31:T504,md`# COVID-19 during 2020 and World Development Indicators by Country In an attempt to add more context to the globally recorded numbers about the COVID-19 outbreak in each country, I have combined COVID-19 data of 2020 with the WorldBank's world development indicators of the previous year. The links to the data sources are: - COVID-19 data: https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide This dataset contains daily COVID-19 cases and related deaths per country from the beginning of the outbreak until the end of 2020 - World Development Indicators: https://databank.worldbank.org/source/world-development-indicators this dataset contains world development indicators for all countries for the year 2019 before the outbreak started. I've aggregated and merged the two datasets in Google Spreadsheets [here](https://docs.google.com/spreadsheets/d/1XzeSHF9i8dbQ1hLEqyYGifyC0wD1yzTIFuPmx_LxLc4/edit?usp=sharing). The combined data can be explored on the interactive scatterplot below. You can select metrics in the drop-downs and see how each country compares. You can also size symbols by population and colour them by continent. Clicking on a continent in the chart's legend will toggle them in the plot.`32:T42b,md`## Disclaimer Given the sensitivity and gravity of the COVID-19 outbreak, I feel that I should state that I am not providing any analysis or conclusions in regards to what some combination metrics might suggest. I leave this up to person exploring the data and people smarter than me. It is also important to keep in mind that the numbers that each country reports on COVID-19 cases might not always be accurate or truthful, depending on a country's testing strategy and government transparency. Finally, regarding that each country is currently still at a different stage of the progression of the outbreak, it is probably too early to compare all countries directly at this stage (you can use the legend to filter scatter plot by certain continents). All this should be taken into consideration when exploring this chart. A similar project has been done by the Worldbank here: https://datanalytics.worldbank.org/covid-dashboard/ Another great resource on statistcs and research about the Coronavirus desease is here: https://ourworldindata.org/coronavirus`33:T6ff,echartOption = ({ backgroundColor: '#FFFFFF', title: { text: `${xMetric.split(" [")[0]} &\n${yMetric} by Country`, padding: [0, 5] }, legend: { top: 35, data: Object.keys(echartsSeries) }, xAxis: { nameLocation: 'center', name: xMetric.split(' [')[0], nameTextStyle: { lineHeight: 25, } }, yAxis: { nameLocation: 'center', nameGap: 60, name: yMetric, type: scatterStyling.includes('log') ? 'log' : 'value' }, tooltip: { showDelay: 0, formatter: params => params.data[3] ? `${params.data[3]}

Population: ${params.data[2]}

${xMetric.split(' [')[0]}: ${params.data[0]}

${yMetric}: ${params.data[1]}

` : '' }, toolbox: { show: true, feature: { dataZoom: { title: { zoom: 'area zooming', back: 'restore area zooming' } }, saveAsImage: { title: 'save as image' } } }, dataZoom: [ { id: 'dataZoomY', type: 'slider', yAxisIndex: [0], showDataShadow: false, left: '92%', width: '15px' }, { id: 'dataZoomX', type: 'slider', xAxisIndex: [0], showDataShadow: false, height: '15px', top: '97%' } ], series: Object.keys(echartsSeries).map(continent => ({ name: continent, data: echartsSeries[continent], type: 'scatter', cursor: 'default', symbolSize: data => scatterStyling.includes('size') ? Math.sqrt(data[2]) / 5e2 : 5, hoverAnimation: false, itemStyle: { color: scatterStyling.includes('colour') ? undefined : '#000000' }, label: { show: true, position: 'right', formatter: param => param.data[3] } })) });34:T431,covidMetricsDefinitions = { const defs = {} defs["Sum of COVID-19 cases"] = "Total of recorded cases of Covid-19"; defs["Sum of COVID-19 related deaths"] = "Total of recorded deaths related to Covid-19"; defs["Sum of COVID-19 cases per million inhabitants"] = "Total of recorded cases of Covid-19 divided by inhabitants and multiplied by 1,000,000. Note that countries with less than one million inhabitants are also included"; defs["Sum of COVID-19 related deaths per million inhabitants"] = "Total of Covid-19 related deaths divided by inhabitants and multiplied by 1,000,000. Note that countries with less than one million inhabitants are also included"; defs["COVID-19 related deaths per COVID-19 Case"] = "Ratio of Total of recorded cases of Covid-19 and recorded deaths related to Covid-19 to date. This ratio should not be interpreted as a nation's fatality rate and should be taken with a grain of salt, since the pandemic is still ongoing and the availablility of testing and accuracy of recorded cases is different for each country"; return defs; };35:[{"id":"4aefe623b3880c7a4cbc3be7009f12d702c5dd9e70e81d2c52be849c48a3d6f1964af6bd1421d8ac6da95595de9db25506b3344955d18c0dae5b36a88283caee","href":"https://static.observableusercontent.com/files/4aefe623b3880c7a4cbc3be7009f12d702c5dd9e70e81d2c52be849c48a3d6f1964af6bd1421d8ac6da95595de9db25506b3344955d18c0dae5b36a88283caee","name":"country-and-continent-codes-list-csv_csv.txt","type":"text/plain","lastModified":1587024527942.73,"size":12344},{"id":"31d581f2338dfcb54e7d0821b79d6f77c3a2d67f69bda90a2e04f50dd34b5e895e901726ffdca0f8f442ff369479e56bfadd5e658dbfb28c3ab1bb392251f5df","href":"https://static.observableusercontent.com/files/31d581f2338dfcb54e7d0821b79d6f77c3a2d67f69bda90a2e04f50dd34b5e895e901726ffdca0f8f442ff369479e56bfadd5e658dbfb28c3ab1bb392251f5df","name":"COVID-19 & World Bank Metrics - Merged Data WDI 23052020.csv","type":"text/csv","lastModified":1590828110863.991,"size":1080668},{"id":"b9c2c5938323e4b4417cb81cd07bb41eb62db078d20b5b10f8be311ce9e51af2125e05175659fb6a8a0404900ad2e7ee423af29ed39471cc9850e9eabfd47445","href":"https://static.observableusercontent.com/files/b9c2c5938323e4b4417cb81cd07bb41eb62db078d20b5b10f8be311ce9e51af2125e05175659fb6a8a0404900ad2e7ee423af29ed39471cc9850e9eabfd47445","name":"COVID-19 & World Bank Metrics - Merged Data WDI 30052020.csv","type":"text/csv","lastModified":1590828110731.059,"size":1080712},{"id":"d2409c296aa18ae7072f20550de068e6fe8a6755b580b91a7fc29d815bae1df87603b24bf27f0a4a7d2396046737b55f705362e157314eb8c1b1d1baba9401cb","href":"https://static.observableusercontent.com/files/d2409c296aa18ae7072f20550de068e6fe8a6755b580b91a7fc29d815bae1df87603b24bf27f0a4a7d2396046737b55f705362e157314eb8c1b1d1baba9401cb","name":"World Development Indicators - Metadata 1.csv","type":"text/csv","lastModified":1618416646072.538,"size":3767807},{"id":"bc542d670904cc7551133730b7b61cda37517f738a6079137a4a3eb8710152b4e4d036c3e7ccf4f24249bd20e5c31cfca03b2b1bf2a9320af657a39efb7118c7","href":"https://static.observableusercontent.com/files/bc542d670904cc7551133730b7b61cda37517f738a6079137a4a3eb8710152b4e4d036c3e7ccf4f24249bd20e5c31cfca03b2b1bf2a9320af657a39efb7118c7","name":"COVID-19 & World Bank Metrics - Merged Data WDI 2020 1.csv","type":"text/csv","lastModified":1618415242696.616,"size":1859282}] 2f:{"title":"COVID-19 during 2020 and World Development Indicators by Country","cells":[{"id":0,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":581,"value":"viewof yMetric = select({\n title: \"Select COVID-19 metric for y-axis:\",\n options: covidMetrics,\n value: \"Sum of COVID-19 cases per million inhabitants\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":599,"value":"viewof xMetric = select({\n title: \"Select World Development Indicator for x-axis:\",\n options: devIndicatorMetrics,\n value: \"GDP per capita (current US$) [NY.GDP.PCAP.CD]\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":506,"value":"viewof scatterStyling = checkbox({\n title: \"Style scatter plot elements\",\n options: [\n { value: \"size\", label: \"Size by population (2018)\" },\n { value: \"colour\", label: \"Colour by continent\" },\n { value: \"log\", label: \"Log on y-axis\" }\n ],\n value: [ \"size\", \"colour\" ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":91,"value":"chartContainer = html`

`","pinned":false,"hidden":false,"mode":"ojs"},{"id":651,"value":"md`**Y-Metric definition**: _${covidMetricsDefinitions[yMetric]}_.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":638,"value":"md`**X-Metric definition**: _${(devIndicatorMetadata.find(d => d[\"Indicator Name\"] === xMetric.split(\" [\")[0]) || { 'Long definition': '' })['Long definition']} Countries that have no data for this metric will not appear on the chart._`","pinned":false,"hidden":false,"mode":"ojs"},{"id":680,"value":"md`---`","pinned":false,"hidden":false,"mode":"ojs"},{"id":535,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":677,"value":"md`\n---\n\nThe chart is made with the [ECharts](https://github.com/apache/incubator-echarts) library and the code is below.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":720,"value":"md`The sections below contain the Echarts chart settings, mapping the data to the correct format for Echarts and initialising the chart.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":126,"value":"$33","pinned":true,"hidden":false,"mode":"ojs"},{"id":85,"value":"echartsInitialised = {\n echarts.init(chartContainer).setOption(echartOption);\n return true;\n};","pinned":true,"hidden":false,"mode":"ojs"},{"id":801,"value":"echartsSeries = {\n const echartsSeries = {};\n mergedSpreadsheetData.forEach(row => {\n if (row.Continent_Name && row[xMetric] !== '..') {\n const echartsRow = [\n parseFloat(row[xMetric]), // x-axis\n parseFloat(row[yMetric]), // y-axis\n row['Population (2019)'], // size\n row['Countries and Territories'].replace(/_/g, ' ') // label\n ];\n \n echartsSeries[row.Continent_Name]\n ? echartsSeries[row.Continent_Name].push(echartsRow)\n : echartsSeries[row.Continent_Name] = [echartsRow];\n }\n });\n return echartsSeries;\n};","pinned":true,"hidden":false,"mode":"ojs"},{"id":717,"value":"md`Here I'm storing all metrics in arrays and filtering out those where more than 100 countries have no recorded data.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":724,"value":"devIndicatorMetrics = {\n const emptyMetricCount = {};\n const filteredDevIndicatorMetrics = [];\n const allDevIndicatorMetrics = Object.keys(mergedSpreadsheetData[0]).slice(14);\n mergedSpreadsheetData.forEach(d => {\n allDevIndicatorMetrics.forEach(m => {\n emptyMetricCount[m] = emptyMetricCount[m] ? emptyMetricCount[m] : 0\n if (d[m] === \"..\") {\n emptyMetricCount[m] = emptyMetricCount[m] + 1;\n }\n })\n })\n allDevIndicatorMetrics.forEach(m => {\n if (emptyMetricCount[m] <= 100) {\n filteredDevIndicatorMetrics.push(m);\n }\n })\n return filteredDevIndicatorMetrics;\n};","pinned":true,"hidden":false,"mode":"ojs"},{"id":141,"value":"covidMetrics = Object.keys(mergedSpreadsheetData[0]).slice(9, 14);","pinned":true,"hidden":false,"mode":"ojs"},{"id":654,"value":"$34","pinned":true,"hidden":false,"mode":"ojs"},{"id":713,"value":"md`Below I am loading the merged data that I exported from the Google Spreadsheet.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":1045,"value":"mergedSpreadsheetData = d3.csvParse(\n await FileAttachment(\"COVID-19 & World Bank Metrics - Merged Data WDI 2020 1.csv\").text(),\n d3.autoType\n).map(row => {\n const countryInfo = countryMetaData.find(cmd =>\n cmd[\"Three_Letter_Country_Code\"] === row[\"Country Code\"]\n );\n return {\n ...countryInfo,\n ...row\n };\n});","pinned":true,"hidden":false,"mode":"ojs"},{"id":1050,"value":"devIndicatorMetadata = d3.csvParse(\n await FileAttachment(\"World Development Indicators - Metadata 1.csv\").text(),\n d3.autoType\n);","pinned":true,"hidden":false,"mode":"ojs"},{"id":759,"value":"countryMetaData = d3.csvParse(\n await FileAttachment(\"country-and-continent-codes-list-csv_csv.txt\").text(),\n d3.autoType\n);","pinned":true,"hidden":false,"mode":"ojs"},{"id":709,"value":"md`Imports`","pinned":false,"hidden":false,"mode":"ojs"},{"id":67,"value":"echarts = require(\"https://cdn.jsdelivr.net/npm/echarts@4.7.0/dist/echarts.min.js\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":586,"value":"import { select, checkbox } from \"@jashkenas/inputs\";","pinned":true,"hidden":false,"mode":"ojs"},{"id":11,"value":"d3 = require(\"d3@5\")","pinned":true,"hidden":false,"mode":"ojs"},{"id":1057,"value":"md`Legacy from when I tried to import data directly from the [Google spreadsheet with all the calculations]((https://docs.google.com/spreadsheets/d/1XzeSHF9i8dbQ1hLEqyYGifyC0wD1yzTIFuPmx_LxLc4/edit?usp=sharing). Because the spreadsheet imports some external data, I ran into performance issues and empty fields, which is why I decided to upload the export into the notebook and update it periodically by hand instead.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":21,"value":"_mergedSpreadsheetData = {\n const url = \"https://docs.google.com/spreadsheets/d/e/2PACX-1vT3VoiA9l4A9Men0BY4y0BA8HiCAkKOdplzWTJ_DsKyfTk9Ex_YbaRB3tcAbiYG-rIce3Dj0vINwP8f/pub?gid=144603776&single=true&output=csv\"\n let spreadsheetData;\n const spreadsheet = await d3.csv(url).then(data => {\n spreadsheetData = [ ... data ];\n });\n return spreadsheetData.map(row => {\n const countryInfo = countryMetaData.find(cmd => cmd[\"Three_Letter_Country_Code\"] === row[\"Country Code\"]);\n return {\n ...countryInfo,\n ...row\n };\n });\n};","pinned":true,"hidden":false,"mode":"ojs"},{"id":630,"value":"_devIndicatorMetadata = {\n const url = \"https://docs.google.com/spreadsheets/d/e/2PACX-1vT3VoiA9l4A9Men0BY4y0BA8HiCAkKOdplzWTJ_DsKyfTk9Ex_YbaRB3tcAbiYG-rIce3Dj0vINwP8f/pub?gid=125150895&single=true&output=csv\"\n let spreadsheetData;\n const spreadsheet = await d3.csv(url).then(data => {\n spreadsheetData = [ ... data ];\n });\n return spreadsheetData;\n};","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W35","stdlib":"1","titling":"auto"}