo']], }) }) let thevariable = variables_metadata.filter(d => (d.dato === 'monto_eitc') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, marginLeft: 95, marginTop: 40, marginBottom: 50, x: { label: 'Año contributivo', 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, domain: ['Federal', 'Local', 'Federal - proyectado', 'Local - proyectado'], 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({ // label: null, tickFormat: "", interval: 1, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: 'Crédito por trabajo - total', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, // fill: d => d['Año contributivo'] === 2023 ? chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() : EA_COLORES_MAP['INDIGO'], z: 'share', fill: d => d['Año contributivo'] !== 2023 ? (d.share === 'Federal' ? EA_COLORES_MAP['INDIGO'] : EA_COLORES_MAP['GREEN']) : (d.share === 'Federal' ? '#cdd1ec' : chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()), // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 2, // strokeDasharray: '10 3', // stroke: d => d['Año contributivo'] === 2023 ? 'black' : 'none', }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, Plot.stackY2({ x: "Año contributivo", y: thevariable.variable, z: 'share', text: d => d3.format('$,.0f')(d[thevariable.variable]) + (d['Año contributivo'] > 2020 ? `\n\n(${d3.format('.1%')(d.share_pct)})` : ''), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', // fontWeight: 'bold', fill: d => !(((d['Año contributivo'] < 2021) & (d.share === 'Federal'))) ? ((d.share === 'Local' | d['Año contributivo'] === 2023) ? 'black' : 'white') : null, })), Plot.text(thedata_original, { x: "Año contributivo", y: thevariable.variable, z: 'share', text: d => d3.format('$,.0f')(d[thevariable.variable]), 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:T117b,grafica_01_bar7 = { let thedata_original = [...eitc_isla_2019_2022, {'Crédito por Trabajo - Total': 1293e6, 'Año contributivo': 2023} ]; let fed_share = { 2019: 0, 2020: 0, 2021: 0.56571353396, 2022: 0.56571353396, 2023: 0.517246539056, } let thedata = [] thedata_original.forEach(d => { thedata.push({...d, share: 'Federal', 'Crédito por Trabajo - Total': d['Crédito por Trabajo - Total']*fed_share[d['Año contributivo']], share_pct: fed_share[d['Año contributivo']] }) thedata.push({...d, share: 'Local', 'Crédito por Trabajo - Total': d['Crédito por Trabajo - Total']*(1-fed_share[d['Año contributivo']]), share_pct: 1-fed_share[d['Año contributivo']], }) }) let thevariable = variables_metadata.filter(d => (d.dato === 'monto_eitc') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, 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', 'Local - Proyectado'], 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: 'Tax year', }), Plot.axisY({ tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Earned income tax credit - Total', }), on(Plot.barY(thedata, { x: "Año contributivo", y: thevariable.variable, // fill: d => d['Año contributivo'] === 2023 ? chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex() : EA_COLORES_MAP['INDIGO'], z: 'share', fill: d => d['Año contributivo'] !== 2023 ? (d.share === 'Federal' ? EA_COLORES_MAP['INDIGO'] : EA_COLORES_MAP['GREEN']) : (d.share === 'Federal' ? '#cdd1ec' : chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()), // stroke: d => d['Año contributivo'] === year_mutable ? 'var(--selected-color)' : 'none', ariaDescription: 'outline-key', strokeWidth: 2, // strokeDasharray: '10 3', // stroke: d => d['Año contributivo'] === 2023 ? 'black' : 'none', }), { // click: function (event, { datum }) { // console.log('bar clicked!', datum) // mutable year_mutable = datum['Año contributivo'] // }, }), Plot.text(thedata, Plot.stackY2({ x: "Año contributivo", y: thevariable.variable, z: 'share', text: d => d3.format('$,.0f')(d[thevariable.variable]) + (d['Año contributivo'] > 2020 ? `\n\n(${d3.format('.1%')(d.share_pct)})` : ''), dy: 7, lineAnchor: 'top', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', // fontWeight: 'bold', fill: d => !(((d['Año contributivo'] < 2021) & (d.share === 'Federal'))) ? ((d.share === 'Local' | d['Año contributivo'] === 2023) ? 'black' : 'white') : null, })), Plot.text(thedata_original, { x: "Año contributivo", y: thevariable.variable, z: 'share', text: d => d3.format('$,.0f')(d[thevariable.variable]), 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 }3e:T93b, { let thedata = eitc_trapezoid_paths; let thevariable = {variable: 'eitc'}; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, marginRight: 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]), }, x: { domain: [0, 50e3], }, 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, { filter: d => d.filing_status === 'single', x: "plateau_middle", y: 'max_credit', text: d => d3.format('$,.0f')(d['max_credit']), dy: -8, lineAnchor: 'bottom', textAnchor: 'middle', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fill: 'black', }), ] }) return theplot }3f:T934, { let thedata = eitc_trapezoid_paths; let thevariable = {variable: 'eitc'}; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, marginTop: 30, marginRight: 30, color: { tickFormat: d => `${d} ${d === 3 ? 'or more ' : ''}dependent${d === 1 ? '' : 's'}`, legend: true, type: 'categorical', range: ['INDIGO', 'TEAL', 'BLUE', 'RED',].map(d => EA_COLORES_MAP[d]), }, x: { domain: [0, 50e3], }, 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: 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, { 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 }40: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 } } })41:T9d2,{ 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 credito_promedio = aq.from(thedata) .rollup({ 'elpromedio': op.mean(thevariable.variable), // '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: 'Earned income tax credit - average', label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'Municipality', tickFormat: d => d === 'OTROS*' ? 'Other*' : d, }), Plot.barX(thedata, { x: thevariable.variable, y: "Municipios", fill: d => d[thevariable.variable] >= 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: thevariable.variable, y: "Municipios", fill: d => d[thevariable.variable] >= credito_promedio ? 'white' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), // Plot.ruleX([0]), Plot.text([0], { x: credito_promedio, dx: 5, y: d => 'Hormigueros', text: d => `Average\nPuerto\nRico\n${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 }42:Tce4,{ let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let thedata = eitc_municipal_2019_2022.filter(d => d['Año contributivo'] === year_select) let planillas_promedio = aq.from(thedata) .rollup({ 'Crédito por Trabajo - Total': op.sum('Crédito por Trabajo - Total'), 'Planillas Total - Crédito por Trabajo': op.sum('Planillas Total - Crédito por Trabajo'), 'Planillas Promedio - Crédito por Trabajo': op.mean('Planillas Total - Crédito por Trabajo') }) .derive({ 'Crédito por Trabajo - Promedio': d => d['Crédito por Trabajo - Total'] / d['Planillas Total - Crédito por Trabajo'] }) .objects()[0]['Planillas Promedio - Crédito por Trabajo'] 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: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'Municipality', tickFormat: d => d === 'OTROS*' ? 'Other*' : d, }), Plot.barX(thedata, { x: thevariable.variable, y: "Municipios", fill: d => d[thevariable.variable] >= planillas_promedio ? EA_COLORES_MAP['INDIGO'] : chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex(), // fill: EA_COLORES_MAP['INDIGO'], // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), Plot.ruleX([planillas_promedio]), Plot.text(thedata, { filter: d => d[thevariable.variable] > 5500, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: "Municipios", // fill: d => d[thevariable.variable] >= credito_promedio ? 'white' : 'black', fill: d => d[thevariable.variable] >= planillas_promedio ? '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] < 5500, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: "Municipios", // fill: d => d[thevariable.variable] >= credito_promedio ? 'white' : 'black', fill: 'black', text: d => d3.format(',.0f')(d[thevariable.variable]), dx: 5, textAnchor: 'start', sort: {y: "x", reverse: true}}), // Plot.ruleX([0]), Plot.text([0], { x: planillas_promedio, dx: 10, y: d => 'Maunabo', text: d => `Average\nPuerto\nRico\n${d3.format(',.0f')(planillas_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }43:Tba7,{ 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 ? 'Municipality specified' : 'Municipality not specified', }, marks: [ // Plot.axisX({ // hide: false, // tickSize: 0, // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisY({ label: 'Families', 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: d => 1, y: thevariable.variable, fill: 'is_defined', }), Plot.text(thedata, Plot.stackY({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: d => 1, y: thevariable.variable, z: 'is_defined', // channels: {z: {reverse: true}}, text: d => d3.format(',.0f')(d[thevariable.variable]) +' '+ `(${d3.format('.1%')(d[thevariable.variable]/total)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d.is_defined ? 'white' : 'black', // stroke: 'black', // textAnchor: 'end', dx: -5, })), // Plot.text(thedata, Plot.selectLast({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), // x: 'Año contributivo', // y: thevariable.variable, // z: 'Anejo CT: Categoría Dependientes Cualificados', // text: d => (d['Anejo CT: Categoría Dependientes Cualificados']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // stroke: 'white', fill: 'black', // textAnchor: 'end', dx: -25, // })), ] }) return theplot }44:T8d4,{ let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thedata_initial = eitc_municipal_2019_2022 .filter(d => d.Municipios !== 'OTROS*') // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => -d[thevariable.variable]).slice(0,5) let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: thedata.map(d => d['Municipios']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, label: 'Municipality', }), Plot.axisY({ label: 'Earned income tax credit - average', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.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: 'Municipios', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Municipios', y: thevariable.variable, text: d => d3.format('$,.0f')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, }), // Plot.text(thedata, Plot.selectLast({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), // x: 'Año contributivo', // y: thevariable.variable, // z: 'Anejo CT: Categoría Dependientes Cualificados', // text: d => (d['Anejo CT: Categoría Dependientes Cualificados']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // stroke: 'white', fill: 'black', // textAnchor: 'end', dx: -25, // })), ] }) return theplot }45:T8d3,{ let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thedata_initial = eitc_municipal_2019_2022 .filter(d => d.Municipios !== 'OTROS*') // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => d[thevariable.variable]).slice(0,5) let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: thedata.map(d => d['Municipios']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, label: 'Municipality', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', label: 'Earned income tax credit - average', tickSize: 0, tickFormat: "$,.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: 'Municipios', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Municipios', 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.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 }46:T883,{ let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let thedata_initial = eitc_municipal_2019_2022 .filter(d => d.Municipios !== 'OTROS*') // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => -d[thevariable.variable]).slice(0,5) let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: thedata.map(d => d['Municipios']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ 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: 'Municipios', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Municipios', 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.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 }47:T882,{ let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let thedata_initial = eitc_municipal_2019_2022 .filter(d => d.Municipios !== 'OTROS*') // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => d[thevariable.variable]).slice(0,5) let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: thedata.map(d => d['Municipios']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ 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: 'Municipios', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Municipios', 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.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 }48:T7fa,grafica_02 = { let thedata = eitc_t6_estado_sexo_2019_2022 .filter(d => d['Año contributivo'] === year_select_2) .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === dato_reducers[dato_input]))[0]; const sex_labels = { 'Femenino': 'Female', 'Masculino': 'Male', 'No marcó o otro': 'Did not mark or other', } let theplot = Plot.plot({ // width: 800, height: 160, marginLeft: 150, // marginRight: 150, marginBottom: 45, x: {}, // fy: {reverse: true}, // y: {tickFormat: "$,.0f"}, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.axisX({ grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => sex_labels[d] ?? d, label: 'Sex', }), Plot.barX(thedata, { x: thevariable.variable, y: 'Sexo Contribuyente', // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], // fillOpacity: 0.5, }), Plot.text(thedata, { x: thevariable.variable, y: 'Sexo Contribuyente', // fy: 'Estado Civil', text: d => d3.format('$,.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 }49:T9ab,grafica_ = { let thedata = eitc_t6_estado_sexo_2019_2022 .filter(d => d['Año contributivo'] === year_select_2) .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, height: 160, marginLeft: 150, // marginRight: 150, marginBottom: 45, x: {}, // fy: {reverse: true}, // y: {tickFormat: "$,.0f"}, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.axisX({ grid: true, axis: "bottom", tickFormat: ",.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ label: 'Sex', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => sex_labels[d] ?? d, }), Plot.barX(thedata, { x: thevariable.variable, y: 'Sexo Contribuyente', // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], // fillOpacity: 0.5, }), Plot.text(thedata, { filter: d => d[thevariable.variable] > 10000, x: thevariable.variable, y: 'Sexo Contribuyente', // fy: 'Estado Civil', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: 'white', fontSize: 14.5, textAnchor: 'end', dx: -5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 10000, x: thevariable.variable, y: 'Sexo Contribuyente', // fy: 'Estado Civil', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: 'black', fontSize: 14.5, textAnchor: 'start', dx: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) return theplot }4a:T95c,grafica_1 = { let thedata = eitc_t6_estado_sexo_2019_2022 .filter(d => d['Año contributivo'] === year_select_2) .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, height: 160, marginLeft: 150, // marginRight: 150, marginBottom: 45, x: {}, // fy: {reverse: true}, // y: {tickFormat: "$,.0f"}, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.axisX({ grid: true, axis: "bottom", tickFormat: ",.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }), Plot.barX(thedata, { x: thevariable.variable, y: 'Sexo Contribuyente', // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], // fillOpacity: 0.5, }), Plot.text(thedata, { filter: d => d[thevariable.variable] > 10000, 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', }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 10000, 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: 'black', fontSize: 14.5, textAnchor: 'start', dx: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) return theplot }4b:Td7e,grafica_20 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; 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: 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: "$,.0f"}, color: { legend: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', tickPadding: 20, }), Plot.axisFx({ fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], //strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { // filter: d => d['Año contributivo'] > 2020, x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, dy: -15, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }4c:T102e,grafica_26 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 60, marginBottom: 45, // x: {}, // fx: { // paddingInner: .2, // reverse: true, // }, fy: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }, // y: {tickFormat: "$,.0f"}, color: { tickFormat: capitalizeOnlyFirstLetter, 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 contribuyente', fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, tickPadding: 25, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, stroke: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fill: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Casado', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), dx: -9, dy: -10, fontSize: 12, fontWeight: 'bold', }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Contribuyente Individual', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), dy: 10, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }4d:T104f,grafica_44 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; 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: 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: "$,.0f"}, color: { legend: true, range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), reverse: true, tickFormat: d => marital_english[d] ?? d, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, label: 'Tax year', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', label: 'Earned income tax credit - Average', }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (sex_english[d] ?? d).replace('Did not mark or other','Did not mark\nor other'), tickPadding: 25, label: 'Sex of filer' }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, stroke: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fill: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Casado', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), dx: -9, dy: -10, fontSize: 12, fontWeight: 'bold', }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Contribuyente Individual', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), dy: 10, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }4e:T104e,grafica_34 = { let thedata = eitc_t6_estado_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; 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: 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: "$,.0f"}, color: { legend: true, range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), tickFormat: d => capitalizeOnlyFirstLetter(d), reverse: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.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: '$,.0f', }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), // Plot.axisFy({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // tickPadding: 25, // }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, stroke: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fill: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Casado', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), textAnchor: 'start', // dx: 9, dy: -10, fontSize: 12, fontWeight: 'bold', }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Contribuyente Individual', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), dy: 12, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }4f:T1064,grafica_42 = { let thedata = eitc_t6_estado_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; 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: 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: "$,.0f"}, color: { legend: true, tickFormat: d => d === 'Casado' ? 'Married' : 'Individual', range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), reverse: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, label: 'Tax year', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', label: 'Earned income tax credit - Average', }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), // Plot.axisFy({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // tickPadding: 25, // }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, stroke: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fill: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Casado', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), textAnchor: 'start', // dx: 9, dy: -10, fontSize: 12, fontWeight: 'bold', }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Contribuyente Individual', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$.0f')(d[thevariable.variable]), dy: 12, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }50:Tff3,grafica_35 = { let thedata = eitc_t6_estado_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 60, 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: "$,.0f"}, color: { legend: true, range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), reverse: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, }), Plot.axisY({ 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: 'Año contributivo', y: thevariable.variable, stroke: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fill: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Casado', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), textAnchor: 'start', // dx: 9, dy: -10, fontSize: 12, fontWeight: 'bold', }), Plot.text(thedata, { filter: d => d['Estado Civil'] === 'Contribuyente Individual', x: 'Año contributivo', y: thevariable.variable, z: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: 12, fontSize: 12, fontWeight: 'bold', textAnchor: 'start', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }51:Tdd8,grafica_21 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 60, marginRight: 120, marginTop: 60, marginBottom: 45, // x: {}, y: { domain: [0, 350e3], }, fx: { paddingInner: .2, reverse: true, }, fy: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }, // y: {tickFormat: "$,.0f"}, color: { legend: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: ',.0f', domain: [0, 420e3], }), 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, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], // strokeWidth: 3, // fill: null, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { // filter: d => d['Año contributivo'] > 2020, x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dx: -18, dy: -10, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }52:Td82,grafica_22 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 60, marginRight: 120, marginTop: 60, marginBottom: 45, // x: {}, y: { domain: [0, 520e3], }, fx: { paddingInner: .2, reverse: true, }, fy: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }, // y: {tickFormat: "$,.0f"}, color: { legend: true, }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', }), Plot.axisY({ 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, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, stroke: EA_COLORES_MAP['INDIGO'], //strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], //strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { // filter: d => d['Año contributivo'] > 2020, x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: -10, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }53:T15ed,grafica_23 = { let thedata = eitc_t6_sexo_2019_2022 // .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; 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: 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) }, 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: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], stroke: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], fill: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['Sexo Contribuyente'] === 'No marcó o otro', x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d[thevariable.variable]), dy: 15, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), Plot.text(thedata, { filter: d => (d['Sexo Contribuyente'] === 'Femenino'), x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d[thevariable.variable]), dy: -15, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), // Plot.text(thedata, { // filter: d => (d['Sexo Contribuyente'] === 'Femenino') & (d['Estado Civil'] === 'Casado'), // x: 'Año contributivo', // y: thevariable.variable, // //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('$,.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('$,.0f')(d[thevariable.variable]), // dy: -10, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), Plot.text(thedata, { filter: d => (d['Sexo Contribuyente'] === 'Masculino') & (d['Año contributivo'] > 2019), x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d[thevariable.variable]), dy: 12, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }54:T1687,grafica_43 = { let thedata = eitc_t6_sexo_2019_2022 // .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let sex_english = { 'Femenino': 'Female', 'Masculino': 'Male', 'No marcó u otro': 'Did not mark or other', 'No marcó o otro': 'Did not mark or other', } let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 70, marginRight: 30, marginTop: 60, marginBottom: 45, // x: {}, fx: { paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {min: 0}, color: { tickFormat: d => sex_english[d] ?? d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.ruleY([0], {stroke: null}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', interval: 1, label: 'Tax year', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', tickPadding: 30, label: 'Earned income tax credit - Average', }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), // Plot.axisFy({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], stroke: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], fill: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => d['Sexo Contribuyente'] === 'No marcó o otro', x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d[thevariable.variable]), dy: 15, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), Plot.text(thedata, { filter: d => (d['Sexo Contribuyente'] === 'Femenino'), x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d[thevariable.variable]), dy: -15, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), // Plot.text(thedata, { // filter: d => (d['Sexo Contribuyente'] === 'Femenino') & (d['Estado Civil'] === 'Casado'), // x: 'Año contributivo', // y: thevariable.variable, // //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('$,.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('$,.0f')(d[thevariable.variable]), // dy: -10, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), Plot.text(thedata, { filter: d => (d['Sexo Contribuyente'] === 'Masculino') & (d['Año contributivo'] > 2019), x: 'Año contributivo', y: thevariable.variable, //fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format('$,.0f')(d[thevariable.variable]), dy: 12, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }55:Tbd2,grafica_27 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ width: 1000, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 60, marginBottom: 70, // x: {}, fy: { // paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {min: 0}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.ruleY([0], {stroke: null}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.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: '$,.0f', }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d.replace(' ', '\n') }), Plot.axisFx({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '' }), Plot.barY(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', fill: 'Sexo Contribuyente', tip: true, }), // Plot.text(thedata, { // filter: d => d['Año contributivo'] > 2020, // x: 'Año contributivo', // y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('.2f')(d['Promedio - Anejo CT Cantidad de dependientes cualificados']), // dy: -10, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), Plot.text(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', text: d => d3.format('$,.0f')(d[thevariable.variable]), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }56:Tcfd,grafica_47 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; 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, tickFormat: d => sex_english[d] ?? 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({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, tickFormat: d => sex_english[d] ?? d, // tickFormat: '', // interval: 1, rotate: 15, textAnchor: 'start', label: 'Sex', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => (d === 'Casado' ? 'Married' : 'Individual').replace(' ', '\n'), label: 'Personal Status', }), Plot.axisFx({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '', label: 'Year', }), Plot.barY(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', fill: 'Sexo Contribuyente', 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 Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', text: d => d3.format('$,.0f')(d[thevariable.variable]), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }57:Tc30,grafica_28 = { let thedata = eitc_t6_estado_sexo_2019_2022 .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'Promedio - Anejo CT Cantidad de dependientes cualificados'} let theplot = Plot.plot({ width: 800, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 60, marginBottom: 45, // x: {}, fy: { // paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {min: 0}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.ruleY([0], {stroke: null}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.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, }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: ',.2f', label: 'Promedio - Dependientes Cualificados' }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d.replace(' ', '\n') }), Plot.axisFx({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '' }), Plot.barY(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', fill: 'Sexo Contribuyente', 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 Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', text: d => d3.format(',.2f')(d[thevariable.variable]), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }58:Tca1,grafica_29 = { let thedata = eitc_t6_estado_sexo_2019_2022 .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; // 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({ // 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: 'Promedio - Ingreso bruto ganado\npara la determinación del Crédito por Trabajo' }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d.replace(' ', '\n') }), Plot.axisFx({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '' }), Plot.barY(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', fill: 'Sexo Contribuyente', 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 Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', text: d => d3.format('$,.0f')(d[thevariable.variable]), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }59:Tdfa,grafica_48 = { let thedata = eitc_t6_estado_sexo_2019_2022 .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'Promedio - Total Ingreso bruto ganado para la determinación del EITC'} let theplot = Plot.plot({ width: 800, // height: 160, marginLeft: 50, marginRight: 120, marginTop: 60, marginBottom: 45, // x: {}, fy: { // paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {min: 0}, color: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, tickFormat: d => sex_english[d] ?? d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.ruleY([0], {stroke: null}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ label: 'Sex', // grid: true, axis: "bottom", tickFormat: "$,.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: d => sex_english[d] ?? d, // tickFormat: '', // interval: 1, // rotate: 15, // textAnchor: 'start', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', // label: 'Promedio - Ingreso bruto ganado\npara la determinación del Crédito por Trabajo' label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, // tickFormat: d => d.replace(' ', '\n'), tickFormat: d => d === 'Casado' ? 'Married' : 'Individual', label: 'Personal Status', }), Plot.axisFx({ label: '\n\n\nYear', fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '' }), Plot.barY(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', fill: 'Sexo Contribuyente', tip: true, }), // Plot.text(thedata, { // filter: d => d['Año contributivo'] > 2020, // x: 'Año contributivo', // y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // text: d => d3.format('.2f')(d['Promedio - Anejo CT Cantidad de dependientes cualificados']), // dy: -10, // fontSize: 12, // fontWeight: 'bold', // z: 'Sexo Contribuyente', // strokeWidth: 3, // }), Plot.text(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, fy: 'Estado Civil', text: d => d3.format('$,.0f')(d[thevariable.variable]), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }5a:Tcc3,grafica_36 = { let thedata = eitc_t6_sexo_2019_2022 .filter(d => d['Año contributivo'] > 2020) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; // 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({ // 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: 'Promedio - Ingreso bruto ganado\npara la determinación del Crédito por Trabajo' }), // Plot.axisFy({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // tickFormat: d => d.replace(' ', '\n') // }), Plot.axisFx({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: '' }), Plot.barY(thedata, { x: 'Sexo Contribuyente', fx: 'Año contributivo', y: thevariable.variable, // fy: 'Estado Civil', fill: 'Sexo Contribuyente', 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 Contribuyente', fx: 'Año contributivo', y: thevariable.variable, // fy: 'Estado Civil', text: d => d3.format('$,.0f')(d[thevariable.variable]), fill: 'white', fontSize: 14.5, lineAnchor: 'top', dy: 5, fontWeight: 'bold', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', }), ] }) add_estilos(theplot) return theplot }5b:T10bb,grafica_24 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 80, // marginRight: 120, marginTop: 60, marginBottom: 50, // x: {}, y: { domain: [0, 330e3], }, fx: { paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {tickFormat: "$,.0f"}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ Plot.ruleY([0], {stroke: null}), // 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: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: ',.0f', tickPadding: 30, }), Plot.axisFx({ fontFamily: 'jaf-bernino-sans', fontSize: 12, }), // Plot.axisFy({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], stroke: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], fill: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => !((d['Sexo Contribuyente'] === 'No marcó o otro') & (d['Estado Civil'] === 'Casado')), x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: -10, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), Plot.text(thedata, { filter: d => ((d['Sexo Contribuyente'] === 'No marcó o otro') & (d['Estado Civil'] === 'Casado')), x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: 10, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }5c:T12be,grafica_37 = { let thedata = eitc_t6_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 80, marginRight: 70, marginTop: 60, marginBottom: 50, // x: {}, y: { domain: [0, 330e3], }, fx: { paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {tickFormat: "$,.0f"}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', // tickPadding: 20, interval: 1, }), Plot.axisY({ 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: 'Año contributivo', y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], stroke: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], fill: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => ((d['Sexo Contribuyente'] === 'Femenino') ), x: 'Año contributivo', y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: -13, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', fill: 'Sexo Contribuyente', strokeWidth: 3, }), Plot.text(thedata, { filter: d => ((d['Sexo Contribuyente'] === 'Masculino') ), x: 'Año contributivo', y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: 13, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', fill: 'Sexo Contribuyente', strokeWidth: 3, textAnchor: 'start', }), Plot.text(thedata, { filter: d => ((d['Sexo Contribuyente'] === 'No marcó o otro') ), x: 'Año contributivo', y: thevariable.variable, // fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: -10, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, // fill: 'Sexo Contribuyente', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }5d:T10b5,grafica_25 = { let thedata = eitc_t6_estado_sexo_2019_2022 // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 80, marginRight: 30, marginTop: 60, marginBottom: 50, // x: {}, y: { domain: [0, 500e3], }, fx: { paddingInner: .2, reverse: true, }, // fy: { // tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, // }, // y: {tickFormat: "$,.0f"}, color: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, legend: true, range: [0,.5, .85].map(color_interp) }, marks: [ Plot.ruleY([0], {stroke: null}), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.frame({ fill: chroma(EA_COLORES_MAP['DARK GREY']).alpha(0.15).hex(), }), Plot.axisX({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, }), Plot.axisY({ 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: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], stroke: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], fill: 'Sexo Contribuyente', // strokeWidth: 3, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { filter: d => !((d['Sexo Contribuyente'] === 'No marcó o otro') & (d['Estado Civil'] === 'Casado')), x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: -10, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), Plot.text(thedata, { filter: d => ((d['Sexo Contribuyente'] === 'No marcó o otro') & (d['Estado Civil'] === 'Casado')), x: 'Año contributivo', y: thevariable.variable, fx: 'Estado Civil', //fy: 'Sexo Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), dy: 10, fontSize: 12, fontWeight: 'bold', z: 'Sexo Contribuyente', strokeWidth: 3, }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }5e:T654,grafica_03 = { let thedata = eitc_t2_edad_2019_2022 .filter(d => d['Año contributivo'] === year_select_3) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad contribuyente`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ label: 'Crédito por trabajo - promedio', tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', y: thevariable.variable, // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, channels: {'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo' }, tip: true, }), Plot.arrow(thedata, { filter: d => isNaN(d['limite_superior']), x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 90 : d['limite_superior'], y: thevariable.variable, }), // Plot.tip(), ] }) add_estilos(theplot) return theplot }5f:T647,grafica_45 = { let thedata = eitc_t2_edad_2019_2022 .filter(d => d['Año contributivo'] === year_select_3) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Age`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: 'Earned income tax credit - Average' }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', y: thevariable.variable, // 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(thedata, { filter: d => isNaN(d['limite_superior']), x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 90 : d['limite_superior'], y: thevariable.variable, }), // Plot.tip(), ] }) add_estilos(theplot) return theplot }60:T61a,grafica_2 = { let thedata = eitc_t2_edad_2019_2022 .filter(d => d['Año contributivo'] === year_select_3) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Age`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', y: thevariable.variable, // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: {'Planillas': thevariable.variable}, }), Plot.arrow(thedata, { filter: d => isNaN(d['limite_superior']), x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 90 : d['limite_superior'], y: thevariable.variable, }), ] }) add_estilos(theplot) return theplot }61:T5a0,grafica_3 = { let thedata = eitc_t2_edad_2019_2022 .filter(d => d['Año contributivo'] === year_select_3) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ marginTop: 35, marginLeft: 60, marginBottom: 45, height: 450, x: {domain: [18,90]}, marks: [ Plot.axisX({ label: `Edad contribuyente`, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, ticks: 11, interval: 5, }), Plot.axisY({ 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(thedata, { x1: "limite_inferior", x2: 'limite_superior', y: thevariable.variable, // stroke: 'white', strokeWidth: 0.3, fill: 'black', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, }), Plot.arrow(thedata, { filter: d => isNaN(d['limite_superior']), x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 90 : d['limite_superior'], y: thevariable.variable, }), ] }) add_estilos(theplot) return theplot }62:T8a1,grafica_4 = { let thedata = eitc_t7_estado_dependientes_2021_2022 .filter(d => d['Año contributivo'] === year_select_4) // .filter(d => d['Estado Civil'] === estado_civil_mutable) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: num_dependientes_options, }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => dependents_labels[d] ?? d, }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, text: d => d3.format('$,.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.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 }63:Tb65,grafica_8 = { let thedata = eitc_t7_estado_dependientes_2021_2022 .filter(d => d['Año contributivo'] === year_select_4) .filter(d => d['Estado Civil'] === estado_civil_mutable) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let max_value = d3.max(thedata, d => d[thevariable.variable]) const fits_inside_bar = d => d[thevariable.variable] > .1*max_value // (d[thevariable.variable] > 20e3) let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: num_dependientes_options, }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => dependents_labels[d] ?? d, }), Plot.axisY({ 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: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: (d => (d['Estado Civil'] === estado_civil_mutable) & fits_inside_bar(d)), x: 'Anejo CT: Categoría Dependientes Cualificados', 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.text(thedata, { filter: (d => (d['Estado Civil'] === estado_civil_mutable) & !fits_inside_bar(d)), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: '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 }64:T86f,grafica_9 = { let thedata = eitc_t7_estado_dependientes_2021_2022 .filter(d => d['Año contributivo'] === year_select_4) // .filter(d => d['Estado Civil'] === estado_civil_mutable) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: num_dependientes_options, }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ 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: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: (d => (d['Estado Civil'] === estado_civil_mutable)), x: 'Anejo CT: Categoría Dependientes Cualificados', 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.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 }65:T434,eitc_t7_dependientes_2019_2022 = aq.from(eitc_t7_estado_dependientes_2021_2022) .groupby('Anejo CT: Categoría Dependientes Cualificados', 'Año contributivo') .rollup({ 'Crédito por Trabajo - Total': op.sum('Crédito por Trabajo - Total'), 'Planillas Total - Crédito por Trabajo': op.sum('Planillas Total - Crédito por Trabajo'), 'Total - Total Ingreso bruto ganado para la determinación del EITC': op.sum('Total - Total Ingreso bruto ganado para la determinación del EITC'), 'Planillas Total - Total Ingreso bruto ganado para la determinación del EITC': op.sum('Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'), }) .derive({ 'Crédito por Trabajo - Promedio': d => d['Crédito por Trabajo - Total'] / d['Planillas Total - Crédito por Trabajo'], 'Promedio - Total Ingreso bruto ganado para la determinación del EITC': d => d['Total - Total Ingreso bruto ganado para la determinación del EITC'] / d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'] }) // .view() .objects()66:T8df,grafica_38 = { let thedata = eitc_t7_dependientes_2019_2022 .filter(d => d['Año contributivo'] === year_select_4) // .filter(d => d['Estado Civil'] === estado_civil_mutable) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: num_dependientes_options, }, marks: [ Plot.axisX({ label: 'Anejo CT: Categoría dependientes cualificados', 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: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, text: d => d3.format('$,.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.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 }67:T8e5,grafica_46 = { let thedata = eitc_t7_dependientes_2019_2022 .filter(d => d['Año contributivo'] === year_select_4) // .filter(d => d['Estado Civil'] === estado_civil_mutable) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: num_dependientes_options, }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => dependientes_english[d] ?? d, label: null, }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, label: 'Earned income tax credit - Average', }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, text: d => d3.format('$,.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.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 }68:Tad0,grafica_39 = { let thedata = eitc_t7_dependientes_2019_2022 .filter(d => d['Año contributivo'] === year_select_4) // .filter(d => d['Estado Civil'] === estado_civil_mutable) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let max_value = d3.max(thedata, d => d[thevariable.variable]) const fits_inside_bar = d => d[thevariable.variable] > .1*max_value // (d[thevariable.variable] > 20e3) let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: num_dependientes_options, }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ 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: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: (d => fits_inside_bar(d)), x: 'Anejo CT: Categoría Dependientes Cualificados', 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.text(thedata, { filter: (d => !fits_inside_bar(d)), x: 'Anejo CT: Categoría Dependientes Cualificados', y: thevariable.variable, text: d => d3.format(',.0f')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, fill: '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 }69:T6e8,{ let thedata = eitc_t7_estado_dependientes_2021_2022 // .filter(d => d['Año contributivo'] === year_mutable) // .filter(d => d['Estado Civil'] === estado_civil_mutable) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ // width: 800, marginLeft: 50, x: { tickFormat: "", interval: 1, domain: [2019, 2020, 2021, 2022], }, y: {tickFormat: "$,.0f", grid: true}, marks: [ // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.line(thedata, { filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Año contributivo', y: thevariable.variable, z: 'Anejo CT: Categoría Dependientes Cualificados', }), Plot.text(thedata, { filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Año contributivo', y: thevariable.variable, text: d => d3.format('$,.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, stroke: 'white', fill: '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 }6a:T6f6,grafica_5 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ width: 800, height: 230, marginLeft: 220, marginBottom: 50, y: { domain: d3.sort(thedata, d => d['Fuente de Mayor Ingreso'] == 'Otro', d => -d[thevariable.variable]).map(d => d['Fuente de Mayor Ingreso']), }, marks: [ Plot.axisX({ tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ tickSize: 0, tickFormat: d => fuente_ingreso_newline_labels[d].replaceAll('\n',' ') ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, label: 'Source of income', }), Plot.barX(thedata, { x: thevariable.variable, y: 'Fuente de Mayor Ingreso', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { x: thevariable.variable, y: 'Fuente de Mayor Ingreso', text: d => d3.format('$,.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'end', dx: -5, }), ] }) return theplot }6b:T92c,grafica_10 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, height: 230, marginLeft: 220, marginBottom: 50, y: { domain: d3.sort(thedata, d => d['Fuente de Mayor Ingreso'] == 'Otro', d => -d[thevariable.variable]).map(d => d['Fuente de Mayor Ingreso']), }, marks: [ Plot.axisX({ tickSize: 0, tickFormat: ",.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ tickSize: 0, tickFormat: d => fuente_ingreso_newline_labels[d].replaceAll('\n',' ') ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, label: 'Source of income', }), Plot.barX(thedata, { x: thevariable.variable, y: 'Fuente de Mayor Ingreso', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d[thevariable.variable] > 50e3, x: thevariable.variable, y: 'Fuente de Mayor Ingreso', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 50e3, x: thevariable.variable, y: 'Fuente de Mayor Ingreso', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'black', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'start', dx: 5, }), ] }) return theplot }6c:T891,grafica_11 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, height: 230, marginLeft: 220, marginBottom: 50, y: { domain: d3.sort(thedata, d => d['Fuente de Mayor Ingreso'] == 'Otro', d => -d[thevariable.variable]).map(d => d['Fuente de Mayor Ingreso']), }, marks: [ Plot.axisX({ tickSize: 0, tickFormat: ",.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickSize: 0, tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.barX(thedata, { x: thevariable.variable, y: 'Fuente de Mayor Ingreso', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d[thevariable.variable] > 50e3, x: thevariable.variable, y: 'Fuente de Mayor Ingreso', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'end', dx: -5, }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 50e3, x: thevariable.variable, y: 'Fuente de Mayor Ingreso', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'black', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'start', dx: 5, }), ] }) return theplot }6d:T717,grafica_40 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 50, marginBottom: 60, x: { domain: d3.sort(thedata, d => d['Fuente de Mayor Ingreso'] == 'Otro', d => -d[thevariable.variable]).map(d => d['Fuente de Mayor Ingreso']), }, marks: [ Plot.axisY({ label: 'Earned income tax credit - average', tickSize: 0, tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisX({ label: 'Largest source of income', tickSize: 0, tickFormat: d => capitalizeOnlyFirstLetter(fuente_ingreso_newline_labels[d] ?? d), fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.barY(thedata, { y: thevariable.variable, x: 'Fuente de Mayor Ingreso', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { y: thevariable.variable, x: 'Fuente de Mayor Ingreso', text: d => d3.format('$,.0f')(d[thevariable.variable]), // 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 }6e:Tb47,grafica_41 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let total = aq.from(thedata).rollup({'total': op.sum(thevariable.variable)}).objects()[0]['total'] let theplot = Plot.plot({ width: 800, height: 130, marginLeft: 30, marginBottom: 50, color: { legend: true, domain: d3.sort(thedata, d => -d[thevariable.variable]).map(d => d['Fuente de Mayor Ingreso']), range: ['INDIGO', 'PINK', 'RED', 'PURPLE', 'GREEN', 'BLUE'].map(d => EA_COLORES_MAP[d]), }, y: { axis: false, // domain: d3.sort(thedata, // d => d['Fuente de Mayor Ingreso'] == 'Otro', // d => -d[thevariable.variable]).map(d => d['Fuente de Mayor Ingreso']), }, marks: [ Plot.axisX({ tickSize: 0, tickFormat: ",.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), // Plot.axisY({ // tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.barX(thedata, { x: thevariable.variable, y: d => 1, order: '-sum', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'Fuente de Mayor Ingreso', tip: true, channels: {'Planillas': thevariable.variable, 'Porciento': d => d3.format('.1%')(d[thevariable.variable]/total)}, }), Plot.text(thedata, Plot.stackX({ filter: d => d[thevariable.variable] > 50e3, x: thevariable.variable, y: d => 1, z: 'Fuente de Mayor Ingreso', text: d => d3.format(',.0f')(d[thevariable.variable]) +'\n'+ `(${d3.format('.1%')(d[thevariable.variable]/total)})`, // stroke: 'white', strokeWidth: 0.3, fill: 'black', order: '-sum', sort: {'y': '-x'}, fill: 'white', fontWeight: 'bold', fontSize: 14.5, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', // textAnchor: 'end', dx: -5, })), // Plot.text(thedata, { // filter: d => d[thevariable.variable] < 50e3, // x: thevariable.variable, // y: 'Fuente de Mayor Ingreso', // text: d => d3.format(',.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'black', // fontWeight: 'bold', // fontSize: 14.5, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // textAnchor: 'start', dx: 5, // }), ] }) return theplot }6f:T6d0,grafica_16 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Fuente de Mayor Ingreso'] !== null) // .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 50, marginBottom: 50, color: { legend: true, }, marks: [ Plot.axisX({ tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, interval: 1, }), Plot.axisY({ tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, z: 'Fuente de Mayor Ingreso', stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, tip: true, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Fuente de Mayor Ingreso', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', // fontWeight: 'bold', // fontSize: 14.5, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // textAnchor: 'end', dx: -5, // }), ] }) return theplot }70:Tf1c,grafica_32 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Fuente de Mayor Ingreso'] !== null) // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 50, marginRight: 150, marginTop: 60, marginBottom: 45, // x: {}, // fx: { // paddingInner: .2, // reverse: true, // }, fy: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }, // y: {tickFormat: "$,.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({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, label: 'Year', }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisFy({ label: 'Largest Source of Income', fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => fuente_ingreso_newline_labels[d] ?? d, tickPadding: 25, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, stroke: EA_COLORES_MAP['INDIGO'], fy: 'Fuente de Mayor Ingreso', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], fy: 'Fuente de Mayor Ingreso', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { // filter: d => d['Estado Civil'] === 'Casado', x: 'Año contributivo', y: thevariable.variable, fy: 'Fuente de Mayor Ingreso', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: -9, dy: 12, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // fill: 'white', // fontSize: 14.5, // textAnchor: 'end', dx: -5, // fontWeight: 'bold', // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // }), ] }) add_estilos(theplot) return theplot }71:Tea8,grafica_33 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Fuente de Mayor Ingreso'] !== null) // .filter(d => d['Año contributivo'] === year_select_2) // .filter(d => d['Estado Civil'] == estado_civil_select_2) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ // width: 800, // height: 160, marginLeft: 90, marginRight: 150, marginTop: 60, marginBottom: 45, // x: {}, // fx: { // paddingInner: .2, // reverse: true, // }, fy: { tickFormat: d => (d === 'No marcó o otro') ? 'No marcó u otro' : d, }, // y: {tickFormat: "$,.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({ // grid: true, axis: "bottom", tickFormat: "$,.0f", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '', tickPadding: 20, interval: 1, }), Plot.axisY({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, tickFormat: '$,.0f', tickPadding: 30, }), // Plot.axisFx({ // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisFy({ fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => fuente_ingreso_newline_labels[d] ?? d, tickPadding: 25, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, stroke: EA_COLORES_MAP['INDIGO'], fy: 'Fuente de Mayor Ingreso', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.dot(thedata, { x: 'Año contributivo', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], fy: 'Fuente de Mayor Ingreso', // stroke: d => `${d['Estado Civil']} - ${d['Sexo Contribuyente']}`, // fy: 'Estado Civil', // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // stroke: EA_COLORES_MAP['INDIGO'], // strokeWidth: 1, // fillOpacity: 0.5, tip: true, }), Plot.text(thedata, { // filter: d => d['Estado Civil'] === 'Casado', x: 'Año contributivo', y: thevariable.variable, fy: 'Fuente de Mayor Ingreso', text: d => d3.format(',.0f')(d[thevariable.variable]), // dx: -9, dy: -12, fontSize: 12, fontWeight: 'bold', }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Sexo Contribuyente', // // fy: 'Estado Civil', // text: d => d3.format('$,.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 }72:T6d8,grafica_17 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Fuente de Mayor Ingreso'] !== null) // .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, color: { legend: true, }, marks: [ Plot.axisX({ tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, interval: 1, }), Plot.axisY({ // tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, z: 'Fuente de Mayor Ingreso', stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Fuente de Mayor Ingreso', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', // fontWeight: 'bold', // fontSize: 14.5, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // textAnchor: 'end', dx: -5, // }), ] }) return theplot }73:T6c3,grafica_18 = { let thedata = eitc_t4_fuente_ingreso_2019_2022 .filter(d => d['Fuente de Mayor Ingreso'] !== null) // .filter(d => d['Año contributivo'] === year_select_5) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, // height: 230, marginLeft: 60, marginBottom: 50, color: { legend: true, }, marks: [ Plot.axisX({ tickSize: 0, tickFormat: "", fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, interval: 1, }), Plot.axisY({ // tickFormat: '$,.0f', tickSize: 0, // tickFormat: d => fuente_ingreso_labels[d] ?? d, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.line(thedata, { x: 'Año contributivo', y: thevariable.variable, z: 'Fuente de Mayor Ingreso', stroke: 'Fuente de Mayor Ingreso', strokeWidth: 2, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], }), // Plot.text(thedata, { // x: thevariable.variable, // y: 'Fuente de Mayor Ingreso', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', // fontWeight: 'bold', // fontSize: 14.5, // fontFamily: 'jaf-bernino-sans', // fontVariant: 'tabular-nums', // textAnchor: 'end', dx: -5, // }), ] }) return theplot }74:T7e1,grafica_6 = { let thedata = eitc_t3_ocupacion_2019_2022 .filter(d => d['Año contributivo'] === year_select_6) ; 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: `The consolidation of "Other professions" responds to the need to preserve taxpayer confidentiality. This category includes the following professions for the year ${year_select_6}: ${year_to_otras_profesiones[year_select_6].map(d=>occupation_labels[d] ?? d).join(', ')}`, y: { domain: d3.sort(thedata, d => d['Ocupación Contribuyente'] == 'Otros listado abajo*', d => -d[thevariable.variable] ).map(d => d['Ocupación Contribuyente']) }, marks: [ Plot.axisX({ tickFormat: '$,.0f', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ label: 'Occupation', tickFormat: d => occupation_labels[d] ?? d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.barX(thedata, { x: thevariable.variable, y: 'Ocupación Contribuyente', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { x: thevariable.variable, y: 'Ocupación Contribuyente', text: d => d3.format('$,.0f')(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', textAnchor: 'end', dx: -5, }), ] }) return theplot }75:T5006,occupation_labels = { const en_espanol = [ "Abogado", "Administrador de Escuela", "Agente de Publicidad", "Agente y Corredor de Bienes Raíces", "Agente y Corredor de Seguros", "Agricultor", "Cajero de Ventas", "Cocinero", "Conductor de Camiones", "Conductor de Otros", "Conserje", "Contador o Auditor", "Electricista", "Enfermera", "Estilista, Barbero y Cosmetólogo", "Gerente y Supervisor de Ventas", "Gerente y Supervisor en General", "Guardia Privado", "Hojalatero", "Ingeniero", "Jardinero", "Maestro de Escuela", "Mecánico y Técnico Automotriz", "Militar", "Obrero de Carga", "Oficial de Corrección", "Oficinista", "Operador de Equipo Farmacéutico", "Operador de Equipo Industrial", "Otros Oficios o Profesiones", "Profesor Universitario", "Programador de Computadoras", "Representante de Servicio al Cliente", "Representante de Ventas al por mayor", "Representante de Ventas al por menor", "Retirado", "Secretaria", "Servicios Domésticos", "Técnico de Refrigeración", "Tecnólogo Médico", "Trabajador Social", "Vendedor al por mayor", "Vendedor al por menor", "Abogado", "Administrador de Escuela", "Agente y Corredor de Seguros", "Agricultor", "Cajero de Ventas", "Camarero", "Carpintero", "Cartero y Mensajero", "Cocinero", "Conductor de Camiones", "Conductor de Otros", "Conserje", "Contador o Auditor", "Contratista", "Electricista", "Embalsamador y Sepulturero", "Enfermera", "Estilista, Barbero y Cosmetólogo", "Farmacéutico o Auxiliar", "Gerente y Supervisor de Industrias", "Gerente y Supervisor en General", "Guardia Privado", "Ingeniero", "Jardinero", "Maestro de Escuela", "Mecánico y Técnico Automotriz", "Militar", "Oficial de Corrección", "Oficial de Institución Financiera", "Oficinista", "Operador de Equipo Farmacéutico", "Operador de Equipo Industrial", "Operador de Equipo Pesado", "Otros Oficios o Profesiones", "Profesor Universitario", "Psicólogo", "Representante de Servicio al Cliente", "Representante de Ventas al por menor", "Retirado", "Secretaria", "Servicios Domésticos", "Tecnólogo Médico", "Trabajador Social", "Vendedor al por mayor", "Vendedor al por menor", "Abogado", "Administrador de Escuela", "Agricultor", "Ajustador de Cuentas", "Albañil", "Asistente de Vuelo", "Asistente Médico", "Bombero", "Cajero de Bancos", "Cajero de Ventas", "Camarero", "Carnicero", "Carpintero", "Cartero y Mensajero", "Cocinero", "Conductor de Camiones", "Conductor de Otros", "Conserje", "Contador o Auditor", "Contratista", "Delineante", "Ebanista", "Electricista", "Enfermera", "Estilista, Barbero y Cosmetólogo", "Farmacéutico o Auxiliar", "Gerente y Supervisor de Industrias", "Gerente y Supervisor de Ventas", "Gerente y Supervisor en General", "Guardia Privado", "Hojalatero", "Ingeniero", "Jardinero", "Maestro de Escuela", "Mecánico Industrial", "Mecánico y Técnico Automotriz", "Militar", "Obrero de Carga", "Obrero de Construcción", "Oficial de Corrección", "Oficial de Institución Financiera", "Oficinista", "Operador de Datos", "Operador de Equipo Eléctrico", "Operador de Equipo Farmacéutico", "Operador de Equipo Industrial", "Operador de Equipo Pesado", "Operador de Equipo Telefónico", "Otros Oficios o Profesiones", "Patólogo del Habla", "Pintor de Estructura", "Plomero", "Profesor Universitario", "Programador de Computadoras", "Propagandista Médico", "Recogedor de Desechos", "Representante de Servicio al Cliente", "Representante de Ventas al por mayor", "Representante de Ventas al por menor", "Retirado", "Secretaria", "Servicios Domésticos", "Soldador", "Técnico de Refrigeración", "Tecnólogo Médico", "Terapista Profesional", "Trabajador Social", "Vendedor al por mayor", "Vendedor al por menor", "Abogado", "Agente y Corredor de Seguros", "Agricultor", "Ajustador de Cuentas", "Albañil", "Asistente Médico", "Bombero", "Cajero de Bancos", "Cajero de Ventas", "Camarero", "Carnicero", "Carpintero", "Cartero y Mensajero", "Cocinero", "Conductor de Camiones", "Conductor de Otros", "Conserje", "Contador o Auditor", "Contratista", "Delineante", "Ebanista", "Electricista", "Enfermera", "Estilista, Barbero y Cosmetólogo", "Farmacéutico o Auxiliar", "Gerente y Supervisor de Industrias", "Gerente y Supervisor de Ventas", "Gerente y Supervisor en General", "Guardia Privado", "Hojalatero", "Ingeniero", "Jardinero", "Maestro de Escuela", "Mecánico Industrial", "Mecánico y Técnico Automotriz", "Militar", "Obrero de Carga", "Obrero de Construcción", "Oficial de Corrección", "Oficial de Institución Financiera", "Oficinista", "Operador de Datos", "Operador de Equipo Eléctrico", "Operador de Equipo Farmacéutico", "Operador de Equipo Industrial", "Operador de Equipo Pesado", "Operador de Equipo Telefónico", "Otros Oficios o Profesiones", "Pintor de Estructura", "Plomero", "Profesor Universitario", "Programador de Computadoras", "Recogedor de Desechos", "Representante de Servicio al Cliente", "Representante de Ventas al por mayor", "Representante de Ventas al por menor", "Retirado", "Secretaria", "Servicios Domésticos", "Soldador", "Técnico de Refrigeración", "Tecnólogo Médico", "Terapista Profesional", "Trabajador Social", "Vendedor al por mayor", "Vendedor al por menor", "Otros listado abajo*", "Otros listado abajo*", "Otros listado abajo*", "Otros listado abajo*", "Atleta Profesional", "Patólogo Forense", "Juez", "Administrador de Escuela", "Agente de Publicidad", "Agente de Viajes", "Agente y Corredor de Bienes Raíces", "Agente y Corredor de Valores", "Agrimensor", "Arquitecto", "Artista, Actor, Bailarín, Cantante", "Asistente de Vuelo", "Avicultor", "Cerrajero", "Científico", "Economista", "Embalsamador y Sepulturero", "Gerente de Sistemas de Información", "Gerente y Supervisor de Finanzas", "Locutor de Radio y Televisión", "Médico Especialista", "Médico Generalista", "Patólogo del Habla", "Planificador", "Propagandista Médico", "Psicólogo", "Radiólogo", "Tapicero", "Veterinario", "Agente de Publicidad", "Agente de Viajes", "Agente y Corredor de Bienes Raíces", "Agente y Corredor de Seguros", "Agente y Corredor de Valores", "Agrimensor", "Arquitecto", "Artista, Actor, Bailarín, Cantante", "Atleta Profesional", "Avicultor", "Cerrajero", "Científico", "Economista", "Embalsamador y Sepulturero", "Gerente de Sistemas de Información", "Gerente y Supervisor de Finanzas", "Legislador y Alcalde", "Locutor de Radio y Televisión", "Médico Especialista", "Médico Generalista", "Planificador", "Psicólogo", "Radiólogo", "Tapicero", "Veterinario", "Agente de Publicidad", "Agente de Viajes", "Agente y Corredor de Bienes Raíces", "Agente y Corredor de Valores", "Agrimensor", "Ajustador de Cuentas", "Albañil", "Arquitecto", "Artista, Actor, Bailarín, Cantante", "Asistente de Vuelo", "Asistente Médico", "Atleta Profesional", "Avicultor", "Bombero", "Cajero de Bancos", "Carnicero", "Cerrajero", "Científico", "Delineante", "Ebanista", "Economista", "Gerente de Sistemas de Información", "Gerente y Supervisor de Finanzas", "Gerente y Supervisor de Ventas", "Hojalatero", "Juez", "Legislador y Alcalde", "Locutor de Radio y Televisión", "Mecánico Industrial", "Médico Especialista", "Médico Generalista", "Obrero de Carga", "Obrero de Construcción", "Operador de Datos", "Operador de Equipo Eléctrico", "Operador de Equipo Telefónico", "Patólogo del Habla", "Patólogo Forense", "Pintor de Estructura", "Planificador", "Plomero", "Programador de Computadoras", "Propagandista Médico", "Radiólogo", "Recogedor de Desechos", "Representante de Ventas al por mayor", "Soldador", "Tapicero", "Técnico de Refrigeración", "Terapista Profesional", "Veterinario", "Agente de Viajes", "Agente y Corredor de Valores", "Agrimensor", "Ajustador de Cuentas", "Albañil", "Arquitecto", "Artista, Actor, Bailarín, Cantante", "Asistente de Vuelo", "Asistente Médico", "Atleta Profesional", "Avicultor", "Bombero", "Cajero de Bancos", "Camarero", "Carnicero", "Carpintero", "Cartero y Mensajero", "Cerrajero", "Científico", "Contratista", "Delineante", "Ebanista", "Economista", "Embalsamador y Sepulturero", "Farmacéutico o Auxiliar", "Gerente de Sistemas de Información", "Gerente y Supervisor de Finanzas", "Gerente y Supervisor de Industrias", "Juez", "Legislador y Alcalde", "Locutor de Radio y Televisión", "Mecánico Industrial", "Médico Especialista", "Médico Generalista", "Obrero de Construcción", "Oficial de Institución Financiera", "Operador de Datos", "Operador de Equipo Eléctrico", "Operador de Equipo Pesado", "Operador de Equipo Telefónico", "Patólogo del Habla", "Patólogo Forense", "Pintor de Estructura", "Planificador", "Plomero", "Propagandista Médico", "Psicólogo", "Radiólogo", "Recogedor de Desechos", "Soldador", "Tapicero", "Terapista Profesional", "Veterinario", ] const en_ingles = [ "Lawyer", "School Administrator", "Press agent", "Real Estate Agent and Broker", "Insurance Agent and Broker", "Farmer", "Sales Cashier", "Chef", "Truck driver", "Driver of Others", "Concierge", "Accountant or Auditor", "Electrician", "Nurse", "Stylist, Barber and Cosmetologist", "Sales Manager and Supervisor", "General Manager and Supervisor", "Private Guard", "Tinsmith", "Engineer", "Gardener", "School teacher", "Automotive Mechanic and Technician", "Military", "Cargo Worker", "Correction Officer", "Clerk", "Pharmaceutical Equipment Operator", "Industrial Equipment Operator", "Other Trades or Professions", "College professor", "Computer programmer", "Customer Service Representative", "Wholesale Sales Representative", "Retail Sales Representative", "Retired", "Secretary", "Domestic Services", "Refrigeration Technician", "Medical Technologist", "Social worker", "wholesale seller", "retail seller", "Lawyer", "School Administrator", "Insurance Agent and Broker", "Farmer", "Sales Cashier", "Waiter", "Carpenter", "Postman and Messenger", "Chef", "Truck driver", "Driver of Others", "Concierge", "Accountant or Auditor", "Contractor", "Electrician", "Embalmer and Gravedigger", "Nurse", "Stylist, Barber and Cosmetologist", "Pharmacist or Assistant", "Manager and Supervisor of Industries", "General Manager and Supervisor", "Private Guard", "Engineer", "Gardener", "School teacher", "Automotive Mechanic and Technician", "Military", "Correction Officer", "Financial Institution Officer", "Clerk", "Pharmaceutical Equipment Operator", "Industrial Equipment Operator", "Heavy equipment operator", "Other Trades or Professions", "College professor", "Psychologist", "Customer Service Representative", "Retail Sales Representative", "Retired", "Secretary", "Domestic Services", "Medical Technologist", "Social worker", "wholesale seller", "retail seller", "Lawyer", "School Administrator", "Farmer", "Account Adjuster", "Construction worker", "Flight Attendant", "Medical assistant", "Firefighter", "Bank Teller", "Sales Cashier", "Waiter", "Butcher", "Carpenter", "Postman and Messenger", "Chef", "Truck driver", "Driver of Others", "Concierge", "Accountant or Auditor", "Contractor", "Draftsman", "Cabinetmaker", "Electrician", "Nurse", "Stylist, Barber and Cosmetologist", "Pharmacist or Assistant", "Manager and Supervisor of Industries", "Sales Manager and Supervisor", "General Manager and Supervisor", "Private Guard", "Tinsmith", "Engineer", "Gardener", "School teacher", "Industrial mechanic", "Automotive Mechanic and Technician", "Military", "Cargo Worker", "Construction worker", "Correction Officer", "Financial Institution Officer", "Clerk", "Data Operator", "Electrical Equipment Operator", "Pharmaceutical Equipment Operator", "Industrial Equipment Operator", "Heavy equipment operator", "Telephone Equipment Operator", "Other Trades or Professions", "Speech Pathologist", "Painter of Structure", "Plumber", "College professor", "Computer programmer", "Medical propagandist", "Debris Collector", "Customer Service Representative", "Wholesale Sales Representative", "Retail Sales Representative", "Retired", "Secretary", "Domestic Services", "Welder", "Refrigeration Technician", "Medical Technologist", "Professional Therapist", "Social worker", "wholesale seller", "retail seller", "Lawyer", "Insurance Agent and Broker", "Farmer", "Account Adjuster", "Construction worker", "Medical assistant", "Firefighter", "Bank Teller", "Sales Cashier", "Waiter", "Butcher", "Carpenter", "Postman and Messenger", "Chef", "Truck driver", "Driver of Others", "Concierge", "Accountant or Auditor", "Contractor", "Draftsman", "Cabinetmaker", "Electrician", "Nurse", "Stylist, Barber and Cosmetologist", "Pharmacist or Assistant", "Manager and Supervisor of Industries", "Sales Manager and Supervisor", "General Manager and Supervisor", "Private Guard", "Tinsmith", "Engineer", "Gardener", "School teacher", "Industrial mechanic", "Automotive Mechanic and Technician", "Military", "Cargo Worker", "Construction worker", "Correction Officer", "Financial Institution Officer", "Clerk", "Data Operator", "Electrical Equipment Operator", "Pharmaceutical Equipment Operator", "Industrial Equipment Operator", "Heavy equipment operator", "Telephone Equipment Operator", "Other Trades or Professions", "Painter of Structure", "Plumber", "College professor", "Computer programmer", "Debris Collector", "Customer Service Representative", "Wholesale Sales Representative", "Retail Sales Representative", "Retired", "Secretary", "Domestic Services", "Welder", "Refrigeration Technician", "Medical Technologist", "Professional Therapist", "Social worker", "wholesale seller", "retail seller", "Others listed below*", "Others listed below*", "Others listed below*", "Others listed below*", "Professional athlete", "Forensic Pathologist", "Judge", "School Administrator", "Press agent", "Travel agent", "Real Estate Agent and Broker", "Securities Agent and Broker", "Surveyor", "Architect", "Artist, Actor, Dancer, Singer", "Flight Attendant", "Poultryman", "Locksmith", "Scientist", "Economist", "Embalmer and Gravedigger", "Information Systems Manager", "Finance Manager and Supervisor", "Radio and Television Announcer", "Medical specialist", "General practitioner", "Speech Pathologist", "Planner", "Medical propagandist", "Psychologist", "Radiologist", "Upholsterer", "Vet", "Press agent", "Travel agent", "Real Estate Agent and Broker", "Insurance Agent and Broker", "Securities Agent and Broker", "Surveyor", "Architect", "Artist, Actor, Dancer, Singer", "Professional athlete", "Poultryman", "Locksmith", "Scientist", "Economist", "Embalmer and Gravedigger", "Information Systems Manager", "Finance Manager and Supervisor", "Legislator and Mayor", "Radio and Television Announcer", "Medical specialist", "General practitioner", "Planner", "Psychologist", "Radiologist", "Upholsterer", "Vet", "Press agent", "Travel agent", "Real Estate Agent and Broker", "Securities Agent and Broker", "Surveyor", "Account Adjuster", "Construction worker", "Architect", "Artist, Actor, Dancer, Singer", "Flight Attendant", "Medical assistant", "Professional athlete", "Poultryman", "Firefighter", "Bank Teller", "Butcher", "Locksmith", "Scientist", "Draftsman", "Cabinetmaker", "Economist", "Information Systems Manager", "Finance Manager and Supervisor", "Sales Manager and Supervisor", "Tinsmith", "Judge", "Legislator and Mayor", "Radio and Television Announcer", "Industrial mechanic", "Medical specialist", "General practitioner", "Cargo Worker", "Construction worker", "Data Operator", "Electrical Equipment Operator", "Telephone Equipment Operator", "Speech Pathologist", "Forensic Pathologist", "Painter of Structure", "Planner", "Plumber", "Computer programmer", "Medical propagandist", "Radiologist", "Debris Collector", "Wholesale Sales Representative", "Welder", "Upholsterer", "Refrigeration Technician", "Professional Therapist", "Vet", "Travel agent", "Securities Agent and Broker", "Surveyor", "Account Adjuster", "Construction worker", "Architect", "Artist, Actor, Dancer, Singer", "Flight Attendant", "Medical assistant", "Professional athlete", "Poultryman", "Firefighter", "Bank Teller", "Waiter", "Butcher", "Carpenter", "Postman and Messenger", "Locksmith", "Scientist", "Contractor", "Draftsman", "Cabinetmaker", "Economist", "Embalmer and Gravedigger", "Pharmacist or Assistant", "Information Systems Manager", "Finance Manager and Supervisor", "Manager and Supervisor of Industries", "Judge", "Legislator and Mayor", "Radio and Television Announcer", "Industrial mechanic", "Medical specialist", "General practitioner", "Construction worker", "Financial Institution Officer", "Data Operator", "Electrical Equipment Operator", "Heavy equipment operator", "Telephone Equipment Operator", "Speech Pathologist", "Forensic Pathologist", "Painter of Structure", "Planner", "Plumber", "Medical propagandist", "Psychologist", "Radiologist", "Debris Collector", "Welder", "Upholsterer", "Professional Therapist", "Vet" ] const thelabels = Object({}) for (let i = 0; i < en_espanol.length; i++) { thelabels[en_espanol[i]] = en_ingles[i] } return thelabels return Object({ 'Otros listado abajo*': 'Others', }) }76:Ta18,grafica_12 = { let thedata = eitc_t3_ocupacion_2019_2022 .filter(d => d['Año contributivo'] === year_select_6) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, marginLeft: 250, marginBottom: 50, caption: `The consolidation of "Other professions" responds to the need to preserve taxpayer confidentiality. This category includes the following professions for the year ${year_select_6}: ${year_to_otras_profesiones[year_select_6].map(d => occupation_labels[d] ?? d).join(', ')}`, y: { domain: d3.sort(thedata, d => d['Ocupación Contribuyente'] == 'Otros listado abajo*', d => -d[thevariable.variable] ).map(d => d['Ocupación Contribuyente']) }, marks: [ Plot.axisX({ tickFormat: ',.0f', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.axisY({ tickFormat: d => occupation_labels[d] ?? d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, label: 'Occupation', }), Plot.barX(thedata, { x: thevariable.variable, y: 'Ocupación Contribuyente', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 60e3, x: thevariable.variable, y: 'Ocupación Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'black', fontWeight: 'bold', fontSize: 12, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'start', dx: 5, }), Plot.text(thedata, { filter: d => d[thevariable.variable] > 60e3, x: thevariable.variable, y: 'Ocupación Contribuyente', text: d => d3.format(',.0f')(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', textAnchor: 'end', dx: -5, }), ] }) return theplot }77:T9ab,grafica_13 = { let thedata = eitc_t3_ocupacion_2019_2022 .filter(d => d['Año contributivo'] === year_select_6) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ width: 800, marginLeft: 250, marginBottom: 50, caption: `La consolidación de "Otras profesiones" 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['Ocupación Contribuyente'] == 'Otros listado abajo*', d => -d[thevariable.variable] ).map(d => d['Ocupación Contribuyente']) }, marks: [ Plot.axisX({ tickFormat: ',.0f', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: d => d === 'Otros listado abajo*' ? 'Otras profesiones' : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.barX(thedata, { x: thevariable.variable, y: 'Ocupación Contribuyente', // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 60e3, x: thevariable.variable, y: 'Ocupación Contribuyente', text: d => d3.format(',.0f')(d[thevariable.variable]), // stroke: 'white', strokeWidth: 0.3, fill: 'black', sort: {'y': '-x'}, fill: 'black', fontWeight: 'bold', fontSize: 12, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', textAnchor: 'start', dx: 5, }), Plot.text(thedata, { filter: d => d[thevariable.variable] > 60e3, x: thevariable.variable, y: 'Ocupación Contribuyente', text: d => d3.format(',.0f')(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', textAnchor: 'end', dx: -5, }), ] }) return theplot }78:Tcf0,year_to_otras_profesiones = { let year_to_string = {2022: ` Agente de Viajes Agente y Corredor de Valores Agrimensor Ajustador de Cuentas Albañil Arquitecto Artista, Actor, Bailarín, Cantante Asistente de Vuelo Asistente Médico Atleta Profesional Avicultor Bombero Cajero de Bancos Camarero Carnicero Carpintero Cartero y Mensajero Cerrajero Científico Contratista Delineante Ebanista Economista Embalsamador y Sepulturero Farmacéutico o Auxiliar Gerente de Sistemas de Información Gerente y Supervisor de Finanzas Gerente y Supervisor de Industrias Juez Legislador y Alcalde Locutor de Radio y Televisión Mecánico Industrial Médico Especialista Médico Generalista Obrero de Construcción Oficial de Institución Financiera Operador de Datos Operador de Equipo Eléctrico Operador de Equipo Pesado Operador de Equipo Telefónico Patólogo del Habla Patólogo Forense Pintor de Estructura Planificador Plomero Propagandista Médico Psicólogo Radiólogo Recogedor de Desechos Soldador Tapicero Terapista Profesional Veterinario `, 2021: ` Agente de Publicidad Agente de Viajes Agente y Corredor de Bienes Raíces Agente y Corredor de Valores Agrimensor Ajustador de Cuentas Albañil Arquitecto Artista, Actor, Bailarín, Cantante Asistente de Vuelo Asistente Médico Atleta Profesional Avicultor Bombero Cajero de Bancos Carnicero Cerrajero Científico Delineante Ebanista Economista Gerente de Sistemas de Información Gerente y Supervisor de Finanzas Gerente y Supervisor de Ventas Hojalatero Juez Legislador y Alcalde Locutor de Radio y Televisión Mecánico Industrial Médico Especialista Médico Generalista Obrero de Carga Obrero de Construcción Operador de Datos Operador de Equipo Eléctrico Operador de Equipo Telefónico Patólogo del Habla Patólogo Forense Pintor de Estructura Planificador Plomero Programador de Computadoras Propagandista Médico Radiólogo Recogedor de Desechos Representante de Ventas al por mayor Soldador Tapicero Técnico de Refrigeración Terapista Profesional Veterinario`, 2020: ` Agente de Publicidad Agente de Viajes Agente y Corredor de Bienes Raíces Agente y Corredor de Seguros Agente y Corredor de Valores Agrimensor Arquitecto Artista, Actor, Bailarín, Cantante Atleta Profesional Avicultor Cerrajero Científico Economista Embalsamador y Sepulturero Gerente de Sistemas de Información Gerente y Supervisor de Finanzas Legislador y Alcalde Locutor de Radio y Televisión Médico Especialista Médico Generalista Planificador Psicólogo Radiólogo Tapicero Veterinario`, 2019: ` Atleta Profesional Patólogo Forense Juez Administrador de Escuela Agente de Publicidad Agente de Viajes Agente y Corredor de Bienes Raíces Agente y Corredor de Valores Agrimensor Arquitecto Artista, Actor, Bailarín, Cantante Asistente de Vuelo Avicultor Cerrajero Científico Economista Embalsamador y Sepulturero Gerente de Sistemas de Información Gerente y Supervisor de Finanzas Locutor de Radio y Televisión Médico Especialista Médico Generalista Patólogo del Habla Planificador Propagandista Médico Psicólogo Radiólogo Tapicero Veterinario` } let year_to_list = {} for (let [year,val_string] of Object.entries(year_to_string)) { year_to_list[year] = val_string.trim('\n').split('\n') } return year_to_list }79:Tca8,{ let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let thedata_initial = aq.from( eitc_t3_ocupacion_2019_2022 .filter(d => d['Año contributivo'] === year_select) ) .derive({ 'is_defined': d => !((d['Ocupación Contribuyente'] === 'Otros listado abajo*') | (d['Ocupación Contribuyente'] === "Otros Oficios o Profesiones")), // 'is_defined': d => !(['Otros listado abajo*', "Otros Oficios o Profesiones"].includes(d['Ocupación Contribuyente'])), }) .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, height: 550, 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 ? 'Ocupación especificada' : 'Ocupación no especificada', }, 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(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: d => 1, y: thevariable.variable, fill: 'is_defined', }), Plot.text(thedata, Plot.stackY({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: d => 1, y: thevariable.variable, z: 'is_defined', // channels: {z: {reverse: true}}, text: d => d3.format(',.0f')(d[thevariable.variable]) +' '+ `(${d3.format('.1%')(d[thevariable.variable]/total)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d.is_defined ? '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 }7a:Tca3,{ let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let thedata_initial = aq.from( eitc_t3_ocupacion_2019_2022 .filter(d => d['Año contributivo'] === year_select) ) .derive({ 'is_defined': d => !((d['Ocupación Contribuyente'] === 'Otros listado abajo*') | (d['Ocupación Contribuyente'] === "Otros Oficios o Profesiones")), // 'is_defined': d => !(['Otros listado abajo*', "Otros Oficios o Profesiones"].includes(d['Ocupación Contribuyente'])), }) .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, height: 550, 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 ? 'Occupation specified' : 'Occupation not specified', }, marks: [ // Plot.axisX({ // hide: false, // tickSize: 0, // fontFamily: 'jaf-bernino-sans', // fontSize: 12, // }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, label: 'Families', }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: d => 1, y: thevariable.variable, fill: 'is_defined', }), Plot.text(thedata, Plot.stackY({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: d => 1, y: thevariable.variable, z: 'is_defined', // channels: {z: {reverse: true}}, text: d => d3.format(',.0f')(d[thevariable.variable]) +' '+ `(${d3.format('.1%')(d[thevariable.variable]/total)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d.is_defined ? 'white' : 'black', // stroke: 'black', // textAnchor: 'end', dx: -5, })), // Plot.text(thedata, Plot.selectLast({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), // x: 'Año contributivo', // y: thevariable.variable, // z: 'Anejo CT: Categoría Dependientes Cualificados', // text: d => (d['Anejo CT: Categoría Dependientes Cualificados']), // // stroke: 'white', strokeWidth: 0.3, fill: 'black', // // sort: {'y': '-x'}, // stroke: 'white', fill: 'black', // textAnchor: 'end', dx: -25, // })), ] }) return theplot }7b:Tabb,{ let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thedata_initial = eitc_t3_ocupacion_2019_2022 .filter(d => !((d['Ocupación Contribuyente'] === 'Otros listado abajo*') | (d['Ocupación Contribuyente'] === "Otros Oficios o Profesiones"))) // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => -d[thevariable.variable]).slice(0,5) let xformats = { 'Stylist, Barber and Cosmetologist': 'Stylist, Barber\nand Cosmetologist', 'Refrigeration Technician': 'Refrigeration\ntechnician', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['Ocupación Contribuyente']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => { let english = occupation_labels[d] ?? d return capitalizeOnlyFirstLetter(xformats[english] ?? english) }, label: 'Occupation', }), Plot.axisY({ label: 'Earned income tax credit - average', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.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: 'Ocupación Contribuyente', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Ocupación Contribuyente', 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.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 }7c:Ta63,{ let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let thedata_initial = eitc_t3_ocupacion_2019_2022 .filter(d => !((d['Ocupación Contribuyente'] === 'Otros listado abajo*') | (d['Ocupación Contribuyente'] === "Otros Oficios o Profesiones"))) // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => -d[thevariable.variable]).slice(0,5) let xformats = { 'Customer Service Representative': 'Customer service\nrepresentative', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['Ocupación Contribuyente']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => { let english = occupation_labels[d] ?? d return capitalizeOnlyFirstLetter(xformats[english] ?? english) }, label: 'Occupation', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, label: 'Families', }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Ocupación Contribuyente', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Ocupación Contribuyente', 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.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 }7d:Ta3f,{ let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thedata_initial = eitc_t3_ocupacion_2019_2022 .filter(d => d.Municipios !== 'Otros listado abajo*') // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => d[thevariable.variable]).slice(0,5) let xformats = { 'Programador de Computadoras': 'Computer\nprogrammer', 'Administrador de Escuela': 'School administrator', 'Ingeniero': 'Engineer', 'Retirado': 'Retired', 'Militar': 'Millitary', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['Ocupación Contribuyente']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => capitalizeOnlyFirstLetter(xformats[d] ?? d), label: 'Occupation', }), Plot.axisY({ label: 'Earned income tax credit - average', 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: 'Ocupación Contribuyente', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Ocupación Contribuyente', 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.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 }7e:Ta74,{ let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let thedata_initial = eitc_t3_ocupacion_2019_2022 .filter(d => !((d['Ocupación Contribuyente'] === 'Otros listado abajo*') | (d['Ocupación Contribuyente'] === "Otros Oficios o Profesiones"))) // Excluir otros .filter(d => d['Año contributivo'] === year_select); let thedata = d3.sort(thedata_initial, d => d[thevariable.variable]).slice(0,5) let xformats = { 'Administrador de Escuela': 'Administrador\nde Escuela', 'Programador de Computadoras': 'Programador de\nComputadoras', 'Agente de Publicidad': 'Agente de\nPublicidad', 'Representante de Ventas al por mayor': 'Representante de\nVentas al\npor mayor', } let theplot = Plot.plot({ // width: 800, marginLeft: 60, marginTop: 30, marginBottom: 60, x: { domain: thedata.map(d => d['Ocupación Contribuyente']), }, marks: [ Plot.axisX({ tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => xformats[d] ?? d, }), Plot.axisY({ 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: 'Ocupación Contribuyente', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'Ocupación Contribuyente', 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.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 }7f:T675,grafica_7 = { let thedata = eitc_t1_escala_ingresos_2019_2022 .filter(d => d['Año contributivo'] === year_select_7) ; let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, y: { domain: [0, 2600], }, marks: [ Plot.axisX({ label: `Gross earned income`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ label: "Earned income tax credit - average", tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: { 'Promedio': thevariable.variable, 'Planillas': 'Planillas Total - Crédito por Trabajo', 'limite_inferior': 'limite_inferior', 'limite_superior': 'limite_superior', }, }), Plot.arrow(thedata, { filter: d => isNaN(d['limite_superior']), x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], y: thevariable.variable, }), // Plot.line(trapezoide_2022, { // x: 'x', // y: 'y', // }), ] }) return theplot }80:T555,grafica_14 = { let thedata = eitc_t1_escala_ingresos_2019_2022 .filter(d => d['Año contributivo'] === year_select_7) ; let thevariable = variables_metadata.filter(d => (d.dato === 'numero_planillas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, marks: [ Plot.axisX({ label: `Gross earned income`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, label: legend_label_hack[thevariable.variable] ?? thevariable.variable, }), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, channels: {'Planillas': thevariable.variable}, tip: true, }), 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 }81:T8a3,grafica_30 = { let thedata = eitc_t1_escala_ingresos_2019_2022 .filter(d => d['Año contributivo'] === year_select_7) ; // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable_individuos = variables_metadata.filter(d => (d.dato === 'monto_eitc_individuos') & (d.promedio_o_total === 'promedio'))[0]; let thevariable_casados = variables_metadata.filter(d => (d.dato === 'monto_eitc_casados') & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: {grid: true,}, fx: {reverse: true,}, marks: [ Plot.axisX({ label: `Ingreso bruto ${thedata[0].tipo_ingreso}`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisFx({ }), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', fx: d => 'Individuos', y: thevariable_individuos.variable, fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: {'Promedio': thevariable_individuos.variable, 'Planillas': 'Planillas Contribuyente Individual - Crédito por Trabajo'}, }), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', fx: d => 'Casados', y: thevariable_casados.variable, fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: {'Promedio': thevariable_casados.variable, 'Planillas': 'Planillas Casados - Crédito por Trabajo'}, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable_individuos.variable, // }), ] }) return theplot }82:T920,grafica_31 = { let thedata = eitc_t1_escala_ingresos_2019_2022 .filter(d => d['Año contributivo'] === year_select_7) ; // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable_individuos = variables_metadata.filter(d => (d.dato === 'monto_eitc_individuos') & (d.promedio_o_total === 'promedio'))[0]; let thevariable_casados = variables_metadata.filter(d => (d.dato === 'monto_eitc_casados') & (d.promedio_o_total === 'promedio'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, width: 1000, y: {grid: true,}, fx: {reverse: true,}, marks: [ Plot.axisX({ label: `Ingreso bruto ${thedata[0].tipo_ingreso}`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisFx({ }), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', fx: d => 'Individuos', y: 'Planillas Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: {'Planillas individuos': 'Planillas Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC'}, }), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', fx: d => 'Casados', y: 'Planillas Casados - Total Ingreso bruto ganado para la determinación del EITC', fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: {'Planillas casados': 'Planillas Casados - Total Ingreso bruto ganado para la determinación del EITC'}, }), // Plot.arrow(thedata, { // filter: d => isNaN(d['limite_superior']), // x1: "limite_inferior", x2: d => isNaN(d['limite_superior']) ? 43000 : d['limite_superior'], // y: thevariable_individuos.variable, // }), ] }) return theplot }83:T534,grafica_15 = { let thedata = eitc_t1_escala_ingresos_2019_2022 .filter(d => d['Año contributivo'] === year_select_7) ; let thevariable = variables_metadata.filter(d => (d.dato === 'total_personas') & (d.promedio_o_total === 'total'))[0]; let theplot = Plot.plot({ marginLeft: 60, marginBottom: 50, marginTop: 30, marks: [ Plot.axisX({ label: `Ingreso bruto ${thedata[0].tipo_ingreso}`, tickFormat: "$,.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.axisY({ tickFormat: ",.0f", tickSize: 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontSize: 12, }), Plot.rectY(thedata, { x1: "limite_inferior", x2: 'limite_superior', y: thevariable.variable, fill: EA_COLORES_MAP['INDIGO'], stroke: 'white', strokeWidth: .5, tip: true, channels: {'inferior': 'limite_inferior', '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 }84:T881,{ // 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 >= 2020) .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: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // 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', }), 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${d3.format('.1%')(rate_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }85:T8a2,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'rate'} let thedata = eitc_participation_rates .filter(d => d.year >= 2020) .filter(d => d.state !== 'National') // .filter(d => d['Año contributivo'] === year_select); let rate_promedio = aq.from(thedata) .rollup({ 'mean_rate': op.mean('rate') }) .objects()[0]['mean_rate'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '.0%', label: 'Participation rate in the Earned Income Tax Credit (take-up rate)', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'State or jurisdiction', }), Plot.ruleX([rate_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('.1%')(d[thevariable.variable]), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), Plot.text([0], { x: rate_promedio, dx: 5, y: d => 'Washington', text: d => `Average\n${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 }86:Tb02,{ let thedata = eitc_participation_rates .filter(d => d.year >= 2020) .filter(d => d.state !== 'National') thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us') .rollup({ rate: op.mean('rate'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata, 'is_us', 'rate') let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { // domain: num_dependientes_options, reverse: true, // interval: 1, }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d ? 'Average 50 states and DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Participation rate in the earned income tax credit', 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: 'rate', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { x: 'is_us', y: 'rate', text: d => d3.format(',.1%')(d['rate']), 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: 'rate', 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 }87:Tb19,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'percentage_of_federal_credit'} // let thedata = eitc_local_governments // // .filter(d => d.year >= 2020) // // .filter(d => d.state !== 'National') // // .filter(d => d['Año contributivo'] === year_select); let thedata = eitc_local_governments_with_zeros let porciento_promedio = aq.from(thedata) .rollup({ 'mean_pct': op.mean('percentage_of_federal_credit') }) .objects()[0]['mean_pct'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1000, // 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: '.0%', label: 'Crédito por Trabajo local como porciento del Crédito por Trabajo federal' }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'Estado', }), Plot.ruleX([porciento_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] >= .15, 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(thedata, { filter: d => d[thevariable.variable] < .15, 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: 'start', sort: {y: "x", reverse: true} }), Plot.text([0], { x: porciento_promedio, dx: 5, y: d => 'Maine', text: d => `Promedio\n${d3.format('.1%')(porciento_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }88:Tb99,{ let thedata = eitc_local_governments_with_zeros let thevariable = {variable: 'percentage_of_federal_credit'}; thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us') .rollup({ percentage_of_federal_credit: op.mean('percentage_of_federal_credit'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata, 'is_us', 'percentage_of_federal_credit'); 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 como porciento del Crédito por Trabajo 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'], }), 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: 'percentage_of_federal_credit', 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 }89:T857,{ // 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, }), 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('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }8a:T87f,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'average_eitc'} let thedata = eitc_returns .filter(d => d.state !== 'International') // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata) .rollup({ 'average_eitc': op.mean('average_eitc') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Federal Earned Income Tax Credit - Average', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'State or jurisdiction', }), 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('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', sort: {y: "x", reverse: true}}), Plot.text([0], { x: eitc_promedio, dx: 5, y: d => 'Washington', text: d => `Average\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }8b:Tb17,{ let thedata = eitc_returns let thevariable = {variable: 'average_eitc'}; thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us') .rollup({ average_eitc: op.mean('average_eitc'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata, 'is_us', 'average_eitc'); let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 30, marginBottom: 50, x: { domain: [true, false], // interval: 1, // reverse: true, }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d ? 'Average 50 states and DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Federal earned income tax credit - average', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.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 }8c:T987,{ // 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: '$,.0f', label: 'Federal earned income tax credit - average', }), Plot.axisX({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: null, }), Plot.ruleY([eitc_promedio]), Plot.barY(thedata, { y: thevariable.variable, x: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {x: "y"}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('$,.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 => `Average\n(without\nPuerto 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 }8d:Ta84,{ // 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: '$,.0f', label: 'Crédito por Trabajo local - Promedio', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'Estado', }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { filter: d => d[thevariable.variable] > 100, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', text: d => d3.format('$,.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('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }8e:Taa4,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'amount'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') .filter(d => d.jurisdiction === 'local') // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata) .rollup({ 'average_eitc': op.mean('amount') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 950, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Earned Income Tax Credit - Local Contribution (Average)', }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: 'State or jurisdiction', }), 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 => `Average\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 }8f:Tb94,{ 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 ? 'Average 50 states and DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Local earned income tax credit - average', 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('+$.0f')(d['change_value']), // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: 'white', fontWeight: 'bold', fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', stroke: 'white', strokeWidth: 8, fill: 'black', // lineAnchor: 'bottom', dy: -8, // textAnchor: 'end', dx: -5, }), ] }) return theplot }90:Td08,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'amount'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata) .rollup({ 'average_eitc': op.mean('amount') }) .objects()[0]['average_eitc'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1200, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, color: { legend: true, domain: ['Federal', 'Local'], range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]) }, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Crédito por Trabajo federal y local - Promedio' }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: "Estado", }), // 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 }91:Td57,{ // 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: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, color: { legend: true, domain: ['Federal', 'Local'], range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), tickFormat: d => d + ' Contribution', }, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Earned Income Tax Credit - Federal and Local Contributions (Average)' }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: "State or jurisdiction", }), // Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], // fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', z: 'jurisdiction', fill: d => d.state === 'Puerto Rico' ? (d.jurisdiction === 'federal' ? EA_COLORES_MAP['INDIGO'] : EA_COLORES_MAP['GREEN']) : (d.jurisdiction === 'federal' ? '#cdd1ec' : chroma(EA_COLORES_MAP['GREEN']).alpha(0.35).hex()), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, Plot.stackX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', // fill: d => d.state === 'Puerto Rico' ? 'white' : 'black', fill: d => d.jurisdiction === 'federal' ? (d.state === 'Puerto Rico' ? 'white' : 'black') : null, text: d => d.share > 0 ? d3.format('$,.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('$,.0f')(eitc_promedio)}`, // textAnchor: 'start', // lineAnchor: 'top', // fontFamily: 'jaf-bernino-sans', // fontWeight: 'bold', // fontSize: 13, // }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }92:Tfbf,{ let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') // .filter(d => d.jurisdiction === 'local') // .filter(d => d['Año contributivo'] === year_select); let thevariable = {variable: 'amount'}; thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us', 'jurisdiction') .rollup({ amount: op.mean('amount'), }) .orderby(aq.desc('is_us')) .objects() let thedata_merged = aq.from(thedata) .groupby('is_us') .rollup({ amount: op.sum('amount'), }) .orderby(aq.desc('is_us')) .objects() let thedata_changes = get_data_changes(thedata_merged, 'is_us', 'amount'); let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 50, marginBottom: 50, x: { domain: [true, false], // interval: 1, // reverse: true, }, color: { legend: true, domain: ['federal', 'local'], range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), tickFormat: d => convertToTitleCase(d), }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, tickFormat: d => d ? 'Promedio 50 estados y DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Crédito por Trabajo local - Promedio', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, Plot.stackY({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'is_us', y: thevariable.variable, fill: 'jurisdiction', // fill: EA_COLORES_MAP['INDIGO'], })), Plot.text(thedata, Plot.stackY({ x: 'is_us', y: thevariable.variable, z: 'jurisdiction', text: d => d3.format('$,.0f')(d[thevariable.variable]), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: 'white', // textAnchor: 'end', dx: -5, })), Plot.line(thedata_merged, { x: 'is_us', y: 'amount', markerEnd: 'arrow', // z: 'Fuente de Mayor Ingreso', // stroke: 'Fuente de Mayor Ingreso', curve: 'step-after', stroke: 'black', strokeWidth: 2, dy: -0, // stroke: 'white', strokeWidth: 0.3, fill: 'black', // sort: {'y': '-x'}, // fill: EA_COLORES_MAP['INDIGO'], tip: true, }), Plot.text(thedata_changes, { // filter: d => [2006, 2022].includes(d.year), x: 'current_domain', y: 'previous_value', dy: -0, text: d => d3.format('+$.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, }), Plot.text(thedata_merged, { // filter: d => [2006, 2022].includes(d.year), x: 'is_us', y: 'amount', dy: -0, text: d => d3.format('$,.0f')(d['amount']), // 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: -10, // textAnchor: 'end', dx: -5, }), ] }) return theplot }93:T10c6,{ 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: 'percent'}; thedata = aq.from(thedata) .derive({ is_us: d => d.state !== 'Puerto Rico' }) .groupby('is_us', 'jurisdiction') .rollup({ amount: op.mean('amount'), }) .orderby(aq.desc('is_us')) .objects() let thedata_merged = aq.from(thedata) .groupby('is_us') .rollup({ total: op.sum('amount'), }) .orderby(aq.desc('is_us')) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_merged)) .derive({'percent': d => d.amount/d.total}) .objects(); console.log('some thedata', thedata); let thedata_changes = get_data_changes(thedata_merged, 'is_us', 'percent'); let theplot = Plot.plot({ width: 400, marginLeft: 60, marginTop: 50, marginBottom: 50, x: { domain: [true, false], // interval: 1, // reverse: true, }, color: { legend: true, domain: ['federal', 'local'], range: ['INDIGO', 'GREEN'].map(d => EA_COLORES_MAP[d]), tickFormat: d => convertToTitleCase(d), }, 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: ".0%", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, Plot.stackY({ // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'is_us', y: thevariable.variable, fill: 'jurisdiction', // fill: EA_COLORES_MAP['INDIGO'], })), Plot.text(thedata, Plot.stackY({ x: 'is_us', y: thevariable.variable, z: 'jurisdiction', 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_merged, { // x: 'is_us', // y: 'percent', // 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_merged, { // filter: d => [2006, 2022].includes(d.year), x: 'is_us', y: d => 1., dy: -0, text: d => d3.format('.0%')(1.0), // 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: -10, // textAnchor: 'end', dx: -5, }), ] }) return theplot }94:Td09,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'share'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') // .filter(d => d['Año contributivo'] === year_select); let eitc_promedio = aq.from(thedata.filter(d => d.jurisdiction === 'federal')) .rollup({ 'share': op.mean('share') }) .objects()[0]['share'] let theplot = Plot.plot({ marginLeft: 150, marginRight: 50, marginBottom: 50, height: 1200, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, color: { legend: false, domain: ['Federal',], range: ['INDIGO',].map(d => EA_COLORES_MAP[d]) }, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: ',.0%', label: 'Crédito por Trabajo - Aportación federal' }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, label: "Estado", }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { filter: d => d.jurisdiction === 'federal', x: 'share', 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({ filter: d => d.jurisdiction === 'federal', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: 'share', 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('.1%')(d.share)}` : '', dx: 0, textAnchor: 'middle', sort: {y: "x", reverse: true} })), Plot.text(thedata, Plot.stackX1({ filter: d => d.jurisdiction === 'federal', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: 'share', y: 'state', fill: d => d.jurisdiction === 'local' ? (d.state === 'Puerto Rico' ? 'white' : '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 => 'Hawaii', z: 'jurisdiction', text: d => `Promedio\n${d3.format(',.1%')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }95:Tbd2,{ let thevariable = {variable: 'share'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') .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 ? 'Average 50 states and DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Earned income tax credit - federal contribution', 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 }96:Tbcf,{ let thevariable = {variable: 'share'} let thedata = eitc_returns_with_local .filter(d => d.scale === 'average') .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 ? 'Average 50 states and DC' : 'Puerto Rico', }), Plot.axisY({ label: 'Earned income tax credit - local contribution', 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 }97: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() }98:Tc51,{ // 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({ marginTop: 40, 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: "Families", }), Plot.axisY({ fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', tickPadding: 20, label: 'Total Earned Income Tax Credit', }), // 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('$,.0f')(eitc_promedio)}`, // textAnchor: 'start', // lineAnchor: 'top', // fontFamily: 'jaf-bernino-sans', // fontWeight: 'bold', // fontSize: 13, // }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }99:T995,{ // const cols_show = ['state', 'below_poverty'] const cols_show = Object.keys(poverty_table[0]) const formatters = { 'below_poverty': '.1%', 'population_under_poverty': ',.0f', 'total_population': ',.0f', 'total_household': ',.0f', 'total_families': ',.0f', 'households_median_income': '$,.0f', 'families_median_income': '$,.0f', } const classes = { 'state': 'left-align first-col', 'below_poverty': 'tabular-nums bold', 'population_under_poverty': 'tabular-nums', 'total_population': 'tabular-nums', 'total_household': 'tabular-nums', 'total_families': 'tabular-nums', 'households_median_income': 'tabular-nums', 'families_median_income': 'tabular-nums last-col', } const row_classes = { 'Puerto Rico': 'highlight-row', } const headers = { 'state': 'State or jurisdiction', 'below_poverty': 'Population under poverty line (%)', 'population_under_poverty': 'Population under poverty line (#)', 'total_population': 'Total population (#)', 'total_household': 'Total households (#)', 'total_families': 'Total families (#)', 'households_median_income': 'Median household income ($)', 'families_median_income': 'Median family income ($)', } return htl.html` ${cols_show.map(c => htl.html``)} ${poverty_table.map((row) => htl.html` ${cols_show.map((col) => htl.html`` )} ` )}

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

` }9a:Taef,{ // 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 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: '$,.0f', label: 'Contribución neta per cápita del gobierno federal a los estados o territorios', }), Plot.axisY({ label: null, fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, // tickSpacing: .3, tickPadding: 80, tickFormat: d => state_labels[d] ?? d, }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { filter: d => d[thevariable.variable] < 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.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('$,.0f')(d[thevariable.variable]), dx: 5, textAnchor: 'start', sort: {y: "x", reverse: true}}), Plot.text([0], { x: eitc_promedio, dx: 5, y: d => 'Michigan', text: d => `Promedio\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }9b:Tf20,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10300, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 70, marginRight: 70, marginBottom: 100, marginTop: 30, width: 1000, height: 500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Contribución neta per cápita del gobierno federal a los estados o territorios', }), 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('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 14000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, rotate: -90, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, textAnchor: 'end', dy: 8, sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] < 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' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', dy: -8, sort: {x: "y", reverse: true}}), Plot.text([0], { y: eitc_promedio, // dx: 5, dy: -8, x: d => 'Michigan', text: d => `Promedio\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }9c:Teff,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10300, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 70, marginRight: 70, marginBottom: 130, marginTop: 30, width: 1000, height: 500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 16, tickFormat: '$,.0f', label: 'Deficits in Net Federal Expenditures per Capita', }), Plot.axisX({ 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.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('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 20000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, y: thevariable.variable, rotate: -90, x: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), // dx: 5, textAnchor: 'end', dy: 8, sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] < 20000, 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('$,.0f')(d[thevariable.variable]), // dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', dy: -8, sort: {x: "y", reverse: true}}), Plot.text([0], { y: eitc_promedio, // dx: 5, dy: -8, x: d => 'Michigan', text: d => `Average\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }9d:Tf4c,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10300, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 130, // marginRight: 70, marginBottom: 50, // marginTop: 30, // width: 1000, height: 700, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 16, tickFormat: '$,.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('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 20000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, // rotate: -90, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', // dy: 8, // sort: {y: "x", reverse: false}, }), Plot.text(thedata, { filter: d => d[thevariable.variable] < 20000, 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('$,.0f')(d[thevariable.variable]), dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', // dy: -8, // sort: {y: "x", reverse: false}, }), Plot.text([0], { x: eitc_promedio, dx: 5, // dy: -8, y: d => 'Michigan', text: d => `Average\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'bottom', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }9e:T1163,{ // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'outflow_per_capita'} let thedata = federal_outflows//.filter(d => d.outflow_per_capita < 0) thedata = aq.from(thedata) .derive({ 'outflow_per_capita': d => d.outflow_per_capita*-1 }) .derive({ 'below_pr': d => d.outflow_per_capita > 10300, }) // .orderby('outflow_per_capita') let eitc_promedio = aq.from(thedata) .rollup({ 'outflow_per_capita': op.mean('outflow_per_capita') }) .objects()[0]['outflow_per_capita'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 240, // marginRight: 70, marginBottom: 50, // marginTop: 30, // width: 1000, height: 1100, caption: 'AÑADIR FUENTE AQUI', // x: {percent: true,}, marks: [ Plot.axisX({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 16, tickFormat: '$,.0s', label: '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: 90, textAnchor: 'end', lineAnchor: 'middle', // tickFormat: d => state_labels[d] ?? d, }), Plot.ruleX([0]), 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['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, // y: thevariable.variable, // x: 'state', // fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', // text: d => d3.format('$,.0f')(d[thevariable.variable]), // // dx: -5, // // textAnchor: 'end', // sort: {x: "y", reverse: true}}), Plot.text(thedata, { filter: d => d[thevariable.variable] >= 20000, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, // rotate: -90, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, textAnchor: 'end', // dy: 8, // sort: {y: "x", reverse: false}, }), Plot.text(thedata, { filter: d => (d[thevariable.variable] < 20000) & (d[thevariable.variable] > 0), 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('$,.0f')(d[thevariable.variable]), dx: 5, stroke: 'white', strokeWidth: 6, textAnchor: 'start', // dy: -8, // sort: {y: "x", reverse: false}, }), Plot.text(thedata, { filter: d => (d[thevariable.variable] < 0), fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, // rotate: -90, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.0f')(d[thevariable.variable]), dx: -5, stroke: 'white', strokeWidth: 6, textAnchor: 'end', // dy: -8, // sort: {y: "x", reverse: false}, }), Plot.text([0], { x: eitc_promedio, dx: -25, // dy: -8, y: d => 'North Dakota', text: d => `Average\nexpenditure\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 }9f:Tbed,{ // 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: '$,.0f', label: 'Federal Government Net Contribution per Capita', }), Plot.axisY({ label: 'State or jurisdiction', fontFamily: 'jaf-bernino-sans', tickSize: 0, fontSize: 12, // tickSpacing: .3, tickPadding: 80, tickFormat: d => state_labels[d] ?? d, }), Plot.ruleX([eitc_promedio]), Plot.barX(thedata, { x: thevariable.variable, y: 'state', // fill: EA_COLORES_MAP['INDIGO'], // fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : '#cdd1ec', fill: d => d.state === 'Puerto Rico' ? EA_COLORES_MAP['INDIGO'] : ( d.below_pr ? chroma(EA_COLORES_MAP['RED']).alpha(1.).hex() : '#cdd1ec' ), // opacity: d => d['País'] === 'Puerto Rico (2023' sort: {y: "x", reverse: true}}), // Plot.ruleX([credito_promedio]), Plot.text(thedata, { filter: d => d[thevariable.variable] < 0, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', fontSize: 14.5, x: thevariable.variable, y: 'state', fill: d => d.state === 'Puerto Rico' ? 'black' : 'black', text: d => d3.format('$,.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('$,.0f')(d[thevariable.variable]), dx: 5, textAnchor: 'start', sort: {y: "x", reverse: true}}), Plot.text([0], { x: eitc_promedio, dx: 5, y: d => 'Michigan', text: d => `Average\n${d3.format('$,.0f')(eitc_promedio)}`, textAnchor: 'start', lineAnchor: 'top', fontFamily: 'jaf-bernino-sans', fontWeight: 'bold', fontSize: 13, }), ] }) // console.log('ugh', theplot) add_estilos(theplot, 'somebar') return theplot }a0:Te98,{ // let thevariable = variables_metadata.filter(d => (d.dato === 'monto_eitc') & (d.promedio_o_total === 'promedio'))[0]; let thevariable = {variable: 'average_eitc'} let estados_incluir = federal_outflows.filter(d => d.outflow_per_capita < 0).map(d => d.state) let estados_below_pr = federal_outflows .filter(d => estados_incluir.includes(d.state)) .filter(d => d.outflow_per_capita < -10301) .map(d => d.state); console.log('some estados_below_pr', estados_below_pr); let thedata = eitc_returns .filter(d => estados_incluir.includes(d.state)) .map(d => { return {...d, below_pr: estados_below_pr.includes(d.state)} }) ; // thedata = aq.from(thedata) // .derive({ // 'outflow_per_capita': d => d.outflow_per_capita*-1 // }) console.log('some thedata', thedata) let eitc_promedio = aq.from(thedata.filter(d => d.state !== 'Puerto Rico')) .rollup({ 'average_eitc': op.mean('average_eitc') }) .objects()[0]['average_eitc'] let state_labels = { 'District of Columbia': 'District of\nColumbia', } let theplot = Plot.plot({ marginLeft: 70, marginRight: 70, marginBottom: 100, marginTop: 30, width: 1000, height: 500, // caption: 'ACTUALIZAR MOVER XAXIS LABEL AL CENTRO', // x: {percent: true,}, marks: [ Plot.axisY({ fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, fontSize: 12, tickFormat: '$,.0f', label: 'Federal earned income tax credit - average', }), 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 => `Average\n(without\nPuerto 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 }a1:Tc67,{ // 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': 'State or jurisdiction', 'below_poverty': 'Población bajo nivel de pobreza (%)', 'population_under_poverty': 'Población bajo nivel de pobreza (#)', 'total_population': 'Total population (#)', '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': 'Federal government expenditures ($)', 'collections': 'Federal government revenues ($)', 'outflow_per_capita': 'Federal government net contribution per capita ($)', 'outflow': 'Federal government net contribution ($)', } 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]}

` }a2:Taec,{ let thedata = [ {year: 2021, renglon: 'bajo', planillas: 290257}, {year: 2021, renglon: 'rebasa', planillas: 62168}, {year: 2021, renglon: 'sobre', planillas: 293282}, {year: 2022, renglon: 'bajo', planillas: 298051}, {year: 2022, renglon: 'rebasa', planillas: 64260}, {year: 2022, renglon: 'sobre', planillas: 286048}, ] 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': 'Families below the poverty line', 'rebasa': 'Families that surpass the poverty line', 'sobre': 'Families over the poverty line', } 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: 'Families', 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'+ `(${d3.format('.0%')(d.pct)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d['renglon'] === 'bajo' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), Plot.text(thedata_totales, ({ x: 'year', y: 'total', text: d => d3.format(',.0f')(d['total']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, // fill: d => d['renglon'] === 'bajo' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }a3:T80c,{ let thedata = [ {year: 2021, personas: 123164, planillas: 62168}, {year: 2022, personas: 126014, planillas: 64260}, ] let theplot = Plot.plot({ width: 400, marginLeft: 60, // marginTop: 30, // marginBottom: 50, x: { // domain: num_dependientes_options, interval: 1, }, marks: [ Plot.axisX({ label: null, tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'People that surpass the federal poverty line', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), // Plot.dot(thedata, {x: "limite_inferior", y: "Crédito por Trabajo - Casados"}), Plot.barY(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'year', y: 'personas', fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, { // filter: (d => d['Estado Civil'] === estado_civil_mutable), x: 'year', y: 'personas', text: d => d3.format(',.0f')(d['personas'])+'\npeople\n\n'+`(${d3.format(',.0f')(d['planillas'])}\nfamilies)`, 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 }a4:T98b,{ let thedata = [ {year: 2021, recibe_eitc: true, planillas: 645707, pct: .53}, {year: 2021, recibe_eitc: false, planillas: 584153, pct: .47}, {year: 2022, recibe_eitc: true, planillas: 648359, pct: .54}, {year: 2022, recibe_eitc: false, planillas: 545151, pct: .46}, ] 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: 400, 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'+ `(${d3.format('.0%')(d.pct)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d['recibe_eitc'] ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), Plot.text(thedata_totales, ({ x: 'year', y: 'total', text: d => d3.format(',.0f')(d['total']), fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', lineAnchor: 'bottom', dy: -7, // fill: d => d['recibe_eitc'] ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }a5:T997,{ let thedata = [ {year: 2021, recibe_eitc: true, planillas: 645707, pct: .53}, {year: 2021, recibe_eitc: false, planillas: 584153, pct: .47}, {year: 2022, recibe_eitc: true, planillas: 648359, pct: .54}, {year: 2022, recibe_eitc: false, planillas: 545151, pct: .46}, ] let thedata_totales = aq.from(thedata) .groupby('year') .rollup({'total': op.sum('planillas')}) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totales)) .derive({pct: d => d.planillas/d.total}) .objects() let theplot = Plot.plot({ width: 400, marginLeft: 65, // marginTop: 30, // marginBottom: 50, x: { // domain: num_dependientes_options, interval: 1, }, color: { legend: true, domain: [true, false], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()], tickFormat: d => d ? 'Received earned income tax credit' : 'Did not receive earned income tax credit', // range: }, marks: [ Plot.axisX({ label: null, tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Families', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: ",.0f", fontSize: 12, }), Plot.barY(thedata, { x: 'year', y: 'planillas', fill: 'recibe_eitc', // fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, Plot.stackY({ x: 'year', y: 'planillas', z: 'recibe_eitc', text: d => d3.format(',.0f')(d['planillas']) +'\n'+ `(${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 }a6:T9e7,{ let thedata = [ {year: 2022, renglon: 'poblacion', variable: 'ingreso', cantidad: 15619}, {year: 2022, renglon: 'poblacion', variable: 'eitc', cantidad: 1731}, {year: 2022, renglon: 'bajo', variable: 'ingreso', cantidad: 10525}, {year: 2022, renglon: 'bajo', variable: 'eitc', cantidad: 2115}, {year: 2022, renglon: 'sobre', variable: 'ingreso', cantidad: 22071}, {year: 2022, renglon: 'sobre', variable: 'eitc', cantidad: 1243}, ] 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 = { 'bajo': 'Families that did not\nsurpass the federal\npoverty line', 'poblacion': 'Families that claimed\nthe earned income\ntax credit', 'sobre': 'Families above the\nfederal poverty line', } 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' ? 'Gross earned income (average)' : 'Earned income tax credit (average)', }, marks: [ Plot.axisX({ label: null, // tickFormat: '', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 16, tickFormat: d => renglon_labels[d] ?? d, }), Plot.axisY({ label: 'Amount', 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']) +' '+ `(${d3.format('.0%')(d.pct)})`, fontSize: 16, fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', fontWeight: 'bold', // lineAnchor: 'top', dy: 7, fill: d => d['variable'] === 'ingreso' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }a7:T86e,{ let thedata = ingreso_credito_por_dependientes_pivoted let thedata_totales = aq.from(thedata) .groupby('Num deps') .rollup({'total': op.sum('cantidad')}) .objects(); thedata = aq.from(thedata) .join_left(aq.from(thedata_totales)) .derive({pct: d => d.cantidad/d.total}) .objects() let renglon_labels = { 'bajo': 'No rebasó la pobreza', 'poblacion': 'Población que reclamó\nel Crédito por Trabajo', 'sobre': 'Fuera de pobreza', } let theplot = Plot.plot({ width: 800, marginLeft: 60, // marginTop: 30, marginBottom: 50, x: { // domain: num_dependientes_options, // interval: 1, }, color: { legend: true, domain: ['Ingreso promedio', 'Credito promedio'], range: [EA_COLORES_MAP['INDIGO'], chroma(EA_COLORES_MAP['INDIGO']).alpha(0.35).hex()], tickFormat: d => d === 'Ingreso promedio' ? 'Gross earned income (average)' : 'Earned income tax credit (average)', }, marks: [ Plot.axisX({ label: 'Dependents', tickFormat: d => d === num_deps_cutoff ? `${num_deps_cutoff} or more` : d, tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, // tickFormat: d => renglon_labels[d] ?? d, }), Plot.axisY({ label: 'Amount', fontFamily: 'jaf-bernino-sans', fontVariant: 'tabular-nums', tickSize: 0, tickFormat: "$,.0f", fontSize: 12, }), Plot.barY(thedata, { x: 'Num deps', y: 'cantidad', fill: 'variable', // fill: EA_COLORES_MAP['INDIGO'], }), Plot.text(thedata, Plot.stackY({ x: 'Num deps', y: 'cantidad', z: 'variable', text: d => d3.format('$,.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'] === 'Ingreso promedio' ? 'white' : 'black', // textAnchor: 'end', dx: -5, })), ] }) return theplot }a8:Te52,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: 'Poverty rate', }), 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 }a9:Te61,aplot_discrete = { let thedata = [ {eitc_incluido: false, below_poverty: .417}, {eitc_incluido: true, below_poverty: .378}, ] 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 ? 'Estimated poverty rate\nafter the earned income\ntax credit' : 'Poverty rate', tickSize: 0, fontFamily: 'jaf-bernino-sans', fontSize: 12, }), Plot.axisY({ label: 'Poverty rate', 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 }aa:Tc8e,{ 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: 'Labor participation rate', }), 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 }ab:Tcb2,{ let thedata = participacion_laboral.filter(d => d.year >= 2017) 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: 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 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 }ac:T4b8,eitc_isla_2019_2022 = { let data = aq.from(eitc_municipal_2019_2022) .groupby('Año contributivo') .rollup({ 'Crédito por Trabajo - Total': d => op.sum(d['Crédito por Trabajo - Total']), "Planillas Total - Crédito por Trabajo": d => op.sum(d["Planillas Total - Crédito por Trabajo"]), 'Total - Total Ingreso bruto ganado para la determinación del EITC': d => op.sum(d['Total - Total Ingreso bruto ganado para la determinación del EITC']), 'Planillas Total - Total Ingreso bruto ganado para la determinación del EITC': d => op.sum(d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC']), "Total de personas": d => op.sum(d["Total de personas"]), }) .derive({ "Crédito por Trabajo - Promedio": d => d['Crédito por Trabajo - Total'] / d["Planillas Total - Crédito por Trabajo"], 'Promedio - Total Ingreso bruto ganado para la determinación del EITC': d => d['Total - Total Ingreso bruto ganado para la determinación del EITC'] / d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'], }) .orderby(aq.desc('Año contributivo')) .objects() // data.forEach(d => add_extra_eitc_columns(d)) return data }ad:T7e8,add_extra_eitc_columns = d => { // Montos de credito por trabajo d["Crédito por Trabajo - Total"] = d["Crédito por Trabajo - Contribuyente Individual"] + d["Crédito por Trabajo - Casados"] d["Planillas Total - Crédito por Trabajo"] = d["Planillas Contribuyente Individual - Crédito por Trabajo"] + d["Planillas Casados - Crédito por Trabajo"] d['Crédito por Trabajo - Promedio'] = d["Crédito por Trabajo - Total"] / d["Planillas Total - Crédito por Trabajo"] d['Crédito por Trabajo - Promedio - Contribuyente Individual'] = d["Crédito por Trabajo - Contribuyente Individual"] / d["Planillas Contribuyente Individual - Crédito por Trabajo"] d['Crédito por Trabajo - Promedio - Casados'] = d["Crédito por Trabajo - Casados"] / d["Planillas Casados - Crédito por Trabajo"] // Ingreso bruto ganado para determinacion del EITC d['Total - Total Ingreso bruto ganado para la determinación del EITC'] = d['Casados - Total Ingreso bruto ganado para la determinación del EITC'] + d['Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC'] d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'] = d['Planillas Casados - Total Ingreso bruto ganado para la determinación del EITC'] + d['Planillas Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC'] d['Promedio - Total Ingreso bruto ganado para la determinación del EITC'] = d['Total - Total Ingreso bruto ganado para la determinación del EITC'] / d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'] // Cantidad de planillas y personas d['Total de personas'] = 1*d["Planillas Contribuyente Individual - Crédito por Trabajo"] + 2*d["Planillas Casados - Crédito por Trabajo"] + d['Anejo CT Cantidad de dependientes cualificados'] d['Promedio - Anejo CT Cantidad de dependientes cualificados'] = d['Anejo CT Cantidad de dependientes cualificados'] / d['Planillas - Anejo CT Cantidad de dependientes cualificados'] }ae:T69c,eitc_municipal_2019_2022 = { let data = eitc_municipal_2019_2022_initial; data.forEach(d => { // Montos de credito por trabajo d["Crédito por Trabajo - Total"] = d["Crédito por Trabajo - Contribuyente Individual"] + d["Crédito por Trabajo - Casados"] d["Planillas Total - Crédito por Trabajo"] = d["Planillas Contribuyente Individual - Crédito por Trabajo"] + d["Planillas Casados - Crédito por Trabajo"] d['Crédito por Trabajo - Promedio'] = d["Crédito por Trabajo - Total"] / d["Planillas Total - Crédito por Trabajo"] // Ingreso bruto ganado para determinacion del EITC d['Total - Total Ingreso bruto ganado para la determinación del EITC'] = d['Casados - Total Ingreso bruto ganado para la determinación del EITC'] + d['Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC'] d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'] = d['Planillas Casados - Total Ingreso bruto ganado para la determinación del EITC'] + d['Planillas Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC'] d['Promedio - Total Ingreso bruto ganado para la determinación del EITC'] = d['Total - Total Ingreso bruto ganado para la determinación del EITC'] / d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'] // Cantidad de planillas y personas d['Total de personas'] = 1*d["Planillas Contribuyente Individual - Crédito por Trabajo"] + 2*d["Planillas Casados - Crédito por Trabajo"] + (isNaN(d['Anejo CT Cantidad de dependientes cualificados']) ? 0 : d['Anejo CT Cantidad de dependientes cualificados']) }) return data }af:T6b8,eitc_returns = { let data = await FileAttachment("eitc_returns@1.csv").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 = (+d.claims.slice(0,-2))*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,-2))*scaler // d.rate = (+d.rate.slice(0, -1))/100 d.average_eitc = +d.average_eitc.replace('$', '').replace(',','') }) data= [{ state: 'Puerto Rico', claims: 646471, total_eitc: 633015702, average_eitc: Math.round(633015702 / 646471), year: 2022, }, ...data] data = aq.from(data).join_left(aq.from(state_abbreviations), 'state').objects() return data }b0:T580,// eitc_t1_escala_ingresos_2019_2022 = { // let data = bruh; // data.forEach(d => { // // Montos de credito por trabajo // d["Crédito por Trabajo - Total"] = d["Crédito por Trabajo - Contribuyente Individual"] + d["Crédito por Trabajo - Casados"] // d["Planillas Total - Crédito por Trabajo"] = d["Planillas Contribuyente Individual - Crédito por Trabajo"] + d["Planillas Casados - Crédito por Trabajo"] // d['Crédito por Trabajo - Promedio'] = d["Crédito por Trabajo - Total"] / d["Planillas Total - Crédito por Trabajo"] // // Ingreso bruto ganado para determinacion del EITC // d['Total - Total Ingreso bruto ganado para la determinación del EITC'] = d['Casados - Total Ingreso bruto ganado para la determinación del EITC'] + d['Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC'] // d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'] = d['Planillas Casados - Total Ingreso bruto ganado para la determinación del EITC'] + d['Planillas Contribuyente Individual - Total Ingreso bruto ganado para la determinación del EITC'] // d['Promedio - Total Ingreso bruto ganado para la determinación del EITC'] = d['Total - Total Ingreso bruto ganado para la determinación del EITC'] / d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'] // }) // return data; // 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['ingreso_ganado_eitc'] : [],\n })\n theinput.value = dato_mutable\n theinput.oninput = () => mutable dato_mutable = theinput.value\n return theinput\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":847,"value":"htl.html`\n

${dato_labels[dato_input]} por año contributivo y municipio

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":50,"value":"$31","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2150,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2311,"value":"municipios_label_hack = Object({\n 'Quebradillas': '\\nQuebradillas',\n 'Camuy': '\\nCamuy',\n 'Guaynabo': '\\nGuaynabo',\n 'Canóvanas': '\\nCanóvanas',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3154,"value":"legend_label_hack = Object({\n 'Crédito por Trabajo - Promedio': 'Earned Income Tax Credit - Average',\n 'Planillas Total - Crédito por Trabajo': 'Families',\n 'Promedio - Total Ingreso bruto ganado para la determinación del EITC': 'Gross Earned Income for determining Earned Income Tax Credit - Average',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1952,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1874,"value":"download_buttons(grafica_01, 'EITC_grafica_01', '#1')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":458,"value":"logica_mapa_highlight = {\n // LOGICA para actualizar el mapa cuando la variable de municipio cambie\n // TODO: Considerar si incluir esto dentro del cell del mapa? o haria un loop?\n // tbf, if it ain't broke, don't fix it (yet)\n if (!muni_mutable) return null;\n\n let highlighted_path = d3.select(grafica_01).selectAll(\"svg g[aria-description='outline-key']\").selectAll('title').nodes().filter(d => d.innerHTML === muni_mutable)[0].parentNode\n let sibs = getSiblings(highlighted_path);\n // console.log('highlighted_path', highlighted_path)\n\n // TODO: TEST\n d3.select(highlighted_path)\n .raise()\n .style(\"fill\", null)\n .style('fill-opacity', 1.0)\n .style('stroke', 'var(--selected-color)').style('stroke-width', 5)\n sibs.forEach(s => \n d3.select(s)\n .style(\"fill\", 'black')\n .style('fill-opacity', 0.6)\n .style('stroke', null)\n )\n \n return highlighted_path\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":699,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1779,"value":"import {serialize, rasterize} from \"@mbostock/saving-svg\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1793,"value":"estilos.tagNam","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":914,"value":"ramp(color_interp)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":919,"value":"color_interp = d3.interpolateHcl(\n chroma.mix('white',EA_COLORES_MAP['INDIGO'], .90, 'lch').hex(),\n EA_COLORES_MAP['LIME'])","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":422,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2068,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2078,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2083,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1878,"value":"download_buttons(grafica_01_bar, 'EITC_grafica_01_barras', '#1 barras')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2415,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5047,"value":"$3a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2664,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2440,"value":"$3c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5035,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5209,"value":"

\n Individual\n

\n \n

\n Casados\n

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5173,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5476,"value":"

\n Single\n

\n \n

\n Married\n

\n","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5469,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5165,"value":"eitc_trapezoid_paths = {\n let data = structuredClone(eitc_trapezoids)\n data.forEach(d => {\n const path = d.path\n path.forEach(p => {\n p['filing_status'] = d.filing_status\n p['kids'] = d.kids\n })\n \n })\n\n return data.map(d => d.path).flat()\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5148,"value":"eitc_trapezoids = {\n const filing_status_options = ['single', 'married'];\n const kids_options = ['0', '1', '2', '3'];\n\n let data = [];\n for (let filing_status of filing_status_options) {\n for (let kids of kids_options) {\n const d = structuredClone(eitc_params_2023[filing_status][kids])\n d['filing_status'] = filing_status\n d['kids'] = +kids\n d['plateau_middle'] = 0.5*(d.phase_in + d.phase_out_start)\n\n d['path'] = [\n {income: 0, eitc: 0, desc: 'start', i: 0},\n {income: d.phase_in, eitc: d.max_credit, desc: 'phase_in_end', i: 1},\n {income: d.phase_out_start, eitc: d.max_credit, desc: 'phase_out_start', i: 2},\n {income: d.phase_out_end, eitc: 0, desc: 'phase_out_end', i: 3},\n ]\n\n data = [...data, d]\n }\n }\n\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5146,"value":"$40","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1339,"value":"function create_year_select() { \n let theinput = Inputs.radio(years, {label: \"Elige un año\",\n // format: d => d.toString(),\n format: d => (dato_input === 'monto_eitc') ? htl.html`${d}` :\n (d < 2021) ? htl.html`~~${d}~~` : htl.html`${d}`,\n value: years[years.length-1],\n disabled: years.filter(d => (dato_input === 'monto_eitc') ? false : (d < 2021))\n })\n // theinput.value = year_mutable;\n // theinput.oninput = () => mutable year_mutable = theinput.value;\n return theinput\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1984,"value":"$41","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2288,"value":"$42","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3921,"value":"### Ranking municipal","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3956,"value":"$43","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3923,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3943,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3946,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3952,"value":"$47","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":993,"value":"htl.html`\n

2. ${dato_labels[dato_input]} según estado civil y sexo

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1341,"value":"viewof year_select_2 = create_year_select()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1393,"value":"viewof estado_civil_select_2 = { \n let theinput = Inputs.radio(estado_civil_options, {label: \"Elige estado civil\",\n // format: d => d.toString(),\n // format: d => (dato_input === 'monto_eitc') ? htl.html`${d}` :\n // (d < 2021) ? htl.html`~~${d}~~` : htl.html`${d}`,\n value: estado_civil_options[0],\n // disabled: years.filter(d => (dato_input === 'monto_eitc') ? false : (d < 2021))\n })\n return theinput\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2163,"value":"### Barras","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1571,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5403,"value":"sex_labels = Object({\n 'Femenino': 'Female',\n 'Masculino': 'Male',\n 'No marcó o otro': 'Did not mark or other',\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1885,"value":"download_buttons(grafica_02, 'EITC_grafica_02', '#2')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1773,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1894,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2165,"value":"### Cambio por año\n\nGrafica de lineas de EITC x Estado civil y sexo donde sea X-axis el año, y-axis es la variable, y facet by la combinacion de sexo y estado civil. Realizar esta para cada una de las 3 variables.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2169,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2329,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5070,"value":"$4d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3807,"value":"eitc_t6_estado_2019_2022 = aq.from(eitc_t6_estado_sexo_2019_2022)\n .groupby('Estado Civil', 'Año contributivo')\n .rollup({\n 'Crédito por Trabajo - Total': op.sum('Crédito por Trabajo - Total'),\n 'Planillas Total - Crédito por Trabajo': op.sum('Planillas Total - Crédito por Trabajo'),\n })\n .derive({\n 'Crédito por Trabajo - Promedio': d => d['Crédito por Trabajo - Total'] / d['Planillas Total - Crédito por Trabajo'],\n })\n.objects()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3804,"value":"$4e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5257,"value":"function capitalizeOnlyFirstLetter(string) {\n return string.charAt(0).toUpperCase() + string.slice(1).toLowerCase();\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5053,"value":"$4f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3830,"value":"$50","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2218,"value":"$51","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2221,"value":"$52","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2224,"value":"### Cambio por año (pero menos facets)\ncon promedio de dependientes por planilla en texto","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3858,"value":"aq.from(eitc_t6_sexo_2019_2022).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3841,"value":"eitc_t6_sexo_2019_2022 = aq.from(eitc_t6_estado_sexo_2019_2022)\n .groupby('Sexo Contribuyente', 'Año contributivo')\n .rollup({\n 'Crédito por Trabajo - Total': op.sum('Crédito por Trabajo - Total'),\n 'Planillas Total - Crédito por Trabajo': op.sum('Planillas Total - Crédito por Trabajo'),\n 'Total - Total Ingreso bruto ganado para la determinación del EITC': op.sum('Total - Total Ingreso bruto ganado para la determinación del EITC'),\n 'Planillas Total - Total Ingreso bruto ganado para la determinación del EITC': op.sum('Planillas Total - Total Ingreso bruto ganado para la determinación del EITC'),\n })\n .derive({\n 'Crédito por Trabajo - Promedio': d => d['Crédito por Trabajo - Total'] / d['Planillas Total - Crédito por Trabajo'],\n 'Promedio - Total Ingreso bruto ganado para la determinación del EITC': d => d['Total - Total Ingreso bruto ganado para la determinación del EITC'] / d['Planillas Total - Total Ingreso bruto ganado para la determinación del EITC']\n })\n.objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2226,"value":"$53","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5059,"value":"$54","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5062,"value":"sex_english = Object({\n 'Femenino': 'Female',\n 'Masculino': 'Male',\n 'No marcó u otro': 'Did not mark or other',\n 'No marcó o otro': 'Did not mark or other',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5066,"value":"marital_english = Object({\n 'Contribuyente Individual': 'Individual',\n 'Casado': 'Married',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2360,"value":"$55","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5536,"value":"$56","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2377,"value":"$57","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4883,"value":"Consolidando la variable de Sexo del grafico anterior:","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4875,"value":"{\n let thedata = eitc_t6_estado_sexo_2019_2022\n .filter(d => d['Año contributivo'] > 2020)\n // .filter(d => d['Estado Civil'] == estado_civil_select_2)\n ;\n // let thevariable = variables_metadata.filter(d => (d.dato === dato_input) & (d.promedio_o_total === 'promedio'))[0];\n let thevariable = {variable: 'Promedio - Anejo CT Cantidad de dependientes cualificados'}\n\n thedata = aq.from(thedata)\n .groupby('Año contributivo', 'Estado Civil')\n .rollup({\n 'Anejo CT Cantidad de dependientes cualificados': op.sum('Anejo CT Cantidad de dependientes cualificados'),\n 'Planillas - Anejo CT Cantidad de dependientes cualificados': op.sum('Planillas - Anejo CT Cantidad de dependientes cualificados'),\n })\n .derive({\n 'Promedio - Anejo CT Cantidad de dependientes cualificados' : d => d['Anejo CT Cantidad de dependientes cualificados'] / d['Planillas - Anejo CT Cantidad de dependientes cualificados']\n })\n .objects();\n\n return aq.from(thedata).view()\n\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2383,"value":"$58","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5548,"value":"$59","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3853,"value":"$5a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2241,"value":"$5b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3864,"value":"$5c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2245,"value":"$5d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1769,"value":"eitc_t6_estado_sexo_2019_2022\n .filter(d => d['Año contributivo'] === year_select_2)\n .filter(d => d['Estado Civil'] == estado_civil_select_2)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":886,"value":"htl.html`\n

3. ${dato_labels[dato_input]} por edad del contribuyente

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1350,"value":"viewof year_select_3 = create_year_select()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":881,"value":"$5e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5078,"value":"$5f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1898,"value":"$60","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1903,"value":"$61","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1888,"value":"download_buttons(grafica_03, 'EITC_grafica_03', '#3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1008,"value":"htl.html`\n

4. ${dato_labels[dato_input]} según dependientes

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1355,"value":"viewof year_select_4 = Inputs.radio([2021,2022], {label: \"Elige un año\",\n format: d => d.toString(),\n // format: d => (dato_input === 'monto_eitc') ? htl.html`${d}` :\n // (d < 2021) ? htl.html`~~${d}~~` : htl.html`${d}`,\n value: years[years.length-1],\n // disabled: years.filter(d => (dato_input === 'monto_eitc') ? false : (d < 2021))\n })","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3900,"value":"### Cambiando segun estado civil","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1052,"value":"viewof estado_civil_select = { \n let theinput = Inputs.radio(estado_civil_options, {label: \"Elige estado civil\",\n // format: d => d.toString(),\n // format: d => (dato_input === 'monto_eitc') ? htl.html`${d}` :\n // (d < 2021) ? htl.html`~~${d}~~` : htl.html`${d}`,\n value: estado_civil_options[0],\n // disabled: years.filter(d => (dato_input === 'monto_eitc') ? false : (d < 2021))\n })\n theinput.value = estado_civil_mutable;\n theinput.oninput = () => mutable estado_civil_mutable = theinput.value;\n return theinput\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1361,"value":"$62","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1905,"value":"$63","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1916,"value":"$64","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5416,"value":"dependents_labels = Object({\n 'No dependiente': 'No dependents',\n '1 dependiente': '1 dependent',\n '2 dependientes': '2 dependents',\n '3 o más dependientes': '3 or more dependents',\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3903,"value":"### Consolidado (solo año cambia, no estado civil)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3890,"value":"$65","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3905,"value":"$66","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5082,"value":"dependientes_english = Object({\n 'No dependiente': 'No dependents',\n '1 dependiente': '1 dependent',\n '2 dependientes': '2 dependents',\n '3 o más dependientes': '3 or more dependents',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5080,"value":"$67","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3913,"value":"$68","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1013,"value":"$69","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1077,"value":"htl.html`\n

5. ${dato_labels[dato_input]} según la fuente de mayor ingreso

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1377,"value":"viewof year_select_5 = create_year_select()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":982,"value":"$6a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1919,"value":"$6b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1925,"value":"$6c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4073,"value":"$6d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4091,"value":"$6e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2127,"value":"eitc_t4_fuente_ingreso_2019_2022","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2115,"value":"$6f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3781,"value":"fuente_ingreso_newline_labels = Object({\n 'Empleado del Gobierno, municipios o Corporaciones Públicas': 'Public government\\nemployee',\n 'Empleado del Gobierno Federal': 'Federal government\\nemployee',\n 'Trabajo por Cuenta Propia': 'Self-employed',\n 'Retirado / Pensionado': 'Retired or pensioned',\n 'Empleado de Empresa Privada': 'Private sector\\nemployee',\n 'Otro': 'Other',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3784,"value":"fuente_ingreso_labels","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3767,"value":"$70","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3794,"value":"$71","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2141,"value":"$72","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2146,"value":"$73","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1519,"value":"fuente_ingreso_labels = Object({\n 'Empleado del Gobierno, municipios o Corporaciones Públicas': 'Empleado del gobierno público',\n 'Empleado del Gobierno Federal': 'Empleado del gobierno federal',\n 'Trabajo por Cuenta Propia': 'Trabajo por cuenta propia',\n 'Retirado / Pensionado': 'Retirado o pensionado',\n 'Empleado de Empresa Privada': 'Empleado de empresa privada',\n 'Otro': 'Otra',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1074,"value":"htl.html`\n

6. ${dato_labels[dato_input]} según ocupación del contribuyente

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1381,"value":"viewof year_select_6 = create_year_select()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":954,"value":"$74","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5439,"value":"\nlistado_ocupaciones = {\n const listado = d3.sort(eitc_t3_ocupacion_2019_2022,\n d => d['Ocupación Contribuyente'] == 'Otros listado abajo*',\n ).map(d => d['Ocupación Contribuyente'])\n\n console.log('listado ocupaciones')\n console.log(listado)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5431,"value":"$75","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1927,"value":"$76","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1934,"value":"$77","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5448,"value":"{\n const other_profs = (Object.values(year_to_otras_profesiones)).flat()\n console.log('other profs')\n console.log(other_profs)\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1404,"value":"$78","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4011,"value":"$79","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5087,"value":"$7a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5495,"value":"occupation_labels","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4020,"value":"$7b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4045,"value":"$7c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4037,"value":"$7d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4062,"value":"$7e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":842,"value":"htl.html`\n

7. ${dato_labels[dato_input]} segun umbral de ingreso

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1387,"value":"viewof year_select_7 = create_year_select()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3762,"value":"trapezoide_2022 = [\n {x: 0, y: 0},\n {x: 10113, y: 1517},\n {x: 16280, y: 1517},\n {x: 26500, y: 0},\n \n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":810,"value":"$7f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1936,"value":"$80","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2390,"value":"$81","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2406,"value":"$82","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1939,"value":"$83","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3194,"value":"## 8. Análisis comparativo con estados","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2510,"value":"$84","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5564,"value":"$85","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4573,"value":"function get_data_changes(thedata, domain_var, value_var) {\n const ugh = domain_var;\n let thedata_changes = aq.from(\n thedata.map(d => {return {...d, domain_var: d[domain_var], value_var: d[value_var]}})\n )\n // console.log('some thedata_changes', thedata_changes.objects())\n\n thedata_changes = thedata_changes\n .derive({\n 'current_domain': d => d['domain_var'],\n 'previous_domain': d => op.lag(d['domain_var']),\n \n 'current_value': d => d['value_var'],\n 'previous_value': d => op.lag(d['value_var']),\n })\n .filter(d => d.previous_value !== undefined)\n .derive({\n 'mid_domain': d => (d.current_domain + d.previous_domain)/2,\n 'change_value': d => d.current_value - d.previous_value,\n })\n .objects();\n // console.log('finished thedata_changes', thedata_changes)\n return thedata_changes\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4293,"value":"$86","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2591,"value":"$87","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4325,"value":"$88","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4312,"value":"// aq.from(eitc_local_governments).view()\neitc_local_governments_with_zeros = aq.from(eitc_returns).select('state')\n .join_left(aq.from(eitc_local_governments))\n .impute({\n percentage_of_federal_credit: d => 0,\n })\n .objects()\n\n","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2545,"value":"$89","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5570,"value":"$8a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4766,"value":"$8b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4664,"value":"$8c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2690,"value":"$8d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5574,"value":"$8e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4776,"value":"$8f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2650,"value":"$90","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5582,"value":"$91","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4782,"value":"$92","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4890,"value":"$93","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4335,"value":"$94","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4347,"value":"$95","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4886,"value":"$96","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2606,"value":"$97","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2550,"value":"$98","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2781,"value":"dot_abbreviation_hack = Object({\n 'AK': 'AK \\n',\n 'CT': ' CT',\n 'DC': '\\n\\n\\n DC',\n 'IA': '\\n\\n\\nIA ',\n 'ID': '\\n\\n\\n\\n ID',\n 'KS': '\\nKS ',\n 'MA': ' MA',\n 'ME': ' ME',\n 'MD': ' MD',\n 'MO': 'MO ',\n 'MN': 'MN ',\n 'MT': '\\n\\n\\n MT',\n 'NC': 'NC ',\n 'ND': '\\n\\n\\n\\n ND',\n 'NH': '\\n\\n\\n\\n NH',\n 'NJ': ' NJ',\n 'NM': 'NM ',\n 'OH': ' OH',\n 'RI': '\\n\\nRI ',\n 'SD': 'SD ',\n 'UT': '\\n\\n UT',\n 'VT': '\\n\\n\\n\\n\\n VT',\n 'WY': '\\n\\n\\n\\nWY ',\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2912,"value":"$99","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4383,"value":"$9a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4920,"value":"$9b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5092,"value":"$9c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5100,"value":"$9d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5150,"value":"### Net federal expenditures per capita\n\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":5124,"value":"$9e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5593,"value":"$9f","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4967,"value":"$a0","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4355,"value":"usaspending_obligations = {\n let data = await FileAttachment(\"usaspending_obligations@1.csv\").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":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4364,"value":"irs_collections = {\n let data = await FileAttachment(\"irs_gross_collections_2022.csv\").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":4381,"value":"federal_outflows","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4367,"value":"federal_outflows = aq.from(poverty_table)\n .select('state', 'total_population')\n .join_full(aq.from(usaspending_obligations))\n .join_full(aq.from(irs_collections))\n .derive({\n 'outflow': d => d.collections - d.obligations, // outflow means FROM the state TO los federales\n })\n .derive({\n 'outflow_per_capita': d => d.outflow / d.total_population,\n })\n .orderby(aq.desc('outflow_per_capita'))\n.objects()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4910,"value":"aq.from(federal_outflows).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4908,"value":"\n \n \n \n \n \n \n \n \n ${cars.slice(0, 5).map(car => htl.html`\n \n \n \n `)}\n \n

Name	Fuel economy	Year
${car[\"name\"]}	${car[\"economy (mpg)\"]} mpg	’${car[\"year\"]}

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4812,"value":"$a1","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4113,"value":"## 9. Impacto en la pobreza","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4123,"value":"### Renglones de pobreza por año","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4162,"value":"$a2","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4115,"value":"### Número de personas que sobrepasan la pobreza por año","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4125,"value":"$a3","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4117,"value":"### Familias que reciben el crédito por trabajo","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4142,"value":"$a4","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5050,"value":"$a5","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4119,"value":"### Ingreso bruto ganado promedio, segun renglon de pobreza (2022)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4183,"value":"$a6","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4121,"value":"### Ingreso bruto ganado promedio, segun número de dependientes","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4234,"value":"$a7","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4415,"value":"$a8","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4549,"value":"aplot.scale('x')//.apply(2014)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4434,"value":"$a9","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4694,"value":"$aa","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4702,"value":"$ab","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4690,"value":"aq.from(participacion_laboral).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4687,"value":"participacion_laboral = {\n let data = await FileAttachment(\"tasa_participacion_laboral@2.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":4554,"value":"aplot_discrete.scale('x')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4406,"value":"aq.from(poverty_puerto_rico).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4404,"value":"poverty_puerto_rico = {\n let data = await FileAttachment(\"PovertyRate2006-2022 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":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5090,"value":"aq.from(ingreso_credito_por_dependientes).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4220,"value":"ingreso_credito_por_dependientes = {\n let data = await FileAttachment(\"ingreso_credito_por_dependientes.csv\").csv({typed: true})\n\n data.forEach(d => {\n d['Total ingreso'] = +d['Total ingreso'].replaceAll(',', '');\n d['Total planillas'] = +(d['Total planillas'].toString()).replaceAll(',', '')\n d['Total credito'] = +(d['Total credito'].toString()).replaceAll(',', '')\n })\n\n // TODO: Consolidar a 3+ dependientes\n\n data.forEach(d => {\n d['Ingreso promedio'] = +(d['Ingreso promedio'].toString()).replaceAll(',', '').replaceAll('$','')\n d['Credito promedio'] = +(d['Credito promedio'].toString()).replaceAll(',', '').replaceAll('$','')\n })\n\n // data = aq.from(data)\n // .fold(['Ingreso promedio', 'Credito promedio'], {as: ['variable', 'cantidad']})\n // .objects()\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4646,"value":"num_deps_cutoff = 3","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4641,"value":"ingreso_credito_por_dependientes_consolidated = {\n let data = ingreso_credito_por_dependientes\n\n data = aq.from(data)\n .derive({\n 'Num deps': aq.escape(d => d['Num deps'] >= num_deps_cutoff ? num_deps_cutoff : d['Num deps'])\n })\n .groupby('Num deps')\n .rollup({\n 'Total ingreso': op.sum('Total ingreso'),\n 'Total planillas': op.sum('Total planillas'),\n 'Total credito': op.sum('Total credito'),\n })\n .derive({\n 'Ingreso promedio': d => d['Total ingreso'] / d['Total planillas'],\n 'Credito promedio': d => d['Total credito'] / d['Total planillas'],\n })\n .objects()\n\n \n \n\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4637,"value":"ingreso_credito_por_dependientes_pivoted = {\n let data = ingreso_credito_por_dependientes_consolidated\n data = aq.from(data)\n .fold(['Ingreso promedio', 'Credito promedio'], {as: ['variable', 'cantidad']})\n .objects()\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1461,"value":"# Apéndice","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1690,"value":"chroma = require(\"chroma-js\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1846,"value":"fonts_link = \"https://use.typekit.net/szl4srx.css\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1850,"value":"fonts_css_content = await fetch(fonts_link).then(r => r.text())","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1788,"value":"// htl.html`\n// \n// `","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1463,"value":"estilos = htl.html`\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5558,"value":"htl.html`\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1826,"value":"estilos_content = d3.select(estilos).select('style').node().textContent","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1866,"value":"function add_estilos(theplot, plotname = null) {\n var styleElement = document.createElementNS('http://www.w3.org/2000/svg', 'style');\n styleElement.textContent = fonts_css_content + '\\n' + estilos_content;\n \n let myplot = d3.select(theplot)\n const toplevel_tag = myplot.node().tagName\n // console.log('toplevel_tag', toplevel_tag)\n\n if (toplevel_tag !== 'svg') {\n myplot.selectAll(\"svg\")\n .each((d, i, nodes) => {\n // if (plotname !== null) {\n // console.log('add_estilos for ' + plotname + ': d, i, nodes:', d, i, nodes)\n // }\n d3.select(nodes[i]).node().insertBefore(\n styleElement,\n nodes[i].firstChild,\n );\n \n })\n } else {\n myplot.node().insertBefore(\n styleElement,\n myplot.firstChild,\n );\n }\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1861,"value":"function download_buttons(svg_embed, filename_no_ext, label_prefix) {\n return html`\n ${DOM.download(serialize(svg_embed), filename_no_ext+'.svg', `${label_prefix} - Download as SVG`)}\n ${DOM.download(rasterize(svg_embed), filename_no_ext+'.png', `${label_prefix} - Download as PNG`)}\n`\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1804,"value":"estilos.children[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1710,"value":"\n$1,234M\n\n
\n\n$1,234M\n\n
\n\n$1,234m\n","pinned":true,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1044,"value":"estado_civil_options = {\n // return [...new Set(eitc_t7_estado_dependientes_2021_2022.map(d => d['Estado Civil']))]\n return [\"Contribuyente Individual\", \"Casado\"]\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1032,"value":"num_dependientes_options = [\"No dependiente\", \"1 dependiente\", \"2 dependientes\", \"3 o más dependientes\", ]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1017,"value":"eitc_t7_estado_dependientes_2021_2022[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":813,"value":"eitc_t1_escala_ingresos_2019_2022[0]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":187,"value":"## En un municipio","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":150,"value":"viewof muni_select = {\n let theinput = Inputs.select([null, ...munis_in_geojson], {label: \"Selecciona un municipio\"});\n theinput.value = muni_mutable;\n theinput.oninput = () => mutable muni_mutable = theinput.value;\n return theinput\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":271,"value":"estilos_viejos = html`\n\n`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":209,"value":"Plot.plot({\n width,\n marginLeft: 50,\n x: {tickFormat: \"\"},\n y: {tickFormat: \"$,.0f\" },\n marks: [\n Plot.ruleY([0]),\n Plot.boxY(eitc_municipal_2019_2022, {\n x: \"Año contributivo\", y: 'Crédito por Trabajo - Promedio',\n // stroke: \"Municipios\",\n }),\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":685,"value":"Plot.plot({\n y: {\n domain: [1400, 2000],\n \n },\n marks: [\n Plot.dot(eitc_municipal_2019_2022, {\n x: 'Promedio - Total Ingreso bruto ganado para la determinación del EITC',\n y: 'Crédito por Trabajo - Promedio',\n tip: true,\n channels: {municipio: \"Municipios\", año: \"Año contributivo\"},\n }),\n Plot.linearRegressionY(eitc_municipal_2019_2022, {\n x: 'Promedio - Total Ingreso bruto ganado para la determinación del EITC',\n y: 'Crédito por Trabajo - Promedio',\n stroke: \"red\",\n clip: true\n })\n ]\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":280,"value":"mapaprueba = {\n // the interactions tested here use the 'on' function from https://observablehq.com/@fil/plot-onclick-experimental-plugin\nlet theplot = Plot.plot({\n // _facet: { data, x: [\"o\", \"o\", \"u\", \"u\"] },\n aspectRatio: 1,\n axis: false,\n className: 'mapaprueba', // ya no necesariamente necesario\n marks: [\n // Plot.frame(),\n on(Plot.geo(municipios_geojson, {\n pointerEvents: 'all',\n }),\n {\n click: function (event, { datum }) {\n let curr_fill = d3.select(event.target).style(\"fill\");\n d3.select(event.target).style(\"fill\", \"red\");\n let sibs = getSiblings(event.target);\n sibs.forEach(s => d3.select(s).style(\"fill\", null))\n console.log('a click event!')\n event.stopPropagation()\n },\n })\n ]\n})\n\n // Click event para el mapa overall\n d3.select(theplot).on(\"click\", () => {\n console.log('some random click there');\n d3.select(theplot).selectAll('svg path').style(\"fill\", null)\n })\n\n return theplot\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":408,"value":"$ac","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":419,"value":"aq.from(eitc_isla_2019_2022).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":48,"value":"municipios_with_data = add_data_to_geojson(\n municipios_geojson,\n eitc_municipal_2019_2022_initial,\n ['municipio', 'Municipios'],\n 'eitc_municipal'\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":60,"value":"munis_in_geojson = new Set(municipios_geojson.features.map(d => d.properties.municipio))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"munis_in_eitc_municipal = new Set(eitc_municipal_2019_2022_initial.map(d => d.Municipios))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":69,"value":"extra_munis_in_eitc_municipal = [...munis_in_eitc_municipal].filter(element => !munis_in_geojson.has(element))","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":75,"value":"JSON.stringify(extra_munis_in_eitc_municipal)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"# Apéndice","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":495,"value":"mutable year_mutable = (2022)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":393,"value":"mutable muni_mutable = (null)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":801,"value":"mutable dato_mutable = ('monto_eitc')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1050,"value":"mutable estado_civil_mutable = (\"Contribuyente Individual\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1036,"value":"mutable num_dependientes_mutable = ('No dependiente')","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1038,"value":"viewof num_dependientes_select = { \n let theinput = Inputs.radio(num_dependientes_options, {label: \"Elige cuantos dependientes\",\n // format: d => d.toString(),\n // format: d => (dato_input === 'monto_eitc') ? htl.html`${d}` :\n // (d < 2021) ? htl.html`~~${d}~~` : htl.html`${d}`,\n value: num_dependientes_options[0],\n // disabled: years.filter(d => (dato_input === 'monto_eitc') ? false : (d < 2021))\n })\n theinput.value = num_dependientes_mutable;\n theinput.oninput = () => mutable num_dependientes_mutable = theinput.value;\n return theinput\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":277,"value":"import {on} from \"@fil/plot-onclick-experimental-plugin\" // It's SO good omg","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":904,"value":"import {EA_COLORES_MAP} from \"22dc6182207c44c8\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":916,"value":"import {ramp} from \"@d3/working-with-color\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":289,"value":"getSiblings = function (elem) {\n // From https://gomakethings.com/how-to-get-all-of-an-elements-siblings-with-vanilla-js/\n \n\t// Setup siblings array and get the first sibling\n\tvar siblings = [];\n\tvar sibling = elem.parentNode.firstChild;\n\n\t// Loop through each sibling and push to the array\n\twhile (sibling) {\n\t\tif (sibling.nodeType === 1 && sibling !== elem) {\n\t\t\tsiblings.push(sibling);\n\t\t}\n\t\tsibling = sibling.nextSibling\n\t}\n\n\treturn siblings;\n\n};","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":109,"value":"years = [2019,2020,2021,2022]","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":9,"value":"eitc_municipal_2019_2022_initial = await FileAttachment(\"eitc_municipal_2019_2022@2.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"eitc_municipal_2019_2022_initial","database":"$undefined"},{"id":877,"value":"$ad","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3089,"value":"variables_metadata.filter(d => d.dato === dato_input)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":94,"value":"$ae","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2322,"value":"select sum(\"Planillas Total - Crédito por Trabajo\")\nfrom eitc_municipal_2019_2022\nwhere \"Año contributivo\" = 2022","pinned":true,"hidden":false,"mode":"sql","author":"$undefined","summary":"$undefined","output":"","database":"var:eitc_municipal_2019_2022"},{"id":803,"value":"bruh = await FileAttachment(\"eitc_t1_escala_ingresos_2019_2022@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"bruh","database":"$undefined"},{"id":2874,"value":"poverty_table = {\n let data = await FileAttachment(\"PovertyIncome-ACS-5y-2022.xlsx\").xlsx()\n data = data.sheet(0, {headers: true})\n data = aq.from(data)\n .rename({\n 'State': 'state',\n 'Below Poverty': 'below_poverty',\n 'Population under Poverty': 'population_under_poverty',\n 'Total Population': 'total_population',\n 'Total Household': 'total_household',\n 'Total Families': 'total_families',\n 'Households Median Income': 'households_median_income',\n 'Families Median Income': 'families_median_income',\n })\n .objects()\n\n const commacast = d => +(d.replaceAll(',', ''))\n data.forEach(d => {\n const vars_commacast = ['population_under_poverty', 'total_population', 'total_household',\n 'total_families', 'households_median_income','families_median_income']\n for (const v of vars_commacast) {\n d[v] = commacast(d[v])\n }\n \n })\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4744,"value":"aq.from(state_abbreviations).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2770,"value":"state_abbreviations = {\n let data = await FileAttachment(\"states_abbreviations.csv\").csv({typed: true})\n data = aq.from(data)\n .rename({\n 'State': 'state',\n 'Abbreviation': 'abbreviation',\n \n })\n .objects()\n data.forEach(d => {\n if (d.state === 'District Of Columbia') d.state = 'District of Columbia'\n\n \n })\n // data= [{\n // state: 'Puerto Rico',\n // percentage_of_federal_credit: .767679,\n // refundable: true,\n // }, ...data]\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2570,"value":"eitc_local_governments = {\n let data = await FileAttachment(\"eitc_local_governments@1.csv\").csv({typed: true})\n data = aq.from(data)\n .rename({\n 'State or Local Government': 'state',\n 'Percentage of Federal Credit': 'percentage_of_federal_credit',\n 'Is Credit Refundable?': 'refundable',\n })\n .objects()\n data.forEach(d => {\n // d.state = convertToTitleCase(d.state)\n d.percentage_of_federal_credit = (+d.percentage_of_federal_credit.slice(0, -(\" percent\".length)))/100\n\n if (d.refundable === 'Yes') d.refundable = true;\n if (d.refundable === 'No') d.refundable = false;\n\n \n })\n data= [{\n state: 'Puerto Rico',\n percentage_of_federal_credit: .767679,\n refundable: true,\n }, ...data]\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4741,"value":"// aq.from(eitc_participation_rates).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2478,"value":"eitc_participation_rates = {\n let data = await FileAttachment(\"eitc_participation_rates.csv\").csv({typed: true})\n data = aq.from(data)\n // .derive({'latest_rate': d => d['Tax Year 2020']})\n .rename({'Participation Rate by State': 'state'})\n .fold(aq.not('state'), {'as': ['year', 'rate']})\n .derive({\n // state: d => op.capital(d.state),\n year: d => +op.replace(d.year, 'Tax Year ', '')\n })\n .objects()\n data.forEach(d => {\n d.state = convertToTitleCase(d.state)\n d.state = state_replace[d.state] ?? d.state\n d.rate = (+d.rate.slice(0, -1))/100\n })\n data= [{\n state: 'Puerto Rico',\n year: 2021,\n rate: .872,\n }, ...data]\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4729,"value":"aq.from(eitc_returns).view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4750,"value":"eitc_returns.filter(d => d.state.startsWith('Cali'))[0].state","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4757,"value":"state_replace = Object({\n \"California \": 'California',\n 'District Of Columbia': 'District of Columbia',\n })","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2528,"value":"$af","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2776,"value":"state_abbreviations","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2504,"value":"function convertToTitleCase(str) {\n if (!str) {\n return \"\"\n }\n return str.toLowerCase().replace(/\\b\\w/g, s => s.toUpperCase());\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1945,"value":"eitc_t1_escala_ingresos_2019_2022 = {\n let data = await FileAttachment(\"eitc_t1_escala_ingresos_2019_2022@1.csv\").csv({typed: true})\n data.forEach(d => add_extra_eitc_columns(d))\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":951,"value":"eitc_t3_ocupacion_2019_2022 = {\n let data = await FileAttachment(\"eitc_t3_ocupacion_2019_2022.csv\").csv({typed: true})\n data.forEach(d => add_extra_eitc_columns(d))\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":980,"value":"eitc_t4_fuente_ingreso_2019_2022 = {\n let data = await FileAttachment(\"eitc_t4_fuente_ingreso_2019_2022.csv\").csv({typed: true})\n data.forEach(d => add_extra_eitc_columns(d))\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":991,"value":"eitc_t6_estado_sexo_2019_2022 = {\n let data = await FileAttachment(\"eitc_t6_estado_sexo_2019_2022.csv\").csv({typed: true})\n data.forEach(d => add_extra_eitc_columns(d))\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1010,"value":"eitc_t7_estado_dependientes_2021_2022 = {\n let data = await FileAttachment(\"eitc_t7_dependientes_2021_2022.csv\").csv({typed: true})\n data.forEach(d => add_extra_eitc_columns(d))\n return data\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":808,"value":"$b0","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":872,"value":"eitc_t2_edad_2019_2022_initial = await FileAttachment(\"eitc_t2_edad_2019_2022.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"eitc_t2_edad_2019_2022_initial","database":"$undefined"},{"id":875,"value":"eitc_t2_edad_2019_2022 = {\n let data = eitc_t2_edad_2019_2022_initial;\n data.forEach(d => add_extra_eitc_columns(d))\n return data;\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":628,"value":"aq.from(eitc_municipal_2019_2022).filter(d => d.Municipios === 'Adjuntas').view()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":644,"value":"variables_metadata = {\n return [\n {variable: 'Crédito por Trabajo - Total', dato: 'monto_eitc', promedio_o_total: 'total'},\n {variable: 'Crédito por Trabajo - Promedio', dato: 'monto_eitc', promedio_o_total: 'promedio'},\n {variable: 'Crédito por Trabajo - Promedio - Contribuyente Individual', dato: 'monto_eitc_individuos', promedio_o_total: 'promedio'},\n {variable: 'Crédito por Trabajo - Promedio - Casados', dato: 'monto_eitc_casados', promedio_o_total: 'promedio'},\n {variable: 'Total - Total Ingreso bruto ganado para la determinación del EITC', dato: 'ingreso_ganado_eitc', promedio_o_total: 'total'},\n {variable: 'Promedio - Total Ingreso bruto ganado para la determinación del EITC', dato: 'ingreso_ganado_eitc', promedio_o_total: 'promedio'},\n {variable: 'Planillas Total - Crédito por Trabajo', dato: 'numero_planillas', promedio_o_total: 'total'},\n {variable: 'Total de personas', dato: 'total_personas', promedio_o_total: 'total'},\n ]\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":649,"value":"dato_labels = Object({\n 'monto_eitc': 'Crédito por Trabajo',\n 'ingreso_ganado_eitc': 'Ingreso bruto ganado para la determinación del EITC',\n 'numero_planillas': 'Total de familias',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3105,"value":"dato_reducers = Object({\n 'monto_eitc': 'promedio',\n 'ingreso_ganado_eitc': 'promedio',\n 'numero_planillas': 'total',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3129,"value":"dato_formatters = Object({\n 'monto_eitc': '$,.0f',\n 'ingreso_ganado_eitc': '$,.0f',\n 'numero_planillas': ',.0f',\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":7,"value":"import {municipios_geojson} from \"22dc6182207c44c8\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":405,"value":"import { aq, op } from '@uwdata/arquero'","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"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":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$Wb1","stdlib":"1","titling":"auto"}} 30:"$2f:body"