30:[] 2e:{"title":"PSET 4","cells":[{"id":0,"value":"md`# PSET 4`","pinned":false,"hidden":false,"mode":"ojs"},{"id":4,"value":"md`## Exploratory Visualizations\nThis problem set is meant to create a foundation of exploratory data vizualizations and data management using VegaLite and Zebras. We emphasize code readability and reusability over performance. Code performance can be measured in many different ways, and is by no means the focus of this course, however, we expect your code to be non-repetitive, clean, and efficient enough to deal with medium-to-large datasets.\n\n## Submission Instructions\nSubmit the link of your assignment on Stellar. \\n\n**Remember to publish your notebook so it is publicly accesible!** \\n\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":14,"value":"md`## 1. Loading data\nWe will be working with two different datasets. Load: \n- [\\`MIG_18022019163824069.csv\\`](https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/6dff0d45ed26352a7a3c7afb4ace0bad1ce8ba20/MIG_18022019163824069.csv)\n- [Country Metadata](https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/90951b60444376eebfcbbee9beca00c083f489f6/Metadata_Country_API_SM.POP.NETM_DS2_en_csv_v2_10473747.csv)\n\n`\n","pinned":false,"hidden":false,"mode":"ojs"},{"id":27,"value":"// Your code here\nUrl1=\"https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/6dff0d45ed26352a7a3c7afb4ace0bad1ce8ba20/MIG_18022019163824069.csv\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":61,"value":"Url2=\"https://gist.githubusercontent.com/cesandoval/b834ac93c07e03ec5205843b97f68017/raw/90951b60444376eebfcbbee9beca00c083f489f6/Metadata_Country_API_SM.POP.NETM_DS2_en_csv_v2_10473747.csv\"","pinned":false,"hidden":false,"mode":"ojs"},{"id":64,"value":"d3 = require(\"d3\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":69,"value":"z = require('https://bundle.run/zebras@0.0.11')","pinned":false,"hidden":false,"mode":"ojs"},{"id":75,"value":"vegalite = require(\"@observablehq/vega-lite@0.1\")","pinned":false,"hidden":false,"mode":"ojs"},{"id":63,"value":"migrant=d3.csv(Url1)","pinned":true,"hidden":false,"mode":"ojs"},{"id":73,"value":"CountryMetadata=d3.csv(Url2)","pinned":false,"hidden":false,"mode":"ojs"},{"id":24,"value":"md `## 2. Joining tables\nJoin the tables based on a common column to obtain a new table.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":266,"value":"join_table=(function () { \n return z.filter(x =>x[\"CO2\"]!=\"TOT\",migrant.map(x => Object.assign(x, CountryMetadata.find(y => y['Country Code'] == x['CO2']))))\n}())","pinned":true,"hidden":false,"mode":"ojs"},{"id":31,"value":"md`## 3. Aggregating and Visualizing Data\n- Group the table by ***IncomeGroup***\n- Sum the immigration values across years by **Variable**\n- Create a grid of **histogram** plots, where every column will represent a different **Variable**, and every row will represent a different **IncomeGroup**.\n - You should **bin** all the values into **12 different bins**. \n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":37,"value":"// Your code here\nGroupIncome=z.groupBy(r => r['IncomeGroup'], join_table)","pinned":true,"hidden":false,"mode":"ojs"},{"id":398,"value":"GroupCountry=z.groupBy(r => r['CO2'], join_table)","pinned":true,"hidden":false,"mode":"ojs"},{"id":401,"value":"allCountries = {\n let allCountries = []\n for (const [key, value] of Object.entries(GroupCountry)) { \n let currCountry = z.gbSum(['Value'], z.groupBy(x => x.Variable, value))\n let currCountryObj = {country: key}\n console.log(GroupCountry[key][0])\n for (const variable of currCountry) {\n currCountryObj[variable.group] = variable.sum\n }\n allCountries.push(currCountryObj)\n }\n return allCountries\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":94,"value":"UniqueIncomeRaw=z.unique((z.getCol('IncomeGroup', join_table)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":130,"value":"UniqueIncome=UniqueIncomeRaw.slice(0,4)","pinned":true,"hidden":false,"mode":"ojs"},{"id":108,"value":"UniqueVar=z.unique((z.getCol('Variable', join_table)))","pinned":false,"hidden":false,"mode":"ojs"},{"id":123,"value":"UniqueCon=z.unique((z.getCol('CO2', join_table)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":82,"value":"GroupSumbug1={\n var fileParse=z.parseNums([\"Value\"], join_table)\n var GroupIncome2=z.groupBy(r => r['IncomeGroup'], fileParse)\n var SumFrame=[]\n for (var i=0; ir.Variable== UniqueVar[a], arr)\n var ValueSum=z.gbSum(\"Value\", z.groupBy(r => r['CO2'], k))\n for (var b=0; bx.group=UniqueCon[b],ValueSum)\n SumFrame.push({\"Country of birth\":UniqueCon[b],\"Variable\": UniqueVar[a],\"IncomeGroup\":UniqueIncome[i],\"Sum\": CountrySum[0]['sum']})\n }\n }\n }\n return SumFrame\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":169,"value":"GroupSumbug2={\n var fileParse=z.parseNums([\"Value\"], join_table)\n var GroupIncome2=z.groupBy(r => r['IncomeGroup'], fileParse)\n var SumFrame=[]\n for (var i=0; ir.Variable== UniqueVar[a], arr)\n for (var b=0; bx.CO2==UniqueCon[b],k)\n var co2Value=z.getCol(\"Value\",CountrySum)\n SumFrame.push({\"Country of birth\":UniqueCon[b],\"Variable\": UniqueVar[a],\"IncomeGroup\":UniqueIncome[i],\"Sum\": co2Value.reduce((a, b) => +a + +b, 0)})\n }\n }\n }\n return SumFrame\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":370,"value":"GroupSumnew={\n var fileParse=z.parseNums([\"Value\"], join_table)\n var GroupIncome2=z.groupBy(r => r['IncomeGroup'], fileParse)\n var SumFrame=[]\n for (var i=0; ir.Variable== UniqueVar[a], arr)\n var UniqueCon=z.unique((z.getCol('CO2', k)))\n for (var b=0; bx.CO2==UniqueCon[b],k)\n var co2Value=z.getCol(\"Value\",CountrySum)\n SumFrame.push({\"Country of birth\":UniqueCon[b],\"Variable\": UniqueVar[a],\"IncomeGroup\":UniqueIncome[i],\"Sum\": co2Value.reduce((a, b) => +a + +b, 0)})\n }\n }\n }\n return SumFrame\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":204,"value":"vegalite({\n title: {\n text : \"Number of migrants\"\n },\n data: {values: GroupSumnew},\n mark: \"bar\",\n height: 120,\n width: 90,\n encoding: {\n x: {\n bin: {\"maxbins\":13}, field: \"Sum\" , type: \"quantitative\", sort:'*'\n },\n \"y\": {\n aggregate:\"count\",field: \"Country of birth\", type: \"quantitative\"\n },\n \"row\": {\n field: \"IncomeGroup\",\n type: \"nominal\"} ,\n \"column\": {\n field: \"Variable\",\n type: \"nominal\"} \n }\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":38,"value":"md` ## 4. Stacked Bar Charts\nWith the dataset from the previous question:\n- Instead of having a different row for every different **IncomeGroup**, create a [**Normalized Stacked Bar Chart**](https://vega.github.io/vega-lite/examples/stacked_bar_normalize.html)\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":43,"value":"// Your code here\nvegalite({\n title: {\n text : \"Number of immigrations\"\n },\n data: {values: GroupSumnew},\n mark: \"bar\",\n height: 120,\n width: 90,\n encoding: {\n x: {\n bin: {\"maxbins\":13}, field: \"Sum\" , type: \"quantitative\", sort:'*'\n },\n \"y\": {\n aggregate:\"count\",field: \"Country of birth\", type: \"quantitative\",\n stack: \"normalize\"\n },\n \"column\": {\n field: \"Variable\",\n type: \"nominal\"}, \n \"color\": {\"field\": \"IncomeGroup\", \"type\": \"nominal\"}\n }\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":46,"value":"md ` ## 5. GDP Scatter Plot\n- Find a dataset that has the **GDP per Capita per Country** for 2017.\n- Upload to **Github** as a gist\n- Load it into your notebook, and join it by **Country Code** to your current dataset\n- Produce a scatterplot that:\n - Shows the immigration level per **Country of Birth** for **2017** on the **X-Axis**\n - Shows the GDP per Capita per **Country** for 2017 on the **Y-Axis**\n - Color the circles and fill of the circles based on the **Region**\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":49,"value":"// Your code here\nUrl3='https://gist.githubusercontent.com/zhengyunhan/33e24de0743afb274b6d791982e13047/raw/d139f36d8fafb0317555e7f77eabe28bbbfad46b/GDP_capita.csv'","pinned":false,"hidden":false,"mode":"ojs"},{"id":219,"value":"GDP=d3.csv(Url3)","pinned":false,"hidden":false,"mode":"ojs"},{"id":229,"value":"Group_Country=z.gbSum(\"Value\", z.groupBy(r => r['CO2'], z.filter(x =>x[\"Year\"]==\"2017\",join_table)))","pinned":true,"hidden":false,"mode":"ojs"},{"id":238,"value":"GDPjoin=Group_Country.map(x => Object.assign(x, GDP.find(y => y['Country Code'] == x['group'])))","pinned":true,"hidden":false,"mode":"ojs"},{"id":244,"value":"GDPfull=GDPjoin.map(x => Object.assign(x, CountryMetadata.find(y => y['Country Code'] == x['Country Code'])))","pinned":true,"hidden":false,"mode":"ojs"},{"id":247,"value":"vegalite({\n data: {values: GDPfull},\n mark: \"point\",\n width: 400,\n encoding: {\n x: {field: \"sum\", type: \"quantitative\", \n \"axis\": {\"title\": \"Number of immigrations in 2017\",\n \"offset\": 20}},\n y: {field: \"2017\", type: \"quantitative\", \n \"axis\": {\"title\": \"GDP per Capita\",\n \"offset\": 10}\n },\n color: {field: \"Region\", type: \"nominal\"}\n }\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":50,"value":"md ` ## 6. Composite Dot Plots\n- Create a dot plot (scatter plot) that: \n - Shows the immigration level per **Country of Birth** on the **Y-Axis**\n - Every dot should represent a different country\n - Shows the years on the **X-Axis**\n- Add a new layer on top of the dot plot that:\n - Draws a line to represent the mean values per year across all years\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":58,"value":"// Your code here\nYear_Country={ \n var UniqueCountry=z.unique((z.getCol('CO2', join_table)))\n var UniqueYear=z.unique((z.getCol('Year', join_table)))\n var SumFrame=[]\n for (var i=0; ir.CO2== UniqueCountry[i], join_table)\n for (var a=0; ar.Year== UniqueYear[a], GroupCountry) \n var co2Value=z.getCol(\"Value\",GroupYear)\n SumFrame.push({\"Country of birth\":UniqueCountry[i],\"Year\": UniqueYear[a],\"Sum\": co2Value.reduce((a, b) => +a + +b, 0)})\n }\n }\n return SumFrame\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":323,"value":"vegalite({\n data: {values: Year_Country},\n mark: \"point\",\n height: 500,\n width: 500,\n encoding: {\n x: {timeUnit: \"year\", field: \"Year\", type: \"temporal\"}, \n y: {field: \"Sum\", type: \"quantitative\"}\n }\n})","pinned":true,"hidden":false,"mode":"ojs"},{"id":353,"value":"Year_Mean={ \n var UniqueYear=z.unique((z.getCol('Year', join_table)))\n var SumFrame=[]\n for (var a=0; ar.Year== UniqueYear[a], Year_Country) \n var co2Value=z.getCol(\"Sum\",GroupYear)\n SumFrame.push({\"Year\": UniqueYear[a],\"Mean\": z.mean(co2Value)})\n }\n return SumFrame\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":361,"value":"vegalite({\n layer:[\n {\n data: {values: Year_Country},\n mark: \"point\",\n height: 500,\n width: 500,\n encoding: {\n x: {timeUnit: \"year\", field: \"Year\", type: \"temporal\"}, \n y: {field: \"Sum\", type: \"quantitative\"}\n }\n },\n {\n data: {values: Year_Mean},\n mark: \"line\",\n height: 500,\n width: 500,\n encoding: {\n x: {timeUnit: \"year\", field: \"Year\", type: \"temporal\"}, \n y: {field: \"Mean\", type: \"quantitative\"},\n color: {\"value\": \"orange\"}\n }\n }\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W30","stdlib":"1","titling":"auto"}