28:null 29:["$","title",null,{"children":"Better Summary Tables | Juans | Observable"}] 2f:Tc8f,// A function to summarize a single column SummarizeColumn = (data, col) => { let content, value, format, finiteFormat, el, chart, missing_label, pct_missing, min, max; const notFiniteFormat = d3.format(",.0f"); // Construct content based on type const type = getType(data, col) const col1 = htl.html`${icon_fns[type]()}${col === "" ? "unlabeled" : col}` switch(type) { // Categorical columns case 'ordinal': format = d3.format(",.0f") // Calculate category percent and count 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] === "") ? "(missing)" : d[0] obj.count = d[1].count obj.pct = d[1].pct return obj }) // Calculate pct. missing pct_missing = data.filter(d => (d[col] === null || d[col] === "")).length / data.length // Create the chart const stack_chart = SmallStack(categories, col) // element to return el = htl.html` ${col1}

${stack_chart}

${pct_format(pct_missing)} `; value = {column: col, type, min:null, max: null, missing:pct_missing, n_categories:categories.length} break; // Date columns case "date": // Calculate and format start / end const start = d3.min(data, d => +d[col]) const end = d3.max(data, d => +d[col]) // Calculate pct. missing pct_missing = data.filter(d => d[col] === null || d[col] === "").length / data.length chart = Histogram(data, col, type) // Element to return el = htl.html` ${col1}

${chart}

${pct_format(pct_missing)} ` value = {column: col, type, min:start, max: end, missing:pct_missing, n_categories:null} break; // Continuous columns default: // Compute values min = d3.min(data, d => +d[col]) max = d3.max(data, d => +d[col]) pct_missing = data.filter(d => d[col] === null || isNaN(d[col])).length / data.length chart = Histogram(data, col, type) // Element to return el = htl.html` ${col1}

${chart}

${pct_format(pct_missing)} ` value = {column: col, type, min, max, missing:pct_missing, n_categories:null} break; } el.value = value; el.appendChild(html``) return el }30:T4f7,SummaryTable = (dataObj, {label="Summary"} = {}) => { 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 value = [] // Create the summary card and track data shape const summaryCard = SummaryCard(data, label) value.n_rows = summaryCard.value.n_rows value.n_columns = summaryCard.value.n_columns value.columns = cols // Compose the element const element = htl.html`

${summaryCard}

${cols.map(d => { const ele = SummarizeColumn(data, d) value.push(ele.value) // get the value from the element return ele })}

Column	Snapshot	Missing

` element.value = value; return element }31:[] 2c:{"id":"05930c2f8350fb92","slug":null,"title":"Better Summary Tables","thumbnail":"9b2dd42f19fcc90773a5012bfe5f7573fda2497853c53450eda60b6019ad2511","access_level":"public","create_time":"$D2024-06-20T02:20:40.997Z","update_time":"$D2024-06-29T03:23:18.049Z","view_time":null,"first_public_time":"$D2024-06-29T03:23:40.172Z","fork":false,"stars":1,"starred":false,"owner":{"type":"team","id":"1be53ef47b72f2b5","login":"juans","name":"Juans","avatar_url":"https://avatars-next.observableusercontent.com/3690d955d0108a92d41458e12571dba94de90eb8c1ddbab3c3e41369942686ea","tier":"starter_2024"},"version":75,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"6342b8ef5215128d","login":"juan-carlos-garcia","name":"Juan Carlos Garcia","avatar_url":"https://avatars-next.observableusercontent.com/a49028003f6484a6da1b44a5ab05fd1c195861117b90e8316427ac6899bc9a45","tier":null}],"members":[{"type":"individual","id":"6342b8ef5215128d","login":"juan-carlos-garcia","name":"Juan Carlos Garcia","avatar_url":"https://avatars-next.observableusercontent.com/a49028003f6484a6da1b44a5ab05fd1c195861117b90e8316427ac6899bc9a45","tier":null,"role":"editor"},{"type":"team","id":"1be53ef47b72f2b5","login":"juans","name":"Juans","avatar_url":"https://avatars-next.observableusercontent.com/3690d955d0108a92d41458e12571dba94de90eb8c1ddbab3c3e41369942686ea","tier":"starter_2024","role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"subscribed":null,"forks":2,"collection":null,"type":"notebook","body":{"title":"Better Summary Tables","cells":[{"id":0,"value":"# Better Summary Tables\n","pinned":false,"hidden":false,"mode":"md"},{"id":6,"value":"aa_poetry_data = d3.csv(\"https://raw.githubusercontent.com/melaniewalsh/responsible-datasets-in-context/main/datasets/aa-periodical-poetry/AAPADA-Periodical-Poetry_1900-1928.csv\", d3.autoType)","pinned":true,"hidden":false,"mode":"ojs"},{"id":4,"value":"formatted_poetry_data = aa_poetry_data.map(d => {\n return Object.fromEntries(\n Object.entries(d).map(([key, value]) => [\n key,\n value === null || value === undefined || value === \"\" ? null : key === \"year\" ? new Date(value, 0, 1) : value\n ])\n );\n});","pinned":true,"hidden":false,"mode":"ojs"},{"id":37,"value":"import { SmallStack, Histogram, SummaryCard, getDateFormat, dateFormat, getType, icon_fns, pct_format } from \"@observablehq/summary-table\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":47,"value":"$2f","pinned":true,"hidden":false,"mode":"ojs"},{"id":35,"value":"$30","pinned":true,"hidden":false,"mode":"ojs"},{"id":41,"value":"viewof new_summary_data = SummaryTable(formatted_poetry_data, {\n label: \"AA Poetry Data\"\n});","pinned":true,"hidden":false,"mode":"ojs"},{"id":44,"value":"null","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W31","stdlib":"1","titling":"auto"}} 2d:"$2c:body"