3d:null 3e:["$","title",null,{"children":"Tabela Resumo | Belo Inv. Research | Observable"}] 44:Tce5,function generateMarkdownSummaryTable( dataObj, { label = "Resumo" } = {}, maxCategories = 5 ) { const data = typeof dataObj.numRows === "function" ? dataObj.objects() : typeof dataObj.toArray === "function" ? dataObj.toArray().map((r) => Object.fromEntries(r)) : dataObj; const sample = data[0] || {}; const cols = data.columns || Object.keys(sample); let markdown = `## ${label}\n\n`; markdown += `**Linhas**: ${data.length} \n`; markdown += `**Colunas**: ${cols.length}\n\n`; markdown += `| Coluna | Visão Geral | Faltante | Distinto | Média | Mediana | DP |\n`; markdown += `|--------|-------------|----------|----------|-------|---------|----|\n`; let hasOthers = false; cols.forEach((col) => { const colType = getType(data, col); const missingPct = ( (data.filter((d) => d[col] == null || d[col] === "").length / data.length) * 100 ).toFixed(1) + "%"; const distinctCount = new Set(data.map((d) => d[col])).size; const mean = colType === "contínuo" ? d3 .mean(data, (d) => +d[col]) .toFixed(1) .replace(".", ",") : "-"; const median = colType === "contínuo" ? d3 .median(data, (d) => +d[col]) .toFixed(1) .replace(".", ",") : "-"; const sd = colType === "contínuo" ? d3 .deviation(data, (d) => +d[col]) .toFixed(1) .replace(".", ",") : "-"; let overview = ""; if (colType === "ordinal") { const categories = d3 .rollups( data, (v) => ({ count: v.length, pct: ((v.length / data.length) * 100).toFixed(1) + "%" }), (d) => d[col] ) .sort((a, b) => b[1].count - a[1].count); let topCategories = categories.slice(0, maxCategories); const othersCount = categories .slice(maxCategories) .reduce((sum, d) => sum + d[1].count, 0); const othersPct = ((othersCount / data.length) * 100).toFixed(1) + "%"; overview = topCategories .map((d) => `${d[0] || "(missing)"}: ${d[1].pct}`) .join(", "); if (categories.length > maxCategories) { overview += `, outros*: ${othersPct}`; hasOthers = true; } } else if (colType === "data") { const dates = data.map((d) => new Date(d[col])).filter((d) => !isNaN(d)); overview = getDateFormatSummary(dates); } else if (colType === "contínuo") { const histogram = d3 .histogram() .value((d) => d[col]) .domain(d3.extent(data, (d) => d[col])) .thresholds(d3.thresholdFreedmanDiaconis(data, (d) => d[col]))(data) .map( (bin) => `[${bin.x0.toFixed(1).replace(".", ",")}, ${bin.x1 .toFixed(1) .replace(".", ",")}): ${bin.length}` ) .join(", "); overview = `${histogram}`; } markdown += `| ${col} | ${overview} | ${missingPct} | ${distinctCount} | ${mean} | ${median} | ${sd} |\n`; }); if (hasOthers) { markdown += `\n*Nota: "outros" inclui todas as categorias adicionais não listadas individualmente.\n`; } return markdown; } 45:T1ad7,function generateMarkdownSummaryTableNested( jsonObj, { label = "Resumo", maxCategories = 3, maxExampleLength = 100 } = {} ) { let markdown = `## ${label}\n\n`; function getType(value) { if (value === null || value === undefined) return "null"; if (Array.isArray(value)) return "array"; return typeof value; } function summarize(data, prefix = "", depth = 0, isTopLevel = false) { if (data === null || data === undefined) return; if (typeof data !== "object") { const colType = getType(data); let example = JSON.stringify(data); if (example.length > maxExampleLength) { example = example.slice(0, maxExampleLength) + "..."; } markdown += `| ${prefix} | ${colType} | - | - | - | - | - | ${example} |\n`; return; } if (!Array.isArray(data)) { const sample = data; const cols = Object.keys(sample); if (isTopLevel) { markdown += `### Resumo da Tabela\n`; markdown += `**Colunas**: ${cols.length}\n\n`; markdown += `| Coluna | Tipo |\n`; markdown += `|--------|------|\n`; } else { markdown += `### ${ prefix ? `${prefix} ` : "" }Visão Geral dos Dados (Profundidade ${depth})\n`; markdown += `**Chaves**: ${cols.length}\n\n`; markdown += `| Chave | Tipo | Exemplo |\n`; markdown += `|--------|-------------|---------|\n`; } cols.forEach((col) => { const fullPath = prefix ? `${prefix}.${col}` : col; const colType = getType(sample[col]); let example = JSON.stringify(sample[col]); if (!isTopLevel && example.length > maxExampleLength) { example = example.slice(0, maxExampleLength) + "..."; } if (isTopLevel) { markdown += `| ${fullPath} | ${colType} |\n`; } else { markdown += `| ${fullPath} | ${colType} | ${example} |\n`; } if (colType === "object" || colType === "array") { summarize(sample[col], fullPath, depth + 1); } }); return; } // Handle arrays const sample = data[0] || {}; const cols = Object.keys(sample); if (depth === 0 && isTopLevel) { markdown += `### Resumo da Tabela\n`; markdown += `**Linhas**: ${data.length} \n`; markdown += `**Colunas**: ${cols.length}\n\n`; markdown += `| Coluna | Visão Geral | Faltante | Distinto | Média | Mediana | DP |\n`; markdown += `|--------|-------------|----------|----------|-------|---------|----|\n`; } else { markdown += `### ${ prefix ? `${prefix} ` : "" }Visão Geral dos Dados (Profundidade ${depth})\n`; markdown += `**Linhas**: ${data.length} \n`; markdown += `**Chaves**: ${cols.length}\n\n`; markdown += `| Coluna | Visão Geral | Faltante | Distinto | Média | Mediana | DP | Exemplo |\n`; markdown += `|--------|-------------|----------|----------|-------|---------|----|---------|\n`; } let hasOthers = false; cols.forEach((col) => { const fullPath = prefix ? `${prefix}.${col}` : col; const colType = getType(data[0][col]); const missingPct = ( (data.filter((d) => d[col] == null || d[col] === "").length / data.length) * 100 ).toFixed(1) + "%"; const distinctCount = new Set(data.map((d) => d[col])).size; const mean = colType === "number" ? d3.mean(data, (d) => +d[col]) ? d3 .mean(data, (d) => +d[col]) .toFixed(1) .replace(".", ",") : "-" : "-"; const median = colType === "number" ? d3.median(data, (d) => +d[col]) ? d3 .median(data, (d) => +d[col]) .toFixed(1) .replace(".", ",") : "-" : "-"; const sd = colType === "number" ? d3.deviation(data, (d) => +d[col]) ? d3 .deviation(data, (d) => +d[col]) .toFixed(1) .replace(".", ",") : "-" : "-"; let examples = [...new Set(data.map((d) => JSON.stringify(d[col])))].join( ", " ); if (examples.length > maxExampleLength) { examples = examples.slice(0, maxExampleLength) + "..."; } let overview = ""; if (colType === "string") { const categories = d3 .rollups( data, (v) => ({ count: v.length, pct: ((v.length / data.length) * 100).toFixed(1) + "%" }), (d) => d[col] ) .sort((a, b) => b[1].count - a[1].count); let topCategories = categories.slice(0, maxCategories); const othersCount = categories .slice(maxCategories) .reduce((sum, d) => sum + d[1].count, 0); const othersPct = ((othersCount / data.length) * 100).toFixed(1) + "%"; overview = topCategories .map((d) => `${d[0] || "(missing)"}: ${d[1].pct}`) .join(", "); if (categories.length > maxCategories) { overview += `, outros*: ${othersPct}`; hasOthers = true; } } else if (colType === "object" || colType === "array") { overview = `Contém ${distinctCount} objetos distintos`; } else if (colType === "number") { const histogram = d3 .histogram() .value((d) => d[col]) .domain(d3.extent(data, (d) => d[col])) .thresholds(d3.thresholdFreedmanDiaconis(data, (d) => d[col]))(data) .map( (bin) => `[${bin.x0.toFixed(1).replace(".", ",")}, ${bin.x1 .toFixed(1) .replace(".", ",")}): ${bin.length}` ) .join(", "); overview = `${histogram}`; } if (depth === 0 && isTopLevel) { markdown += `| ${fullPath} | ${overview} | ${missingPct} | ${distinctCount} | ${mean} | ${median} | ${sd} |\n`; } else { markdown += `| ${fullPath} | ${overview} | ${missingPct} | ${distinctCount} | ${mean} | ${median} | ${sd} | ${examples} |\n`; } if (colType === "object" || colType === "array") { summarize( data.map((d) => d[col]).filter((d) => d != null), fullPath, depth + 1 ); } }); if (hasOthers) { markdown += `\n*Nota: "outros" inclui todas as categorias adicionais não listadas individualmente.\n`; } } // Determine if the top level is a table-like array const isTableLike = Array.isArray(jsonObj) && jsonObj.every((item) => typeof item === "object" && !Array.isArray(item)); summarize(jsonObj, "", 0, isTableLike); // Initial call with check for table-like data return markdown; }46:T76f,SummaryCard = (data, label = "Resumo") => { // Computar valores const sample = data[0] || {}; const cols = data.columns || Object.keys(sample); const col_data = cols.map((d) => { return { label: d === "" ? "sem rótulo" : d, type: getType(data, d) }; }); const n_columns = col_data.length; const n_rows = data.length; // Criar a linha de cabeçalho como um gráfico const header_plot = addTooltips( Plot.cellX(col_data, { fill: (d) => colorMap.get(d.type).color, title: (d) => `${d.label}\n(${d.type})` }).plot({ x: { axis: null }, width: 100, height: 10, color: { domain: [...colorMap.values()].map((d) => d.color) }, style: { overflow: "visible" } }), { stroke: "black", "stroke-width": "3px" } ); // Criar as colunas como um gráfico const col_plot = Plot.cellX(col_data, { fill: (d) => colorMap.get(d.type).color, fillOpacity: 0.3 }).plot({ x: { axis: null }, width: 100, height: 80, color: { domain: [...colorMap.values()].map((d) => d.color) } }); // Construir o elemento const arrow_styles = ` display: inline-block; vertical-align: top; transform-origin: 0 0; transform: rotate(90deg); margin-top: 20px; position: absolute; left: 114px; top: 54px; `; const ele = htl.html`

${label}

${d3.format(",.0f")(n_columns)} ⟶

${header_plot} ${col_plot} ${d3.format( ",.0f" )(n_rows)}
⟶

`; ele.value = { n_rows, n_columns }; return ele; }47:T113d,// Função para resumir uma única coluna SummarizeColumn = (data, col) => { let content, value, format, finiteFormat, el, chart, missing_label, pct_missing, min, max, median, mean, sd; const notFiniteFormat = d3.format(",.0f"); // Construir conteúdo com base no tipo const type = getType(data, col); const col1 = htl.html`${icon_fns[ type ]()}${ col === "" ? "sem rótulo" : col }`; switch (type) { // Colunas categóricas case "ordinal": format = d3.format(",.0f"); // Calcular percentagem e contagem da categoria const categories = d3 .rollups( data, (v) => ({ count: v.length, pct: v.length / data.length || 1 }), (d) => d[col] ) .sort((a, b) => b[1].count - a[1].count) .map((d) => { let obj = {}; obj[col] = d[0] === null || d[0] === "" ? "(ausente)" : d[0]; obj.count = d[1].count; obj.pct = d[1].pct; return obj; }); // Calcular percentagem ausente pct_missing = data.filter((d) => d[col] === null || d[col] === "").length / data.length; // Criar o gráfico const stack_chart = SmallStack(categories, col); // Elemento a ser retornado el = htl.html` ${col1}

${stack_chart}

${pct_format(pct_missing)} - - - `; value = { column: col, type, min: null, max: null, mean: null, median: null, sd: null, missing: pct_missing, n_categories: categories.length }; break; // Colunas de data case "data": // Calcular e formatar início / fim const start = d3.min(data, (d) => +d[col]); const end = d3.max(data, (d) => +d[col]); mean = d3.mean(data, (d) => +d[col]); median = d3.median(data, (d) => +d[col]); sd = d3.deviation(data, (d) => +d[col]); // Calcular percentagem ausente pct_missing = data.filter((d) => d[col] === null || d[col] === "").length / data.length; chart = Histogram(data, col, type); // Elemento a ser retornado el = htl.html` ${col1}

${chart}

${pct_format(pct_missing)} - - - `; value = { column: col, type, min: start, max: end, mean: null, median: null, sd: null, missing: pct_missing, n_categories: null }; break; // Colunas contínuas default: // Calcular valores min = d3.min(data, (d) => +d[col]); max = d3.max(data, (d) => +d[col]); mean = d3.mean(data, (d) => +d[col]); median = d3.median(data, (d) => +d[col]); sd = d3.deviation(data, (d) => +d[col]); if (Number.isFinite(sd)) { finiteFormat = d3.format(",." + d3.precisionFixed(sd / 10) + "f"); format = (x) => Number.isFinite(x) ? finiteFormat(x) : notFiniteFormat(x); } else { format = notFiniteFormat; } pct_missing = data.filter((d) => d[col] === null || isNaN(d[col])).length / data.length; chart = Histogram(data, col, type); // Elemento a ser retornado el = htl.html` ${col1}

${chart}

${pct_format(pct_missing)} ${format(mean)} ${format(median)} ${format(sd)} `; value = { column: col, type, min, max, mean, median, sd, missing: pct_missing, n_categories: null }; break; } el.value = value; el.appendChild(html``); return el; }48:T880,SummaryTable = (dataObj, { label = "Resumo" } = {}) => { // Function to check if a value is a valid date string and not already a Date object const isDateString = (value) => { // Check if the value is a string and matches common date patterns return ( typeof value === "string" && !isNaN(Date.parse(value)) && !value.includes("Invalid Date") ); }; // Function to parse columns that are date strings into Date objects const parseDateColumns = (data) => { const cols = Object.keys(data[0] || {}); cols.forEach((col) => { if (data.some((row) => isDateString(row[col]))) { data.forEach((row) => { if (isDateString(row[col])) { row[col] = new Date(row[col]); } }); } }); }; const data = typeof dataObj.numRows === "function" ? dataObj.objects() : typeof dataObj.toArray === "function" ? dataObj.toArray().map((r) => Object.fromEntries(r)) : dataObj; // Parse date columns parseDateColumns(data); const sample = data[0] || {}; const cols = data.columns || Object.keys(sample); let value = []; // Criar o cartão de resumo e rastrear a forma dos dados const summaryCard = SummaryCard(data, label); value.n_rows = summaryCard.value.n_rows; value.n_columns = summaryCard.value.n_columns; value.columns = cols; // Compor o elemento const element = htl.html`

${summaryCard}

${cols.map((d) => { const ele = SummarizeColumn(data, d); value.push(ele.value); // obter o valor do elemento return ele; })}

Coluna	Visão Geral	Faltante	Média	Mediana	DP

`; element.value = value; return element; }49:T5a0,SmallStack = (categoryData, col, maxCategories = 100) => { // Obter uma barra empilhada horizontal const label = categoryData.length === 1 ? " categoria" : " categorias"; let chartData = categoryData; let categories = 0; if (chartData.length > maxCategories) { chartData = categoryData.filter((d, i) => i < maxCategories); const total = d3.sum(categoryData, (d) => d.count); const otherCount = total - d3.sum(chartData, (d) => d.count); let other = {}; other[col] = "Outras categorias..."; other.count = otherCount; other.pct = other.count / total; chartData.push(other); } return addTooltips( Plot.barX(chartData, { x: "count", fill: col, y: 0, title: (d) => d[col] + "\n" + pct_format(d.pct) }).plot({ color: { scheme: "blues" }, marks: [ Plot.text([0, 0], { x: 0, frameAnchor: "bottom", dy: 10, text: (d) => d3.format(",.0f")(categoryData.length) + `${label}` }) ], style: { paddingTop: "0px", paddingBottom: "15px", textAnchor: "start", overflow: "visible" }, x: { axis: null }, color: { domain: chartData.map((d) => d[col]), scheme: "blues", reverse: true }, height: 30, width: 205, y: { axis: null, range: [30, 3] } }), { fill: "darkblue" } ); }4a:T75d,Histogram = (data, col, type = "continuous") => { // Calcular cor + média const barColor = colorMap.get(type).brighter; const mean = d3.mean(data, (d) => d[col]); // Formatar a média const extent = d3.extent(data, (d) => d[col]); const format = type === "data" ? getDateFormat(extent) : Math.floor(extent[0]) === Math.floor(extent[1]) ? d3.format(",.2f") : d3.format(",.0f"); const rules = [{ label: "média", value: mean }]; return addTooltips( Plot.plot({ height: 55, width: 240, style: { display: "inline-block" }, x: { label: "", ticks: extent, tickFormat: format }, y: { axis: null }, marks: [ Plot.rectY( data, Plot.binX( { y: "count", title: (elems) => { // calcular faixa para os elementos const [start, end] = d3.extent(elems, (d) => d[col]); let barFormat; if (type === "data") { barFormat = getDateFormat([start, end]); } else { barFormat = d3.format( Math.floor(start) === Math.floor(end) ? ",.2f" : ",.0f" ); } return `${elems.length} linhas\n[${barFormat( start )} até ${barFormat(end)}]`; } }, { x: col, fill: barColor } ) ), Plot.ruleY([0]), Plot.ruleX(rules, { x: "value", strokeWidth: 2, title: (d) => `${d.label} ${col}: ${format(d.value)}` }) ], style: { marginLeft: -17, background: "none", overflow: "visible" } }), { opacity: 1, fill: colorMap.get(type).color } ); }4b:T483,// Define the getDateFormat function using locale-aware format functions function getDateFormat(extent) { // Define locale-aware format functions const formatMillisecond = locale.format(".%L"), formatSecond = locale.format(":%S"), formatMinute = locale.format("%I:%M"), formatHour = locale.format("%I %p"), formatDay = locale.format("%a %d"), formatWeek = locale.format("%b %d"), formatMonth = locale.format("%B"), formatYear = locale.format("%Y"); return extent[1] - extent[0] > d3.utcYear.offset(extent[0], 1) - extent[0] ? formatYear : extent[1] - extent[0] > d3.utcMonth.offset(extent[0], 1) - extent[0] ? formatMonth : extent[1] - extent[0] > d3.utcWeek.offset(extent[0], 1) - extent[0] ? formatWeek : extent[1] - extent[0] > d3.utcDay.offset(extent[0], 1) - extent[0] ? formatDay : extent[1] - extent[0] > d3.utcHour.offset(extent[0], 1) - extent[0] ? formatHour : extent[1] - extent[0] > d3.utcMinute.offset(extent[0], 1) - extent[0] ? formatMinute : extent[1] - extent[0] > d3.utcSecond.offset(extent[0], 1) - extent[0] ? formatSecond : formatMillisecond; }4c:T7ba,icon_fns = ({ ordinal: () => html`

`, data: () => html`

`, contínuo: () => html`

` })4d:[{"id":"78466fd621db89a8708d6af36834c45c977f3f95c44f91a380bf7c985f03db2d03849aef300f8caa2bce9605f5229fe9813ed4f168c75f9b0abb4e960400faf0","href":"https://static.observableusercontent.com/files/78466fd621db89a8708d6af36834c45c977f3f95c44f91a380bf7c985f03db2d03849aef300f8caa2bce9605f5229fe9813ed4f168c75f9b0abb4e960400faf0","name":"palmer-penguins.csv","type":"text/csv","lastModified":1719977535500.496,"size":13490},{"id":"e6a0a608bc7de32cd40d81c8b4f3bc3ea7934a0d6cee4575e6afff74ed8729a37971f6d6dba623cb8f98eeb91dc68306586373341be5a1565c92208ba512effb","href":"https://static.observableusercontent.com/files/e6a0a608bc7de32cd40d81c8b4f3bc3ea7934a0d6cee4575e6afff74ed8729a37971f6d6dba623cb8f98eeb91dc68306586373341be5a1565c92208ba512effb","name":"dadosInflacao.csv","type":"text/csv","lastModified":1722991488696.224,"size":10796}] 42:{"id":"61fe08cd7d1d2e28","slug":"summary","title":"Tabela Resumo","thumbnail":"e6334647ad7e7eb88a40047a831baeef315d7847a2d43904f1896b57e4fe9b3b","access_level":"public","create_time":"$D2024-07-03T03:32:15.500Z","update_time":"$D2025-05-27T17:33:16.499Z","view_time":null,"first_public_time":"$D2024-07-03T06:44:31.623Z","has_unpublished_edits":null,"fork":{"id":"253d3c1b5eb8410c","slug":"summary-table","title":"Summary Table","owner":{"id":"f35c755083683fe5","login":"observablehq","name":"Observable"}},"stars":0,"starred":false,"owner":{"type":"team","id":"09eac3a95d21904b","login":"belo","name":"Belo Inv. Research","avatar_url":"https://avatars-next.observableusercontent.com/4ec7f31fc7075ff56bb5e46ef17f39cffe1cb072782912e4ff34f0688e0ddfdd","tier":"pro_2024","bio":"Belo is a think tank specialized in alternative data in finance. The company unveils insights from unconventional data sources for investors and corporations."},"version":3078,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"497d8fdfb82a7b80","login":"poliveira","name":"Paulino Oliveira","avatar_url":"https://avatars-next.observableusercontent.com/c6e154047ee5f7c3824127e7da7f7191a34219417002d99bca1dfd3970de0e9b","tier":null,"bio":"CEO & Founder @belo"}],"members":[{"type":"individual","id":"497d8fdfb82a7b80","login":"poliveira","name":"Paulino Oliveira","avatar_url":"https://avatars-next.observableusercontent.com/c6e154047ee5f7c3824127e7da7f7191a34219417002d99bca1dfd3970de0e9b","tier":null,"role":"editor"},{"type":"team","id":"09eac3a95d21904b","login":"belo","name":"Belo Inv. Research","avatar_url":"https://avatars-next.observableusercontent.com/4ec7f31fc7075ff56bb5e46ef17f39cffe1cb072782912e4ff34f0688e0ddfdd","tier":"pro_2024","role":"viewer"}],"comments":[{"id":"124cfdb9add7a75a","reply_to_id":null,"content":"TO-DO: recreate with duck-db for efficiency.","create_time":"$D2024-07-03T05:06:35.437Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"497d8fdfb82a7b80","login":"poliveira","name":"Paulino Oliveira","avatar_url":"https://avatars-next.observableusercontent.com/c6e154047ee5f7c3824127e7da7f7191a34219417002d99bca1dfd3970de0e9b","tier":null}},{"id":"5c6ea1926432aa62","reply_to_id":null,"content":"TO-DO: date formatting is inconsistent","create_time":"$D2024-07-15T18:35:09.572Z","update_time":null,"resolved":false,"user":{"type":"individual","id":"497d8fdfb82a7b80","login":"poliveira","name":"Paulino Oliveira","avatar_url":"https://avatars-next.observableusercontent.com/c6e154047ee5f7c3824127e7da7f7191a34219417002d99bca1dfd3970de0e9b","tier":null}}],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"titling":"auto","title":"Tabela Resumo","cells":[{"id":1946,"value":"# Tabela Resumo\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":1938,"value":"md`

Translation of the [Observable summary table](https://observablehq.com/@observablehq/summary-table), with an extra method for markdown summaries.

\n`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":0,"value":"Quer uma visão geral rápida de um conjunto de dados? Para começar, importe a função (experimental) `SummaryTable` deste caderno:\n\n~~~js\n// Importe a função para o seu notebook\nimport { SummaryTable } from \"@observablehq/summary-table\"\n~~~\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1120,"value":"Em seguida, passe seus dados para a função `SummaryTable`. Como exemplo, aqui está uma tabela resumo do conjunto de dados Penguins (descrito em detalhe [aqui](https://observablehq.com/@observablehq/plot-exploration-penguins)). Você também pode fazer upload dos seus próprios dados usando este botão:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1856,"value":"// Load the data (e.g., a .csv file)\nviewof data = dataInput({\n initialValue: await FileAttachment(\"palmer-penguins.csv\").csv({ typed: true })\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3071,"value":"weather","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":200,"value":"// Create a view of the summary statistics\nviewof summary_data = SummaryTable(data, {label: \"Penguins Data\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3049,"value":"inflacao = FileAttachment(\"dadosInflacao.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2933,"value":"SummaryTable(inflacao)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1998,"value":"md`${await generateMarkdownSummaryTable(inflacao)}`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3077,"value":"md`${generateMarkdownSummaryTable(penguins)}`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2292,"value":"generateMarkdownSummaryTable(penguins)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1995,"value":"$44","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2140,"value":"md`${generateMarkdownSummaryTableNested(matchesDataset)}`","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3069,"value":"matchesDataset","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2161,"value":"sampleGeoJson = d3.json(\n \"https://gist.githubusercontent.com/wavded/1200773/raw/e122cf709898c09758aecfef349964a8d73a83f3/sample.json\"\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2281,"value":"matchesDataset = d3.json(\n \"https://raw.githubusercontent.com/openfootball/football.json/master/2015-16/en.1.json\"\n)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2164,"value":"import { data as flareData } from \"@d3/nested-treemap\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2216,"value":"$45","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2136,"value":"covidData = [\n {\n country: \"USA\",\n state: \"California\",\n cases: [\n {\n date: \"2020-01-22\",\n confirmed: 0,\n deaths: 0,\n recovered: 0\n },\n {\n date: \"2020-01-23\",\n confirmed: 1,\n deaths: 0,\n recovered: 0\n }\n // more daily data...\n ]\n },\n {\n country: \"USA\",\n state: \"New York\",\n cases: [\n {\n date: \"2020-01-22\",\n confirmed: 0,\n deaths: 0,\n recovered: 0\n },\n {\n date: \"2020-01-23\",\n confirmed: 0,\n deaths: 0,\n recovered: 0\n }\n // more daily data...\n ]\n }\n // more states and countries...\n]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":787,"value":"Os valores calculados são expostos como uma _view_ e podem ser usados como você desejar:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":791,"value":"// Use the summary data (an array of objects, with additional properties)\nsummary_data","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1496,"value":"Por exemplo, você pode então criar um gráfico separado da porcentagem de ausência de cada coluna:\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1500,"value":"addTooltips(\n Plot.barX(summary_data, {\n x: \"missing\",\n y: \"column\",\n fill: \"lightblue\",\n title: (d) =>\n `Percent Missing (${d.column}): ${d3.format(\".1%\")(d.missing)}`\n }).plot({\n x: {\n tickFormat: d3.format(\".1%\")\n },\n y: {\n domain: summary_data\n .sort((a, b) => b.missing - a.missing)\n .map((d) => d.column),\n label: \"\"\n },\n marginLeft: 80,\n caption: \"Percent missing for each variable in the dataset\"\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":794,"value":"## Implementação\nA `SummaryTable` é dividida em algumas peças componíveis.\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":812,"value":"### SummaryCard\n","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":798,"value":"// A small inset showing the # of rows and columns, and their types\nSummaryCard(data)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":552,"value":"$46","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":819,"value":"### SummarizeColumn","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":810,"value":"// A small inset showing the type of column, the categories, and a few (unlabeled) summary stats\nviewof col_summary = SummarizeColumn(weather, \"date\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":8,"value":"$47","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":190,"value":"### Summary Table","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":196,"value":"$48","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":839,"value":"## Helper functions","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3001,"value":"Histogram(weather, \"date\", \"data\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3009,"value":"typeof new Date()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1957,"value":"$49","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":64,"value":"$4a","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1963,"value":"// Configurando o formato de data para Português Brasileiro\nlocale = d3.timeFormatLocale({\n dateTime: \"%A, %e de %B de %Y. %X\",\n date: \"%d/%m/%Y\",\n time: \"%H:%M:%S\",\n periods: [\"AM\", \"PM\"],\n days: [\n \"domingo\",\n \"segunda-feira\",\n \"terça-feira\",\n \"quarta-feira\",\n \"quinta-feira\",\n \"sexta-feira\",\n \"sábado\"\n ],\n shortDays: [\"dom\", \"seg\", \"ter\", \"qua\", \"qui\", \"sex\", \"sáb\"],\n months: [\n \"janeiro\",\n \"fevereiro\",\n \"março\",\n \"abril\",\n \"maio\",\n \"junho\",\n \"julho\",\n \"agosto\",\n \"setembro\",\n \"outubro\",\n \"novembro\",\n \"dezembro\"\n ],\n shortMonths: [\n \"jan\",\n \"fev\",\n \"mar\",\n \"abr\",\n \"mai\",\n \"jun\",\n \"jul\",\n \"ago\",\n \"set\",\n \"out\",\n \"nov\",\n \"dez\"\n ]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1900,"value":"$4b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1894,"value":"function dateFormat(date) {\n // Define locale-aware format functions\n const formatMillisecond = locale.format(\".%L\"),\n formatSecond = locale.format(\":%S\"),\n formatMinute = locale.format(\"%I:%M\"),\n formatHour = locale.format(\"%I %p\"),\n formatDay = locale.format(\"%a %d\"),\n formatWeek = locale.format(\"%b %d\"),\n formatMonth = locale.format(\"%B\"),\n formatYear = locale.format(\"%Y\");\n\n // Define the dateFormat function using locale-aware format functions\n function dateFormat(date) {\n return (\n d3.timeSecond(date) < date\n ? formatMillisecond\n : d3.timeMinute(date) < date\n ? formatSecond\n : d3.timeHour(date) < date\n ? formatMinute\n : d3.timeDay(date) < date\n ? formatHour\n : d3.timeMonth(date) < date\n ? d3.timeWeek(date) < date\n ? formatDay\n : formatWeek\n : d3.timeYear(date) < date\n ? formatMonth\n : formatYear\n )(date);\n }\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1671,"value":"$4c","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1549,"value":"colorMap = new Map(\n [\n [\"ordinal\", \"rgba(78, 121, 167, 1)\"],\n [\"contínuo\", \"rgba(242, 142, 44, 1)\"],\n [\"data\", \"rgba(225,87,89, 1)\"]\n ].map((d) => {\n const col = d3.color(d[1]);\n const color_copy = _.clone(col);\n color_copy.opacity = 0.6;\n return [d[0], { color: col.formatRgb(), brighter: color_copy.formatRgb() }];\n })\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1306,"value":"getType = (data, column) => {\n for (const d of data) {\n const value = d[column];\n if (value == null) continue;\n if (typeof value === \"number\") return \"contínuo\";\n if (value instanceof Date || !isNaN(new Date(value))) return \"data\";\n return \"ordinal\";\n }\n // se todos forem nulos, retornar ordinal\n return \"ordinal\";\n}","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":3055,"value":"formatDate(inflacao[0].data)","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1773,"value":"pct_format = d3.format(\".1%\");","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2028,"value":"### Handling Dates","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"","database":"$undefined"},{"id":2030,"value":"function formatDate(value) {\n const date = new Date(value);\n if (isNaN(date)) return value; // Return the value as is if it's not a valid date\n\n const options = { year: \"numeric\", month: \"short\", day: \"numeric\" };\n return date.toLocaleDateString(\"pt-BR\", options); // Format date to Brazilian Portuguese\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2032,"value":"function formatMonthYear(value) {\n const date = new Date(value);\n if (isNaN(date)) return value; // Return the value as is if it's not a valid date\n\n const options = { year: \"numeric\", month: \"short\" };\n return date.toLocaleDateString(\"pt-BR\", options); // Format date to Brazilian Portuguese\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":2034,"value":"function getDateFormatSummary(dates) {\n const formattedDates = dates.map((date) => formatDate(date));\n const distinctDates = [...new Set(formattedDates)];\n return `Min: ${formatDate(Math.min(...dates))}, Max: ${formatDate(\n Math.max(...dates)\n )}, Distinct: ${distinctDates.length}`;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":856,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1066,"value":"import {addTooltips} from \"@mkfreeman/plot-tooltip\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"},{"id":1857,"value":"import {dataInput} from \"@john-guerra/file-input-with-default-value\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","tool":"$undefined","providerMetadata":"$undefined","attachments":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W4d","stdlib":"1"}} 43:"$42:body"