2f:T5b6,{ const tidy_region = tidy( region, select(['Region', 'Sex', 'Minutes']), pivotWider({namesFrom: 'Sex', valuesFrom: 'Minutes'}), mutate({Gap: d => d.Women - d.Men}) ) const plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, width: 500, marginBottom: 50, inset: 8, grid: true, x: {domain: [1500,2500], ticks: 5, tickSize: 0, label: "Men (mean average minutes per week)", labelOffset: 50}, y: {domain: [1500,2500], ticks: 5, tickSize: 0, label: "Women (mean average minutes per week)"}, marks: [ Plot.link([1], {x1: () => 1500, y1: () => 1500, x2: () => 2500, y2: () => 2500, strokeDasharray: 8}), Plot.dot(tidy_region, {x: "Men", y: "Women", r: 6, fill: "#27A0CC", opacity: 0.7, stroke: "#FFFFFF", strokeWidth: 1, title: (d) => `${d.Region} \n Women: ${d3.format(",.0f")(d.Women)} mins \n Men: ${d3.format(",.0f")(d.Men)} mins \n Gap: ${d.Gap} mins`}), Plot.text(tidy_region, {x: 2000, y: 2000, text: ["Gender parity"], rotate: -38, dy: -10, textAnchor: "start", fontSize: 12, fill: "#000000"}) ], caption: html`

Source: UK HETUS, 2015

`, tooltip: {fill: "#FFFFFF", opacity: 1, stroke: "#27A0CC"} }) return plot }30:T901,{ const tidy_data = tidy( age, select(['Age group', 'Sex', 'Minutes']), pivotWider({namesFrom: 'Sex', valuesFrom: 'Minutes'}), mutate({Gap: d => d.Women - d.Men}) ) const gap_plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, marginLeft: 80, marginBottom: 50, x: {grid: true, domain: [250,450], label: "Mean average minutes per day", labelOffset: 50, tickSize: 0}, y: {domain: d3.sort(tidy_data, d => d.Gap).map(d => d["Age group"]), label: null, tickSize: 0}, marks: [ Plot.arrow(tidy_data, {x1: d => d.Women, x2: d => d.Men, y: "Age group", stroke: "#27A0CC", strokeWidth: 2, title: (d) => `${d["Age group"]} \n ${d.Gap} minutes`}), ], caption: html`

Source: UK HETUS, 2015

`, tooltip: {stroke: "#dddddd", strokeWidth: 2} }) const plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, marginLeft: 80, marginBottom: 50, x: {grid: true, domain: [250,450], label: "Mean average minutes per day", labelOffset: 50, tickSize: 0}, y: {domain: age.filter(d => d.Sex === "Women").sort((a, b) => b.Minutes - a.Minutes).map(d => d["Age group"]), label: null, tickSize: 0}, color: {legend: false, domain: ["Women", "Men"], range: ["#8F5EF8", "#2CC3AB"], style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}}, marks: [ Plot.ruleY(age, Plot.groupY({x1: "min", x2: "max"}, {x: "Minutes", y: "Age group", stroke: "#dddddd", strokeWidth: 2.5})), Plot.dot(age, {x: "Minutes", y: "Age group", r: 5, fill: "Sex", title: (d) => `${d["Age group"]} \n ${d.Sex}\n ${d3.format(",.0f")(d.Minutes)} minutes`}) ], caption: html`

Source: UK HETUS, 2015

`, tooltip: {fill: "#dddddd", opacity: 1, "stroke-width": "0px"} }) return toggle_gap_age === true ? gap_plot : plot; }31:T922,{ const tidy_data = tidy( occupation, select(['Occupation', 'Sex', 'Minutes']), pivotWider({namesFrom: 'Sex', valuesFrom: 'Minutes'}), mutate({Gap: d => d.Women - d.Men}) ) const gap_plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, marginLeft: 180, marginBottom: 50, x: {grid: true, domain: [200,400], label: "Mean average minutes per day", labelOffset: 50, tickSize: 0}, y: {domain: d3.sort(tidy_data, d => d.Gap).map(d => d.Occupation), label: null, tickSize: 0}, marks: [ Plot.arrow(tidy_data, {x1: d => d.Women, x2: d => d.Men, y: "Occupation", stroke: "#27A0CC", strokeWidth: 2, title: (d) => `${d.Occupation} \n ${d.Gap} minutes`}), ], caption: html`

Source: UK HETUS, 2015

`, tooltip: {stroke: "#dddddd", strokeWidth: 2} }) const plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, marginLeft: 180, marginBottom: 50, x: {grid: true, domain: [200,400], label: "Mean average minutes per day", labelOffset: 50, tickSize: 0}, y: {domain: occupation.filter(d => d.Sex === "Women").sort((a, b) => b.Minutes - a.Minutes).map(d => d.Occupation), label: null, tickSize: 0}, color: {legend: false, domain: ["Women", "Men"], range: ["#8F5EF8", "#2CC3AB"], style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}}, marks: [ Plot.ruleY(occupation, Plot.groupY({x1: "min", x2: "max"}, {x: "Minutes", y: "Occupation", stroke: "#dddddd", strokeWidth: 2.5})), Plot.dot(occupation, {x: "Minutes", y: "Occupation", r: 5, fill: "Sex", title: (d) => `${d.Occupation} \n ${d.Sex}\n ${d3.format(",.0f")(d.Minutes)} minutes`}) ], caption: html`

Source: UK HETUS, 2015

`, tooltip: {fill: "#dddddd", opacity: 1, "stroke-width": "0px"} }) return toggle_gap_occupation === true ? gap_plot : plot; }32:T93b,{ const tidy_data = tidy( work_status, select(['Work status', 'Sex', 'Minutes']), pivotWider({namesFrom: 'Sex', valuesFrom: 'Minutes'}), mutate({Gap: d => d.Women - d.Men}) ) const gap_plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, marginLeft: 180, marginBottom: 50, x: {grid: true, domain: [250,550], label: "Mean average minutes per day", labelOffset: 50, tickSize: 0}, y: {domain: d3.sort(tidy_data, d => d.Gap).map(d => d["Work status"]), label: null, tickSize: 0}, marks: [ Plot.arrow(tidy_data, {x1: d => d.Women, x2: d => d.Men, y: "Work status", stroke: "#27A0CC", strokeWidth: 2, title: (d) => `${d["Work status"]} \n ${d.Gap} minutes`}), ], caption: html`

Source: UK HETUS, 2015

`, tooltip: {stroke: "#dddddd", strokeWidth: 2} }) const plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, marginLeft: 180, marginBottom: 50, x: {grid: true, domain: [250,550], label: "Mean average minutes per day", labelOffset: 50, tickSize: 0}, y: {domain: work_status.filter(d => d.Sex === "Women").sort((a, b) => b.Minutes - a.Minutes).map(d => d["Work status"]), label: null, tickSize: 0}, color: {legend: false, domain: ["Women", "Men"], range: ["#8F5EF8", "#2CC3AB"], style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}}, marks: [ Plot.ruleY(work_status, Plot.groupY({x1: "min", x2: "max"}, {x: "Minutes", y: "Work status", stroke: "#dddddd", strokeWidth: 2.5})), Plot.dot(work_status, {x: "Minutes", y: "Work status", r: 5, fill: "Sex", title: (d) => `${d["Work status"]} \n ${d.Sex}\n ${d3.format(",.0f")(d.Minutes)} minutes`}) ], caption: html`

Source: UK HETUS, 2015

`, tooltip: {fill: "#dddddd", opacity: 1, "stroke-width": "0px"} }) return toggle_gap_work_status === true ? gap_plot : plot; }33:[{"id":"51daabdb444f9f854a239f950489551477c742301a369a231a70365e399a5cf0ef3f4ac9c763ddc3dfb993242d114b8c66522a4f18c7c8840d12cffda1a00028","href":"https://static.observableusercontent.com/files/51daabdb444f9f854a239f950489551477c742301a369a231a70365e399a5cf0ef3f4ac9c763ddc3dfb993242d114b8c66522a4f18c7c8840d12cffda1a00028","name":"occupation.csv","type":"text/csv","lastModified":1653663986269.02,"size":886},{"id":"17b203e2b97fc65b45ed54f021ada4dd5ec8e5f242d15a451d4a1751ab2db25f2d7cdeb88a9eab35576eeabfa5db1de565a140b52d6300b72f2df53300c1da12","href":"https://static.observableusercontent.com/files/17b203e2b97fc65b45ed54f021ada4dd5ec8e5f242d15a451d4a1751ab2db25f2d7cdeb88a9eab35576eeabfa5db1de565a140b52d6300b72f2df53300c1da12","name":"work_status.csv","type":"text/csv","lastModified":1653666060416.241,"size":781},{"id":"db9ab8618ca7e081eef485b2d1938e73cb320cab5df73c65dea4bebb39bd2896eaacdc543360cdb797793d56c10426c371f5679fbc750fe5110b620670662434","href":"https://static.observableusercontent.com/files/db9ab8618ca7e081eef485b2d1938e73cb320cab5df73c65dea4bebb39bd2896eaacdc543360cdb797793d56c10426c371f5679fbc750fe5110b620670662434","name":"region.csv","type":"text/csv","lastModified":1653662135379.798,"size":1070},{"id":"caab490ae1e14eceaa05082842daf3e812531260bd2aa86ab75e9cb53752d9d17863d9fa98279f10ddb202c220e8dd0dd2c03844768555b865c105d5ae48aab7","href":"https://static.observableusercontent.com/files/caab490ae1e14eceaa05082842daf3e812531260bd2aa86ab75e9cb53752d9d17863d9fa98279f10ddb202c220e8dd0dd2c03844768555b865c105d5ae48aab7","name":"age.csv","type":"text/csv","lastModified":1653662139271.108,"size":411}] 2d:{"title":"Gender leisure gap, 2015","cells":[{"id":0,"value":"# Gender leisure gap, 2015","pinned":false,"hidden":false,"mode":"md"},{"id":788,"value":"

On average, UK ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} in 2015 spent 5 hours and 29 minutes per day on leisure activities, while UK ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} spent 6 hours and 9 minutes per day. That is equivalent to 40 minutes less leisure time.

","pinned":false,"hidden":false,"mode":"html"},{"id":1039,"value":"---\n### Geography","pinned":false,"hidden":false,"mode":"md"},{"id":776,"value":"

The scatter plot below shows average weekly leisure time for ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} and ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} in the United Kingdom. The dashed line indicates gender parity so the further a country or region is from the diagonal line the greater the leisure gap. The leisure gap is greatest in the North West of England where ${textcolor(\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} on average spend 7 hours less than ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} on leisure activities. By contrast, in Northern Ireland there is gender parity in leisure time. Both ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} and ${textcolor(\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} spend on average 30 hours and 36 minutes on leisure activities per week.

","pinned":false,"hidden":false,"mode":"html"},{"id":739,"value":"

Mean weekly leisure time by region and sex

In employment, UK countries and regions, 2015

","pinned":false,"hidden":false,"mode":"html"},{"id":711,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":1041,"value":"---\n### Age","pinned":false,"hidden":false,"mode":"md"},{"id":880,"value":"

${textcolor (\"Women\", {background: '#8F5EF8', color: '#FFFFFF'})} had less leisure time than ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} across all age groups. The largest difference in the amount of leisure time taken was amongst those aged 16 to 24 years with ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} spending 60 minutes less on average. The gap was smallest amongst those aged 35 to 44 years with ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} spending an average 24 minutes more on leisure activities.

\n
","pinned":false,"hidden":false,"mode":"html"},{"id":815,"value":"

Mean daily leisure time by age and sex

Aged 8 and above, UK, 2015

","pinned":false,"hidden":false,"mode":"html"},{"id":821,"value":"viewof toggle_gap_age = Inputs.toggle({\n label: \"Sort by leisure gap\",\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":798,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":1044,"value":"---\n### Occupation","pinned":false,"hidden":false,"mode":"md"},{"id":927,"value":"

An inequality in leisure time exists between ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} and ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} across occupations. The largest gap in leisure time was amongst those working in administrative or sales and customer services jobs with ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} having around an hour less time than ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})}. However, there is gender parity in skilled trades with ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} and ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} spending around 4 hours and 36 minutes on leisure activities.

\n
","pinned":false,"hidden":false,"mode":"html"},{"id":923,"value":"

Mean daily leisure time by occupation and sex

UK, 2015

","pinned":false,"hidden":false,"mode":"html"},{"id":908,"value":"viewof toggle_gap_occupation = Inputs.toggle({\n label: \"Sort by leisure gap\",\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":906,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1048,"value":"---\n### Work status","pinned":false,"hidden":false,"mode":"md"},{"id":946,"value":"

${textcolor (\"Women\", {background: '#8F5EF8', color: '#FFFFFF'})} who worked part-time had 24 minutes more leisure time than ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} working full-time whilst ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} who worked part-time had 72 more minutes than ${textcolor(\"men\", {background: '#2CC3AB', color: '#FFFFFF'})} working full-time. It is likely that ${textcolor (\"women\", {background: '#8F5EF8', color: '#FFFFFF'})} who work part-time are using more of their time in unpaid work than men.

\n
","pinned":false,"hidden":false,"mode":"html"},{"id":944,"value":"

Mean daily leisure time by work status and sex

UK, 2015

","pinned":false,"hidden":false,"mode":"html"},{"id":952,"value":"viewof toggle_gap_work_status = Inputs.toggle({\n label: \"Sort by leisure gap\",\n value: false\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":950,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":697,"value":"

References

","pinned":false,"hidden":false,"mode":"html"},{"id":60,"value":"---\n### Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":507,"value":"import {tidy, select, mutate, pivotWider} from \"@pbeshai/tidyjs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":373,"value":"import {textcolor} from \"@observablehq/text-color-annotations-in-markdown\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":218,"value":"region = d3.csvParse(\n await FileAttachment(\"region.csv\").text(),\n d3.autoType\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":795,"value":"age = (await FileAttachment(\"age.csv\").csv()).map(d => ({\n 'Age group': d['Age group'],\n Sex: d.Sex,\n Hours: +d.Hours,\n hhmmss: d3.timeParse(\"%H:%M:%S\")(d.hhmmss),\n Minutes: +d.Minutes\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":903,"value":"occupation = (await FileAttachment(\"occupation.csv\").csv()).map(d => ({\n Occupation: d.Occupation,\n Sex: d.Sex,\n Hours: +d.Hours,\n hhmmss: d3.timeParse(\"%H:%M:%S\")(d.hhmmss),\n Minutes: +d.Minutes\n}))","pinned":false,"hidden":false,"mode":"ojs"},{"id":941,"value":"work_status = (await FileAttachment(\"work_status.csv\").csv()).map(d => ({\n 'Work status': d['Work status'],\n Sex: d.Sex,\n Hours: +d.Hours,\n hhmmss: d3.timeParse(\"%H:%M:%S\")(d.hhmmss),\n Minutes: +d.Minutes\n}))","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}