30:T5d1,step1 = md`### Good Data Sources * **[Open Development Myanmar](https://data.opendevelopmentmyanmar.net/dataset):** Open Development Myanmar (ODMm) is part of the Open Development Network, a coalition of organizations co-managing a common open data and open knowledge platform aggregating and sharing information, maps and data to illuminate development trends in the Mekong region at country and regional levels. The platform and network are coordinated by East West Management Institute’s Open Development Initiative. * **[MIMU](https://www.themimu.info/election):** The Myanmar Information Management Unit, or MIMU, provides information management services to strengthen analysis and decision-making of the humanitarian and development community in Myanmar. It maintains a common data and information repository with data from various sources on all sectors, countrywide, at the lowest administrative unit for which it is available. * **[The Ananda](https://theananda.org/en/tools):** Research, data and evidence are not widely employed in public discussion about politics in Myanmar. Ananda promotes the use of data in public discussion about politics and provoking public debates through data. They invite researchers, journalists and individuals to collaborate on making data accesible and understandable. * **[Open Hluttaw]():** Open Hluttaw, of Myanma Fifth Estate, has done some great digitization of UEC records, but their data is not yet publicly available on their website. `31:T539,step2 = md`## Data Types There are two primary data types which this module uses \`.csv\` and \`\`.json\`\`. In the [fourth module](), we will also use extensions of \`\`.json\`\` called \`geoJSON\` and \`topoJSON\` for mapping, but don't worry about that now. *Here's another little secret. Everything eventually is turned into a \`\`.json\`\`.* ### Comman-Separated Values (\`\`.csv\`\`) A comma-separated values file is a delimited text file that uses a comma to separate values. Each line of the file is a data record. Each record consists of one or more fields, separated by commas. The use of the comma as a field separator is the source of the name for this file format. A \`\`.csv\`\` file typically stores tabular data (numbers and text) in plain text, in which case each line will have the same number of fields. ### JavaScript Object Notation (\`\`.json\`\`) JavaScript Object Notation is an open-standard file format or data interchange format that uses human-readable text to transmit data objects consisting of attribute–value (also called key-value) pairs and array data types. It is a very common data format, with a diverse range of applications, and is a language-independent data format. It was derived from JavaScript, but many modern programming languages include code to generate and parse \`.json\` data. `32:T517,pyithuHluttaw = [ { id: "National League for Democracy", seats: 255, color: '#EC1B23' }, { id: "The Tatmadaw", seats: 110, color: '#BA0C0D' }, { id: "Kokang Democracy and Unity Party", seats: 30, color: '#FE6058' }, { id: "Wa Democratic Party", seats: 30, color: '#13A0E7' }, { id: "Union Solidarity and Development Party", seats: 30, color: '#006B31' }, { id: "Arakan National Party", seats: 12, color: '#EB1D27' }, { id: "Shan Nationalities League for Democracy", seats: 12, color: '#FFE63B' }, { id: "Vacant", seats: 7, color: '#e8e8e8' }, { id: "Ta'ang National Party", seats: 3, color: '#0CB8FF' }, { id: "Pa-O National Organisation", seats: 3, color: '#28166F' }, { id: "Zomi Congress for Democracy", seats: 2, color: '#CC232A' }, { id: "Lisu National Development Party", seats: 2, color: '#DC0723' }, { id: "Kachin State Democracy Party", seats: 2, color: '#1D7706' }, { id: "National Unity Party", seats: 1, color: '#CC0000' }, { id: "Mon National Party", seats: 1, color: '#CC232A' }, { id: "Independent", seats: 1, color: '#e8e8e8' } ]33:[{"id":"40c2bb78f4fea618a8c39a0fab65a7ab4b115787e61d117e752d437359a11e83a48431bba74df2965b0a6ce47a855656fe8a7a83d1e2cc980d2e0962dbc4f880","href":"https://static.observableusercontent.com/files/40c2bb78f4fea618a8c39a0fab65a7ab4b115787e61d117e752d437359a11e83a48431bba74df2965b0a6ce47a855656fe8a7a83d1e2cc980d2e0962dbc4f880","name":"Pyithu_Hluttaw_Results_(Total).csv","type":"text/csv","lastModified":1582533831769.088,"size":40176},{"id":"3f0c5fa0000eeb2a85e845595353382f45885f3f782729ad2a147ca046ee75cdc324d33ad993fbb63b513819fa89c8f322df21e875d3251a20f6b37e1622b504","href":"https://static.observableusercontent.com/files/3f0c5fa0000eeb2a85e845595353382f45885f3f782729ad2a147ca046ee75cdc324d33ad993fbb63b513819fa89c8f322df21e875d3251a20f6b37e1622b504","name":"Pyithuhluttaw(Constituencies)-Table 1.csv","type":"text/csv","lastModified":1582788229391.283,"size":183355},{"id":"64fdd69a027736fb69a0fee32faa4217d2b194ff79aa3df90323036cddcbbe23dd501aaa28e989fdbed36373f132430abf8de45cf49e4d5b816643003bcceac2","href":"https://static.observableusercontent.com/files/64fdd69a027736fb69a0fee32faa4217d2b194ff79aa3df90323036cddcbbe23dd501aaa28e989fdbed36373f132430abf8de45cf49e4d5b816643003bcceac2","name":"Pyithu Hluttaw Results (By Party)@1.csv","type":"text/csv","lastModified":1582788784143.438,"size":212187},{"id":"b04605f3597536b62fb93a732ffdf22481a901692b9bc7a91b0b84c66773a57efd3cecfac16d365a26b2090c5ebbe52c5d2f92c3f8806dc1658ad45eaa54fd2b","href":"https://static.observableusercontent.com/files/b04605f3597536b62fb93a732ffdf22481a901692b9bc7a91b0b84c66773a57efd3cecfac16d365a26b2090c5ebbe52c5d2f92c3f8806dc1658ad45eaa54fd2b","name":"Pyithuhluttaw(Constituencies)-Table 1@2.csv","type":"text/csv","lastModified":1582791289774.682,"size":196442}] 2e:{"title":"Manipulating Data","cells":[{"id":0,"value":"toc2 = md`# Manipulating Data\n

Finding, importing, and cleaning data is the foundation of all visualizations
\n\n 1. Good Data Sources\n 2. Data Types\n 3. Loading The Data\n 4. Tables\n 5. Onto Visuals\n

\n
\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":44,"value":"$30","pinned":false,"hidden":false,"mode":"ojs"},{"id":52,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":87,"value":"step3 = md`## Loading The Data\nThis module explores the two most simple ways to load data into Observable, [File Attachments](https://observablehq.com/@observablehq/file-attachments) and [File Hosting](https://observablehq.com/@observablehq/introduction-to-data#files). All the data for these modules has already been loaded.\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"md`### File Attachments\n\nRead this [short tutorial](https://observablehq.com/@observablehq/file-attachments) to get to speed on file attachments. Essentially, it bundles a file from your computer with the webpage, so everyone who views the webpage has access to it.\n\nRead this [short tutorial]() to get up to speed on file hosting. Essentially, it puts the file somewhere safe online, and then accesses it everytime the webpage opens. We'll go through file hosting now.\n\n\n#### To attatch a file to your notebook, type:\n\\`\\`\\`js\nFileAttachment(\"census2014.csv\").url()\n\\`\\`\\`\n\n#### To automatically convert it into a \\`.json\\`, type:\n\\`\\`\\`js\nd3.csv(await FileAttachment('census2014@1.csv').url(), d3.autoType)\n\\`\\`\\`\n\n#### To turn view your data in an interactive table, type:\n\\`\\`\\`js\nviewof census2014Table = table(census2014, { title: \"2014 Census\" })\n\\`\\`\\`\n`","pinned":false,"hidden":false,"mode":"ojs"},{"id":189,"value":"step4 = md`## Tables\n\nTables are a great way to check on our data. We can ask questions like:\n\n1. Is all the data we wanted here?\n2. Does it look like we expected it to?\n3. Did it upload properly?\n\nBecause we can search and filter as well, we can check on specific data points, like a township or population count. *Click the magnifying glass and type in a township you want to inspect*`","pinned":false,"hidden":false,"mode":"ojs"},{"id":128,"value":"viewof pyithu2015Table = table(pyithu_2015_totals, {\n title: \"2015 Pyithu Hluttaw Results\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":403,"value":"md`### Some Analysis\n*The beauty of the table is that it allows us to test some quick assumptions.*\n\n* Total population in your selection: **${Intl.NumberFormat().format(\n Math.round(\n pyithu2015Table.data.reduce((sum, d) => sum + d.population_total, 0)\n )\n)}**\n* Average % voter registration in your selection: **${(\n (pyithu2015Table.data.reduce((sum, d) => sum + d.percent_registration, 0) /\n pyithu2015Table.data.length) *\n 100\n).toFixed(2)}%**\n* Average % voter turnout in your selection: **${(\n (pyithu2015Table.data.reduce((sum, d) => sum + d.percent_turnout, 0) /\n pyithu2015Table.data.length) *\n 100\n).toFixed(2)}%**`","pinned":false,"hidden":false,"mode":"ojs"},{"id":327,"value":"step5 = md`---\n## Onto Visuals\nNow that we have loaded and inspected our data, let's begin making our first chart in the next module. **Note:** This module did not include any information on cleaning data. All data was cleaned before being loaded. Cleaning is, however, a very time consuming step.`","pinned":false,"hidden":false,"mode":"ojs"},{"id":175,"value":"md`---\n## Appendix`","pinned":false,"hidden":false,"mode":"ojs"},{"id":449,"value":"pyithu_2015_by_township = d3.csv(\n await FileAttachment(\"Pyithuhluttaw(Constituencies)-Table 1@2.csv\").url(),\n d3.autoType\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":480,"value":"pyithu_2015_by_township_PARTIAL = d3.csv(\n await FileAttachment(\"Pyithu Hluttaw Results (By Party)@1.csv\").url(),\n d3.autoType\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":42,"value":"pyithu_2015_totals = d3.csv(\n await FileAttachment('Pyithu_Hluttaw_Results_(Total).csv').url(),\n d3.autoType\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":385,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":18,"value":"myanmarState = d3.json(\n await \"https://gist.githubusercontent.com/elibenton/6bed32b6a5dbc314a6a0538b57996678/raw/861bcf09e7a59d6a82017ebe8cfbac62e1bedd83/myanmar-state-borders.json\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":10,"value":"myanmarTownship = d3.json(\n await \"https://gist.githubusercontent.com/elibenton/4267387b3cd591105c41a58fd7b5a173/raw/9473de517c814f5b7e7e840fff520a75330c0e96/myanmar.json\"\n)","pinned":true,"hidden":false,"mode":"ojs"},{"id":12,"value":"d3 = require('d3@5.15.0/dist/d3.min.js')","pinned":false,"hidden":false,"mode":"ojs"},{"id":136,"value":"import { table } from \"@tmcw/tables/2\"","pinned":false,"hidden":false,"mode":"ojs"}],"files":"$W33","stdlib":"1","titling":"auto"}