2b:null 2c:["$","title",null,{"children":"Exploring Factors Impacting Economic Growth | parthivi-varshney | Observable"}] 31:T64e,## Importance Analyzing population growth and life expectancy changes is important for several reasons: - Resource Management: Population growth affects the demand for resources such as food, water, and energy. Analyzing population growth can help policymakers and planners anticipate future demands and plan for the sustainable management of resources. - Healthcare Planning: Life expectancy changes provide important information about the health status of a population. Analyzing trends in life expectancy can help healthcare providers plan for future health services and allocate resources more effectively. - Economic Planning: Population growth and changes in life expectancy can also have significant economic impacts. For example, a rapidly growing population may require more infrastructure and public services, while an aging population may require increased healthcare and social services. Analyzing these changes can help policymakers and economists plan for future economic growth and development. - Social Implications: Changes in population growth and life expectancy can also have important social implications, including changes in family dynamics, workforce participation, and social support systems. Understanding these changes can help communities and policymakers plan for the social and cultural impacts of demographic shifts. Overall, analyzing population growth and life expectancy changes is crucial for understanding the current and future needs of a population, and for planning and developing policies and programs that support sustainable economic, social, and environmental outcomes.32:T54c,viewof choropleth = { const map = vl .markGeoshape() .data( vl .topojson("https://unpkg.com/world-atlas@2/countries-110m.json") .feature("countries") ) .transform( // Transform is advanced vl .lookup("properties.name") .from( // Lookup is even more advanced vl .lookupData(Array.from(lifeExpectancy.entries())) .key("0") .fields("1") ) .as("data") ) .encode( vl .color() .fieldQ("data.life_expectancy") .legend({ orient: "top" , title: "Life Expectancy"}) .scale({ domain: [20, 85], scheme: 'spectral' }), vl.tooltip([ { field: "properties.name", title: "Country" }, { field: "data.life_expectancy", title: "Life Expectancy (years)" } ]) ); const labels = vl .markText() .data( Array.from(lifeExpectancy.values()).filter((d) => countriesForLabel.includes(d.Country) ) ) .encode( vl.text().fieldN("Label"), vl.latitude().fieldQ("latitude"), vl.longitude().fieldQ("longitude") ); return vl .layer(map, labels) .project(vl.projection("equalEarth")) .width(width*0.85) .height(width / 2.5) .autosize({ type: "fit-x", contains: "padding" }) .render(); }33:T408,In developed countries, where the population tends to be older, **the aging population can put a strain on the economy**. As the number of people retiring and leaving the workforce increases, there can be a shortage of skilled workers, which can limit economic growth. Furthermore, an aging population typically requires more healthcare and social services, which can strain government budgets.
In contrast, in developing countries, **where the population tends to be younger, there is often a larger pool of potential workers, which can be beneficial for economic growth**. However, it's important that these young people are properly educated and trained, as an unskilled workforce can limit economic growth. Moreover, developing countries need to provide enough job opportunities for their young population, or else they may face challenges like youth unemployment and social unrest.
**The graph below represents the change in the percentage of each age group that constitutes the total population over last 50 years.**34:T6ca,In developed countries, where gender equality is generally more advanced, **a balanced gender ratio in the workforce can increase productivity, as both men and women contribute equally to economic growth**. Furthermore, having more women in leadership positions can lead to more diverse perspectives and better decision-making, which can improve business outcomes. Studies have also shown that increasing women's participation in the workforce can lead to higher GDP and economic growth.
In developing countries, where gender inequality is often more prevalent, **a low percentage of female participation in the workforce can limit economic growth**. Gender disparities in access to education and job opportunities can lead to a lack of skilled female workers, which can reduce productivity and hinder innovation. Moreover, a gender wage gap can lead to lower household incomes, which can affect economic stability and growth.
In summary, the gender composition of a population can have a significant impact on economic growth in both developed and developing countries. In order to promote sustained economic growth, it's important to work towards gender equality in education, job opportunities, and leadership positions. By ensuring that women have equal access to resources and opportunities, countries can tap into the full potential of their workforce, leading to increased productivity, innovation, and economic growth.
**The graphs below illustrates how the proportion of women has changed relative to men in both developed and developing countries over the last 50 years. Use the checkbox below to switch between developed and developing or select both developed and developing countries and see the world trend.**35:T93c,men_women_pop_diffs = { const data = joined_world_pop_with_gender_diff.objects().filter(d => status.includes(d.Status) && d.gender === "female") if(status.includes("Developing")) { const chart = vl .markLine({ tooltip: true, }) // .select(hover) .data(data) .encode( vl .y() .fieldQ("gender_avg_change") .aggregate("sum") .scale({ zero: false }) .axis({title : "Proportion of Female Population Compared to Male", format: "p"}), vl.x().fieldT("Year").timeUnit("year"), vl.detail().fieldN("Country"), vl.color().if("datum.Country === 'Oman'", vl.value("firebrick")).value("#ccc3"), vl.tooltip([{field: "Year", type: "ordinal", timeUnit: "year", title: "Year"}, {field: "Country", title: "Country"}, {field: "gender_avg_change", aggregate: "sum", format: ".2p", title:"Proportion of Female Population Compared to Male"}]) ) const info = vl .markText() // .transform(vl.filter(hover)) .data(data.filter(d => d.Country === "Oman")) .encode( vl.text().fieldN("Country"), vl.color().value("black") ) return vl.layer(chart, info).width(width*0.7).height(300).render() } else{ const chart = vl .markLine({ tooltip: true, }) // .select(hover) .data(data) .encode( vl .y() .fieldQ("gender_avg_change") .aggregate("sum") .scale({ zero: false }) .axis({title : "Proportion of Female Population Compared to Male", format: "p"}), vl.x().fieldT("Year").timeUnit("year"), vl.detail().fieldN("Country"), vl.color().if("datum.Country === 'Singapore'", vl.value("firebrick")).value("#ccc3"), vl.tooltip([{field: "Year", type: "temporal", timeUnit: "year", title: "Year"}, {field: "Country", title: "Country"}, {field: "gender_avg_change", aggregate: "sum", format: ".2p", title:"Proportion of Female Population Compared to Male"}]) ) const info = vl .markText() // .transform(vl.filter(hover)) .data(data.filter(d => d.Country === "Singapore")) .encode( vl.text().fieldN("Country"), vl.color().value("black") ) return vl.layer(chart, info).width(width*0.7).height(300).render() } } 36:T4e9,## Marks & Channels (How?) 1) **Bar Chart** * **Marks:** Line * **Channels:** * **Length (Size):** To Identify gender_avg_change i.e the percentage of women that constitute towards the overall population as compared to men in any particular year. * Attribute: Gender_avg_change * Task: T1 * Encoding : **Separate, Order & Align** * **Horizontal Position (x-axis):** To show years. * Attribute: Year * Task: T2 * Encoding **Separate, Order, Align** * **Vertical Position (y-axis):** To show population composition of women as compared to men across last 50 years. * Attribute: Gender_avg_change * Task: T3 * Encoding: **Express** 2) **Line Chart** * **Marks:** Line * **Channels:** * **Color Hue:** To Identify the country, red indicates the country with most significant change while grey lines represent other countries. * Attribute: Country * Task: T1 * Encoding : **Separate, & Align** * **Horizontal Position (x-axis):** To show years. * Attribute: Year * Task: T2 * Encoding **Separate, Order, Align** * **Vertical Position (y-axis):** To show population composition of women as compared to men. * Attribute: Gender_avg_change * Task: T3 * Encoding: **Express** 37:T470,The change in life expectancy can significantly impact the economic growth of a country. Increasing life expectancy can lead to a larger elderly population, which can impact healthcare costs, pensions, and social welfare programs. Additionally, **longer lifespans can lead to longer working lives, which can contribute to increased economic output and productivity**. However, the aging population can also lead to a decrease in the size of the labor force and a potential shortage of skilled workers. Countries may need to invest in new technologies and adapt policies to accommodate a larger elderly population. Nevertheless, **longer life expectancy can also create new opportunities for growth in industries such as healthcare, pharmaceuticals, and retirement services**. Overall, changes in life expectancy can have a significant impact on the economy, and countries must prepare accordingly to mitigate potential challenges and maximize opportunities.
**The graph below represents the life expectancy of the country over the last 50 years. You can choose the country of your choice using the drop down menu above the graph.**38:T407,Different age groups can have varying impacts on the economic growth of a country. Younger age groups, such as children and teenagers, are not typically contributors to the workforce but are significant consumers, as they rely on parents and guardians for financial support. In contrast, the working-age population, **typically ages 25-64, are the primary contributors to the labor force and generate the bulk of economic output**. **Older age groups, such as retirees, may have limited participation in the workforce, but can also be significant consumers and contributors to the economy through retirement savings and spending**. Demographic shifts in age groups can impact economic growth, as changes in the labor force participation and consumption patterns can affect productivity and demand for goods and services.
**The graph below represents how the population of different age groups in the country changed over the last 50 years. You can choose the country of your choice using the drop down menu above the graph.**39:T678,country_age_group = { const data = joined_data_by_age.objects().filter(d => filtered_country.includes(d.Country)) const popByAgeGroup = vl.markLine({point: true, tooltip : true}) .data(data) .encode( vl.x().fieldO("Year").axis({ title: "Year" }).timeUnit("year").title("Year"), vl.y().fieldQ("population_by_age").axis({ title: "Total Population", format: "~s"}).title("Population"), vl.color().fieldO("age_group").title("Age Group").scale({ domain:["0-14", "15-29", "30-44", "45-59", "60-74", "75-89", "90-100"], range:["#AAF0D1", "#7FFFD4", "#40E0D0", "#43BFC7", "#3B9C9C", "#007C80", "#033E3E"] }) // .sort(["90-100", "75-89", "60-74", "45-59", "30-44", "15-29", "0-14"]), // vl.tooltip([{field: "Year", type: "temporal", timeUnit: "year", title: "Year"}, {field: "Country", title: "Country"}, {field: "population_by_age", format: "~s", title: "Population by age_group (k)"}, {field: "age_group", title: "Age Group"}]) ) .height(300) .width(width*0.7) const averageWorldPop = vl.markLine({tooltip : true}) .data(data) .encode( vl.x().fieldT("Year").axis({ title: "Year" }), vl.y().sum("population_by_age").axis({ title: "Total Population"}).aggregate('mean'), vl.color().value("firebrick"), vl.tooltip([{field: "Year", type: "temporal", timeUnit: "year", title: "Year"}, {field: "Country", title: "Country"}, {field: "population_by_age", aggregate:"mean", format: "~s", title: "Average Population across all age_groups (k)"}]) ) .height(300) .width(1000) return ( vl .layer(popByAgeGroup, averageWorldPop) .render() ); }3a:T4d6,The composition of the male and female population can impact the economic growth of a country in various ways. Firstly, gender balance can affect the size of the labor force, as gender gaps in education and employment opportunities can limit the potential workforce. **A balanced gender composition can lead to a more efficient allocation of talent and resources, resulting in increased productivity and economic output**. Secondly, promoting gender equality can lead to increased entrepreneurship and innovation, as women are empowered to participate fully in economic activities. Thirdly, gender balance can affect consumption patterns, as women and men often have different preferences and spending behaviors, which can influence market demand and the composition of the economy. **In summary, achieving gender balance and equality can have a positive impact on economic growth and development**.
**The graphs below represent the total population of the chosen country grouped by male and female. You can click on either bar (male or female) to see the percentage difference in population between women and men (gender_avg_change) over the last 50 years. You can also select the country of your choice using the drop down menu**.3b:T627,viewof country_gender_diff = { const hover = vl.selectSingle().fields("gender"); const genderBarPlot = vl .markBar({tooltip: true}) .data(joined_world_pop_with_gender_diff.objects().filter(d => d.Country === filtered_country && d.Year === "2020")) .select(hover) .encode( vl.y().fieldN("gender"), vl.x().fieldQ("population_by_gender").title("Population of Men & Women").axis({format: "~s"}), vl.color().if(hover, vl.color().fieldN("gender")).value("#3333"), vl.tooltip([{field: "population_by_gender", title: "Total Population", format: "~s"}, {field: "gender", title: "Gender"}]) ) .height(200) .width(500); const gender_based_percent = vl .markBar({tooltip: true}) .data(joined_world_pop_with_gender_diff.objects().filter(d => d.Country === filtered_country)) .transform(vl.filter(hover)) .encode( vl.x().fieldO("Year").axis({title: "Year"}).timeUnit("year"), // .timeUnit("year").axis({title: "Year"}), vl.y().fieldQ("gender_avg_change").axis({title : "Relative Percentage", format:"p"}), vl.color().fieldN("gender"), vl.tooltip([{field: "Year", type: "ordinal", title: "Year", timeUnit: "year"}, {field: "gender_avg_change", format:".2p", aggregate: "mean", title: "Relative Propotion in Population (%)"}, {field: "gender", title: "Gender"}]) ) .width(width*0.7) return vl .vconcat(genderBarPlot, gender_based_percent) // .data(joined_world_pop_with_gender_diff.objects().filter(d => d.Country === filtered_country)) .render(); }3c:T4ed,## Marks & Channels (How?) 1) **Graph 1** * **Marks:** Line * **Channels:** * **Color Hue:** To show the gender. * Attribute: Gender * Task: T1 * Encoding : **Separate, & Align** * **Length (Size):** To show population_by_gender. * Attribute: Population_by_gender * Task: T3 * Encoding : **Separate, Order & Align** * **Horizontal Position (x-axis):** To show population_by_gender. * Attribute: Population_by_gender * Task: T2 * Encoding **Express** * **Vertical Position (y-axis):** To show gender. * Attribute: Gender * Task: T1 * Encoding: **Separate, & Align** 2) **Graph 2** * **Marks:** Line * **Channels:** * **Color Hue:** To Identify the gender * Attribute: Gender * Encoding : **Separate, & Align** * **Length (Size):** To show gender_avg_change. * Attribute: Gender_avg_change * Task: T1 * Encoding : **Separate, Order & Align** * **Horizontal Position (x-axis):** To show years. * Attribute: Year * Task: T2, T3 * Encoding **Separate, Order, Align** * **Vertical Position (y-axis):** To show gender_avg_change that is population composition of women as compared to men. * Attribute: Gender_avg_change * Task: T1 * Encoding: **Express** 3d:T410,updated_world_population = { return world_population_data.map(d => { return { "Country" : d.Location, "Year" : d.Time, "TotalMale" : +d.PopMale*1000, "TotalFemale" : +d.PopFemale*1000, "Total" : +d.PopTotal*1000, "0-14" :((Number(d.PopTotal_0_4) + Number(d.PopTotal_5_9) + Number(d.PopTotal_10_14))*1000).toFixed(3), "15-29" : ((Number(d.PopTotal_15_19) + Number(d.PopTotal_20_24) + Number(d.PopTotal_25_29))*1000).toFixed(3), "30-44" : ((Number(d.PopTotal_30_34) + Number(d.PopTotal_35_39) + Number(d.PopTotal_40_44))*1000).toFixed(3), "45-59" : ((Number(d.PopTotal_45_49) + Number(d.PopTotal_50_54) + Number(d.PopTotal_55_59))*1000).toFixed(3), "60-74" : ((Number(d.PopTotal_60_64) + Number(d.PopTotal_65_69) + Number(d.PopTotal_70_74))*1000).toFixed(3), "75-89" : ((Number(d.PopTotal_75_79) + Number(d.PopTotal_80_84) + Number(d.PopTotal_85_89))*1000).toFixed(3), "90-100" : ((Number(d.PopTotal_90_94) + Number(d.PopTotal_95_99))*1000).toFixed(3) } }) }3e:T991,filteredLE_Country = filtered_LE.map(d => { if(d.country_name === "Bahamas, The"){ return {...d, "Country":"Bahamas"} } else if(d.country_name === "Brunei Darussalam"){ return {...d, "Country":"Brunei"} } else if(d.country_name === "Cabo Verde"){ return {...d, "Country":"Cape Verde"} } else if(d.country_name === "Cote d'Ivoire"){ return {...d, "Country":"Côte d'Ivoire"} } else if(d.country_name === "Congo, Dem. Rep."){ return {...d, "Country":"Congo [DRC]"} } else if(d.country_name === "Congo, Rep."){ return {...d, "Country":"Congo [Republic]"} } else if(d.country_name === "Czechia"){ return {...d, "Country":"Czech Republic"} } else if(d.country_name === "Egypt, Arab Rep."){ return {...d, "Country":"Egypt"} } else if(d.country_name === "Gambia, The"){ return {...d, "Country":"Gambia"} } else if(d.country_name === "Hong Kong SAR, China"){ return {...d, "Country":"Hong Kong"} } else if(d.country_name === "Iran, Islamic Rep."){ return {...d, "Country":"Iran"} } else if(d.country_name === "Kyrgyz Republic"){ return {...d, "Country":"Kyrgyzstan"} } else if(d.country_name === "Lao PDR"){ return {...d, "Country":"Laos"} } else if(d.country_name === "Macao SAR, China"){ return {...d, "Country":"Macao"} } else if(d.country_name === "Micronesia, Fed. Sts."){ return {...d, "Country":"Micronesia"} } else if(d.country_name === "Myanmar"){ return {...d, "Country":"Myanmar [Burma]"} } else if(d.country_name === "Russian Federation"){ return {...d, "Country":"Russia"} } else if(d.country_name === "Sao Tome and Principe"){ return {...d, "Country":"São Tomé and Príncipe"} } else if(d.country_name === "Slovak Republic"){ return {...d, "Country":"Slovakia"} } else if(d.country_name === "Korea, Rep."){ return {...d, "Country":"South Korea"} } else if(d.country_name === "Syrian Arab Republic"){ return {...d, "Country":"Syria"} } else if(d.country_name === "Turkiye"){ return {...d, "Country":"Turkey"} } else if(d.country_name === "Virgin Islands (U.S.)"){ return {...d, "Country":"U.S. Virgin Islands"} } else if(d.country_name === "Venezuela, RB"){ return {...d, "Country":"Venezuela"} } else if(d.country_name === "Venezuela, RB"){ return {...d, "Country":"Venezuela"} } else{ return {...d, "Country":d.country_name} } })3f:T7a6,// https://observablehq.com/@uwdata/multi-view-composition?collection=@uwdata/visualization-curriculum // https://vega.github.io/vega-lite-api/ answer = { const hover = vl.selectSingle("hover").fields(["Year"]).nearest(true).on("mouseover"); const dateField = vl.x().fieldT("Year"); const age_groupSymbolFields = [ vl.y().fieldQ("population_by_age").stack("normalize").axis({title : "Population Composition of Age Groups ", format: "~s"}), vl.color().fieldN("age_group") ]; // Select and transform 'points' are 'rule' dependent in the layering const pointsSelect = marks.includes("rule") ? {} : hover; const pointsTransform = marks.includes("rule") ? vl.filter(hover) : vl.filter("true"); const lines = vl.markArea({points: true}) .encode([dateField].concat(age_groupSymbolFields)); const points = vl.markPoint() .encode([dateField].concat(age_groupSymbolFields)) .select(pointsSelect) .transform(pointsTransform); const rule = vl.markRule() .encode( dateField, vl.opacity().if(hover, vl.value(0.3)).value(0), vl.tooltip([...new Set(joined_data_by_age.objects().map(s => s.age_group))]), ) .select(hover) .transform(vl.pivot("age_group").value("population_by_age").groupby("Year")); const linesPointsLayers = marks.filter(m => ["lines","points"].includes(m)).map(m => eval(m)); const ruleLayer = marks.filter(m => ["rule"].includes(m)).map(m => eval(m)); //return vl.layer([vl.layer([lines, points]), rule]) return vl.layer([vl.layer(linesPointsLayers)].concat(ruleLayer)) .data(joined_data_by_age.objects().filter(d => d.Country === filtered_country)) .width(width*0.7) .height(300) .render(); } // answer40:[{"id":"a7ed0aebd8f0fed30f96b0abbf466522028f619a06b55477379f57a116b162a0181d7eaf03469a31a3e92a32c140bec568a047b8aaaa1210df4bac1bf0b034a0","href":"https://static.observableusercontent.com/files/a7ed0aebd8f0fed30f96b0abbf466522028f619a06b55477379f57a116b162a0181d7eaf03469a31a3e92a32c140bec568a047b8aaaa1210df4bac1bf0b034a0","name":"Life_Expectancy_Data.csv","type":"text/csv","lastModified":1682039674042.098,"size":306486},{"id":"ba5e98b149b014c9b97c52f3c8c784a9c1cb886ed6ce5f5f75e5b4d67842ce2d3c4c31ebce9946b80177ec905d8c4a960b399e3730f8f29c956ffe0cf1701d3a","href":"https://static.observableusercontent.com/files/ba5e98b149b014c9b97c52f3c8c784a9c1cb886ed6ce5f5f75e5b4d67842ce2d3c4c31ebce9946b80177ec905d8c4a960b399e3730f8f29c956ffe0cf1701d3a","name":"global life expectancy dataset.csv","type":"text/csv","lastModified":1682039674042.098,"size":101555},{"id":"799d2ca0e992548172d29ad330a6518da369ceb834fc008e17b80357b415816fe07a971b39a02136ff78d0422b149b000608b8781b5eab18acf9446cba7ecb5b","href":"https://static.observableusercontent.com/files/799d2ca0e992548172d29ad330a6518da369ceb834fc008e17b80357b415816fe07a971b39a02136ff78d0422b149b000608b8781b5eab18acf9446cba7ecb5b","name":"PopulationByAgeSex.csv","type":"text/csv","lastModified":1682039674042.098,"size":39760076},{"id":"864b1aeed9012d95cf683140d9c9eebe80d08f158ed513d6c851830a8cf2e48815f0de86b0fe0a9358ec9a2002971e0e80cf525c15de14bca7e3e7864693b656","href":"https://static.observableusercontent.com/files/864b1aeed9012d95cf683140d9c9eebe80d08f158ed513d6c851830a8cf2e48815f0de86b0fe0a9358ec9a2002971e0e80cf525c15de14bca7e3e7864693b656","name":"world_country_and_usa_states_latitude_and_longitude_values@1.csv","type":"text/csv","lastModified":1682039674042.098,"size":11205},{"id":"c674b38a8191c8b76a20938957245be6e1f36b30465d4be494d8c30c4bfb45525c8999e68801d9333b55093e3d0f9421bb9bbfd451b8ab700223b8c7e4802707","href":"https://static.observableusercontent.com/files/c674b38a8191c8b76a20938957245be6e1f36b30465d4be494d8c30c4bfb45525c8999e68801d9333b55093e3d0f9421bb9bbfd451b8ab700223b8c7e4802707","name":"life_expectancy_by_country.csv","type":"text/csv","lastModified":1682039674042.098,"size":522461}] 2f:{"id":"1e5815082c47dd33","slug":null,"title":"Exploring Factors Impacting Economic Growth","thumbnail":"4d079f94e363c47e10c8852d7787c53fc27c22cd352cbdedda23166b66409781","access_level":"public","create_time":"$D2023-04-21T01:14:34.042Z","update_time":"$D2023-04-27T07:09:46.945Z","view_time":null,"first_public_time":"$D2023-04-21T01:14:34.042Z","has_unpublished_edits":null,"fork":{"id":"7261873d32d2eee5","slug":"exploring-demographic-patterns","title":"Exploring Demographic Patterns: Population by Age Group and Life Expectancy Around the World","owner":{"id":"29c4858a5f0f02f2","login":"parthivi-varshney","name":""}},"stars":0,"starred":false,"owner":{"type":"team","id":"29c4858a5f0f02f2","login":"parthivi-varshney","name":"","avatar_url":"https://avatars-next.observableusercontent.com/a01697cc8144442f2a54ed31fc7c9c20b22e30c1a6fe52d5b38bd526bf019a9d","tier":"starter_2024"},"version":5008,"publish_version":null,"role":null,"authored":false,"authors":[{"type":"individual","id":"36cdff01b518af24","login":"parthivi-varshney","name":"","avatar_url":"https://avatars-next.observableusercontent.com/a01697cc8144442f2a54ed31fc7c9c20b22e30c1a6fe52d5b38bd526bf019a9d","tier":null}],"members":[{"type":"individual","id":"36cdff01b518af24","login":"parthivi-varshney","name":"","avatar_url":"https://avatars-next.observableusercontent.com/a01697cc8144442f2a54ed31fc7c9c20b22e30c1a6fe52d5b38bd526bf019a9d","tier":null,"role":"editor"}],"comments":[],"custom_thumbnail":false,"trashed":false,"lapsed":null,"subscribed":null,"forks":1,"collection":null,"type":"notebook","body":{"title":"Exploring Factors Impacting Economic Growth","cells":[{"id":0,"value":"# Exploring Factors Impacting Economic Growth\nAuthor : [Parthivi Varshney](https://observablehq.com/@pv1995)
\nCourse : [Data Visualization / Information Visualization](https://johnguerra.co/classes/data_visualization_spring_2023/)
\nInstructor : [John Alexis Guerra Gomez](https://observablehq.com/@john-guerra)
\nWebsite Source Code : [Click Here](https://github.com/pv1995/FactorsImpactingEconomy)
\nWebsite Link : [Click Here](http://economic-catalysts.surge.sh/)
\nUsability Test Report : [Click Here](https://1drv.ms/w/s!An1LJK5feKNJmFW8phzOmrH4ZnCg?e=mIcWG6)
\nVideo : [Click Here](https://www.youtube.com/watch?v=UVBnKjScoR4)
\nTweet : [Click Here](https://twitter.com/parthivivarshn1/status/1651038340542795777?s=46&t=AGci7c6wYsdLUslAuCtpjg)
\nPresentation Slides : [Click Here](https://www.canva.com/design/DAFfO7oX8ns/0WEeGrufR_jDPCvE6j5LRA/edit?utm_content=DAFfO7oX8ns&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton) \n\n---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2194,"value":"## Introduction\nOver the past 50 years, we have seen significant changes in both population growth and life expectancy around the world. These demographic shifts have had a profound impact on the global economy, as changes in population size and age structure can affect economic growth, productivity, and consumption patterns. In particular, as life expectancy have increased and populations have aged, we have seen significant changes in labor markets, healthcare systems, and social welfare policies. Understanding the complex relationships between population growth, life expectancy, and the economy is crucial for policymakers and economists seeking to promote sustainable economic growth and social well-being in the 21st century.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4,"value":"$31","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2211,"value":"## Data Sources \n - [World Population data](https://www.kaggle.com/datasets/ahmethoso/wpp-population-by-age-and-sex?select=PopulationByAgeSex.csv)\n - [Life Expectancy Data](https://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who)\n - [Global Life Expectancy Data (1971-2020)](https://www.kaggle.com/datasets/hasibalmuzdadid/global-life-expectancy-historical-dataset)\n - Dataset Type - Tabular\n - Data Availability - Static (Public & Free to Use) ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2216,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1634,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1636,"value":"## Story Line - Part 1 - Analyzing World Demographics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1955,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1957,"value":"## Factor 1 - Life Expectancy Around the World ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2995,"value":"The changes in life expectancy can have significant implications for the world economy. **Longer life expectancy can increase economic growth, as people are able to work and contribute for longer and tend to save more for retirement, leading to more investment.** However, longer life expectancy can also create economic challenges, particularly in countries with aging populations, which can limit economic growth, increase the burden on public pension systems, and strain government budgets due to increased demand for healthcare and social services. To manage the economic impact of changes in life expectancy, it's important for countries to invest in education and training for younger workers, as well as healthcare and social services for older populations.
\n**The graph below is a world map that shows the life expectancy of different countries. You can hover over the countries to find their life expectancy or select the country you want to find using the search-select box below.**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3024,"value":"## Insight\nAccording to recent statistics, China boasts the highest life expectancy of the three major countries, with an average of 78.08 years. The USA follows closely with a life expectancy of 77.28 years, while India trails behind with an average life expectancy of 70.16 years, lagging behind China by nearly 8 years.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3080,"value":"viewof countriesForLabel = multiAutoSelect({\n options: Array.from(lifeExpectancy.keys()),\n label: \"Countries with Label\",\n value: this?.value || [\"United States of America\", \"India\", \"China\"]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2276,"value":"#### Change in Life Expectancy of Countries from 1971 to 2020","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2441,"value":"$32","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3591,"value":"[_Data Source_](https://www.kaggle.com/datasets/hasibalmuzdadid/global-life-expectancy-historical-dataset)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2653,"value":"viewof filtered_year = Scrubber(Array.from(d3.group(coordinates_LE, d => d.year), ([key]) => key).sort(d3.ascending), {delay, loop: false})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3543,"value":"**To see how the life expectancy of the world has changes over the last 50 years click the above play button. You can also drag the slider to choose the year of your choice.**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3029,"value":"## Data Abstraction (What?)\n* **Dataset type:** Tabular \n* **Availability:** Static\n* **Attributes used:**\n * Country\n * Attribute Type - Categorical\n * Life_Expectancy\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential\n * Latitude (attribute used but not shown)\n * Attribute Type - Quantitative\n * Ordering Direction - Diverging\n * Longitude (attribute used but not shown)\n * Attribute Type - Quantitative\n * Ordering Direction - Diverging","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3031,"value":"## Task Abstraction (Why?)\n* **T1** : Identify the country **[Identify, Feature]**.\n* **T2** : Identify the life expectancy of the country **[Identify, Feature]**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3033,"value":"## Marks & Channels (How?)\n* **Marks:** Point\n* **Channels:** \n * **Color Saturation:** To Identify the life expectancy of a country.\n * Attribute: Life Expectancy\n * Task: T2\n * Encoding : **Separate**\n * **Position:** To Identify the country, which has been positioned geographically by its latitude and longitude.\n * Attribute: Country\n * Task: T1\n * Encoding : **Separate**","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":37,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1626,"value":"## Factor 2 - Population of Different Age Groups","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2998,"value":"$33","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3009,"value":"## Insight\nOver the past five decades, there has been a significant decrease in the percentage of individuals aged 0-14 within the overall population of both developed and developing nations. In contrast, the percentage of people aged 45 and above in the overall population has increased.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3190,"value":"#### Population Composition of Developed & Developing Countries by Age Group\n_Hover over the area for more information_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3085,"value":"age_group_country_wise_pct_faceted = vl\n .markArea({ tooltip: true })\n .data(joined_data_by_age)\n .encode(\n vl.x().fieldQ(\"Year\").axis({title: \"Year\",format:\"c\",tickCount:4}).title(\"Year\"),\n vl.color().fieldO(\"age_group\").scale({\n domain:[\"0-14\", \"15-29\", \"30-44\", \"45-59\", \"60-74\", \"75-89\", \"90-100\"],\n range:[\"#FFA500\", \"#7FFFD4\", \"#40E0D0\", \"#43BFC7\", \"#3B9C9C\", \"#007C80\", \"#033E3E\"]\n }),\n vl.stroke().value(4),\n vl.y().sum(\"population_by_age\").stack(\"normalize\").axis({title : \"Population Composition of Age Groups (%)\", format: \".2p\"}).title(\"Population\"),\n vl.order(\"y\"),\n vl.column(\"Status\")\n )\n .height(400)\n .width(width * 0.3)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3084,"value":"[_Data Source_](https://www.kaggle.com/datasets/ahmethoso/wpp-population-by-age-and-sex?select=PopulationByAgeSex.csv)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3011,"value":"## Data Abstraction (What?)\n* **Dataset type:** Tabular \n* **Availability:** Static\n* **Attributes used:**\n * Age_Group\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential\n * Year\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential\n * Normalized Sum of Population_by_age (%) - Derived Attribute\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3013,"value":"## Task Abstraction (Why?)\n* **T1** : Identify the age group of the population **[Identify, Feature]**.\n* **T2** : Identify the population composition of age group 15-29 in the year 1995 **[Identify, Feature]**.\n* **T3** : Identify the years that had higher population of age group 0-14 **[Identify, Feature]**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3007,"value":"## Marks and Channels (How?)\n* **Marks:** Area\n* **Channels:** \n * **Color Saturation:** To Identify different age groups\n * Attribute: Age_group\n * Task: T1\n * Encoding : **Separate, Order**\n * **Area:** To show change in population composition of different age groups.\n * Attribute: Normalized Sum of Population_by_age\n * Task: T2\n * Encoding : **Express**\n * **Horizontal Position (x-axis):** To show years.\n * Attribute: Year\n * Task: T3\n * Encoding **Separate, Order, Align**\n * **Vertical Position (y-axis):** To show population composition of different age groups..\n * Attribute: Normalized Sum of Population_by_ag\n * Task: T2\n * Encoding: **Express** ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1629,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1632,"value":"## Factor 3 - Composition of Female Population Compared to Male","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2976,"value":"$34","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3016,"value":"## Insight\nUntil 2003, women made up the majority of the world's population. However, in 2004 there was a sudden decline in the proportion of women, resulting in men becoming the dominant gender in terms of population. Presently, women account for 0.7% less of the global population than men.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3258,"value":"viewof status = Inputs.checkbox([\"Developed\", \"Developing\"], {value: [\"Developing\", \"Developed\"], label: \"Status\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3290,"value":"#### Graph 1 : Composition of Female Population in ${status} Countries as Compared to Male (+ve indicates more female, -ve indicates more male)\n_Hover over the bars for more information_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1604,"value":"female_pop_diff = vl.markBar({tooltip : true})\n .data(joined_world_pop_with_gender_diff.objects().filter(d => status.includes(d.Status) && d.gender === \"female\"))\n .encode(\n vl.x().fieldO(\"Year\").axis({title: \"Year\"}).timeUnit(\"year\"),\n vl.y().fieldQ(\"gender_avg_change\").aggregate(\"mean\").axis({title : \"Proportion of Female Population Compared to Male\", format: \".2p\"}).title(\"Relative Women Population (%)\"),\n // vl.tooltip([{field: \"Year\", type: \"temporal\", timeUnit: \"year\", title: \"Year\"}, {field: \"gender_avg_change\", format:\".2p\", aggregate: \"mean\", title: \"Proportion of Female Population Compared to Male\"}])\n )\n .width(width*0.7)\n .render()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3589,"value":"[_Data Source_](https://www.kaggle.com/datasets/ahmethoso/wpp-population-by-age-and-sex?select=PopulationByAgeSex.csv)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3535,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3307,"value":"## Insight\nOman experienced the most substantial decline in the proportion of its female population among all developing countries. In 1970, the country had 1% more females than males, but by 2019, the gender imbalance had reversed, with 32% fewer women than men.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3301,"value":"#### Graph 2 : Change in Composition of Female Population in ${status} Countries as Compared to Male \n_Hover over the line for more information_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":992,"value":"$35","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3342,"value":"[_Data Source_](https://www.kaggle.com/datasets/ahmethoso/wpp-population-by-age-and-sex?select=PopulationByAgeSex.csv)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3018,"value":"## Data Abstraction (What?)\n* **Dataset type:** Tabular \n* **Availability:** Static \n \n1) **Bar Chart**\n * **Attributes used:**\n * Year\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential\n * Gender_avg_change - Derived Attribute\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential\n \n2) **Line Chart**\n * **Attributes used:**\n * Countries\n * Attribute Type - Categorical\n * Year\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential\n * Gender_avg_change - Derived Attribute\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3020,"value":"## Task Abstraction (Why?)\n\n1) **Bar Chart**\n * **T1** : Identify the gender_avg_change in the year 1990 **[Identify, Feature]**.\n * **T2** : Identify the year in which gender_avg_change was -0.37% **[Identify, Feature]**.\n * **T3** : Identify how the gender_avg_change has changed across last 50 years **[Identify, Trend]**.\n \n2) **Line Chart**\n * **T1** : Identify the country that saw the most drastic change in gender_avg_change in last 50 years **[Identify, Feature]**.\n * **T2** : Identify the trend of gender_avg_change for Oman in last 50 years **[Identify, Trend]**.\n * **T3** : Identify the gender_avg_change of Oman in 1995 **[Identify, Feature]**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3022,"value":"$36","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1968,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1966,"value":"## Story Line - Part 2 - Analyzing Country Demographics","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1970,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":340,"value":"## Factor 1 - Trend of Life Expectancy in the Country","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2993,"value":"$37","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3042,"value":"## Insight\nThe life expectancy in India has steadily increased from 48.39 years in 1971 to 69.88 years in 2020, representing a gain of more than 21 years.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3583,"value":"Inputs.bind(Inputs.select(unique_countries,{\n value: \"India\",\n label: \"Countries :\"\n}), viewof filtered_country)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4241,"value":"#### Life expectancy of ${filtered_country} over last 50 years\n_Hover over the points for more information_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2107,"value":"LE_Country = {\n const barMark = vl.markRule({tooltip : true})\n .encode(\n vl.y().fieldQ(\"Life_Expectancy\"),\n vl.x().fieldO(\"Year\")\n )\n const lineMark = vl.markLine({point : true})\n .encode(\n vl.y().fieldQ(\"Life_Expectancy\"),\n vl.x().fieldO(\"Year\"),\n vl.color().value(\"firebrick\"),\n vl.tooltip([{field: \"Life_Expectancy\",format:\".2f\", title: \"Life Expectancy\"},{field: \"Year\", title: \"Year\"}, vl.fieldN(\"Country\")])\n )\n return vl.layer(barMark, lineMark)\n .data(pivot_life_expectancy_data.filter(d => d.Country === filtered_country))\n .width(width*0.7)\n .render()\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4233,"value":"[_Data Source_](https://www.kaggle.com/datasets/hasibalmuzdadid/global-life-expectancy-historical-dataset)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3056,"value":"## Data Abstraction (What?)\n* **Dataset type:** Tabular \n* **Availability:** Static\n* **Attributes used:**\n * Year\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential\n * Life Expectancy\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3058,"value":"## Task Abstraction (Why?)\n* **T1** : Identify the life expectancy in the year 1990 **[Identify, Feature]**.\n* **T2** : Identify the year in which the life expectancy was 55.25 **[Identify, Feature]**.\n* **T3** : Identify the trend of life expectancy over 50 years **[Identify, Trend]**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3060,"value":"## Marks & Channels (How?)\n* **Marks:** Line & Points\n* **Channels:** \n * **Length:** To show life expectancy in any particular Year.\n * Attribute: Normalized Sum of Population_by_ag\n * Task: T1\n * Encoding : **Express**\n * **Horizontal Position (x-axis):** To show years.\n * Attribute: Year\n * Task: T2\n * Encoding **Separate, Order, Align**\n * **Vertical Position (y-axis):** To show life expectancy.\n * Attribute: Life_Expectancy\n * Task: T1\n * Encoding: **Express** ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":35,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":335,"value":"## Factor 2 - Population Growth by Age Group in the Country","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2989,"value":"$38","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3035,"value":"## Insight\nIn 2019, India had the largest population in the age group of 15-29, while the population of the age group 0-14 experienced a sudden decline. Additionally, the populations of the age groups 0-14, 15-29, 30-44, and 45-59 were all above the average population.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1978,"value":"viewof filtered_country = Inputs.select(unique_countries,{\n value: \"India\",\n label: \"Countries :\"\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4243,"value":"#### Population change of different age groups in ${filtered_country} over last 50 years\n_Hover over the points to get more information_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4730,"value":"

\n ----- Average Population \n

","pinned":false,"hidden":false,"mode":"html","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":810,"value":"$39","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4252,"value":"[_Data Source_](https://www.kaggle.com/datasets/ahmethoso/wpp-population-by-age-and-sex?select=PopulationByAgeSex.csv)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3044,"value":"## Data Abstraction (What?)\n* **Dataset type:** Tabular \n* **Availability:** Static\n* **Attributes used:**\n * Year\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential\n * Population_by_age\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential\n * Age_Group\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3046,"value":"## Task Abstraction (Why?)\n* **T1** : Identify the age group that has the highest population in year 2018 **[Identify, Feature]**.\n* **T2** : Identify the population of age group 15-29 in the year 1990 **[Identify, Feature]**.\n* **T3** : Identify the years in which the population of age group 0-14 was highest **[Identify, Feature]**.\n* **T4** : Identify the trend of population change of age group 30-44 across last 50 years **[Identify, Trend]**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3048,"value":"## Marks & Channels (How?)\n* **Marks:** Line\n* **Channels:** \n * **Color Saturation:** To show different age groups.\n * Attribute: Age_group\n * Task: T1\n * Encoding : **Separate, Order, Align**\n * **Horizontal Position (x-axis):** To show years.\n * Attribute: Year\n * Task: T3\n * Encoding **Separate, Order, Align**\n * **Vertical Position (y-axis):** To show population_by_age.\n * Attribute: Population_by_age\n * Task: T2\n * Encoding: **Express** ","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":1997,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":333,"value":"## Factor 3 - Gender-Based Population Composition in the Country","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2986,"value":"$3a","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3039,"value":"## Insight\nIn 2009, men made up approximately 4.06% more of the population than women in India. However, since then, the population of women has gradually increased. As of today, women still make up less of the population than men, but the difference has narrowed to 3.93%.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3573,"value":"Inputs.bind(Inputs.select(unique_countries,{\n value: \"India\",\n label: \"Countries :\"\n}), viewof filtered_country)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4246,"value":"#### Graph 1: ${filtered_country}'s total population grouped by gender as of 2020\n_Click the bars to update the graph below_
\n_Hover over the bars for more information_","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2942,"value":"$3b","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4249,"value":"#### Graph 2: Percentage difference between female and male population in ${filtered_country} 👆","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":4254,"value":"[_Data Source_](https://www.kaggle.com/datasets/ahmethoso/wpp-population-by-age-and-sex?select=PopulationByAgeSex.csv)","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3050,"value":"## Data Abstraction (What?)\n* **Dataset type:** Tabular \n* **Availability:** Static \n \n1) **Graph 1**\n * **Attributes used:**\n * Gender\n * Attribute Type - Categorical\n * Population_by_gender\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential\n \n2) **Graph 2**\n * **Attributes used:**\n * Gender\n * Attribute Type - Categorical\n * Year\n * Attribute Type - Ordinal\n * Ordering Direction - Sequential\n * Gender_avg_change - Derived Attribute\n * Attribute Type - Quantitative\n * Ordering Direction - Sequential","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3052,"value":"## Task Abstraction (Why?)\n1) **Graph 1**\n * **T1** : Identify the gender **[Identify, Feature]**.\n * **T2** : Identify the population of female **[Identify, Feature]**.\n * **T3** : Compare the population of male and female **[Compare, Feature]**.\n \n2) **Graph 2**\n * **T1** : Identify the gender_avg_change in the year 1990 **[Identify, Feature]**.\n * **T2** : Identify the year in which gender_avg_change was -3.8% **[Identify, Feature]**.\n * **T3** : Identify how the gender_avg_change has changed across last 50 years **[Identify, Trend]**.","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":3054,"value":"$3c","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":641,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2300,"value":"## Appendix","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":2660,"value":"delay = 500","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3078,"value":"fmtAge = d3.format(\"0.2f\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4851,"value":"unique_countries = [...new Set(joined_world_pop_with_gender_diff.objects().map(d => d.Country))].sort()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2543,"value":"lifeExpectancy = new Map(\n coordinates_LE_new\n .filter((d) => d.year === filtered_year)\n .map((d) => [\n d.Country,\n {\n life_expectancy: (+d.value).toFixed(2),\n Label: `${d.Country} (${fmtAge(d.value)})`,\n latitude: +d.latitude,\n longitude: +d.longitude,\n Country: d.Country,\n Year : d.year\n }\n ])\n)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1279,"value":"mutable highlighted = \"\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1337,"value":"age_groups = [\"0-14\",\"15-29\",\"30-44\",\"45-59\",\"60-74\",\"75-89\",\"90-100\"]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1334,"value":"age_group_series = d3.stack()\n .keys(age_groups)\n (world_pop_groupby_year.objects().filter(d => d.Status === status))\n .map(d => (d.forEach(v => v.key = d.key), d))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1294,"value":"age_group_x = d3.scaleBand()\n .domain(Array.from(new Set(filtered_world_population.map(d => d.Year))))\n .range([age_group_margin.left, width ])\n .padding(0.4)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1292,"value":"age_group_y = d3.scaleLinear()\n .domain([0, d3.max(age_group_series, d => d3.max(d, d => d[1]))])\n .rangeRound([age_group_height - age_group_margin.bottom, age_group_margin.top])","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1290,"value":"age_group_color = d3.scaleOrdinal()\n .domain(age_group_series.map(d => d.key))\n .range(d3.schemeBlues[age_group_series.length])\n .unknown(\"#ccc\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1286,"value":"age_group_xAxis = g => g\n .attr(\"transform\", `translate(0,${age_group_height - age_group_margin.bottom})`)\n .call(d3.axisBottom(age_group_x).tickSizeOuter(0))\n .call(g => g.selectAll(\".domain\").remove())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1284,"value":"age_group_yAxis = g => g\n .attr(\"transform\", `translate(${age_group_margin.left},0)`)\n .call(d3.axisLeft(age_group_y).ticks(null, \"s\"))\n .call(g => g.selectAll(\".domain\").remove())","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1302,"value":"age_group_height = 300","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1282,"value":"age_group_margin = ({top: 10, right: 10, bottom: 20, left: 40})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":903,"value":"viewof joined_data_by_age = aq\n .from(pivot_life_expectancy_data)\n .join(aq.from(filtered_world_population_with_age_group) )\n .join(aq.from(coordinates))\n .filter(aq.escape(d => d.Year >= \"1971\" && d.Year <= \"2020\")).view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":805,"value":"viewof filtered_world_population_with_age_group = aq.from(filtered_world_population).fold([\"0-14\", \"15-29\", \"30-44\", \"45-59\", \"60-74\", \"75-89\", \"90-100\"],{ as: [\"age_group\", \"population_by_age\"] }).view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1671,"value":"viewof joined_world_pop_with_gender_diff = aq\n .from(pivot_life_expectancy_data)\n .join(aq.from(world_population_with_gender_diff))\n .join(aq.from(coordinates))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1602,"value":"viewof world_population_with_gender_diff = aq\n .from(filtered_world_population)\n .rename({ TotalMale: \"male\", TotalFemale: \"female\" })\n .derive({\n avg_change_women_pop: aq.escape(\n (d) => (+d.female - +d.male) / (+d.male + +d.female)\n )\n })\n .derive({\n avg_change_men_pop: aq.escape(\n (d) => (+d.male - +d.female) / (+d.male + +d.female)\n )\n })\n .fold([\"male\", \"female\"], { as: [\"gender\", \"population_by_gender\"] })\n .fold([\"avg_change_women_pop\",\"avg_change_men_pop\"],{as:[\"gender_avg_label\",\"gender_avg_change\"]})\n.filter((d) =>( d[\"gender\"]===\"female\" && d[\"gender_avg_label\"]===\"avg_change_women_pop\") || ( d[\"gender\"]===\"male\" && d[\"gender_avg_label\"]===\"avg_change_men_pop\"))\n .view()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1425,"value":"world_pop_groupby_year = aq.table(world_pop_with_status).groupby(\"Year\",\"Status\")\n .rollup({ \"0-14\" : op.mean('0-14'),\n \"15-29\" : op.mean('15-29'),\n \"30-44\" : op.mean('30-44'),\n \"45-59\" : op.mean('45-59'),\n \"60-74\" : op.mean('60-74'),\n \"75-89\" : op.mean('75-89'),\n \"90-100\" : op.mean('90-100')})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":920,"value":"world_pop_with_status = aq\n .from(pivot_life_expectancy_data)\n .join(aq.from(filtered_world_population) )\n .join(aq.from(coordinates))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":166,"value":"filtered_world_population = updated_world_population.filter(d => {\n if (countries.includes(d.Country) && (d.Year>=1971 && d.Year<=2020))\n {\n return d\n }\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2593,"value":"countries = [...new Set(pivot_life_expectancy_data.map(d => d.Country))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2514,"value":"coordinates = aq\n .from(pivot_life_expectancy_data)\n .join(aq.from(world_lat_lang))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":258,"value":"pivot_life_expectancy_data = {\n const pivot_object = []\n updated_life_expectancy.forEach( d => {\n for(const key of Object.keys(d)){\n if(!isNaN(key) && key>=1971){\n const obj = {\n \"Country\" : d.Country,\n \"Status\" : d.Status,\n \"Year\" : key,\n \"Life_Expectancy\" : d[key]\n }\n pivot_object.push(obj)\n }\n }\n })\n return pivot_object\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":78,"value":"updated_life_expectancy = {\nconst life_expectancy_data_modified = [...life_expectancy_data, {\"Country\" : \"United States\",\"Status\" : \"Developed\"}]\nconst life_expectancy_status = [];\nglobal_life_expectancy.forEach((outer)=>{\n life_expectancy_data_modified.forEach((inner)=>{\n if(outer[\"Country Name\"] === inner[\"Country\"]) {\n life_expectancy_status.push({ \"Country\": outer[\"Country Name\"], ...outer, Status: inner.Status})\n }\n })\n})\nlife_expectancy_status.forEach(d => {\n delete d[\"Country Name\"]\n if(d.Country === \"United States\"){\n d.Country = \"United States of America\"\n }\n}) \n\nreturn life_expectancy_status.filter((v,i,a)=>a.findIndex(v2=>(v[\"Country\"] === v2[\"Country\"]))===i)\n}","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":117,"value":"$3d","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":27,"value":"### Data Sample","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":29,"value":"#### Life Expectancy Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":51,"value":"life_expectancy_data1 = await FileAttachment(\"Life_Expectancy_Data.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"life_expectancy_data1","database":"$undefined"},{"id":62,"value":"globalLifeExpectancyDataset = await FileAttachment(\"global life expectancy dataset.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"globalLifeExpectancyDataset","database":"$undefined"},{"id":31,"value":"#### World Population By Age Group Data","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":44,"value":"populationbyagesex = await FileAttachment(\"PopulationByAgeSex.csv\").csv({typed: \"auto\"})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"populationbyagesex","database":"$undefined"},{"id":33,"value":"---","pinned":false,"hidden":false,"mode":"md","author":"$undefined","summary":"$undefined","output":"","database":"$undefined"},{"id":556,"value":"import {multiAutoSelect} from \"@john-guerra/multi-auto-select\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":531,"value":"import {interval} from '@mootari/range-slider@1786'","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":70,"value":"global_life_expectancy = FileAttachment(\"global life expectancy dataset.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2596,"value":"g_co = [...new Set(global_life_expectancy.map(d => d[\"Country Name\"]))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2599,"value":"l_co = [...new Set(life_expectancy_data.map(d => d[\"Country\"]))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":18,"value":"life_expectancy_data = FileAttachment(\"Life_Expectancy_Data.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":20,"value":"world_population_data = FileAttachment(\"PopulationByAgeSex.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2527,"value":"world_lat_lang.map(d => {\n delete d.country_code\n delete d.country\n delete d.usa_state_code\n delete d.usa_state_latitude\n delete d.usa_state_longitude\n delete d.usa_state\n return d\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2518,"value":"world_lat_lang = world_coordinates.map(d => {\n \n return {...d, \"Country\" : d.country}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2525,"value":"world_country_and_usa_states_latitude_and_longitud = await FileAttachment(\"world_country_and_usa_states_latitude_and_longitude_values@1.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"world_country_and_usa_states_latitude_and_longitud","database":"$undefined"},{"id":3548,"value":"coordinates_LE_with_southsudan.filter(d => d.Country===\"South Sudan\")","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3563,"value":"coordinates_LE_new = coordinates_LE_with_southsudan.map(d => {\n if(d.Country === \"South Sudan\"){\n return {...d, \"Country\": \"S. Sudan\"}\n }\n \n return {...d}\n})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3555,"value":"coordinates_LE_with_southsudan = [...coordinates_LE,...SouthSudan_CentralAfrican_Data]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2646,"value":"coordinates_LE = coordinates_with_LE.objects().map(d => {\n if(d.Country === \"United States\"){\n return {...d, \"Country\": \"United States of America\"}\n }\n else if(d.Country === \"Congo [DRC]\"){\n return {...d, \"Country\": \"Dem. Rep. Congo\"}\n }\n else if(d.Country === \"Congo [Republic]\"){\n return {...d, \"Country\": \"Congo\"}\n }\n else if(d.Country === \"Central African Republic\"){\n return {...d, \"Country\": \"Central African Rep.\"}\n }\n return {...d}\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2611,"value":"coordinates_with_LE = aq\n.from(newLE_Country)\n.join(aq.from(world_lat_lang))","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2723,"value":"country_coor = world_lat_lang.map(d => d.Country)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2512,"value":"world_coordinates = FileAttachment(\"world_country_and_usa_states_latitude_and_longitude_values@1.csv\").csv()","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2607,"value":"le_co = [...new Set(newLE_Country.map(d => d.Country))]","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2616,"value":"newLE_Country = filteredLE_Country.map(d => {\n delete d.country_code\n delete d.country_name\n return d\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":3546,"value":"SouthSudan_CentralAfrican_Data = filteredLE_Country.filter(d => d.Country === \"South Sudan\")","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2614,"value":"$3e","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2604,"value":"filtered_LE = LE.filter(d => d.year>=1971)","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2606,"value":"life_expectancy_by_country = await FileAttachment(\"life_expectancy_by_country.csv\").csv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"life_expectancy_by_country","database":"$undefined"},{"id":2602,"value":"LE = FileAttachment(\"life_expectancy_by_country.csv\").csv()","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":758,"value":"import {aq, op} from \"@uwdata/arquero\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1351,"value":"import {legend} from \"@d3/color-legend\"","pinned":true,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":1584,"value":"import {StackedBarChart} from \"@d3/diverging-stacked-bar-chart\"\n","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":2655,"value":"import {Scrubber} from \"@mbostock/scrubber\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4329,"value":"import {checkbox} from \"@jashkenas/inputs\"","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4327,"value":"viewof marks = checkbox({\n title: md `[Multi-View Composition](https://observablehq.com/@uwdata/multi-view-composition?collection=@uwdata/visualization-curriculum) Layers`,\n description: \"Select which mark layers to render\",\n options: [\n { value: \"lines\", label: \"Lines\" },\n { value: \"points\", label: \"Points\" },\n { value: \"rule\", label: \"Rule\"}\n ],\n value: [\"lines\",\"points\", \"rule\"]\n})","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"},{"id":4322,"value":"$3f","pinned":false,"hidden":false,"mode":"ojs","author":"$undefined","summary":"$undefined","output":"$undefined","database":"$undefined"}],"files":"$W40","stdlib":"1","titling":"auto"}} 30:"$2f:body"