2e:null 2f:["$","title",null,{"children":"Unpaid Work | Don McCurdy | Observable"}] 34:T480,summary = md`## Share of paid and unpaid work On an average day, in an average country worldwide, women spend ${ Math.round(100 * (dfPercent.stat.mean('WomenPaidTime') + dfPercent.stat.mean('WomenUnpaidTime')) / (dfPercent.stat.mean('MenPaidTime') + dfPercent.stat.mean('MenUnpaidTime'))) - 100 }% more time working then men. Despite that, women's work is disproportionately unpaid labor(${ Math.round(100 * dfPercent.stat.mean('WomenUnpaidTime') / (dfPercent.stat.mean('WomenPaidTime') + dfPercent.stat.mean('WomenUnpaidTime'))) }% of total hours), such as domestic and care efforts. Men's work, by contrast, is only rarely unpaid (${ Math.round(100 * dfPercent.stat.mean('MenUnpaidTime') / (dfPercent.stat.mean('MenPaidTime') + dfPercent.stat.mean('MenUnpaidTime'))) }% of total hours). *For the purposes of this [UNSD study](https://unstats.un.org/unsd/gender/timeuse/index.html), subsistence labor is counted as paid labor. These statistics are related to SDG indicator 5.4.1 – "Time spent on unpaid domestic and care work, by sex, age and location."* `35:T80d,dfPercent = { const paidTime = (group, gender) => group .filter((d) => d.get('Gender') === gender && d.get('TimeUse') === 'Paid') .getRow(0).get('AvgTime'); const unpaidTime = (group, gender) => group .filter((d) => d.get('Gender') === gender && d.get('TimeUse') === 'Unpaid') .getRow(0).get('AvgTime'); const totalTime = (group, gender) => group .filter((d) => d.get('Gender') === gender) .stat.sum('AvgTime'); const _df = dfCleaned .groupBy('Country') .aggregate((g) => ({ Country: g.getRow(0).get('Country'), Code: g.getRow(0).get('Code'), Flag: g.getRow(0).get('Flag'), MenPaidTime: paidTime(g, 'Men'), MenUnpaidTime: unpaidTime(g, 'Men'), MenPaidPercent: 100 * paidTime(g, 'Men') / totalTime(g, 'Men'), MenUnpaidPercent: 100 * unpaidTime(g, 'Men') / totalTime(g, 'Men'), WomenPaidTime: paidTime(g, 'Women'), WomenUnpaidTime: unpaidTime(g, 'Women'), WomenPaidPercent: 100 * paidTime(g, 'Women') / totalTime(g, 'Women'), WomenUnpaidPercent: 100 * unpaidTime(g, 'Women') / totalTime(g, 'Women'), }), 'GroupStats') .map((d) => d .set('Country', d.get('GroupStats').Country) .set('Code', d.get('GroupStats').Code) .set('Flag', d.get('GroupStats').Flag) .set('MenPaidTime', Number(d.get('GroupStats').MenPaidTime.toFixed(2))) .set('MenUnpaidTime', Number(d.get('GroupStats').MenUnpaidTime.toFixed(2))) .set('MenPaidPercent', Number(d.get('GroupStats').MenPaidPercent.toFixed(2))) .set('MenUnpaidPercent', Number(d.get('GroupStats').MenUnpaidPercent.toFixed(2))) .set('WomenPaidTime', Number(d.get('GroupStats').WomenPaidTime.toFixed(2))) .set('WomenUnpaidTime', Number(d.get('GroupStats').WomenUnpaidTime.toFixed(2))) .set('WomenPaidPercent', Number(d.get('GroupStats').WomenPaidPercent.toFixed(2))) .set('WomenUnpaidPercent', Number(d.get('GroupStats').WomenUnpaidPercent.toFixed(2))) .delete('GroupStats')); return _df; }36:T48d,dfCleaned = { let _df = dfRaw // Omit the partial 18+ study in Argentina. .filter((d) => d.get('Country') !== 'Argentina' || d.get('Age') !== '18+') // Omit Malaysia, which has no paid data. .filter((d) => d.get('Country') !== 'Malaysia') // Filter to prefer studies focused on adults. .sortBy('Age', 'DESC') .dropDuplicates('Country', 'TimeUse', 'Gender', 'Area', 'Year') // Filter to prefer more recent studies. .sortBy('Year', 'DESC') .dropDuplicates('Country', 'TimeUse', 'Gender', 'Area') .withColumn('RowKey', (d) => [d.get('Country'), d.get('TimeUse'), d.get('Gender')].join(':')); // Group rows that are equivalent other than Area; compute average time for that group. let _groupAvg = _df .sortBy('Area', 'DESC') .groupBy('RowKey') .aggregate((d) => d.stat.mean('AvgTime'), '_AvgTime') // Re-associate the average times with original rows, and format. return _df .innerJoin(_groupAvg, ['RowKey', 'RowKey']) .drop('AvgTime') .dropDuplicates('RowKey') .rename('_AvgTime', 'AvgTime') .select('Country', 'Code', 'Flag', 'Year', 'Gender', 'TimeUse', 'AvgTime'); } 37:T54d,dfRaw = { let rows = d3.csvParse(await FileAttachment('VIZ5_2020_April - Unpaid Work.csv').text()); // Name normalization. const nameConversions = ({ 'Iran (Islamic Republic of)': 'Iran', 'China, Hong Kong Special Administrative Region': 'Hong Kong', 'Republic of Korea': 'Korea, Republic of', 'United Kingdom of Great Britain and Northern Ireland': 'United Kingdom', 'The former Yugoslav Republic of Macedonia': 'Macedonia', 'United Republic of Tanzania': 'Tanzania', 'Republic of Moldova': 'Moldova', 'Bolivia (Plurinational State of)': 'Bolivia', 'Lao People\'s Democratic Republic': 'Lao', 'United States of America': 'United States', 'R�union': 'Reunion' }); // Normalize country names, giving us two-letter codes and flags. rows.forEach((row) => { row.Country = nameConversions[row.Country] || row.Country; row.Code = countryEmoji.code(row.Country); row.Flag = countryEmoji.flag(row.Code); if (!row.Code) throw new Error('Could not find country: ' + row.Country); }); rows.forEach((row) => { row['Time use'] = row['Time use'].startsWith('Paid') ? 'Paid' : 'Unpaid'; }); return new DF.DataFrame(rows) .drop('Survey Availability') .rename('Time use', 'TimeUse') .rename('Average Time (hours)', 'AvgTime') .dropMissingValues(['AvgTime']); }38:T49e,function *createBarChart(data, units = '') { const canvasEl = document.createElement('canvas'); canvasEl.style.height = '1500px'; canvasEl.style.width = '100%'; yield canvasEl; // canvasEl.style.height = '1000px'; const ctx = canvasEl.getContext('2d'); const chart = new Chart(ctx, { type: 'horizontalBar', data, options: { maintainAspectRatio: false, scales: { xAxes: [{ stacked: true, beginAtZero: true, ticks: { display: false, // callback: (value) => commas(value) + `${units}` } }], yAxes: [{ beginAtZero: true, stacked: true, autoSkip: false, // barThickness: 20, }] }, tooltips: { callbacks: { label: function(tooltipItem, data) { const label = data.datasets[tooltipItem.datasetIndex].label; const v = data.datasets[tooltipItem.datasetIndex].data[tooltipItem.index]; return label + ': ' + commas(v) + `${units}`; } } } } }); return canvasEl; }39:[{"id":"a368475f89c75557fdbc68ac8be390c362fd32255a91c30e632096b97c5f6bda4fc51fcecd8be211b306d3fefb961e85396049ae1d3f3940e12d065c5892431a","href":"https://static.observableusercontent.com/files/a368475f89c75557fdbc68ac8be390c362fd32255a91c30e632096b97c5f6bda4fc51fcecd8be211b306d3fefb961e85396049ae1d3f3940e12d065c5892431a","name":"VIZ5_2020_April - Unpaid Work.csv","type":"text/csv","lastModified":1586120298226.163,"size":70907}] 32:{"id":"45e9f1e62abdc7d9","slug":"unpaid-work","title":"Unpaid Work","thumbnail":"8a698263ad5ce8767e539e1c857bc5eabd7e035fc9bc500f488386ce7c42f192","access_level":"public","create_time":"$D2020-04-05T20:58:10.705Z","update_time":"$D2020-04-07T06:24:18.118Z","view_time":null,"first_public_time":"$D2020-04-07T05:20:41.356Z","has_unpublished_edits":null,"fork":false,"stars":2,"starred":false,"owner":{"type":"team","id":"33be3b16b6048934","login":"donmccurdy","name":"Don McCurdy","avatar_url":"https://avatars-next.observableusercontent.com/820f052fc667c91be64227f07caf56500c469583fc33e4929ada72fa52e7df07","tier":"starter_2024"},"version":797,"publish_version":797,"role":null,"authored":false,"authors":[{"type":"individual","id":"e90f448d6afe2c05","login":"donmccurdy","name":"Don McCurdy","avatar_url":"https://avatars-next.observableusercontent.com/820f052fc667c91be64227f07caf56500c469583fc33e4929ada72fa52e7df07","tier":null}],"members":[{"type":"individual","id":"e90f448d6afe2c05","login":"donmccurdy","name":"Don McCurdy","avatar_url":"https://avatars-next.observableusercontent.com/820f052fc667c91be64227f07caf56500c469583fc33e4929ada72fa52e7df07","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":2,"collection":{"id":"b9eeaa6df4e5793f","slug":"vis-makeovermonday","title":"Vis #MakeoverMonday","owner":{"id":"33be3b16b6048934","login":"donmccurdy","name":"Don McCurdy"}},"type":"notebook","body":{"title":"Unpaid Work","cells":[{"id":0,"value":"md`# Unpaid Work\n\n[Week 14, 2020](https://data.world/makeovermonday/2020w14) of [#MakeoverMonday](https://data.world/makeovermonday). I intended for this to be a bit of practice with different styles of visualization; it ended up being mostly a deep-dive into [dataframe.js](https://gmousse.gitbooks.io/dataframe-js/#dataframe-js), which I hadn't used before. Most time was spent data cleaning, discovering and resolving a few issues mentioned below. Got a bit more comfortable with dataframe.js by the end of the visualization, but probably should have just formatted the data in another tool before bringing it into JavaScript, in retrospect.\n\n- Original visualization: [Unpaid work: Allocation of time and time-use](https://unstats.un.org/unsd/gender/timeuse/index.html)\n- Data source: [UN Stats](https://unstats.un.org/unsd/gender/timeuse/index.html)\n`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":592,"value":"$34","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":769,"value":"md`| | Men (avg) | Women (avg) |\n|-|-----|-------|\n| Paid | ${ dfPercent.stat.mean('MenPaidTime').toFixed(2) } hours | ${ dfPercent.stat.mean('WomenPaidTime').toFixed(2) } hours |\n| Unpaid | ${ dfPercent.stat.mean('MenUnpaidTime').toFixed(2) } hours | ${ dfPercent.stat.mean('WomenUnpaidTime').toFixed(2) } hours |\n\n***`\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":749,"value":"md`

Share of paid and unpaid work, by gender

`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":595,"value":"main = createGenderPercentChart('Women')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":593,"value":"function createGenderPercentChart(gender) {\n const _df = dfPercent\n .sortBy('WomenPaidPercent');\n\n return createBarChart({\n\n labels: _df.select('Country', 'Code', 'Flag')\n .dropDuplicates()\n .toCollection()\n .map((d) => d.Country + ' ' + d.Flag),\n\n datasets: [\n {\n label: 'Unpaid (Men)',\n data: _df.toCollection().map((d) => d.MenUnpaidPercent),\n backgroundColor: PALETTE[2],\n },\n {\n label: 'Paid (Men)',\n data: _df.toCollection().map((d) => d.MenPaidPercent),\n backgroundColor: PALETTE[4],\n },\n {\n label: 'Paid (Women)',\n data: _df.toCollection().map((d) => d.WomenPaidPercent),\n backgroundColor: PALETTE[5],\n },\n {\n label: 'Unpaid (Women)',\n data: _df.toCollection().map((d) => d.WomenUnpaidPercent),\n backgroundColor: PALETTE[3]\n }\n ]\n\n }, '%');\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":402,"value":"md`## Hours`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":510,"value":"createGenderChart('Men')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":512,"value":"createGenderChart('Women')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":507,"value":"function createGenderChart(gender) {\n const _df = df\n .filter((d) => d.get('Gender') === gender)\n .sortBy('Country');\n const header = _df.select('Country', 'Code', 'Flag').dropDuplicates().toCollection();\n const paidDataset = {label: 'Paid', data: [], backgroundColor: PALETTE[1]};\n const unpaidDataset = {label: 'Unpaid', data: [], backgroundColor: PALETTE[2]};\n const datasets = _df.toCollection()\n .forEach((d) => {\n if (d.TimeUse.startsWith('Paid')) {\n paidDataset.data.push(d.AvgTime);\n return;\n }\n unpaidDataset.data.push(d.AvgTime);\n });\n return createBarChart({\n labels: header.map((d) => d.Country + ' ' + d.Flag),\n datasets: [paidDataset, unpaidDataset]\n }, 'hours');\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":665,"value":"md`## Notes\n\n**Resources:**\n\n- Country names, codes, and flag emoji: [country-emoji](https://github.com/meeDamian/country-emoji)\n- [DataFrame.js](https://gmousse.gitbooks.io/dataframe-js/#dataframe-js)\n\n**Fixed:**\n- [x] ~~Omit a bad study in Argentina.~~\n- [x] ~~Omit Malaysia, with no useable data.~~\n- [x] ~~Data got corrupted, had to re-run from the top.~~\n- [x] ~~Labels for last ~2 countries don't seem to fit on the page.~~`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":11,"value":"md`## Data cleaning`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":160,"value":"df.show(15)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":490,"value":"df = dfCleaned","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":555,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":181,"value":"$36","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":138,"value":"$37","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":315,"value":"md`## Helpers`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":317,"value":"$38","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":344,"value":"function commas(x) {\n return x;//.toFixed(2).replace(/\\B(?=(\\d{3})+(?!\\d))/g, \",\");\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":268,"value":"md`## Dependencies`","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":379,"value":"PALETTE = ['#ffffcc','#c7e9b4','#7fcdbb','#41b6c4','#1d91c0','#225ea8','#0c2c84'] // https://colorbrewer2.org/#type=sequential&scheme=YlGnBu&n=7","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":312,"value":"Chart = {\n const _Chart = await require('https://cdnjs.cloudflare.com/ajax/libs/Chart.js/2.9.3/Chart.bundle.js');\n // _Chart.defaults.global.tooltipTemplate = '<%= commas(value) %>';\n _Chart.defaults.global.scaleLabel = \"<%=parseInt(value).toLocaleString()%>\";\n _Chart.defaults.global.tooltipTemplate = \"<%if (label){%><%=label%>: <%}%><%=value.toLocaleString()%>\";\n return _Chart;\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":5,"value":"d3 = require('d3@5')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":123,"value":"DF = require('https://bundle.run/dataframe-js@1.4.3')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":17,"value":"countryEmoji = require('https://bundle.run/country-emoji@1.5.0')","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W39","stdlib":"1","titling":"auto"}} 33:"$32:body"