2f:T56e,{ const filtered_data = data.filter(d => d.group === grouping) const tidy_data = tidy( filtered_data, select(['activity', 'wave', 'variable', 'minutes']), pivotWider({ namesFrom: 'wave', valuesFrom: 'minutes' }), mutate({ change: d => d['Mar-22'] - d['2014/15'] }) ) const plot = Plot.plot({ style: {fontSize: "14px", fontFamily: "'Open Sans', sans-serif"}, width: width, marginLeft: 150, x: {grid: true, labelOffset: 50, tickSize: 0}, y: {domain: d3.sort(tidy_data, d => -d.change).map(d => d.activity), label: null, tickSize: 0}, facet: {data: tidy_data, x: "variable"}, fx: {label: "Average daily minutes", labelOffset: 45}, marks: [ Plot.arrow(tidy_data, {x1: d => d["2014/15"], x2: d => d["Mar-22"], y: "activity", stroke: (d) => d.change > 0 ? "#206095" : "#F66068", strokeWidth: 3, title: (d) => `${d.activity} \n ${d.change.toFixed(1)} minutes`}), Plot.frame({stroke: "#bdbdbd", strokeWidth: 1}) ], caption: html`

Source: Office for National Statistics – Time Use Surveys

`, tooltip: {stroke: "#dddddd", strokeWidth: 2} }) return plot }30:[{"id":"38cfe49ac4b6085841dc0c8a120421a26d3d79794f636857711441ad76ae98f8bc141b023142ab25e91019c77b779d757b51a1b2bb0d1160e246fd3156da729a","href":"https://static.observableusercontent.com/files/38cfe49ac4b6085841dc0c8a120421a26d3d79794f636857711441ad76ae98f8bc141b023142ab25e91019c77b779d757b51a1b2bb0d1160e246fd3156da729a","name":"data@5.csv","type":"text/csv","lastModified":1681291459643.576,"size":31866}] 2d:{"title":"Changes in time use","cells":[{"id":0,"value":"# Changes in time use","pinned":false,"hidden":false,"mode":"md"},{"id":200,"value":"

UK, 2014/15 (UKTUS) and March 2022 (OTUS)

","pinned":false,"hidden":false,"mode":"html"},{"id":220,"value":"

The arrows indicate a ${textcolor(\"rise\", {background: '#206095', color: '#FFFFFF'})} or a ${textcolor(\"decline\", {background: '#F66068', color: '#FFFFFF'})} in average daily minutes

","pinned":false,"hidden":false,"mode":"html"},{"id":7,"value":"viewof grouping = Inputs.select([\"Sex\",\"Age group\",\"Income band\",\"Employment status\",\"Dependent children\",\"Sex and dependent children\",\"Total\"], {sort: false, label: \"Split by:\", value: \"Total\"})","pinned":false,"hidden":false,"mode":"ojs"},{"id":15,"value":"$2f","pinned":false,"hidden":false,"mode":"ojs"},{"id":234,"value":"

The Online Time Use Survey (OTUS) can also be split by working pattern (full-time / part-time), disability status, sexuality and ethnicity.

","pinned":false,"hidden":false,"mode":"md"},{"id":136,"value":"---\n## Appendix","pinned":false,"hidden":false,"mode":"md"},{"id":11,"value":"import {tidy, select, mutate, pivotWider} from \"@pbeshai/tidyjs\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":101,"value":"import {Plot} from \"@mkfreeman/plot-tooltip\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":191,"value":"import {textcolor} from \"@observablehq/text-color-annotations-in-markdown\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"data = (await FileAttachment(\"data@5.csv\").csv()).map(d => ({\n activity: d.activity,\n wave: d.wave,\n group: d.group,\n variable: d.variable,\n minutes: +d.minutes, \n}))","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W30","stdlib":"1","titling":"auto"}