2f:T6a4,plot1 = Plot.plot({ width, height: width>1000 ? 0.6*width : width>500 ? 0.8*width : 350, marginLeft: width>1000 ? 60 : width>500 ? 60 : 30, marginBottom: width>1000 ? 80 : width>500 ? 60 : 30, marginTop: width>1000 ? 60 : width>500 ? 40 : 20, marginRight: width>1000 ? 50 : width>500 ? 40 : 20, x: {tickFormat:"d", grid:true, ticks:width>600 ? [1990,1995,2000,2005,2010,2015,2020,2025] : [1990,2000,2010,2020], tickSize: width>500 ? 10 : 5, label: "Year", labelArrow: false}, y: {tickFormat: d=> d.toFixed(1) , ticks: width>500 ? [1.5,2.1,2.5,3,3.5,4,4.5] : [1.4,2.4,3.4,4.4], domain:[1.4,4.5], grid: true, label: "TFR (children per women)", labelArrow: false}, color: {legend: false, domain:domain1, range: range1}, marks: [ Plot.frame({strokeWidth:.5}), Plot.ruleY([2.1], {strokeWidth:1}), Plot.line(df1, {x:"annee", y: "n2", stroke:"paysEn", strokeWidth: width>1200 ? 8 : width>500 ? 5 : 3, }), Plot.dot(df1,{x: "annee", y: "n", z: "paysEn", fill:"transparent", stroke: "transparent", channels: { annee: {value:"annee", label:""}, pays: {value: "paysEn", label:""}, n: {value: "n", label: "TFR:"} }, tip: {format: {x:false,y:false,z: false,stroke:false, pays:true, annee:"d",n: d=> `${d.toFixed(1)} children`}, strokeWidth:0} }), Plot.text(["Replacement rate"], {x:1992.5, y: 1.75, textAnchor:"start", lineAnchor:"top"}), Plot.arrow([1], {x1:1992.3, x2:1991.5, y1: 1.70, y2:2.07, bend:20}), ] })30:T1018,boxPlotGraph1 = htl.html `

${title1} ${legend1} ${plot1}

Z. Ouadah-Bedidi, I. Bouchoucha, S. Abdellatif, Population & Societies, 644, May 2026, INED.

Guide: In Tunisia, the average number of children per women fell to 1.58 children per woman after having risen to 2.4 in the mid-2010s.

Scope: Women aged 15–49 years.

Sources: WHO and MSP (Algeria); HCP (Morocco); INS (Tunisia), see Box 1.

`31:T8d7,plot2 = Plot.plot({ width , height: .8*width, marginTop: width>1000 ? 80 : 50, marginLeft: 60, marginBottom: 110, y: {label: "Fertility rate by age (%)", labelArrow:false, domain: [0,20], ticks:[0,5,10,15,20], tickSize: width>500 ? 10 : 5, grid: true}, x: {label: "Age group", labelArrow:false, labelAnchor: "center", labelOffset:90, ticks: [1,2,3,4,5,6,7], tickSize:10, grid: false, domain:[0.75,7.25]}, color: {legend:false, domain: domain2, range: range2}, marks: [ Plot.line(df2, {filter: d=> legend2L.includes(d.paysEn), x:"ageNum",y:"n", stroke:"transparent", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 1, channels: { pays: {value:"paysEn", label: ""}, age: {value:"ageEn", label: ""}, n : {value:"n", label:""}, }, tip: {format:{x: false, y: false,stroke:false,z:false, pays: true, age: true, n: d=> `${d.toFixed(1)}%` }, strokeWidth:0} }), Plot.line(df2, {filter: d=> legend2L.includes(d.paysEn) && d.anneeNum==1, x:"ageNum",y:"n", stroke:"paysEn", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 1, }), Plot.line(df2, {filter: d=> legend2L.includes(d.paysEn) && d.anneeNum==2, x:"ageNum",y:"n", stroke:"paysEn", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 1, strokeDasharray: [15,10] }), Plot.line(df2, {filter: d=> legend2L.includes(d.paysEn) && d.anneeNum==3, x:"ageNum",y:"n", stroke:"paysEn", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 1, strokeDasharray: [4,10] }), Plot.text(["15–19"], {x:1, y:-.4, lineAnchor:"top"}), Plot.text(["20–24"], {x:2, y:-.4, lineAnchor:"top"}), Plot.text(["25–29"], {x:3, y:-.4, lineAnchor:"top"}), Plot.text(["30–34"], {x:4, y:-.4, lineAnchor:"top"}), Plot.text(["35–39"], {x:5, y:-.4, lineAnchor:"top"}), Plot.text(["40–44"], {x:6, y:-.4, lineAnchor:"top"}), Plot.text(["45–49"], {x:7, y:-.4, lineAnchor:"top"}), Plot.ruleY([0], {strokeWidth:0.5}) , Plot.ruleX([0.75], {strokeWidth:0.5}) ] })32:T998,plot2B = Plot.plot({ width, height: width>500 ? 0.8*width : 350, marginTop: width>1000 ? 80 : 50, marginLeft: width>500 ? 50 : 30, marginBottom: 100, y: {label: "Fertility rate by age (%)", labelArrow:false, domain: [0,20], ticks: [0,5,10,15,20], grid: true}, x: {label: "Age group", labelArrow:false, labelAnchor: "center", labelOffset: width>500 ? 70 : 80, domain:[0.75,7.25], ticks: [1,2,3,4,5,6,7]}, color: {legend:false, domain: domain2, range: range2}, marks: [ Plot.line(df2, {filter: d=> legend2LB.includes(d.paysFr), x:"ageNum",y:"n", stroke:"transparent", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 2, channels: { pays: {value:"paysEn", label: ""}, age: {value:"ageEn", label: ""}, n : {value:"n", label:""}, }, tip: {format:{x: false, y: false,stroke:false,z:false, pays: true, age: true, n: d=> `${d.toFixed(1)}%` }, strokeWidth:0} }), Plot.line(df2, {filter: d=> legend2LB.includes(d.paysEn) && d.anneeNum==1, x:"ageNum",y:"n", stroke:"paysEn", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 1, }), Plot.line(df2, {filter: d=> legend2LB.includes(d.paysEn) && d.anneeNum==2, x:"ageNum",y:"n", stroke:"paysEn", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 1, strokeDasharray: [15,5] }), Plot.line(df2, {filter: d=> legend2LB.includes(d.paysEn) && d.anneeNum==3, x:"ageNum",y:"n", stroke:"paysEn", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 1, strokeDasharray: [4,6] }), Plot.text([width>500 ? "15–19" : "15\n|\n19"], {x:1, y:-1, lineAnchor:"top"}), Plot.text([width>500 ? "20–24" : "20\n|\n24"], {x:2, y:-1, lineAnchor:"top"}), Plot.text([width>500 ? "25–29" : "25\n|\n29"], {x:3, y:-1, lineAnchor:"top"}), Plot.text([width>500 ? "30–34" : "30\n|\n34"], {x:4, y:-1, lineAnchor:"top"}), Plot.text([width>500 ? "35–39" : "35\n|\n39"], {x:5, y:-1, lineAnchor:"top"}), Plot.text([width>500 ? "40–49" : "40\n|\n49"], {x:6, y:-1, lineAnchor:"top"}), Plot.text([width>500 ? "50–54" : "50\n|\n54"], {x:7, y:-1, lineAnchor:"top"}), Plot.ruleY([0], {strokeWidth:0.5}), Plot.ruleX([0.75], {strokeWidth:0.5}) ] })33:T1542,boxPlotGraph2 = htl.html `

${title2}

${plot2}

${viewof legend2L} ${legDash}

${viewof legend2LB} ${legDashL}

${plot2B}

Z. Ouadah-Bedidi, I. Bouchoucha, S. Abdellatif, Population & Societies, 644, May 2026, INED.

Guide: In Algeria en 2014, for 100 women aged 20 to 24,8 children were born.

Note: Values in parentheses show mean age at childbirth, calculated from age-specific fertility rates.

Scope: Women aged 15–49 years.

Sources: WHO and MSP (Algeria); HCP (Morocco); INS (Tunisia), see Box 1.

`34:T692,plot3 = Plot.plot({ width, height: width>1000 ? 0.65*width : width>500 ? 0.7*width : 350, marginLeft: width>1200 ? 100 : width> 700 ? 90 : width>500 ? 70 : 30, marginBottom: width>1200 ? 80 : width> 700 ? 70 : width>500 ? 60 : 30, marginRight: width> 700 ? 50 : 20, marginTop: width>1200 ? 70 : width> 700 ? 40 : 30, x: {label: "Year", labelArrow:false, labelAnchor:"center", tickFormat:"d", ticks: width>500 ? [1990,1995,2000,2005,2010,2015,2020] : [1990,2000,2010,2020], tickSize: width>500 ? 10 : 5, domain: [1990,2020]}, y: {label: "Prevalence of contraception use (%)", labelArrow:false, domain: [34,75], ticks: [35,40,45,50,55,60,65,70,75] ,grid: true}, color: {legend:false, domain:domainCol, range:rangeCol }, marks: [ Plot.frame({strokeWidth:0.5}), Plot.line(filter3, { x:"annee", y:"n", stroke:"catNum", strokeWidth: width>1000 ? 5 : width>500 ? 3 : 2, tip: false}), Plot.dot(filter3, {x:"annee", y:"nTip", fill:"transparent", stroke: "transparent", strokeWidth: width>1000 ? 3 : width>500 ? 2 : 1, r: width>1000 ? 7 : width>500 ? 4 : 2, channels: { annee: {value:"annee", label:""}, met: {value:"metEn", label:""}, pays: {value: "paysEn", label:""}, n: {value: "nTip", label: ""} }, tip: {format: {x:false,y:false,z: false,stroke:false, pays:true, annee:"d", met: true, n: d=> `${d.toFixed(1)}%`}, strokeWidth:0} }, ) ] })35:T10d2,boxPlotGraph3 = htl.html `

${title3}

All methods ${viewof legend3A} Modern methods ${viewof legend3B}

${plot3}

Z. Ouadah-Bedidi, I. Bouchoucha, S. Abdellatif, Population & Societies, 644, May 2026, INED.

Guide: In Morocco, rate of contraceptive use (all methods) reached 70% in 2018, or 58% counting only use of modern methods.

Scope: Married women aged 15–49.

Sources: WHO and MSP (Algeria); HCP (Morocco); INS (Tunisia), see Box 1.

`36:Tb66, 37:T5a5,filterLegend = ( domain, range, subdomain = null ) => { const defaultChecked = subdomain === null ? domain : subdomain; const value = new Map( domain.map(d => [d, defaultChecked.includes(d)]) ); const _set = () => set( node, [...value].filter(d => d[1]).map(d => d[0]) ); const oninput = e => { value.set(e.target.id, e.target.checked); _set(); }; const selectAll = () => { domain.forEach(d => value.set(d, true)); node.querySelectorAll("input[type=checkbox]") .forEach(cb => cb.checked = true); _set(); }; const selectNone = () => { domain.forEach(d => value.set(d, false)); node.querySelectorAll("input[type=checkbox]") .forEach(cb => cb.checked = false); _set(); }; const node = htl.html`

${domain.map((d, i) => htl.html`

${d}

`)}

`; _set(); return node; }; 38:T6cd,filterLegendLong = ( domain, range, subdomain = null ) => { const defaultChecked = subdomain === null ? domain : subdomain; const value = new Map( domain.map(d => [d, defaultChecked.includes(d)]) ); const _set = () => set( node, [...value].filter(d => d[1]).map(d => d[0]) ); const oninput = e => { value.set(e.target.id, e.target.checked); _set(); }; const selectAll = () => { domain.forEach(d => value.set(d, true)); node.querySelectorAll("input[type=checkbox]") .forEach(cb => cb.checked = true); _set(); }; const selectNone = () => { domain.forEach(d => value.set(d, false)); node.querySelectorAll("input[type=checkbox]") .forEach(cb => cb.checked = false); _set(); }; const node = htl.html`

${domain.map((d, i) => htl.html`

${d}

`)}

`; _set(); return node; }; 39:[{"id":"7439df02f5172249769f48f56459d9e5175481c9743d13bdbc6bf408c6d507af73aaae8381b56bad135c9075144c402580637e6f36e9cd605ce86d0778cdc220","href":"https://static.observableusercontent.com/files/7439df02f5172249769f48f56459d9e5175481c9743d13bdbc6bf408c6d507af73aaae8381b56bad135c9075144c402580637e6f36e9cd605ce86d0778cdc220","name":"n644_1@7.csv","type":"text/csv","lastModified":1778847201970.192,"size":3330},{"id":"301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","href":"https://static.observableusercontent.com/files/301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","name":"n644_2@5.csv","type":"text/csv","lastModified":1778847201970.192,"size":4352},{"id":"1d83fcff4f51b568e6159c46a9979bdd146c26ea91ae09d684f47f5b5686a37955785bad8e10ab58cce491f083a7fac3f38f8355afe13c9d73c0ad34d1ec0b13","href":"https://static.observableusercontent.com/files/1d83fcff4f51b568e6159c46a9979bdd146c26ea91ae09d684f47f5b5686a37955785bad8e10ab58cce491f083a7fac3f38f8355afe13c9d73c0ad34d1ec0b13","name":"n644_3@10.csv","type":"text/csv","lastModified":1778847201970.192,"size":11877},{"id":"301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","href":"https://static.observableusercontent.com/files/301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","name":"n644_2.csv","type":"text/csv","lastModified":1779083901833.528,"size":4352},{"id":"dcbc715eccf31d53018c227f8f6f6c0b78e2c7018a6daf5b1bb01ed96cd04e55fc883cc7a28bb840247355d2a21888bbd3c9d11510f21aeab7dc60cc9b34554e","href":"https://static.observableusercontent.com/files/dcbc715eccf31d53018c227f8f6f6c0b78e2c7018a6daf5b1bb01ed96cd04e55fc883cc7a28bb840247355d2a21888bbd3c9d11510f21aeab7dc60cc9b34554e","name":"n644_2@3.csv","type":"text/csv","lastModified":1779084315455.322,"size":4362},{"id":"cbe99e2fa0a017537212fc525d90486ac190734d11b9b0a60111d95dbafc0ed30d0d46c16839e97f3196e946de8b09c00c9b92c9fe912547bcf1dd7053864a51","href":"https://static.observableusercontent.com/files/cbe99e2fa0a017537212fc525d90486ac190734d11b9b0a60111d95dbafc0ed30d0d46c16839e97f3196e946de8b09c00c9b92c9fe912547bcf1dd7053864a51","name":"n644_3.csv","type":"text/csv","lastModified":1779082313410.182,"size":13977},{"id":"301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","href":"https://static.observableusercontent.com/files/301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","name":"n644_2@1.csv","type":"text/csv","lastModified":1779084212986.401,"size":4352},{"id":"301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","href":"https://static.observableusercontent.com/files/301e7ca2e6a7a45e4df4ba2a6f5a63f248c3e3a7f21b69bea50939427495cf3b9dc2788d814de0ffda8df7fe7f5e1920120eecff78ae486c06f703227a705c13","name":"n644_2@2.csv","type":"text/csv","lastModified":1779084251872.128,"size":4352},{"id":"fc661ac9673531e575e88601da634f9a93a211a9a25f00f67347bc603b5b97ae0062d69dcecdb77c6aa1c38b081c581106d70c53dac8db4762f2422207406f04","href":"https://static.observableusercontent.com/files/fc661ac9673531e575e88601da634f9a93a211a9a25f00f67347bc603b5b97ae0062d69dcecdb77c6aa1c38b081c581106d70c53dac8db4762f2422207406f04","name":"n644_2@4.csv","type":"text/csv","lastModified":1779108470098.228,"size":4302}] 2d:{"title":"n644 (EN) G1 à G3","cells":[{"id":0,"value":"# n644 (EN) G1 à G3","pinned":false,"hidden":false,"mode":"md"},{"id":5,"value":"pal100 = [\n \"#ff564e\", //rouge\n \"#ff7aad\", //rose\n \"#522673\", //mauve\n \"#0042eb\", //bleu\n \"#5cd49e\", //vert\n \"#ffb322\", //jaune\n \"#222330\", //black\n \"#d1d1d1\" //gris\n]","pinned":false,"hidden":false,"mode":"ojs"},{"id":7,"value":"## Graphique 1","pinned":false,"hidden":false,"mode":"md"},{"id":9,"value":"df1 = FileAttachment(\"n644_1@7.csv\").csv({typed: true}) ","pinned":false,"hidden":false,"mode":"ojs"},{"id":52,"value":"title1 = html`

Figure 1. Total fertility rate of women in the Maghreb (1990–2024)

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":83,"value":"domain1 = [\"Algeria\",\"Morocco\",\"Tunisia\"]","pinned":false,"hidden":false,"mode":"ojs"},{"id":85,"value":"range1 = [pal100[4], pal100[0], pal100[3]]","pinned":false,"hidden":false,"mode":"ojs"},{"id":30,"value":"legend1 = Plot.legend({swatchSize:40, swatchHeight:3, color:{domain: domain1, range: range1}})","pinned":false,"hidden":false,"mode":"ojs"},{"id":11,"value":"$2f","pinned":true,"hidden":false,"mode":"ojs"},{"id":26,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":129,"value":"## Graphique 2","pinned":false,"hidden":false,"mode":"md"},{"id":147,"value":"title2 = html`

Figure 2. Fertility rates in the Maghreb by maternal age group (2004–2024)

` ","pinned":false,"hidden":false,"mode":"ojs"},{"id":131,"value":"df2 = FileAttachment(\"n644_2@4.csv\").csv({typed:true})","pinned":false,"hidden":false,"mode":"ojs"},{"id":133,"value":"domain2 = [...new Set(df2.map(d=>(d.paysEn)))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":154,"value":"range2 = [\"#1f764f\",\"#33c081\",\"#9de5c5\", \"#c80900\", pal100[0], \"#ff9a95\", \"#00288d\", pal100[3], \"#5a88ff\" ]","pinned":false,"hidden":false,"mode":"ojs"},{"id":168,"value":"viewof legend2L = filterLegendLong(domain2,range2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":258,"value":"viewof legend2LB = filterLegendLong(domain2,range2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":799,"value":"legDash = Plot.plot({\nwidth:200, \nheight:70,\nmarginLeft:0, \nmarginRight:0,\nmarginBottom:10,\n\nx: {domain:[0,0.2], axis:false},\n\nmarks : [ \n\nPlot.ruleY([-1], {x1:0, x2:0.07, stroke: \"#d1d1d1\",\n strokeWidth: width>1000 ? 4 : width>500 ? 3 : 2,\n strokeDasharray:[5,5]}),\nPlot.text([\"2019, 2023, 2024\"], {x:0.08, y:-1, textAnchor:\"start\", className: \"dash\"}),\n\n \nPlot.ruleY([0], {x1:0, x2:0.07, stroke: \"#d1d1d1\",\n strokeWidth: width>1000 ? 4 : width>500 ? 3 : 2,\n strokeDasharray:[15,5]}),\nPlot.text([\"2014\"], {x:0.08, y:0, textAnchor:\"start\", className: \"dash\"}),\n\nPlot.ruleY([1], {x1:0, x2:0.07, stroke: \"#d1d1d1\",\n strokeWidth: width>1000 ? 4 : width>500 ? 3 : 2,}),\nPlot.text([\"2004, 2005\"], {x:0.08, y:1, textAnchor:\"start\", className: \"dash\"}) \n]\n\n \n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":903,"value":"legDashL = Plot.plot({\nwidth:200, \nheight:70,\nmarginLeft:0, \nmarginRight:0,\nmarginBottom:10,\n \nx: {domain:[0,0.2], axis:false},\n\nmarks : [ \n\nPlot.ruleY([-1], {x1:0, x2:0.07, stroke: \"#d1d1d1\",\n strokeWidth: width>1000 ? 4 : width>500 ? 3 : 2, strokeDasharray:[5,5]}),\nPlot.text([\"2019, 2023, 2024\"], {x:0.08, y:-1, textAnchor:\"start\", className: \"dash\"}),\n\n \nPlot.ruleY([0], {x1:0, x2:0.07, stroke: \"#d1d1d1\",\n strokeWidth: width>1000 ? 4 : width>500 ? 3 : 2, strokeDasharray:[15,5]}),\nPlot.text([\"2014\"], {x:0.08, y:0, textAnchor:\"start\", className: \"dash\"}),\n\n \nPlot.ruleY([1], {x1:0, x2:0.07, stroke: \"#d1d1d1\",\n strokeWidth: width>1000 ? 4 : width>500 ? 3 : 2}),\nPlot.text([\"2004, 2005\"], {x:0.08, y:1, textAnchor:\"start\", className: \"dash\"}) \n]\n\n \n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":174,"value":"$31","pinned":true,"hidden":false,"mode":"ojs"},{"id":245,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":184,"value":"$33","pinned":true,"hidden":false,"mode":"ojs"},{"id":378,"value":"## Graphique 3","pinned":false,"hidden":false,"mode":"md"},{"id":491,"value":"**`domain` et `range` pour filterlegend**","pinned":false,"hidden":false,"mode":"md"},{"id":590,"value":"df3 = FileAttachment(\"n644_3.csv\").csv({typed:true})","pinned":true,"hidden":false,"mode":"ojs"},{"id":383,"value":"domain3 = [...new Set(df3.map(d=>(d.paysEn)))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":448,"value":"range3 = [...new Set(df3.map(d=>(d.color)))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":411,"value":"domain3c = domain3.concat(domain3)","pinned":false,"hidden":false,"mode":"ojs"},{"id":387,"value":"range3a = [\"#2ca871\", \"#ff160b\",pal100[3]]","pinned":false,"hidden":false,"mode":"ojs"},{"id":391,"value":"range3b = [\"#9de5c5\", \"#ffaaa6\",\"#759cff\",]","pinned":false,"hidden":false,"mode":"ojs"},{"id":413,"value":"range3C = range3a.concat(range3b)","pinned":false,"hidden":false,"mode":"ojs"},{"id":496,"value":"**Les leux checkbox**","pinned":false,"hidden":false,"mode":"md"},{"id":389,"value":"viewof legend3A = filterLegend(domain3,range3a)","pinned":true,"hidden":false,"mode":"ojs"},{"id":401,"value":"viewof legend3B = filterLegend(domain3,range3b)","pinned":true,"hidden":false,"mode":"ojs"},{"id":498,"value":"**Filtrage de la base**","pinned":false,"hidden":false,"mode":"md"},{"id":515,"value":"title3 = html`

Figure 3. Use of contraception in the Maghreb

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":434,"value":"X = df3.filter(d=>legend3A.includes(d.paysEn) && d.met==\"TM\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":437,"value":"Xb = df3.filter(d=>legend3B.includes(d.paysEn) && d.met==\"MM\" )","pinned":false,"hidden":false,"mode":"ojs"},{"id":440,"value":"filter3 = X.concat(Xb)","pinned":false,"hidden":false,"mode":"ojs"},{"id":470,"value":"rangeCol= [...new Set(filter3.map(d=>(d.color)))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":500,"value":"**Domain et range pour le graph**","pinned":false,"hidden":false,"mode":"md"},{"id":474,"value":"domainCola= [...new Set(X.map(d=>(d.catNum)))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":478,"value":"domainColb= [...new Set(Xb.map(d=>(d.catNum)))]","pinned":false,"hidden":false,"mode":"ojs"},{"id":480,"value":"domainCol = domainCola.concat(domainColb)","pinned":false,"hidden":false,"mode":"ojs"},{"id":418,"value":"$34","pinned":true,"hidden":false,"mode":"ojs"},{"id":513,"value":"$35","pinned":true,"hidden":false,"mode":"ojs"},{"id":99,"value":"### CSS & fonctions","pinned":false,"hidden":false,"mode":"md"},{"id":101,"value":"$36","pinned":false,"hidden":false,"mode":"html"},{"id":161,"value":"function set(input, value) {\n input.value = value;\n input.dispatchEvent(new Event(\"input\", {bubbles: true}));\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":163,"value":"$37","pinned":false,"hidden":false,"mode":"ojs"},{"id":165,"value":"$38","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W39","stdlib":"1","titling":"auto"}