VizHub Forks Graph as of Sept. 11 2019

VizHubForks.json

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The data comes from [Wikipedia: List of continents by population](https://en.wikipedia.org/wiki/List_of_continents_by_population#Regional_and_continental_(sub)totals_from_1950_to_2016).","lastUpdatedTimestamp":1538737014,"height":500,"imagesUpdatedTimestamp":1541535657},{"id":"95f3ad7d101941b4a3b1869811b358d6","documentType":"visualization","owner":"13540669","title":"Making a Bar Chart","description":"","lastUpdatedTimestamp":1540476136,"height":500,"imagesUpdatedTimestamp":1541535679},{"id":"25a9dbd980a145a2906f8901f355bd42","documentType":"visualization","owner":"27960935","title":"Shapes with SVG and CSS","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540910090,"height":500,"imagesUpdatedTimestamp":1541535690},{"id":"8a50b76cbdc147089983d9889ef2672d","documentType":"visualization","owner":"27960935","title":"Bowl of Fruit - Gerenal Update Pattern","description":"","lastUpdatedTimestamp":1540910098,"height":500,"imagesUpdatedTimestamp":1541535701},{"id":"b9a7597acd7c439385e5264dbaf5a694","documentType":"visualization","owner":"34223598","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; 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encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535562195,"imagesUpdatedTimestamp":1541535921},{"id":"551ea85b74844fa2b1c09a522d0afe7a","documentType":"visualization","owner":"42813309","title":"Assignment 1 Tweak a Face!","description":"Author: Weijie Pang\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535562761,"imagesUpdatedTimestamp":1541535932},{"id":"d06f9a594e95458c8e7e54bcd5fae603","documentType":"visualization","owner":"42813309","title":"Assignment 1 Tweak a Face!","description":"Author: Weijie Pang\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1548606976,"height":500,"imagesUpdatedTimestamp":1548606984},{"id":"57c956a58f36451f9a364c97e2b3bed8","documentType":"visualization","owner":"34223598","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js ","lastUpdatedTimestamp":1541565022,"height":500,"imagesUpdatedTimestamp":1541565026},{"id":"ae49aeb6a87c4f60ba5f25f5eed5992d","documentType":"visualization","owner":"42821433","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540847147,"height":500,"imagesUpdatedTimestamp":1541535965},{"id":"9470efe1ff2c440cb7372653ba64dea6","documentType":"visualization","owner":"7958306","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541560058,"height":500,"imagesUpdatedTimestamp":1541560059},{"id":"dd44f8fcdc8346ff90bddd63572bf638","documentType":"visualization","owner":"68416","title":"Responding to Resize","description":"","lastUpdatedTimestamp":1562323785,"height":500,"imagesUpdatedTimestamp":1562323797},{"id":"3ac8ab3834ea4b7f9de523181f394abe","documentType":"visualization","owner":"3117142","title":"Making a barchart","description":"\n\n","lastUpdatedTimestamp":1535622656,"imagesUpdatedTimestamp":1541535998},{"id":"16ecd0e2b2ef48d2af5a1cc8aab9fb46","documentType":"visualization","owner":"3117142","title":"Customising numbers","description":"\n\n","lastUpdatedTimestamp":1551114086,"height":500,"imagesUpdatedTimestamp":1551114088},{"id":"077ab0562e6a45ab998fcf5bc87da8fe","documentType":"visualization","owner":"3117142","title":"Making a barchart","description":"\n\n","lastUpdatedTimestamp":1551181713,"height":500,"imagesUpdatedTimestamp":1551181719},{"id":"fff4e634f51c4fb1b8fea263a579b553","documentType":"visualization","owner":"13540669","title":"Child Kidnap data 2014","description":"This bar chart shows the kidnapping in 2014. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n","lastUpdatedTimestamp":1535639994,"imagesUpdatedTimestamp":1541536031},{"id":"e6e1782e79f34e75898c49d4ed50abea","documentType":"visualization","owner":"68416","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538829645,"height":500,"imagesUpdatedTimestamp":1541536042},{"id":"787901fd254a4fdcbdb510205505af1b","documentType":"visualization","owner":"36131688","title":"Making a Bar Chart","description":"This bar chart shows the largest fast food restaurant chains in the world. The data comes from Wikipedia in [List of the largest fast food restaurant chains](https://en.wikipedia.org/wiki/List_of_the_largest_fast_food_restaurant_chains).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541646431,"height":500,"imagesUpdatedTimestamp":1541646441},{"id":"3d3a4cef56984cd6afa947bea5797d5d","documentType":"visualization","owner":"36131688","title":" The top 10 largest fast food restaurant chains in the world","description":"This bar chart shows the top 10 fast food restaurant in the world. The data comes from Wikipedia in [List of the largest fast food restaurant chains](https://en.wikipedia.org/wiki/List_of_the_largest_fast_food_restaurant_chains).\n\n","lastUpdatedTimestamp":1541625006,"height":500,"imagesUpdatedTimestamp":1541625013},{"id":"8ed582f7de004170a5e36575cabc4fc7","documentType":"visualization","owner":"13914434","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most valuable companies in the world. \n\nThe data comes from the year 2018 estimate in [The 10 Most Valuable Brands in 2018](hhttps://www.inc.com/business-insider/amazon-google-most-valuable-brands-brand-finance-2018.html).","lastUpdatedTimestamp":1541647110,"height":500,"imagesUpdatedTimestamp":1541647114},{"id":"97fb22799572422595808dabcc007c73","documentType":"visualization","owner":"1780801","title":"Geert Is The Best!","description":"","lastUpdatedTimestamp":1535746076,"imagesUpdatedTimestamp":1541536086},{"id":"2ecce423e0cd47289e1078c107229dc5","documentType":"visualization","owner":"36267844","title":"Countries with Highest Number of Space Travellers ","description":"This bar chart shows the number of space travellers (astronauts, but sometimes civilians too!) per country. The eight countries represented here are the countries with the highest number of space travellers in the World. The data is from [World Atlas](https://www.worldatlas.com/articles/countries-with-the-most-space-travelers.html).\n\n","lastUpdatedTimestamp":1541458717,"height":500,"imagesUpdatedTimestamp":1541536097},{"id":"5a59837f9ea94246a23af195924e8b49","documentType":"visualization","owner":"68416","title":"USA Fresh Fruit Consumption","description":"This data is about fresh fruit consumption in the US. The data covers 1980 - 2016.\n\nThis data comes from the USDA Fruit and Tree Nut Yearbook - Table G36, via [Data.world: TBT Week 19 - Fresh Fruit Consumption - 1980 to 2016](https://data.world/throwback-thurs/tbt-week-19-fresh-fruit-consumption-1980-to-2016)","lastUpdatedTimestamp":1538737014,"height":500,"imagesUpdatedTimestamp":1541536108},{"id":"9a6e06687834470191dc19496f4d30eb","documentType":"visualization","owner":"31802591","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"956dff8d5ffd4be0a1f7ce6d56e9c4a9","documentType":"visualization","owner":"31802591","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1535828020,"imagesUpdatedTimestamp":1541536119},{"id":"370f2866692f4923a7b1c23758cad569","documentType":"visualization","owner":"31802591","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"85767f4469e24fa785150751232aceda","documentType":"visualization","owner":"31802591","title":"Data Table Summary: IMF","description":"The dataset consists of fiscal variables for a large panel of countries. \nVariables include:\n1) Government Revenue\n2) Government Expenditure\n3) Government Primary Expenditure\n4) Interest Paid on Public Dept\n5) Government Primary Balance\n6) Gross Public Dept\n7) GDP\n\nNote: All variables are represented as percentage of GDP\n\n\nThis dataset is used in one of the [International Monetary Fund's paper on Fiscal Prudence and Profligacy](http://www.imf.org/external/np/fad/histdb/).","lastUpdatedTimestamp":1536161332,"imagesUpdatedTimestamp":1541536130},{"id":"359a6cfed76c4876b528cf4324ce0b4a","documentType":"visualization","owner":"13180246","title":"Top 10 highest grossing movies of all time","description":"This bar graph shows the top 10 highest grossing movies of all time.\n\n[Source of the data](https://www.newsday.com/entertainment/movies/the-biggest-box-office-hits-of-all-time-1.5369007)\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554697914,"height":500,"imagesUpdatedTimestamp":1554697919},{"id":"6ca4760f14c0491e9c95db10fb4c8ae0","documentType":"visualization","owner":"31802591","title":"Data Table Summary: IMF","description":"The dataset consists of fiscal variables for a large panel of countries. \nVariables include:\n1) Government Revenue\n2) Government Expenditure\n3) Government Primary Expenditure\n4) Interest Paid on Public Dept\n5) Government Primary Balance\n6) Gross Public Dept\n7) GDP\n\nNote: All variables are represented as percentage of GDP\n\n\nThis dataset is used in one of the [International Monetary Fund's paper on Fiscal Prudence and Profligacy](http://www.imf.org/external/np/fad/histdb/).","imagesUpdatedTimestamp":100},{"id":"d2f6bd291a0a4476b3ca92eaac5027ef","documentType":"visualization","owner":"31802591","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536613795,"imagesUpdatedTimestamp":1541536152},{"id":"f628e771e8924f37a1338a6dd80db542","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Credit Lending","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"39fa20a7534f42ad8717247d0d778c8e","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Credit Lending","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"06d6d81fee274081b768e32cd8d7f911","documentType":"visualization","owner":"31802591","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"7196c98a51784d48baecfe291a10e5db","documentType":"visualization","owner":"31802591","title":"Data Table Summary","description":"This data consists of loan sanctioned in the first quarter of 2018 by the Lending Club. It consists of variables such as 1) Loan Amount Requested 2) Loan Amount Funded\n3) Term of Loan 4) Home Ownership of the Lender 5) Month Issued 6) State of Residency\n\nThis data comes [Lending Club website](https://www.lendingclub.com/info/download-data.action).\n","lastUpdatedTimestamp":1535832813,"imagesUpdatedTimestamp":1541536163},{"id":"815ca2b85bbf4d86bfb531178c6f9754","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Lending Club","description":"This data consists of loan sanctioned in the first quarter of 2018 by the Lending Club. It consists of variables such as 1) Loan Amount Requested 2) Loan Amount Funded\n3) Term of Loan 4) Home Ownership of the Lender 5) Month Issued 6) State of Residency\n\nThis data comes [Lending Club website](https://www.lendingclub.com/info/download-data.action).\n","lastUpdatedTimestamp":1535832939,"imagesUpdatedTimestamp":1541536174},{"id":"a60b500b243140de9963cbd04d011fc3","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Lending Club","description":"\nThis data consists of loan sanctioned in the first quarter of 2018 by the Lending Club. It consists of variables such as 1) Loan Amount Requested 2) Loan Amount Funded\n3) Term of Loan 4) Home Ownership of the Lender 5) Month Issued 6) State of Residency\n\nThis data comes from [Lending Club website](https://www.lendingclub.com/info/download-data.action).\n","lastUpdatedTimestamp":1535833023,"imagesUpdatedTimestamp":1541536185},{"id":"139fb35652de4e8fbe77ad68ef66c287","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Lending Club","description":"This data consists of loans less than $5000 sanctioned in the first quarter of 2018 by the Lending Club. It consists of variables such as 1) Loan Amount Requested 2) Loan Amount Funded 3) Term of Loan 4) Home Ownership of the Lender 5) Month Issued 6) State of Residency\n\nThis data comes from [Lending Club website](https://www.lendingclub.com/info/download-data.action).\n\n","lastUpdatedTimestamp":1535834552,"imagesUpdatedTimestamp":1541536196},{"id":"c7ae314c434a4d15acfaea85397eef5b","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Lending Club","description":"This data consists of loans less than $5000 sanctioned in the first quarter of 2018 by the Lending Club. It consists of variables such as 1) Loan Amount Requested 2) Loan Amount Funded 3) Term of Loan 4) Home Ownership of the Lender 5) Month Issued 6) State of Residency\n\nThis data comes from [Lending Club website](https://www.lendingclub.com/info/download-data.action).\n","lastUpdatedTimestamp":1535834592,"imagesUpdatedTimestamp":1541536207},{"id":"5d3a12b6424d4ef68d842a67f87a3fe5","documentType":"visualization","owner":"13914434","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about the information of all the companies in Fortune 500 in 2017. The fields include company name, number of employees, previous rank, revenues, revenue change, profits, profit change, assets, and market value.\n\nThis data is the limited edition that comes from [Fortune Magazine](www.fortune.com/fortune500).","lastUpdatedTimestamp":1535838817,"imagesUpdatedTimestamp":1541536218},{"id":"2c6be5f933ab4cf496a3f42d3fab1800","documentType":"visualization","owner":"13914434","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about the calories of all the fast food in US. The data was collected in January of 2017 by looking through online nutritional information provided by fast food restaurant chains.\n\nThis data comes from the author of this [Statcrunch - Nutritional Data for Fast Food 2017](https://www.statcrunch.com/app/index.php?dataid=2323899).","lastUpdatedTimestamp":1535838677,"imagesUpdatedTimestamp":1541536229},{"id":"958815f5907d46c6acfd1e4f05d09ddc","documentType":"visualization","owner":"26421182","title":"Let's make a face with d3.js!","description":"","lastUpdatedTimestamp":1538865645,"height":500,"imagesUpdatedTimestamp":1541536240},{"id":"2742db0b281b4e7183669d7401c83625","documentType":"visualization","owner":"13914434","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about batting, pitching, and fielding statistics for MLB teams from 2013 to 2016.\n\nThis data comes from the author of this post [Statcrunch - MLB Team Stats 2013-2016](https://www.statcrunch.com/app/index.php?dataid=1953762).","lastUpdatedTimestamp":1535838666,"imagesUpdatedTimestamp":1541536251},{"id":"b289ed5439714c3fb76785e753d35c4b","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Cricket","description":"The dataset consists of all the cricket one day internationals played between 1971 to 2017. \nVariables include:\n1) Scorecard\t\n2) Team 1 \n3) Team 2 \n4) Winner \n5) Margin\n6) Ground\n7) Match Date\n\n\nThis dataset is obtained from [Kaggle](https://www.kaggle.com/jaykay12/odi-cricket-matches-19712017#originalDataset.csv).","lastUpdatedTimestamp":1535845414,"imagesUpdatedTimestamp":1541536262},{"id":"f7ad66bce9184d9e940379ffb8882801","documentType":"visualization","owner":"26421182","title":"The top 10 most populous countries","description":"This bar chart shows population of the top 10 most populous countries. \n\nThe data comes from the year 2018 estimate in [World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/). ","lastUpdatedTimestamp":1542884788,"height":500,"imagesUpdatedTimestamp":1542884789},{"id":"a1835fbbe4164a5d804a097f7c61b6a0","documentType":"visualization","owner":"4958548","title":"Shapes with SVG & CSS","description":"Davids code along Curan Kehellers example tutorial of how to use JavaScript objects, arrays and modules.","lastUpdatedTimestamp":1535903398,"imagesUpdatedTimestamp":1541536284},{"id":"7aa49897461a4301baeebf5cf6cc1aa8","documentType":"visualization","owner":"4958548","title":"Cars Report","description":"Davids code along Curan Kehellers example tutorial of how to use JavaScript objects, arrays and modules.","lastUpdatedTimestamp":1535902286,"imagesUpdatedTimestamp":1541536295},{"id":"7b2156a5442449b0bc7e68938abedd4f","documentType":"visualization","owner":"26421182","title":"Notes Frequency of a Song","description":"This bar chart shows the music notes from a MIDI song with filename <1a2d8218a9e42cb6b1cdabd5f54b5c56.mid>, track number #12. Also in [Song's Note Scatter Plot](https://vizhub.com/rubens2005/9d9e94d6dbc14a73abda9c7d4bea3b00).\n\nThe data comes from the [Million Song Dataset](https://labrosa.ee.columbia.edu/millionsong/).\n\nThis song is 'Poor Little Fool'. More information: [Wikipedia, the free encyclopedia](https://en.wikipedia.org/wiki/Poor_Little_Fool).\n","lastUpdatedTimestamp":1542220561,"height":500,"imagesUpdatedTimestamp":1542220568},{"id":"6ded1feaf10c405c858e40af3adc5022","documentType":"visualization","owner":"4958548","title":"Cars Report","description":"Davids code along Curan Kehellers example tutorial of how to use JavaScript objects, arrays and modules.","imagesUpdatedTimestamp":100},{"id":"45a98cc8b1cc401e95897c24a1130f1a","documentType":"visualization","owner":"4958548","title":"Cars report","description":"","lastUpdatedTimestamp":1535902876,"imagesUpdatedTimestamp":1541536317},{"id":"4ad4204a9be9468e840fda99844eb3c0","documentType":"visualization","owner":"4958548","title":"Shapes with SVG & CSS","description":"Davids code along Curan Kehellers example tutorial of how to use JavaScript objects, arrays and modules.","lastUpdatedTimestamp":1535903579,"imagesUpdatedTimestamp":1541536328},{"id":"ea57a232a5c246b0b4cbd2fe3afd0b01","documentType":"visualization","owner":"31802591","title":"Untitled","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535907535,"imagesUpdatedTimestamp":1541536339},{"id":"a79f497493854d3a975938b5837c96cf","documentType":"visualization","owner":"4958548","title":"Davids D3-face","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535919011,"imagesUpdatedTimestamp":1541536350},{"id":"33a8973c91be46fb8ab5b19765c90433","documentType":"visualization","owner":"31802591","title":" Grad ","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535910425,"imagesUpdatedTimestamp":1541536361},{"id":"4ec51598e9e141c0bf42a69826fb0dae","documentType":"visualization","owner":"31802591","title":"Making a Bar Chart","description":"","lastUpdatedTimestamp":1535916006,"imagesUpdatedTimestamp":1541536372},{"id":"5a341e1364c640eeab0b0466a6a078cb","documentType":"visualization","owner":"31802591","title":"Making a Bar Chart","description":"","lastUpdatedTimestamp":1535916572,"imagesUpdatedTimestamp":1541536383},{"id":"bed94dc24f764ca88f743708ae179f29","documentType":"visualization","owner":"31802591","title":"Premier League Player Stats","description":"This bar chart shows the top 10 goal scorers of English Premier League 2017-18. The data comes from the [premier league website](https://www.premierleague.com/stats/top/players/goals?se=79).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/34y8fAUsVzI\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541476280,"height":500,"imagesUpdatedTimestamp":1541536394},{"id":"a2bf886c19b541eba83d416b49ece14e","documentType":"visualization","owner":"4958548","title":"Davids D3-face","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535919962,"imagesUpdatedTimestamp":1541536405},{"id":"84b8201d43ee42068f50291a07967a2b","documentType":"visualization","owner":"4958548","title":"Davids D3-face","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535925752,"imagesUpdatedTimestamp":1541536416},{"id":"b01f91c1a7a9479ea9852d74c0413a0d","documentType":"visualization","owner":"4958548","title":"Davids D3-face","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535925768,"imagesUpdatedTimestamp":1541536427},{"id":"baafa76b13dc404da32a449a0691d76a","documentType":"visualization","owner":"4958548","title":"Davids D3-face","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1535926302,"imagesUpdatedTimestamp":1541536438},{"id":"26132f0076c24b9796afe3a4ed60b786","documentType":"visualization","owner":"4958548","title":"Davids D3-face","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537993900,"height":500,"imagesUpdatedTimestamp":1541536449},{"id":"eca44d910ce34d45a45df0fff9d9b53f","documentType":"visualization","owner":"7958306","title":"Making a Bar Chart","description":"This bar chart shows the top 10 technologies used by StackOverflow survey respondents in 2018 by percentage [2018 Developer Survey Results](https://insights.stackoverflow.com/survey/2018/#technology).\n","lastUpdatedTimestamp":1541560075,"height":500,"imagesUpdatedTimestamp":1541560081},{"id":"ad9792236e564989afdbbbafcf39eb5b","documentType":"visualization","owner":"13540669","title":"Asian Games 2018 Medal Tally","description":"This bar chart shows the total medals per country in Asian Games 2018. [Asian Games 2018 Medal Tally](https://www.sportskeeda.com/sports/asian-games-medals-table).\n","lastUpdatedTimestamp":1540547718,"height":500,"imagesUpdatedTimestamp":1541536495},{"id":"581a3798fc214b3ea5b31a2842c70ef5","documentType":"visualization","owner":"26421182","title":"Structuring D3 code with ES6 classes","description":"This code is based on code from Elliot Bentley’s Block [774b87bf0f7482599419d1e7da9ed918](http://bl.ocks.org/ejb/774b87bf0f7482599419d1e7da9ed918).\n\n For more information, see his blog post [A better way to structure D3 code](http://ejb.github.io/2017/08/09/a-better-way-to-structure-d3-code-es6-version.html).","lastUpdatedTimestamp":1539263642,"height":500,"imagesUpdatedTimestamp":1541536506},{"id":"c6740149745e4d2eb0bdceda3b9052fa","documentType":"visualization","owner":"8374102","title":"Nick's Music Listening History","description":"This data is collected by a service called [last.fm](https://last.fm), which has been aggregating my music plays via iTunes and Spotify since 2008. The data shows the top 20 artists by play count, as tracked by the service. The data was accessed from my [profile](https://last.fm/user/philosiphicus) via a service called [last.fm to csv](https://benjaminbenben.com/lastfm-to-csv/), using my username (philosiphicus).","lastUpdatedTimestamp":1539004354,"height":500,"imagesUpdatedTimestamp":1541536518},{"id":"5ac8f269da2b46b2a7bbd3888f39c846","documentType":"visualization","owner":"13540669","title":"India's Medal Tally in Asian Games 2018","description":"This bar chart shows the total medals per country in Asian Games 2018. [India's Medal Tally in Asian Games 2018](https://www.sportskeeda.com/sports/india-medal-tally-asian-games-2018).\n","lastUpdatedTimestamp":1550238644,"height":500,"imagesUpdatedTimestamp":1550238654},{"id":"eed72277fa7a4decb175b7a5bca1ace9","documentType":"visualization","owner":"1944891","title":"Dataset #1 Packet Headers","description":"This program prints a summary of a data table.\n\nThis data is packet headers from mixed normal and malicious network traffic. This dataset was truncated from its original size of 151,643 lines (~22MB).\n\nThis data was generated in 2016 by [Stratosphere labs](https://www.stratosphereips.org/datasets-overview/) network traffic captures. This particular [dataset](https://mcfp.felk.cvut.cz/publicDatasets/CTU-Mixed-Capture-4/) comes with other network traffic log sources as well as a description of the actions taken to produce the traffic.","lastUpdatedTimestamp":1541729909,"height":500,"imagesUpdatedTimestamp":1541729911},{"id":"ab94a68223e94f73bbb890786eb0c0cb","documentType":"visualization","owner":"34223598","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541562464,"height":500,"imagesUpdatedTimestamp":1541562475},{"id":"0841328a93af402b8b35414fb3f1731f","documentType":"visualization","owner":"26421182","title":"Project and Activity List","description":"This program prints a summary of a data table.\n\nThis data is about real projects.\n\nThis data comes from [Operations Research & Scheduling Research Group](http://www.projectmanagement.ugent.be/?q=research/data/realdata).","lastUpdatedTimestamp":1539264608,"height":500,"imagesUpdatedTimestamp":1541536562},{"id":"c04ffcba9f5743a0802ef5ad8025fbfb","documentType":"visualization","owner":"36267844","title":"NASA and Russian EVA Activity Data","description":"This program prints a summary of a data table.\n\nThis data is about Extra-vehicular Activity (EVA) performed by the US and Russia.\n\nThis data comes from NASA [Open Data Portal](https://data.nasa.gov/Raw-Data/Extra-vehicular-Activity-EVA-US-and-Russia/9kcy-zwvn). I have removed the 'purpose' column in the original dataset.","lastUpdatedTimestamp":1541458446,"height":500,"imagesUpdatedTimestamp":1541536573},{"id":"1fe395420b3a4cc1a64b70929f956bdd","documentType":"visualization","owner":"36131688","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"d1b071a85442451f8757e83499d86387","documentType":"visualization","owner":"11801614","title":"Hello wtf VizHub !","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536041744,"imagesUpdatedTimestamp":1541536584},{"id":"08611b6fd9a14cc9b6bd9a82ec1f3fa0","documentType":"visualization","owner":"36131688","title":"Fast Food Restaurants Across America_1","description":"\nThis data is about locations of fast food restaurant. The data contains 10,000 records.\n\nThis data comes from Kaggle in [Fast Food Restaurants Across America](https://www.kaggle.com/datafiniti/fast-food-restaurants/home).","lastUpdatedTimestamp":1540003391,"height":500,"imagesUpdatedTimestamp":1541536595},{"id":"4802e9f91a084e9d919c11551a39abaa","documentType":"visualization","owner":"11801614","title":"title is Hello Dave!","description":"readme is MarkDown","lastUpdatedTimestamp":1536067776,"imagesUpdatedTimestamp":1541536607},{"id":"159a2b1243ef435686d507e6bc437141","documentType":"visualization","owner":"27960935","title":"(HW) The World's Highest Paid Athletes in 2018","description":"This bar chart ranks the top 10 highest-paid athletes. Their payments include their salaries and the endorsements. The data was collect from [Forbes: The World's Highest-Paid Athletes](https://www.forbes.com/athletes/#59a62f3655ae).","lastUpdatedTimestamp":1541639399,"height":500,"imagesUpdatedTimestamp":1541639401},{"id":"0ab09c21c70146c8bb35b46265d71168","documentType":"visualization","owner":"36267844","title":"Public Transport Journeys by Type of Transport London Data","description":"This program prints a summary of a data table.\n\nThis data is about the number of passenger journeys each  type of public transport completes in London from 2010 to July 2018.\n\nThis data comes from the London Data Store [Transport for London (TfL)](https://data.london.gov.uk/dataset/public-transport-journeys-type-transport).\n\n\nMore information about the data (from the website):\nNumber of journeys on the public transport network by TFL reporting period, by type of transport. Data is broken down by bus, underground, DLR, tram, Overground and cable car.\n\nPeriod lengths are different in periods 1 and 13, and the data is not adjusted to account for that.\nDocklands Light Railway journeys are based on automatic passenger counts at stations.\nOverground and Tram journeys are based on automatic on-carriage passenger counts. \nReliable Overground journey numbers have only been available since October 2010.\n\nThe Emirates Air Line cable car service began 28 June 2012. ","lastUpdatedTimestamp":1541458455,"height":500,"imagesUpdatedTimestamp":1541536629},{"id":"c351b3a30d2b44059f5eb54555cd22d6","documentType":"visualization","owner":"68416","title":"Delete This - but upload the data as a dataset","description":"This program prints a summary of JSON data.\n\nThis data is about global emissions over time.\n\nThis data comes from [ClimateWatch: Historical GHG Emissions](https://www.climatewatchdata.org/ghg-emissions?source=32&version=1). The data was extracted from this [API endpoint](https://www.climatewatchdata.org/api/v1/emissions?location=WORLD&sector=389%2C409&source=32).","lastUpdatedTimestamp":1538992018,"height":500,"imagesUpdatedTimestamp":1541536640},{"id":"325f6f76c47f46c6b72a2003fbc7eee8","documentType":"visualization","owner":"36267844","title":"London Average Air Quality Levels Data","description":"This program prints a summary of a data table.\n\nThis data is about the monthly average air quality in London starting in January 2008 until August 2018.\n\nThis data comes from the London Datastore [London Average Air Quality Levels](https://data.london.gov.uk/dataset/london-average-air-quality-levels).","lastUpdatedTimestamp":1541458471,"height":500,"imagesUpdatedTimestamp":1541536651},{"id":"77c86441960c4c9784e2730e74e5f357","documentType":"visualization","owner":"1944891","title":"Dataset #2 Network Flows","description":"This program prints a summary of a data table.\n\nThis data is network flows from mixed normal and malicious network traffic. This dataset was truncated from its original size of 151,643 lines (~22MB).\n\nThis data was generated in 2016 by [Stratosphere labs](https://www.stratosphereips.org/datasets-overview/) network traffic captures. This particular [dataset](https://mcfp.felk.cvut.cz/publicDatasets/CTU-Mixed-Capture-4/) comes with other network traffic log sources as well as a description of the actions taken to produce the traffic.","lastUpdatedTimestamp":1536077214,"imagesUpdatedTimestamp":1541536662},{"id":"13a678ad79394ec7a86e4aede1b4e9cd","documentType":"visualization","owner":"1944891","title":"Dataset #3: Netflow with GUI Info","description":"This program prints a summary of a data table.\n\nThis data is netflow data with correlated keyboard, mouse, and gui activity fields. This is a novel data source representing windows on a desktop (user interactions)\n\nThis data comes from a private data source provided by a professor at WPI. This professor has cleared this data set for public use.","lastUpdatedTimestamp":1541730814,"height":500,"imagesUpdatedTimestamp":1541730817},{"id":"39b9d6a9910d4995b52122c564f45e99","documentType":"visualization","owner":"36131688","title":"World Happiness Report","description":"\nThis data is the World Happiness Report. The data contains the countries' happiness rank, economy(GDP), life expectancy and more.\n\nThis data comes from Kaggle in [World Happiness Report](https://www.kaggle.com/unsdsn/world-happiness).","lastUpdatedTimestamp":1536536443,"imagesUpdatedTimestamp":1541536684},{"id":"cc5582bc1b824808b6ae28b790d309e8","documentType":"visualization","owner":"36131688","title":"World Bank Data","description":"\n\nThis is World Bank's data, which contains country population, fertility rate, and life expectancy.\n\nThis data comes from Kaggle in [World Bank Data (1960 to 2016)](https://www.kaggle.com/gemartin/world-bank-data-1960-to-2016#life_expectancy.csv).","lastUpdatedTimestamp":1539993067,"height":500,"imagesUpdatedTimestamp":1541536696},{"id":"a03f4170a7fb47618f67e3147ac2187e","documentType":"visualization","owner":"19274272","title":"Top 10 Best Selling Albums  Bar chart","description":"This bar chart shows unit sales of the top 10 best selling albums of all time. The data comes from the Business Insider article [The 50 best-selling albums of all time](https://www.businessinsider.com/50-best-selling-albums-all-time-2016-9#18-the-beatles-the-beatles-1967-1970-33).","lastUpdatedTimestamp":1541646655,"height":500,"imagesUpdatedTimestamp":1541646656},{"id":"0c7017e1045040999703c35023175b68","documentType":"visualization","owner":"42813309","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"d8d3a0e54c6d4c34923c7e908da1a15e","documentType":"visualization","owner":"19274272","title":"Multidimentional Poverty Measure","description":"This program prints a summary of a data table.\n\nThis data is about the poverty levels in certain countries.\n\nThis data comes from Kaggle [Multidimensional Poverty Measures](https://www.kaggle.com/ophi/mpi).","lastUpdatedTimestamp":1541646666,"height":500,"imagesUpdatedTimestamp":1541646667},{"id":"d02cedf9727b48b9acadba547df3212f","documentType":"visualization","owner":"4958548","title":"Davids D3 Bar Chart: General Governemnt Debt ","description":"This bar chart shows the percentage of general government debts of the ten highest ranking countries in 2015. \nGeneral governemnt debt means the ratio of general gross government debt to the governments GDP. \n\n\nThe data originates from the [OECD](https://data.oecd.org/gga/general-government-debt.htm) and is from the year 2015.","lastUpdatedTimestamp":1537993881,"height":500,"imagesUpdatedTimestamp":1541536740},{"id":"131760a1d49b48578ac80f97ece1a154","documentType":"visualization","owner":"19274272","title":"Countries of the world","description":"This program prints a summary of a data table.\n\nThis data is about the current state of the countries in the world.\n\nThis data comes from Kaggle [Countries of the World](https://www.kaggle.com/fernandol/countries-of-the-world).","lastUpdatedTimestamp":1541646668,"height":500,"imagesUpdatedTimestamp":1541646678},{"id":"e31f613ec1194a1d8882792912ca364e","documentType":"visualization","owner":"19274272","title":"US minimum wage","description":"This program prints a summary of a data table.\n\nThis data is about the minimum wage in all of the states in the United States and how it compares to the value of today's dollar.\n\nThis data comes from Kaggle [US Minimum Wage by State from 1968 to 2017](https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017).","lastUpdatedTimestamp":1541646668,"height":500,"imagesUpdatedTimestamp":1541646689},{"id":"3b1d62cbf4a24d7492112b45f7d54ef3","documentType":"visualization","owner":"1944891","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1537889140,"height":500,"imagesUpdatedTimestamp":1541536774},{"id":"edb719330dba44949318200ead60e1a1","documentType":"visualization","owner":"1944891","title":"Most Popular Programming Languages","description":"These data are represent the top programming languages according to the number of corresponding active repositories on GitHub. The data was taken from GitHub's publically available datasets available through an [API](https://developer.github.com/v3/) or through their [archive](https://www.gharchive.org/).","lastUpdatedTimestamp":1541729794,"height":500,"imagesUpdatedTimestamp":1541729809},{"id":"8b3adc05cf4d4bc2a25e80f9d6e939a1","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536092904,"imagesUpdatedTimestamp":1541536796},{"id":"449a390a26f44907ab739238b5a9ad5f","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536092922,"imagesUpdatedTimestamp":1541536807},{"id":"d36e971e242b46dab84f16b8a0d64d51","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536093157,"imagesUpdatedTimestamp":1541536818},{"id":"528ce14d89b743f1a7a2e57db0d7ac76","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536093227,"imagesUpdatedTimestamp":1541536829},{"id":"af4925a0afd447b4a6bd9f44e2724201","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536093383,"imagesUpdatedTimestamp":1541536840},{"id":"6c0ab602d8874a9ab904ef61afe2e7fc","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536093472,"imagesUpdatedTimestamp":1541536851},{"id":"66f911d46b254079a7365e95e16cf051","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536093493,"imagesUpdatedTimestamp":1541536862},{"id":"ba89b83bdb624efbba97d1ffff2ed797","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536093717,"imagesUpdatedTimestamp":1541536873},{"id":"2dd4c2f61e0743538a85e711e7f1a307","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536094225,"imagesUpdatedTimestamp":1541536884},{"id":"16675d506c794992a16134d98042882f","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536094305,"imagesUpdatedTimestamp":1541536895},{"id":"2efd5748266a4442aed5fd7815bd35fd","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536094341,"imagesUpdatedTimestamp":1541536906},{"id":"b4850c891f944cbb95f909de82a218f6","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"2d3e7facece2472296c51e582985c8a5","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"d9eaf6d54d7e4022a0c7d10c310b9332","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536106819,"imagesUpdatedTimestamp":1541536917},{"id":"e1d3149c68ad4dcda21662b1aeadf35d","documentType":"visualization","owner":"42821433","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540847134,"height":500,"imagesUpdatedTimestamp":1541536928},{"id":"b26b70f478b247c8bc0abb24f01e418a","documentType":"visualization","owner":"42821433","title":"Making a Bar Chart","description":"This bar chart shoes the top 10 most played live Pearl Jam Songs. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536106693,"imagesUpdatedTimestamp":1541536939},{"id":"9a195adacb7649db8fe503321810b69a","documentType":"visualization","owner":"42821433","title":"Making a Bar Chart","description":"This bar chart shows the top 10 most played live Pearl Jam Songs","lastUpdatedTimestamp":1536106729,"imagesUpdatedTimestamp":1541536950},{"id":"664541f05e214a50826679f12dc793e6","documentType":"visualization","owner":"42821433","title":"Making a Bar Chart","description":"This bar chart shows the top 10 most played live Pearl Jam Songs\n[PearlJam](https://www.setlist.fm/stats/pearl-jam-23d6b80b.html)","lastUpdatedTimestamp":1536106824,"imagesUpdatedTimestamp":1541536961},{"id":"84d0464022db4ce0810e0261fe757430","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536106917,"imagesUpdatedTimestamp":1541536972},{"id":"3fca5c5ad69344cb84a753bfe9b47a50","documentType":"visualization","owner":"42821433","title":"Making a Bar Chart","description":"This bar chart shows the top 10 most played live Pearl Jam Songs\n[PearlJam](https://www.setlist.fm/stats/pearl-jam-23d6b80b.html)","lastUpdatedTimestamp":1541555546,"height":500,"imagesUpdatedTimestamp":1541555546},{"id":"6b571b9449fe430cbea05689b135859b","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise export worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536107066,"imagesUpdatedTimestamp":1541536983},{"id":"b5026184163f44ed8002c3fa756e6bf7","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandiseexport in US dollar at current prices (Millions) worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536107587,"imagesUpdatedTimestamp":1541536994},{"id":"2e9b9ad192bf4863bbeb34f57da224d0","documentType":"visualization","owner":"26421182","title":"Coding Challenge #114: Bubble Sort Visualization","description":"This is the  [Coding Challenge #114: Bubble Sort Visualization](https://www.youtube.com/watch?v=67k3I2GxTH8) from [The Coding Train](https://www.youtube.com/user/shiffman/videos).","lastUpdatedTimestamp":1538866535,"height":500,"imagesUpdatedTimestamp":1541537005},{"id":"30c071b147524521a55b5709724bf1ea","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise exports in US dollar at current prices (Millions) worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536107783,"imagesUpdatedTimestamp":1541537016},{"id":"93c8d2f4436b4714b0d97d68a0e2c0b0","documentType":"visualization","owner":"42813309","title":"Making a Bar Chart","description":"This bar chart shows the Top 10 total merchandise exports in US dollar at current prices (Millions) worldwise. The data comes from the year 2017 in [World Trade Organization: Total Merchandise Export 2017](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541040555,"height":500,"imagesUpdatedTimestamp":1541537027},{"id":"cbe5a64056784210b410a159a240825c","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536113093,"imagesUpdatedTimestamp":1541537038},{"id":"16dd16aeeee44eb0ac7f74cf0c754b46","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Network of world merchandise trade in 2016.\n\nThis data comes from the year 2018 estimate in [World Trade Organization: Network of world merchandise trade 2016](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1536113341,"imagesUpdatedTimestamp":1541537049},{"id":"f166164af8774f608e90148025f24828","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\nThe reporters several continents, important economy groups and countries. The continents reports are Africa, Asia excluding Hong Kong re-exports, Australia and New Zealand, Europe, Middle East, North America, South and Central America and the Caribbean. The economy groups are Commonwealth of Independent States (CIS), including associate and former member States, European Union (28), Four East Asian traders. And country reporters are Brazil, Japan, Mexico, Russian Federation.\n\nThe exports data is divided into six indicators. They are Agricultural products, Fuels and mining products, Manufactures, Power generating machinery, Non-electric machinery, Electrical machinery.","lastUpdatedTimestamp":1538690415,"height":500,"imagesUpdatedTimestamp":1541537060},{"id":"437bc56be5774468ab264d0b6c9561db","documentType":"visualization","owner":"11801614","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536126447,"imagesUpdatedTimestamp":1541537072},{"id":"5f89c1c4b9164832ad9982880a9f018c","documentType":"visualization","owner":"68416","title":"Auto MPG Summary","description":"This program prints a summary of a data table.\n\nThis data is about cars.\n\nThis data comes from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1542565801,"height":500,"imagesUpdatedTimestamp":1542565806},{"id":"79f3bd0b69a94e9ab92f193c1d42e997","documentType":"visualization","owner":"13540669","title":"India's Medal Tally in Asian Games 2018","description":"This bar chart shows the total medals per country in Asian Games 2018. [India's Medal Tally in Asian Games 2018](https://www.sportskeeda.com/sports/india-medal-tally-asian-games-2018).\n","imagesUpdatedTimestamp":100},{"id":"3e2fc3e159a04550b2ea3e3c969cd03d","documentType":"visualization","owner":"13540669","title":"India's Medal Tally in Asian Games 2018 summary","description":"This program prints a summary of a data table.\n\nThis data is about India's Medal Tally in Asian Games 2018.\n\nThis data comes from [India's Medal Tally in Asian Games 2018](https://www.sportskeeda.com/sports/india-medal-tally-asian-games-2018).","lastUpdatedTimestamp":1540391572,"height":500,"imagesUpdatedTimestamp":1541537094},{"id":"08f25267d9464f8d8c39e0b3373fe7e3","documentType":"visualization","owner":"13540669","title":"Asian Games 2018 Medal Tally customizing the Axes ","description":"This bar chart shows the total medals per country in Asian Games 2018. [Asian Games 2018 Medal Tally](https://www.sportskeeda.com/sports/asian-games-medals-table).\n","lastUpdatedTimestamp":1536143498,"imagesUpdatedTimestamp":1541537106},{"id":"a44b38541b6e47a4afdd2dfe67a302c5","documentType":"visualization","owner":"68416","title":"Customizing Axes","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/). It also demonstrates customization of D3 axes.\n\nSee also the first bar chart visualization that this one builds on: [Making a Bar Chart](https://vizhub.com/curran/dd44f8fcdc8346ff90bddd63572bf638).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/c3MCROTNN8g?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1560115699,"height":500,"imagesUpdatedTimestamp":1560115706},{"id":"35cc2ba9df0d47e88db5508bbe8df2b8","documentType":"visualization","owner":"13540669","title":"Customizing Axes","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n","lastUpdatedTimestamp":1536430867,"imagesUpdatedTimestamp":1541537128},{"id":"2fb8cbae5c3e44febefaab3ec7759178","documentType":"visualization","owner":"13540669","title":"Customizin Axes -vertical bar chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n","lastUpdatedTimestamp":1536320298,"imagesUpdatedTimestamp":1541537139},{"id":"c4ab607f8a2c417abfd5bed066b8d2d2","documentType":"visualization","owner":"13540669","title":"Scattor Plot","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n","lastUpdatedTimestamp":1540479098,"height":500,"imagesUpdatedTimestamp":1541537150},{"id":"a9ec621b1c36439aa2a65e0c28462d7a","documentType":"visualization","owner":"68416","title":"Ordinal Scatter Plot","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1561611587,"height":500,"imagesUpdatedTimestamp":1561611594},{"id":"9247d4d42df74185980f7b1f7504dcc5","documentType":"visualization","owner":"68416","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1560119640,"height":500,"imagesUpdatedTimestamp":1560119648},{"id":"af35f372a6de485f9694ecd78e1cc1ba","documentType":"visualization","owner":"13540669","title":"Cars Scattor Plot","description":"This Scattor Plot shows relation between Horsepower and weight of the cars. The data comes from UCI Machine Learning Repository [UCI Machine Learning Repository 2017]().\n\n","lastUpdatedTimestamp":1536160036,"imagesUpdatedTimestamp":1541537184},{"id":"739f00f0dbd54d67b6cf52eaf472a705","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536158090,"imagesUpdatedTimestamp":1541537195},{"id":"30b3b90e61f6476282fadd2b4ebf27ff","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"1411647c951941b0886a5eff5ea99594","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Gross Domestic Product (GDP) of all countries between 1960 - 2017.\n\nThis data comes from [World Bank national accounts data: GDP (current US$) 1960-2017](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD).","lastUpdatedTimestamp":1536158312,"imagesUpdatedTimestamp":1541537206},{"id":"5ec1f434dace4e21a6c9fa1f33f48be6","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Gross Domestic Product (GDP) of all countries between 1960 - 2017.\n\nThis data comes from [World Bank national accounts data: GDP (current US$) 1960-2017](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD).","imagesUpdatedTimestamp":100},{"id":"d2bc8a07c7ef4c8c99a34ffb49c546ea","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Gross Domestic Product (GDP) of all countries between 1960 - 2017.\n\nThis data comes from [World Bank national accounts data: GDP (current US$) 1960-2017](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD).","imagesUpdatedTimestamp":100},{"id":"0868941d44824220ba809087cf8c4d97","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Gross Domestic Product (GDP) of all countries between 1960 - 2017.\n\nThis data comes from [World Bank national accounts data: GDP (current US$) 1960-2017](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD).","imagesUpdatedTimestamp":100},{"id":"2415a15ee1b64af3a32ed280a1140b3a","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536158730,"imagesUpdatedTimestamp":1541537217},{"id":"f77c004d83464154a031e5ac10a44798","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536158828,"imagesUpdatedTimestamp":1541537228},{"id":"280258323cc343afa2f0a85e18b8595c","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536158849,"imagesUpdatedTimestamp":1541537239},{"id":"9b984ea219854cdea45d3f197ddc5579","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536158874,"imagesUpdatedTimestamp":1541537250},{"id":"a68c625f1230415798200b4ae566c2ac","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536158922,"imagesUpdatedTimestamp":1541537261},{"id":"9c29f5961a0740438f141eda7d9554b4","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"173f3add1f1c4876bded2bf89181478c","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"8d0ebeb9019649bdbb46440be615679b","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"9b9ab6e8e18d4ce89e19b01696c7d660","documentType":"visualization","owner":"13540669","title":"Cars Scattor Plot","description":"This Scattor Plot shows relation between Horsepower and weight of the cars. The data comes from UCI Machine Learning Repository [UCI Machine Learning Repository 2017]().\n\n","lastUpdatedTimestamp":1540479108,"height":500,"imagesUpdatedTimestamp":1541537272},{"id":"79c03cafc261475d8bd949fbd88711a5","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"109370ec4fe142b7be1213c08fd95592","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"61c2a15ab2e347cd958ebc115c046135","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536159671,"imagesUpdatedTimestamp":1541537284},{"id":"78e458f29d674295b7cb80bd9767b0e1","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"78d41c401614458e9c110da1aacf623e","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"52cc86e8491b4d4499b3258d97421835","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Gross Domestic Product of all countries between 1970 and 2017.\n\nThis data comes from [World Bank national accounts data: GDP (current US$) 1970-2017 ](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536159756,"imagesUpdatedTimestamp":1541537295},{"id":"15209f0943224dae9737a5ec8eb337cb","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about the Gross Domestic Product of all countries between 1970 and 2017.\n\nThis data comes from [World Bank national accounts data: GDP (current US$) 1970-2017](https://data.worldbank.org/indicator/NY.GDP.MKTP.CD).","lastUpdatedTimestamp":1538615506,"height":500,"imagesUpdatedTimestamp":1541537306},{"id":"036c8d7718104759b887dcd0c647c8e1","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Gross Domestic Product of all countries between 1970 and 2017.\n\nThis data comes from [World Bank national accounts data: GDP (current US$) 1970-2017 ](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"9b77318cce3e4ff6b082f9e3332d1d96","documentType":"visualization","owner":"31802591","title":"Data Table Summary: Lending Club","description":"\nThis data consists of loan sanctioned in the first quarter of 2018 by the Lending Club. It consists of variables such as 1) Loan Amount Requested 2) Loan Amount Funded\n3) Term of Loan 4) Home Ownership of the Lender 5) Month Issued 6) State of Residency\n\nThis data comes from [Lending Club website](https://www.lendingclub.com/info/download-data.action).\n","imagesUpdatedTimestamp":100},{"id":"e77eb4a098fe47a2a80155f713ab6d26","documentType":"visualization","owner":"13540669","title":"India Medals Scattor Plot","description":"This Scattor Plot shows relation between Horsepower and weight of the cars. The data comes from UCI Machine Learning Repository [UCI Machine Learning Repository 2017]().\n\n","lastUpdatedTimestamp":1536161220,"imagesUpdatedTimestamp":1541537318},{"id":"90ca82b80d2d4cd6bd6683dc34e6efa2","documentType":"visualization","owner":"31802591","title":"Data Table Summary: IMF","description":"The dataset consists of fiscal variables for a large panel of countries. \nVariables include:\n1) Government Revenue\n2) Government Expenditure\n3) Government Primary Expenditure\n4) Interest Paid on Public Dept\n5) Government Primary Balance\n6) Gross Public Dept\n7) GDP\n\nNote: All variables are represented as percentage of GDP\n\n\nThis dataset is used in one of the [International Monetary Fund's paper on Fiscal Prudence and Profligacy](http://www.imf.org/external/np/fad/histdb/).","imagesUpdatedTimestamp":100},{"id":"0d5f000a8b6243ce85ff9b57e4999160","documentType":"visualization","owner":"31802591","title":"Revised Data Table Summary: IMF","description":"The dataset consists of fiscal variables for a large panel of countries. Variables include: 1) Government Revenue 2) Government Expenditure 3) Government Primary Expenditure 4) Interest Paid on Public Dept 5) Government Primary Balance 6) Gross Public Dept 7) GDP\n\nNote: All variables are represented as percentage of GDP\n\nThis dataset is used in one of the International Monetary Fund's paper on Fiscal Prudence and Profligacy.","lastUpdatedTimestamp":1536161620,"imagesUpdatedTimestamp":1541537329},{"id":"dcc07384cfcc43cda1a219a6b5d979d3","documentType":"visualization","owner":"31802591","title":"Data  Summary: IMF","description":"The dataset consists of fiscal variables for a large panel of countries. \nVariables include:\n1) Government Revenue\n2) Government Expenditure\n3) Government Primary Expenditure\n4) Interest Paid on Public Dept\n5) Government Primary Balance\n6) Gross Public Dept\n7) GDP\n\nNote: All variables are represented as percentage of GDP\n\n\nThis dataset is used in one of the [International Monetary Fund's paper on Fiscal Prudence and Profligacy](http://www.imf.org/external/np/fad/histdb/).","lastUpdatedTimestamp":1536161503,"imagesUpdatedTimestamp":1541537340},{"id":"cc8b74aebf9d4f818fde09c26eb7daf1","documentType":"visualization","owner":"8374102","title":"Data Table Summary - Dataset 1","description":"This [data](https://github.com/fivethirtyeight/guns-data) included here is related to the [Gun Deaths In America](https://fivethirtyeight.com/features/gun-deaths/) project from the website FiveThirtyEight. Included are data describing deaths caused by guns in America between 2012 and 2014.\n\nThe data includes the year, month, and location of the incident, the \"intent\" behind the death, whether or not it was committed by police, and demographic information about the victim. ","lastUpdatedTimestamp":1536175559,"imagesUpdatedTimestamp":1541537351},{"id":"77c34d0085e84e92a9da493ec3508f37","documentType":"visualization","owner":"8374102","title":"Data Table Summary - Dataset 2","description":"These data were scraped from the [Coffee Quality Institute](https://database.coffeeinstitute.org/) by James LeDoux, and are currently hosted in his [GitHub repo](https://github.com/jldbc/coffee-quality-database). The data originally included just over 1,300 ratings; the last 300 or so were removed in order to fit the criteria for this project.\n\nAttributes shown in the dataset include the source and manufacturer, certain methods of manufacturing, and taste and flavor ratings.","lastUpdatedTimestamp":1536175520,"imagesUpdatedTimestamp":1541537362},{"id":"888c286a9f7c4b5cacfde366bf169b02","documentType":"visualization","owner":"8374102","title":"Data Table Summary - Dataset 3","description":"This dataset is similar to the dataset I used for my bar chart assignment. I pulled the last month or so of data from my [last.fm profile](https://last.fm/user/philosiphicus) using [this service](https://mainstream.ghan.nl/export.html) (which is different than the previous service I used, because I wanted to see if it would pull different data). \n\nThe data includes the Unix and traditional timestamp for when I listened to the track, as well as the artist/album/track information. Sometimes the data also includes [MusicBrainz](https://musicbrainz.org) IDs for the artist/album/track. If I were to use this dataset going forward, I would like to add certain other information (specifically, genre or \"mood\") to the dataset somehow. This data also only includes from early August, because larger datasets weren't saving properly in VizHub.","lastUpdatedTimestamp":1536176117,"imagesUpdatedTimestamp":1541537373},{"id":"b3cc89e676b74ed0945cf06e042ab153","documentType":"visualization","owner":"34223598","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541562466,"height":500,"imagesUpdatedTimestamp":1541562486},{"id":"690927d1f7cf44d9ba7efbd6ac366e65","documentType":"visualization","owner":"34223598","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541562468,"height":500,"imagesUpdatedTimestamp":1541562497},{"id":"b93f7fac241443efa7667a10c8647231","documentType":"visualization","owner":"34223598","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541562470,"height":500,"imagesUpdatedTimestamp":1541562508},{"id":"70dca933d70a4269828b0e068ca2d86e","documentType":"visualization","owner":"34223598","title":"Making a Bar Chart","description":"This bar chart shows cars with their MPG.\nthe source comes from https://perso.telecom-paristech.fr/eagan/class/igr204/datasets\n","lastUpdatedTimestamp":1541562472,"height":500,"imagesUpdatedTimestamp":1541562519},{"id":"ca3bf83b501a49f09553ad32720b6527","documentType":"visualization","owner":"34223598","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about A dataset of about 400 cars with 8 characteristics such as horsepower, acceleration, etc.\n\nThis data comes from https://perso.telecom-paristech.fr/eagan/class/igr204/datasets","lastUpdatedTimestamp":1539657645,"height":500,"imagesUpdatedTimestamp":1541537406},{"id":"1c5e149216a74595ab149f7b19135212","documentType":"visualization","owner":"34223598","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about This dataset consists of three files: diaper change pattern of a baby in its first 2.5 months\n\nThis data comes from https://perso.telecom-paristech.fr/eagan/class/igr204/datasets\n\nNOTE: would like to really replace this with the real data from my work(health dataset for members physical activity) still working on gathering the data and making sure its publicly available dataset. ","lastUpdatedTimestamp":1536540740,"imagesUpdatedTimestamp":1541537417},{"id":"724c9c66b2d34c7099a983aca2f31fe2","documentType":"visualization","owner":"34223598","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about This dataset consists of three files: sleep periods of a baby in its first 2.5 months\n\nThis data comes from https://perso.telecom-paristech.fr/eagan/class/igr204/datasets","lastUpdatedTimestamp":1536191746,"imagesUpdatedTimestamp":1541537428},{"id":"63e9ba8ca85446a8a477006fde14d44b","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","lastUpdatedTimestamp":1536195089,"imagesUpdatedTimestamp":1541537439},{"id":"3f0b8a08d8924832bdd663a14113ef7f","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","lastUpdatedTimestamp":1536195172,"imagesUpdatedTimestamp":1541537450},{"id":"2ab979cd317d4a51a7300ff01ac50a91","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"d372a265661b40f3b30c84e5d363d39f","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about the daily highest and lowest stock prices for top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years.\n\nThis data comes from the daily highest and lowest stock prices [Yahoo finance: Basic Material Section](https://finance.yahoo.com/screener/predefined/basic_materials/).","lastUpdatedTimestamp":1538615503,"height":500,"imagesUpdatedTimestamp":1541537461},{"id":"2fcd1db2241c46b4adcae939958ece02","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"3cc838724edd4b6a94288bf6e921cd99","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Network of world merchandise trade in 2016.\n\nThis data comes from the year 2018 estimate in [World Trade Organization: Network of world merchandise trade 2016](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","imagesUpdatedTimestamp":100},{"id":"002c5d06848f48069965932a5c1b0f8c","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Network of world merchandise trade in 2016.\n\nThis data comes from the year 2018 estimate in [World Trade Organization: Network of world merchandise trade 2016](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","imagesUpdatedTimestamp":100},{"id":"b9c052b1b48947df96f75ca27cc2c865","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"7f6e7f596e514a08a0303c4e957d6cc1","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"80fc05184c8b43078cd7d2a1f8a7cbb6","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"ed9cecd58df2492b904dde671797c00f","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"40511d6f36234079b7f6e831f4bec840","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"f3d740d4381e4cb78ae09e5b76fd5924","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","lastUpdatedTimestamp":1536195567,"imagesUpdatedTimestamp":1541537473},{"id":"63361e0d93244566957d3a5ad95462f0","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"10059388763549e698d10236f90612d5","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536195604,"imagesUpdatedTimestamp":1541537484},{"id":"13d92715d06b4b8a9753e2513d0ed7be","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"49fa9a48354048b0937d6c727d5450a2","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"9ea5fcf21a7742ea8332483d5883166e","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"a87c74285e754ac2a6a060ca124cbafe","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"de5a60a4fc304bb7974a8b6bb8de5cc2","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"26d71f6cf5214ece99e9c98a32a1992f","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"0dd68853fd0c4b98b7e76cad55a0c485","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the daily highest and lowest stock prices for the top five volumn companies (RIG, FCX, VALE, SWN, HAL) in Basic Materials section for five years [Yahoo finance: World Population Prospects 2017](https://finance.yahoo.com/sector/basic_materials).","imagesUpdatedTimestamp":100},{"id":"777d7a6fd60b4e5eba8bc669d62d22bc","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"2c9ad297fd6e48a3b22312a427220298","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1536195779,"imagesUpdatedTimestamp":1541537495},{"id":"1c83def7597f4484ae325330f55bf89c","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"1a9c1d6dcdf44e4696017b804582a211","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"6ad9327f154a45b9aacb800e177bfbe7","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"04a22755e2964ae48fab28895557964f","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"bd59676b0eeb48a7a59e5abb41b41a94","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"6a8a6d6a3cb9465ab2a488cafb818e83","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"9b3e574f14874d6fbfaf08696c76e58a","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is the daily highest and lowest stock price of the top five volumn companies in Basic Materials for five years.\n\nThis data comes from historical data [Yahoo Finance: Basic Material section](https://finance.yahoo.com/sector/basic_materials).","lastUpdatedTimestamp":1536196130,"imagesUpdatedTimestamp":1541537506},{"id":"171afe4ad160449fb1fc689d32bd8cb6","documentType":"visualization","owner":"34223598","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","imagesUpdatedTimestamp":100},{"id":"d131cb66253d4f88b06f76897211625a","documentType":"visualization","owner":"68416","title":"Temperature in San Francisco Scatter Plot","description":"This scatter plot shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1538737014,"height":500,"imagesUpdatedTimestamp":1541537517},{"id":"be87c468038141a187a4ed486f4daa80","documentType":"visualization","owner":"13540669","title":"Temperature in San Francisco","description":"This line chart is about temperature variation of city San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n","lastUpdatedTimestamp":1536233018,"imagesUpdatedTimestamp":1541537529},{"id":"012b5b20ce894b0fa7dc98ef3a0b43a5","documentType":"visualization","owner":"68416","title":"Temperature in San Francisco Line Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\nMade in this video:\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/0vKYFsTLtbA\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>","lastUpdatedTimestamp":1558122152,"height":500,"imagesUpdatedTimestamp":1558122154},{"id":"96232eb35fb844088486f2b77b2d1bd6","documentType":"visualization","owner":"13540669","title":" Line Chart:Temperature in San Francisco","description":"This line chart is about temperature variation of city San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n","lastUpdatedTimestamp":1540548725,"height":500,"imagesUpdatedTimestamp":1541537552},{"id":"585f19b2564e484188f4c60f1faf828e","documentType":"visualization","owner":"68416","title":"Temperature in San Francisco Area Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1541752980,"height":500,"imagesUpdatedTimestamp":1541753016},{"id":"156eca6823634fceb6a1e356963bd0e6","documentType":"visualization","owner":"13540669","title":" Area Chart:Temperature in San Francisco","description":"This line chart is about temperature variation of city San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n","lastUpdatedTimestamp":1536234756,"imagesUpdatedTimestamp":1541537575},{"id":"900cb204023748b9a8bdf2273bdefe03","documentType":"visualization","owner":"68416","title":"World Population Area Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\nMade in this video:\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/0vKYFsTLtbA\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>","lastUpdatedTimestamp":1559695376,"height":500,"imagesUpdatedTimestamp":1559695383},{"id":"94ce9b466131447ea90fb1f30f693a14","documentType":"visualization","owner":"13540669","title":" Area Chart:World Population","description":"World Population Area Chart. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n","lastUpdatedTimestamp":1536318560,"imagesUpdatedTimestamp":1541537599},{"id":"785847eb9ab8402e83835251fb063f59","documentType":"visualization","owner":"27960935","title":"Grad_Students Data Table Summary","description":"This data is about basic earnings and labor force information of graduate students broken down by programs in the US between 2010-2012.\n\nThis data comes from a story by FiveThirtyEight [The Economic Guide To Picking A College Major](https://fivethirtyeight.com/features/the-economic-guide-to-picking-a-college-major/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537920874,"height":500,"imagesUpdatedTimestamp":1541537611},{"id":"dd138fc121364fc683b321bd387c9d38","documentType":"visualization","owner":"26421182","title":"Custominzing Axes","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n","lastUpdatedTimestamp":1536258045,"imagesUpdatedTimestamp":1541537622},{"id":"cc62ba93515548a2ae9f9b4faabc5c94","documentType":"visualization","owner":"68416","title":"Pentagon","description":"Inspired by a figure from chapter 5 of Tamara Munzner's excellent book \"Visualization Analysis and Design\". Imported from [Bl.ocks.org: Pentagon](https://bl.ocks.org/curran/8b4b7791fc25cfd2c459e74f3d0423f2).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/uS9I2_A7bx8?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538410039,"height":800,"imagesUpdatedTimestamp":1541537633},{"id":"9d5d78bab7a6443885671c2c855f4dee","documentType":"visualization","owner":"26421182","title":"Scatter Plot","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n","lastUpdatedTimestamp":1536260060,"imagesUpdatedTimestamp":1541537644},{"id":"7d1e0577222e46fc971cecf4e96edb29","documentType":"visualization","owner":"26421182","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n","lastUpdatedTimestamp":1536265100,"imagesUpdatedTimestamp":1541537655},{"id":"a1744a1c56434578a2cead2c3f5e6383","documentType":"visualization","owner":"30349340","title":"Cars Report","description":"","lastUpdatedTimestamp":1537568135,"height":500,"imagesUpdatedTimestamp":1541537667},{"id":"133d5df7abaa415da103ddcf81ee7eca","documentType":"visualization","owner":"30349340","title":"Cars Report","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"7c7276be21af4fb486e650a651c5d3d9","documentType":"visualization","owner":"30349340","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537568145,"height":500,"imagesUpdatedTimestamp":1541537678},{"id":"5f2ccaf00c4a44879c6ea8ddd1a22bcb","documentType":"visualization","owner":"27960935","title":"Drinks Data Table Summary","description":"This data is about alcohol consumption by country.\n\nThis data comes from a story by FiveThirtyEight [Dear Mona Followup: Where Do People Drink The Most Beer, Wine And Spirits](https://fivethirtyeight.com/features/dear-mona-followup-where-do-people-drink-the-most-beer-wine-and-spirits/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536272969,"imagesUpdatedTimestamp":1541537689},{"id":"4e1fe1de9a5c4491bfcc2281ee1ac008","documentType":"visualization","owner":"42821433","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about avocados.\n\nThis data comes from [Hass Avocado Board](http://www.hassavocadoboard.com/retail/volume-and-price-data).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540847157,"height":500,"imagesUpdatedTimestamp":1541537700},{"id":"a19e8c05dcde4281bd1c43cb34ff41b4","documentType":"visualization","owner":"42821433","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about beer.\n\nThis data comes from [Jean-Nicholas Hould](https://www.kaggle.com/nickhould/craft-cans/home).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536712418,"imagesUpdatedTimestamp":1541537712},{"id":"68a902c29c2846559e46071e4412497a","documentType":"visualization","owner":"42821433","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Wallmart Sales.\n\nThis data comes from [Wallmart](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536274226,"imagesUpdatedTimestamp":1541537724},{"id":"6c24eab6025e45ed848b1b090e3efc7e","documentType":"visualization","owner":"27960935","title":"Traffic_Deaths Data Table Summary","description":"This data is about distribution of road traffic deaths by country and is broken down to types of road user.\n\nThis data comes from WHO [Reported distribution of road traffic deaths by type of road user](http://apps.who.int/gho/data/node.main.A998?lang=en).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536274586,"imagesUpdatedTimestamp":1541537736},{"id":"b2e7cfd16676411ab7f216510b098cbe","documentType":"visualization","owner":"42821433","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about Wallmart Sales.\n\nThis data comes from [Wallmart](https://www.kaggle.com/c/walmart-recruiting-store-sales-forecasting/data).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541555576,"height":500,"imagesUpdatedTimestamp":1541555581},{"id":"264ba90edffe457d8bdb86d23cfbe849","documentType":"visualization","owner":"36131688","title":"Fast Food Restaurants Across America_2","description":"\nThis data is about locations of fast food restaurant. The data contains 10,000 records.\n\nThis data comes from Kaggle in [Fast Food Restaurants Across America](https://www.kaggle.com/datafiniti/fast-food-restaurants/home).","lastUpdatedTimestamp":1536282079,"imagesUpdatedTimestamp":1541537747},{"id":"630301c951f146e2a00caeb1c3bd668c","documentType":"visualization","owner":"36131688","title":"Fast Food Restaurants Across America_3","description":"\nThis data is about locations of fast food restaurant. The data contains 10,000 records.\n\nThis data comes from Kaggle in [Fast Food Restaurants Across America](https://www.kaggle.com/datafiniti/fast-food-restaurants/home).","lastUpdatedTimestamp":1538962330,"height":500,"imagesUpdatedTimestamp":1541537759},{"id":"a2269bbc8b0c4aca97b8a1b8745a45a4","documentType":"visualization","owner":"30349340","title":"HTML Cars Report","description":"","lastUpdatedTimestamp":1536283138,"imagesUpdatedTimestamp":1541537771},{"id":"af91248c82134f1fb19983c11e65eac6","documentType":"visualization","owner":"13180246","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about the results and statistics of all games over 9 seasons in thhe English Premier League (the soccer league in England).\n\nThis data comes from http://www.football-data.co.uk/ which was then hosted on [Datahub](https://datahub.io/sports-data/english-premier-league).\n\nIt contains several statistics of each game like the number of goals scored, the shots made or the number of yellow/red cards for all teams.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541567138,"height":500,"imagesUpdatedTimestamp":1541567146},{"id":"b119d45190714ac59f15c1cd0b9c508e","documentType":"visualization","owner":"7958306","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about outbreaks in the USA from 2015 to 2016.\n\nThis data comes from the CDC National Outbreak Reporting System [(NORS)](https://wwwn.cdc.gov/norsdashboard/)\n","lastUpdatedTimestamp":1541560140,"height":500,"imagesUpdatedTimestamp":1541560149},{"id":"22e6c9ba742844bc8bacc76da48aa62f","documentType":"visualization","owner":"13180246","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about the stats (from the popular video games) of Pokemon from the first 3 generations (first 386 pokemon).\n\nThis data comes from [Pokeapi](https://pokeapi.co/).\n\nI called the API for the statistics of the first three generations of Pokemon (since I've only played those games). I cleaned the JSON file received to retain some basic statistics, like the attack stat, defence, etc., of the Pokemon and converted the data into CSV.\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541567149,"height":500,"imagesUpdatedTimestamp":1541567157},{"id":"a528a964b44e4dff88ac17ce8606f033","documentType":"visualization","owner":"7958306","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about injuries caused by home workshop power tools in 2017.\n\nThis data comes from the year 2017 [US Consumer product safety commission](https://www.cpsc.gov/cgibin/NEISSQuery/CaseDetail.aspx?JobId=i12e2FZ9C2P%2fDUaqpqlKRQ%3d%3d&Title=9OYR9kUytIsLilKZieD5xg%3d%3d&OutputFormat=xr0S%2bkBMh05FUtUJOHTVqQ%3d%3d&Type=v0Dpcp2JcG93HTGffrGMT6V6GBbmxC9Tf%2fM5FmggZ1M%3d&UserAff=5x08cgz9T6YPDAZJzvlZjA%3d%3d&UserAffOther=9OYR9kUytIsLilKZieD5xg%3d%3d)\n","lastUpdatedTimestamp":1541560123,"height":500,"imagesUpdatedTimestamp":1541560125},{"id":"370b734b9af4401cb7ae4e4478f85cb3","documentType":"visualization","owner":"7958306","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about all known man made satalites as of 4/30/18.\n\nThis data comes from the Union Of Concerned Scientists [Data](https://www.ucsusa.org/nuclear-weapons/space-weapons/satellite-database#.W5Hbp-hKhaQ)\n","lastUpdatedTimestamp":1541559120,"height":500,"imagesUpdatedTimestamp":1541559121},{"id":"459a7ee55a4e4465811a470f984bf585","documentType":"visualization","owner":"13180246","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is from a study about speed dating.\n\nThis data comes from [Columbia Business School ](http://www.stat.columbia.edu/~gelman/arm/examples/speed.dating/).\n\nThis study was conducted by researchers Ray Fisman and Sheena Iyengar for their paper [Gender Differences in Mate Selection: Evidence From a Speed Dating Experiment](http://faculty.chicagobooth.edu/emir.kamenica/documents/genderDifferences.pdf).\n\nThe data was gathered in a set of experimental speed dating events. They were asked to rate their dates (and themselves) on several attributes like Intelligence, Ambitions, Attractivness, etc. It has a wide variety of variables that are explained in the doc file in the source link.\n\nI couldn't upload the dataset which was around 4.7MB (after some trimming). The full dataset has around 8000 rows. But since the submission deadline is in a couple of hours, I'm uploading a part of it and I hope I can use the complete dataset for my visualizations.\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541648451,"height":500,"imagesUpdatedTimestamp":1541648455},{"id":"607a261492e24c308707c3ae413b3981","documentType":"visualization","owner":"68416","title":"Hello Topologica.js!","description":"Pick a color with 3 sliders.\n\nThis is a small introductory example demonstrating use of [Topologica.js](https://github.com/datavis-tech/topologica).\n\nInspired by [Observable: Multi-Value Inputs](https://beta.observablehq.com/@mbostock/multi-value-inputs).","lastUpdatedTimestamp":1541752984,"height":500,"imagesUpdatedTimestamp":1541753027},{"id":"b8567c9394024e6ba31f08f93defcfb1","documentType":"visualization","owner":"3117142","title":"Data Table Summary","description":"\n\n","lastUpdatedTimestamp":1536319313,"imagesUpdatedTimestamp":1541537860},{"id":"42bc08d8cf524d35b458079d5d1c1eb7","documentType":"visualization","owner":"13540669","title":"Chandrapur district Area Details","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n","lastUpdatedTimestamp":1540477250,"height":500,"imagesUpdatedTimestamp":1541537872},{"id":"35f7faa2144e42ce916b5a72730ccb6d","documentType":"visualization","owner":"13540669","title":" Maharashtra districts SC population","description":"This bar chart shows districtwise SC And ST Population For The Year 2011(Maharashtra State). The data comes from  [Districtwise SC And ST Population For The Year 1991,2001 And 2011](https://mahasdb.maharashtra.gov.in/SDB_Reports/Population_Census/HTML/Districtwise%20SC%20And%20ST%20Population%20For%20The%20Year%201991,2001%20And%202011.html).\n\n","lastUpdatedTimestamp":1541756707,"height":500,"imagesUpdatedTimestamp":1541756711},{"id":"64723a51fa484eeaa46276260a0ee896","documentType":"visualization","owner":"13540669","title":" Maharashtra districts SC population","description":"This bar chart shows districtwise Scheduled Caste(SC) Population For The Year 2011(Maharashtra State). The data comes from  [Districtwise SC And ST Population For The Year 1991,2001 And 2011](https://mahasdb.maharashtra.gov.in/SDB_Reports/Population_Census/HTML/Districtwise%20SC%20And%20ST%20Population%20For%20The%20Year%201991,2001%20And%202011.html).\n\n","lastUpdatedTimestamp":1542473843,"height":500,"imagesUpdatedTimestamp":1542473843},{"id":"852a6000ca3e45d99d2deff2e7a5ceaa","documentType":"visualization","owner":"13540669","title":" Maharashtra districts ST population","description":"This bar chart shows districtwise  Scheculed Tribe(ST) Population For The Year 2011(Maharashtra State). The data comes from  [Districtwise SC And ST Population For The Year 1991,2001 And 2011](https://mahasdb.maharashtra.gov.in/SDB_Reports/Population_Census/HTML/Districtwise%20SC%20And%20ST%20Population%20For%20The%20Year%201991,2001%20And%202011.html).\n\n","lastUpdatedTimestamp":1536325264,"imagesUpdatedTimestamp":1541537906},{"id":"76fe00815d294bc3bfe948346949ee43","documentType":"visualization","owner":"3117142","title":"Scatter plot","description":"\n\n","lastUpdatedTimestamp":1536328869,"imagesUpdatedTimestamp":1541537918},{"id":"14c4758b672a498093b3042930d14e8b","documentType":"visualization","owner":"3117142","title":"Scatter plot: Quantitative","description":"\n\n","lastUpdatedTimestamp":1539006820,"height":500,"imagesUpdatedTimestamp":1541537929},{"id":"a73e25d2b98041d085d73565f03f849f","documentType":"visualization","owner":"3117142","title":"Cars Scatter plot","description":"\n\n","lastUpdatedTimestamp":1553259713,"height":500,"imagesUpdatedTimestamp":1553259714},{"id":"b14e93545d454ec49f12e4f6779fd585","documentType":"visualization","owner":"1944891","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars","lastUpdatedTimestamp":1541729882,"height":500,"imagesUpdatedTimestamp":1541729889},{"id":"bad6e1331c6e449da25fda17e5118462","documentType":"visualization","owner":"1944891","title":"Average Packet Length Scatter Plot","description":"This scatter plot shows data about packet headers. The y-Axis shows protocol names sorted by the average length of their corresponding packets. The x-axis show the average packet length in number of bytes.\n\nThis data was generated in 2016 by [Stratosphere labs](https://www.stratosphereips.org/datasets-overview/) network traffic captures. This particular [dataset](https://mcfp.felk.cvut.cz/publicDatasets/CTU-Mixed-Capture-4/) comes with other network traffic log sources as well as a description of the actions taken to produce the traffic.\n\nThe data in this scatter plot was modified from the original dataset. The columns were grouped by \"Protocol\" and then the length field was aggregated to obtain the average value per protocol.\n\nThe tool-tip code was taken from user \"d3noob\" on [bl.ocks.org](http://bl.ocks.org/d3noob/a22c42db65eb00d4e369)","lastUpdatedTimestamp":1567209119,"height":500,"imagesUpdatedTimestamp":1567209125},{"id":"dc7870c9fe3840a9a4e2249db513ff39","documentType":"visualization","owner":"36267844","title":"Marks and Channels Shape using D3","description":"Shape attempt made by Kathleen Cachel\n\n\nI learned from the [face](https://vizhub.com/curran/be771477cb974c938cd8603dd8b59d32) and [pentagon](https://vizhub.com/curran/cc62ba93515548a2ae9f9b4faabc5c94) examples. Along with looking up some syntax at [dashing d3](https://www.dashingd3js.com/svg-basic-shapes-and-d3js).\n\n4","lastUpdatedTimestamp":1541458485,"height":500,"imagesUpdatedTimestamp":1541537974},{"id":"ee9bb2827d614d26a571e00bf54dbf03","documentType":"visualization","owner":"68416","title":"Bowl of Fruit - General Update Pattern","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IyIAR65G-GQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1560122793,"height":500,"imagesUpdatedTimestamp":1560122800},{"id":"390ceab02b0e4d379240a9f34e0425b2","documentType":"visualization","owner":"8879989","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536432123,"imagesUpdatedTimestamp":1541538007},{"id":"78a44c1f3ce74ef8a87a0270f43dc227","documentType":"visualization","owner":"43082707","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536436065,"imagesUpdatedTimestamp":1541538018},{"id":"1f2a66ae539948779ea24c3d09c00653","documentType":"visualization","owner":"43082707","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536436689,"imagesUpdatedTimestamp":1541538031},{"id":"d312d1d380604f548693084cb4801689","documentType":"visualization","owner":"43082707","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536436873,"imagesUpdatedTimestamp":1541538042},{"id":"a6fbc5cf72d444ddbe7fca121e758852","documentType":"visualization","owner":"43082707","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541650283,"height":500,"imagesUpdatedTimestamp":1541650283},{"id":"028e0f6355e3443080f48a3fdf4a6e07","documentType":"visualization","owner":"43082707","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541020252,"height":500,"imagesUpdatedTimestamp":1541538064},{"id":"21efde63c0834031a9073cac70097f48","documentType":"visualization","owner":"43082707","title":"The Best-selling Video Games of All Time","description":"This bar chart shows the top 10 best-selling video games until now. \nThe data are collected from wikipedia [List of best-selling video games] (https://en.wikipedia.org/wiki/List_of_best-selling_video_games).\n","lastUpdatedTimestamp":1536445243,"imagesUpdatedTimestamp":1541538075},{"id":"bcdc4559ea49454587b38b9e4296db24","documentType":"visualization","owner":"43082707","title":"The Best-selling Video Games of All Time","description":"This bar chart shows the top 10 best-selling video games until now. \nThe data are collected from Wikipedia: [List of best-selling video games](https://en.wikipedia.org/wiki/List_of_best-selling_video_games).","lastUpdatedTimestamp":1541650333,"height":500,"imagesUpdatedTimestamp":1541650338},{"id":"c2bc8e8e1278460e9461088469b1a772","documentType":"visualization","owner":"8374102","title":"Coffee Scatter Plot","description":"These data were scraped from the [Coffee Quality Institute (CQI)](https://database.coffeeinstitute.org/) by James LeDoux, and are currently hosted in his [GitHub repo](https://github.com/jldbc/coffee-quality-database). This dataset ([hosted on VizHub](https://vizhub.com/OxfordComma/datasets/coffee-data)) includes a number of attributes that describe the taste on a scale from 1 to 10, and I thought that flavor and aroma could be correlated. This graphic shows the comparison of flavor versus aroma for the first 500 samples in the dataset. It seems as though I was correct, and that flavor and aroma are correlated. 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Therefor the x-axis is in percent.\n\n\n\nThe data originates from the [OECD](https://data.oecd.org/gga/general-government-debt.htm) and is from the year 2015.","lastUpdatedTimestamp":1537993911,"height":500,"imagesUpdatedTimestamp":1541538130},{"id":"3684ab7426e54d1ebf1c58c00d5a6ace","documentType":"visualization","owner":"68416","title":"Bowl of Fruit - Nested General Update Pattern - Outtake","description":"","lastUpdatedTimestamp":1536501206,"imagesUpdatedTimestamp":1541538141},{"id":"d66746c9f1954650a83690c8d876ee6b","documentType":"visualization","owner":"27960935","title":"HW Scatter Plot","description":"This scatter plot shows data about various candy brands, from [The Ultimate Halloween Candy Power Ranking](https://fivethirtyeight.com/features/the-ultimate-halloween-candy-power-ranking/). The visualization basically shows that there is not much relationship between the sugar content and the candy price.\n","lastUpdatedTimestamp":1541643632,"height":500,"imagesUpdatedTimestamp":1541643636},{"id":"50d09a00211c45dba7c17e25bfa8315a","documentType":"visualization","owner":"27960935","title":"","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536502068,"imagesUpdatedTimestamp":1541538164},{"id":"be56669be25846918e102c35fc6662d9","documentType":"visualization","owner":"27960935","title":"Customizing axes","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540913400,"height":500,"imagesUpdatedTimestamp":1541538175},{"id":"9857017449ed40688201d91d79814a6d","documentType":"visualization","owner":"68416","title":"Bowl of Fruit - Animated Transitions","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IyIAR65G-GQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537181043,"imagesUpdatedTimestamp":1541538186},{"id":"98903f08461946e4b9431ba34c104108","documentType":"visualization","owner":"27960935","title":"Scatter Plot","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537927947,"height":500,"imagesUpdatedTimestamp":1541538197},{"id":"e29388179ce74ec9b57235a6e023427c","documentType":"visualization","owner":"42821433","title":"Lines","description":"Inspired by a figure from chapter 5 of Tamara Munzner's excellent book \"Visualization Analysis and Design\".\n\n","lastUpdatedTimestamp":1536522722,"imagesUpdatedTimestamp":1541538208},{"id":"66b73434bd6948c9b037310079b81165","documentType":"visualization","owner":"8374102","title":"Bar Chart Recreation","description":"A recreation of a bar chart for Homework 3.\n\n<iframe width=\"560\" height=\"315\" src=\"\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536543912,"imagesUpdatedTimestamp":1541538219},{"id":"74b564ba55a444f6b639d4d817a56f3e","documentType":"visualization","owner":"42821433","title":"Lines","description":"Inspired by a figure from chapter 5 of Tamara Munzner's excellent book \"Visualization Analysis and Design\".\n\n","imagesUpdatedTimestamp":100},{"id":"5ccfafb08ae34dbd94a58d2e239d7e50","documentType":"visualization","owner":"42821433","title":"Lines","description":"Inspired by a figure from chapter 5 of Tamara Munzner's excellent book \"Visualization Analysis and Design\".\n\n","imagesUpdatedTimestamp":100},{"id":"ec0dbbc4db9444bdac3bae7385ed026d","documentType":"visualization","owner":"13914434","title":"Fastfood Scatter Plot","description":"This scatter plot shows data about the protien that is contained in each item on the fastfood menu, from [Nutritional Data for Fast Food 2017](https://www.statcrunch.com/app/index.php?dataid=2323899).","lastUpdatedTimestamp":1541022676,"height":500,"imagesUpdatedTimestamp":1541538230},{"id":"ad35eec19d264a2280495569ed22284d","documentType":"visualization","owner":"36131688","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540998303,"height":500,"imagesUpdatedTimestamp":1541538242},{"id":"c4f1d402d46847e1b98eb5b087666b9d","documentType":"visualization","owner":"36131688","title":" The top 10 largest fast food restaurant chains in the world","description":"This bar chart shows the top 10 fast food restaurant in the world. The data comes from Wikipedia in [List of the largest fast food restaurant chains](https://en.wikipedia.org/wiki/List_of_the_largest_fast_food_restaurant_chains).\n\n","lastUpdatedTimestamp":1541647127,"height":500,"imagesUpdatedTimestamp":1541647136},{"id":"984dbf393e5a4374b57b3d43ad80db73","documentType":"visualization","owner":"36131688","title":"Life expectancy vs Happiness Score Scatter Plot","description":"This scatter plot shows the relationship between Life Expectancy and Happiness Score in the 2017 World Happiness Report. The definition of Life Expectancy from Kaggle - World Happiness Report is \"the extent to which Life expectancy contributed to the calculation of the Happiness Score\". \n\nThis data is from [Kaggle - World Happiness Report](https://www.kaggle.com/unsdsn/world-happiness).\n\n","lastUpdatedTimestamp":1541647432,"height":500,"imagesUpdatedTimestamp":1541647438},{"id":"eae51bfa16ae409d9761db736f7d4f6e","documentType":"visualization","owner":"34223598","title":"Baby Diaper pattern","description":"This scatter plot shows data about baby diaper pattern\n\n","lastUpdatedTimestamp":1536538230,"imagesUpdatedTimestamp":1541538276},{"id":"a29fd9bb2d904ff3a5e01a738e0a4ce5","documentType":"visualization","owner":"34223598","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541562492,"height":500,"imagesUpdatedTimestamp":1541562530},{"id":"24900686b48048db82b4cd689f07b649","documentType":"visualization","owner":"34223598","title":"Cereals Scatter Plot","description":"This plot gives the info on how cereals with sugar content (sugar being the most important ingredient to cut down for healthy diet) are rated.\nThis scatter plot shows data about cereals, from https://perso.telecom-paristech.fr/eagan/class/igr204/datasets","lastUpdatedTimestamp":1541562493,"height":500,"imagesUpdatedTimestamp":1541562541},{"id":"ce600276a8da4322952dcf33171bad43","documentType":"visualization","owner":"13914434","title":"Re-create Marks & Channels Graphic","description":"Demonstrates the curve using line and curve in D3.js","lastUpdatedTimestamp":1536593651,"imagesUpdatedTimestamp":1541538309},{"id":"7a7d73de18214adcaa510a64fa64cc47","documentType":"visualization","owner":"3117142","title":"Borehole Dummy Data","description":"This is dummy data and will be used as a template to represent GSI Borehole data in the future.","lastUpdatedTimestamp":1539019800,"height":500,"imagesUpdatedTimestamp":1541538320},{"id":"f4819c5b1213423baf5e6e9f9abb67f8","documentType":"visualization","owner":"13540669","title":"Bowl of Fruit - General Update Pattern","description":"","lastUpdatedTimestamp":1540547667,"height":500,"imagesUpdatedTimestamp":1541538332},{"id":"7f4137a77b564607ae2791ab1e49cf7e","documentType":"visualization","owner":"68416","title":"Bowl of Fruit - General Update Pattern Special Cases","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IyIAR65G-GQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541271341,"height":500,"imagesUpdatedTimestamp":1541538343},{"id":"46f35974ae1041dd88ba655bf0a473b7","documentType":"visualization","owner":"13540669","title":"Bowl of Fruit - General Update Pattern","description":"","lastUpdatedTimestamp":1536575334,"imagesUpdatedTimestamp":1541538354},{"id":"823093872b334945b1e9db92f68a916d","documentType":"visualization","owner":"3117142","title":"Borehole Dummy Data - Linechart ","description":"This is dummy data and will be used as a template to represent GSI Borehole data in the future.","lastUpdatedTimestamp":1553275297,"height":500,"imagesUpdatedTimestamp":1553275302},{"id":"c387465b38914414bd9123ebf2f2114f","documentType":"visualization","owner":"13540669","title":"Bowl of Fruit Create bowl- General Update Pattern","description":"","lastUpdatedTimestamp":1536575866,"imagesUpdatedTimestamp":1541538377},{"id":"edd144cca7fc47c2a2534d7985d406e2","documentType":"visualization","owner":"222798","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536585462,"imagesUpdatedTimestamp":1541538388},{"id":"06025f66820b4408b9caf5ec408d67b4","documentType":"visualization","owner":"222798","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536581516,"imagesUpdatedTimestamp":1541538399},{"id":"593a19fb026547b190b3d07bb6fdec91","documentType":"visualization","owner":"222798","title":"Let's make a face with D3.js!","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nDemonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n","lastUpdatedTimestamp":1536784635,"imagesUpdatedTimestamp":1541538410},{"id":"e7953667ebd24a1c9bd1b9f7d0cd20aa","documentType":"visualization","owner":"13540669","title":" General Update pattern","description":"","lastUpdatedTimestamp":1536592405,"imagesUpdatedTimestamp":1541538421},{"id":"470cc056dd734db0bed59d24b16668a3","documentType":"visualization","owner":"15847634","title":"Shapes with SVG and CSS","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n","lastUpdatedTimestamp":1536602867,"imagesUpdatedTimestamp":1541538432},{"id":"7cb0852301234723b72c15199c2dc1db","documentType":"visualization","owner":"30349340","title":"Map D3","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n","lastUpdatedTimestamp":1537568199,"height":500,"imagesUpdatedTimestamp":1541538443},{"id":"6ec01c2a9e474c6297e3ea48122fe325","documentType":"visualization","owner":"26421182","title":"Bowl of Fruit - General Update Pattern","description":"","lastUpdatedTimestamp":1537828804,"height":500,"imagesUpdatedTimestamp":1541538454},{"id":"040aadea1c6740df819f410f2828a552","documentType":"visualization","owner":"68416","title":"Treemap from bl.ocks.org","description":"An example of porting a work from bl.ocks.org into VizHub. This is a direct port of [Mike Bostock’s Block: Treemap](https://bl.ocks.org/mbostock/4063582), modified only to split into separate files, and to fit in the space available.","lastUpdatedTimestamp":1536597119,"imagesUpdatedTimestamp":1541538465},{"id":"41b0049ea70647ee93740f560282b2ac","documentType":"visualization","owner":"15847634","title":"A Face with D3","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536619258,"imagesUpdatedTimestamp":1541538476},{"id":"c16c636a99ef478a91f9a67391ddba1d","documentType":"visualization","owner":"68416","title":"Looping Transitions Example","description":"An example of looping transitions.\n\nForked from [Ming Zhang's Face](https://vizhub.com/Nikkor/593a19fb026547b190b3d07bb6fdec91).","lastUpdatedTimestamp":1538737016,"height":500,"imagesUpdatedTimestamp":1541538487},{"id":"7fde0fe9f76b4f9eb585b85152a40502","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541476280,"height":500,"imagesUpdatedTimestamp":1541538500},{"id":"ce68822bc2144a81adf4b1caca0bb0a5","documentType":"visualization","owner":"30349340","title":"Hello VizHub!","description":"","lastUpdatedTimestamp":1537568157,"height":500,"imagesUpdatedTimestamp":1541538511},{"id":"782a4e3513384c1aa337f437cbe89b23","documentType":"visualization","owner":"26421182","title":"Bowl of Fruit - General Update Pattern","description":"","lastUpdatedTimestamp":1536619032,"imagesUpdatedTimestamp":1541538522},{"id":"d42de315e9644240afc32e6b686271a0","documentType":"visualization","owner":"31802591","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536612981,"imagesUpdatedTimestamp":1541538533},{"id":"cc7cc622f1aa48cfa3fe9d3d4fcbba13","documentType":"visualization","owner":"31802591","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536613923,"imagesUpdatedTimestamp":1541538546},{"id":"ad919ee2b4fd49ceae6d3b071cbc336f","documentType":"visualization","owner":"31802591","title":"Tax revenue over time plot","description":"This scatter plot shows the revenue generated through taxes by the United States government from 1800 to 2011. The y-axis reports the revenue as a percentage of GDP and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1541476280,"height":500,"imagesUpdatedTimestamp":1541538557},{"id":"b4337906165d4f59bd73e92afe2bb389","documentType":"visualization","owner":"36267844","title":"London Air Quality Scatter Plot","description":"This scatter plot shows data about the average monthly readings of Ozone and Nitrogen Dioxide in London, UK. The data comes from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. \n\n","lastUpdatedTimestamp":1541458751,"height":500,"imagesUpdatedTimestamp":1541538569},{"id":"af28a7e7cb684871b21233bbf9a7df30","documentType":"visualization","owner":"26421182","title":"Temperature in San Francisco Scatter Plot","description":"This scatter plot shows one week of temperature (in degree Celsius) in San Francisco. The data comes from [Date Canva](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1536630071,"imagesUpdatedTimestamp":1541538581},{"id":"75664c550315467a9e6936a265adad4f","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"c4d08153b63547a69ae8526542fa30ad","documentType":"visualization","owner":"222798","title":"Marathons with Most Finishers in 2018(As of 9/11)","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis bar chart shows marathons with the top 10 most finishers. The data comes from the year 2018 estimate as of today in [Which marathon had the most finishers last year?](http://findmymarathon.com/statistics.php).","lastUpdatedTimestamp":1538446513,"height":500,"imagesUpdatedTimestamp":1541538592},{"id":"83bbf7743a674cfc98344f2951363ccf","documentType":"visualization","owner":"26421182","title":"Temperature in San Francisco Line Chart","description":"This scatter plot shows one week of temperature (in degree Celsius) in San Francisco. The data comes from [Date Canva](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1536621619,"imagesUpdatedTimestamp":1541538603},{"id":"0f50f70ee8ad48509800f971114b5e54","documentType":"visualization","owner":"26421182","title":"Temperature in San Francisco Area Chart","description":"This scatter plot shows one week of temperature (in degree Celsius) in San Francisco. The data comes from [Date Canva](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1536622283,"imagesUpdatedTimestamp":1541538615},{"id":"5f4b0be7f8a0419fabe4c9853780944c","documentType":"visualization","owner":"26421182","title":"World Population Area Chart","description":"This scatter plot shows one week of temperature (in degree Celsius) in San Francisco. The data comes from [Date Canva](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1536624273,"imagesUpdatedTimestamp":1541538627},{"id":"eff829bf63054112be627f19031340d8","documentType":"visualization","owner":"13180246","title":"Shapes with SVG and CSS","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536629376,"imagesUpdatedTimestamp":1541538639},{"id":"cef183992ddc4d62b6f0d2fba75d38c2","documentType":"visualization","owner":"27960935","title":"HW Re-create Marks & Channels Graphic","description":"Recreate 4 shapes using d3.symbol.\n","lastUpdatedTimestamp":1540913795,"height":500,"imagesUpdatedTimestamp":1541538650},{"id":"0cb12755faf0441abc86399b8341669c","documentType":"visualization","owner":"7958306","title":"Marks & Channels Graphic","description":"A simple example of the treemap visualization. The goal was to attempt to reproduce a visualization from the Marks & Channels Graphic from Chapter 5.\n\nThis code is based on the example of the treemap in Curran's viz: https://vizhub.com/curran/040aadea1c6740df819f410f2828a552","lastUpdatedTimestamp":1541560158,"height":500,"imagesUpdatedTimestamp":1541560160},{"id":"f8b6925137dc48a89f65079b7c23d6ca","documentType":"visualization","owner":"43166904","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"83965995ebe245a2b5f4a48c9e275a02","documentType":"visualization","owner":"43166904","title":"Barchart","description":"\n","lastUpdatedTimestamp":1536657868,"imagesUpdatedTimestamp":1541538672},{"id":"7193fbd9cc284361ad9a444075c55c6d","documentType":"visualization","owner":"222798","title":"Daily Sales for B. 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The data vendor will upload latest data for previous month at the first week of each month.","lastUpdatedTimestamp":1536784697,"imagesUpdatedTimestamp":1541538683},{"id":"19a3e64aea314507b012bb39488515e4","documentType":"visualization","owner":"6235472","title":"Make a bar chart","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).","lastUpdatedTimestamp":1536678137,"imagesUpdatedTimestamp":1541538694},{"id":"3394bd63ce354700abd00856b45fe904","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536673176,"imagesUpdatedTimestamp":1541538705},{"id":"0d57ddb09387473e982f1c32d367fec2","documentType":"visualization","owner":"31802591","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"681e81fc59974fda8c4a2a389425b5a0","documentType":"visualization","owner":"31802591","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536678146,"imagesUpdatedTimestamp":1541538716},{"id":"d6995ec40a0f466c9a157c9fde83b512","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"This scatter plot shows data about the world trades and taxes for 69 countries in 2015, from [World bank: Auto MPG Data Set](https://data.worldbank.org/indicator).\n\nThe graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","lastUpdatedTimestamp":1536677728,"imagesUpdatedTimestamp":1541538727},{"id":"5b60b071ae7442c2a08ff07230b26cf7","documentType":"visualization","owner":"36131688","title":"Re-create Marks and Channels Graphic","description":"Graphics by Nai-tan!\nInspired by Curran's video [Shapes](https://vizhub.com/curran/366c38ba5ebc4631b4bd936f3b709744)\n","lastUpdatedTimestamp":1541625038,"height":500,"imagesUpdatedTimestamp":1541625046},{"id":"349dd78295b34ae6a35d88c72ef3279f","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"This scatter plot shows data about the world trades and taxes for 69 countries in 2015, from [World bank: Auto MPG Data Set](https://data.worldbank.org/indicator).\n\nThe graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","lastUpdatedTimestamp":1541001582,"height":500,"imagesUpdatedTimestamp":1541538749},{"id":"68d2557b5824473196c04e532729a517","documentType":"visualization","owner":"15847634","title":"Basic Bar Chart","description":"","lastUpdatedTimestamp":1536945118,"imagesUpdatedTimestamp":1541538761},{"id":"f8dc0329c9ec4be581dc87cb147eddd1","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n\nCode fragments used from [stackoverflow](https://stackoverflow.com/questions/12786797/draw-rectangles-dynamically-in-svg)\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536690769,"imagesUpdatedTimestamp":1541538772},{"id":"690ff7552af64bbdbac465ada4dd0eec","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536682221,"imagesUpdatedTimestamp":1541538783},{"id":"2f8b36b17a824787b804605551d8a8b3","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"a0c77b523596412ca99c71a8b07112f7","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"3befe5a1300b4fb089a61f03124b14cb","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536683231,"imagesUpdatedTimestamp":1541538794},{"id":"37ff21c75d0d48cc8591664645a09eeb","documentType":"visualization","owner":"31802591","title":"Dataviz 2018 Assignment Graphics","description":"Dataviz 2018 Assignment Graphics recreated using shapes with SVG and CSS notebook. \n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536684932,"imagesUpdatedTimestamp":1541538805},{"id":"379d74672e6a4289b9cbd3960aeaa92e","documentType":"visualization","owner":"4958548","title":"NBA All-Stars from 2000-2016","description":"This program prints a summary of a data table.\n\nThis data is about NBA All-Stars from 2000 to 2016.\n\nThis data comes from [data.world – NBA All Stars 2000-2016](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nIt was uploaded by [Gabe Salzer](https://data.world/gmoney) who credits [basketball.realgm.com](http://basketball.realgm.com/nba/allstar/game/rosters/2001) as his source.\n","lastUpdatedTimestamp":1536686980,"imagesUpdatedTimestamp":1541538816},{"id":"ef394a2e19644513a6da3b3e47b91bf8","documentType":"visualization","owner":"4958548","title":"NBA All-Stars from 2000-2016","description":"This program prints a summary of a data table.\n\nThis data is about NBA All-Stars from 2000 to 2016.\n\nThis data comes from [data.world – NBA All Stars 2000-2016](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nIt was uploaded by [Gabe Salzer](https://data.world/gmoney) who credits [basketball.realgm.com](http://basketball.realgm.com/nba/allstar/game/rosters/2001) as his source.\n","imagesUpdatedTimestamp":100},{"id":"ded0de8c8c834629b07b5a731302db97","documentType":"visualization","owner":"4958548","title":"World Happiness 2017","description":"This program prints a summary of a data table.\n\nThis data is about the happiness score in various countries.\n\nThis data comes from [www.kaggle.com](https://www.kaggle.com/alperuzum/world-happiness-2017/data).\n\nIt was uploaded by [Alper Üzüm](https://www.kaggle.com/alperuzum) who credits the [World Happiness Report](https://www.kaggle.com/unsdsn/world-happiness) as his source.\n","lastUpdatedTimestamp":1536688492,"imagesUpdatedTimestamp":1541538839},{"id":"dcbf02ee39ee47529d1c40c44724c3b7","documentType":"visualization","owner":"4958548","title":"Boston Housing data","description":"This program prints a summary of a data table.\n\nThis data is about the housing in Boston, pulling together information like crime rate or nitrogen oxides concentration. \n\nThis data comes from [www.kaggle.com](https://www.kaggle.com/c/boston-housing/data).\n\nThe original data source is cited on the [Overview Site](https://www.kaggle.com/c/boston-housing).\n","lastUpdatedTimestamp":1536689273,"imagesUpdatedTimestamp":1541538851},{"id":"4f3bba4379dc471785c97cc71ec1f2e9","documentType":"visualization","owner":"4958548","title":"Boston Housing data","description":"This program prints a summary of a data table.\n\nThis data is about the housing in Boston, pulling together information like crime rate or nitrogen oxides concentration. \n\nThis data comes from [www.kaggle.com](https://www.kaggle.com/c/boston-housing/data).\n\nThe original data source is cited on the [Overview Site](https://www.kaggle.com/c/boston-housing).\n","imagesUpdatedTimestamp":100},{"id":"5677f4f0e839446ab7dce4fcc43a0fa1","documentType":"visualization","owner":"42813309","title":"World Trades vs Taxes in 2015","description":"This scatter plot shows data about the world trades and taxes for 69 countries in 2015, from [World bank: High-technology exports & Taxes on international trade](https://data.worldbank.org/indicator).\n\nThis graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","lastUpdatedTimestamp":1541040558,"height":500,"imagesUpdatedTimestamp":1541538862},{"id":"5ec8b8ee761649f4957fafac8ac337fd","documentType":"visualization","owner":"42813309","title":"Shapes with SVG and CSS","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"6d4f256783424748b4589e06974aedf6","documentType":"visualization","owner":"42813309","title":"Re-create Marks and Channels Graphic","description":"An example of Re-create 2D Marks & size Channels Graphic.\n\n","lastUpdatedTimestamp":1541040556,"height":500,"imagesUpdatedTimestamp":1541538874},{"id":"5ea75c0c07664900bfa9d1f26ace5e0b","documentType":"visualization","owner":"42813309","title":"Shapes with SVG and CSS","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536694853,"imagesUpdatedTimestamp":1541538885},{"id":"cb64ed8e294b41569bfb49e933e9de96","documentType":"visualization","owner":"19274272","title":"Minimum Wage Scatter Plot","description":"This scatter plot shows data about Minimum Wages, from [Kaggle: US Minimum Wage by State from 1968 to 2017](https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017/home).","lastUpdatedTimestamp":1541647264,"height":500,"imagesUpdatedTimestamp":1541647270},{"id":"e7e8968370d644a887171cb9a20508c9","documentType":"visualization","owner":"42821433","title":"Cars Scatter Plot","description":"This scatter plot shows data about the world trades and taxes for 69 countries in 2015, from [World bank: High-technology exports & Taxes on international trade](https://data.worldbank.org/indicator).\n\nThis graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","lastUpdatedTimestamp":1536714331,"imagesUpdatedTimestamp":1541538908},{"id":"312b8321c2ca41189c02ed5d68a975a9","documentType":"visualization","owner":"26421182","title":"Looping Transitions Example - Ft. Rubiana","description":"An example of looping transitions. Ft. Rubiana.\n\nForked from [Curran's Face](https://vizhub.com/curran/c16c636a99ef478a91f9a67391ddba1d).\n\nForked from [Ming Zhang's Face](https://vizhub.com/Nikkor/593a19fb026547b190b3d07bb6fdec91).\n","lastUpdatedTimestamp":1536750952,"imagesUpdatedTimestamp":1541538920},{"id":"c21f08a18f094d4ab048c733e84eaf59","documentType":"visualization","owner":"42821433","title":"Cars Scatter Plot","description":"This plot shows the average cost of an avocado vs the total volume of avocados sold every week from 2016 to 2018, from [Hass Avocado Board](https://www.kaggle.com/neuromusic/avocado-prices/home).\n\n","lastUpdatedTimestamp":1536714569,"imagesUpdatedTimestamp":1541538932},{"id":"69ab5ffc2cb840f1a106ee1f127cb80d","documentType":"visualization","owner":"42821433","title":"Cars Scatter Plot","description":"This scatter plot shows data about the world trades and taxes for 69 countries in 2015, from [World bank: High-technology exports & Taxes on international trade](https://data.worldbank.org/indicator).\n\nThis graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","imagesUpdatedTimestamp":100},{"id":"551f510c5a9b449ba0c91149a7e30ec5","documentType":"visualization","owner":"42821433","title":"Cars Scatter Plot","description":"This plot shows the average cost of an avocado vs the total volume of avocados sold every week from 2016 to 2018, from [Hass Avocado Board](https://www.kaggle.com/neuromusic/avocado-prices/home).\n\nThanks to Weijie Pang for the Legend code","lastUpdatedTimestamp":1540659979,"height":500,"imagesUpdatedTimestamp":1541538944},{"id":"88154465b445467694c30db5982895c4","documentType":"visualization","owner":"42821433","title":"Avocados Scatter Plot","description":"This plot shows the average cost of an avocado vs the total volume of avocados sold every week from 2016 to 2018, from [Hass Avocado Board](https://www.kaggle.com/neuromusic/avocado-prices/home).\n\nThanks to Weijie Pang for the Legend code","lastUpdatedTimestamp":1536714732,"imagesUpdatedTimestamp":1541538955},{"id":"4355aa9a49d445cc95577c7a72be4732","documentType":"visualization","owner":"42821433","title":"Avocados Scatter Plot","description":"This plot shows the average cost of an avocado vs the total volume of avocados sold every week from 2016 to 2018, from [Hass Avocado Board](https://www.kaggle.com/neuromusic/avocado-prices/home).\n\nThanks to Weijie Pang for the Legend code","lastUpdatedTimestamp":1537921089,"height":500,"imagesUpdatedTimestamp":1541538967},{"id":"c10bfd526c4d49d2a12709df31b8a83f","documentType":"visualization","owner":"34223598","title":"Shapes with SVG and CSS","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541562495,"height":500,"imagesUpdatedTimestamp":1541562552},{"id":"5618e01fb1154f0ba9c93e536e022993","documentType":"visualization","owner":"34223598","title":"Re-create Marks & Channels Graphic!","description":"Re-create Marks & Channels Graphic","lastUpdatedTimestamp":1541562497,"height":500,"imagesUpdatedTimestamp":1541562563},{"id":"1103abab80414a30b27d4517de471a5a","documentType":"visualization","owner":"7958306","title":"Satellites Scatter Plot","description":"This data is about the launch mass of a satellite vs the year it was launched.\n\nThis data comes from the Union Of Concerned Scientists [Data](https://www.ucsusa.org/nuclear-weapons/space-weapons/satellite-database#.W5Hbp-hKhaQ)\n","lastUpdatedTimestamp":1541560178,"height":500,"imagesUpdatedTimestamp":1541560184},{"id":"9c5d2f1f8f2b481f9c2f736f1c667ffa","documentType":"visualization","owner":"30349340","title":"Hello VizHub!","description":"","lastUpdatedTimestamp":1536725650,"imagesUpdatedTimestamp":1541539012},{"id":"38c3ed5b94804f5e8e1deecfbaeabdaa","documentType":"visualization","owner":"13180246","title":"Pokemon Scatter Plot","description":"This scatter plot shows data about a couple of stats of Pokemon from the popular video games. The data was downloaded from the open sourced [PokeAPI](http://pokeapi.co).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554698381,"height":500,"imagesUpdatedTimestamp":1554698392},{"id":"e6b9fbcb3b624272a1b41ff5059f1343","documentType":"visualization","owner":"222798","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536858987,"imagesUpdatedTimestamp":1541539035},{"id":"80116cd367864479a4cfe7956fad77ff","documentType":"visualization","owner":"222798","title":"Daily Promotion and Sales of V-Tech Toys Tmall Flagstore","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis dataset is about promtion and sales for a toys brand called V-tech in tmall.com in 2018.\n\nThis dataset comes from the toys market research project of Rocabin Trade Co., Ltd for 2018 [Promotion and Sales for Tmall Toys Flagstores](https://goo.gl/fts8b9).\n\nThe data is a daily incremental file which will be uploaded daily by Rocabin Trade Co., Ltd. The sample only includes one-day sales volume.","lastUpdatedTimestamp":1536784710,"imagesUpdatedTimestamp":1541539046},{"id":"d8acd8a41bb94f9aa9e5ae89cea94bff","documentType":"visualization","owner":"222798","title":"Daily Price for V-tech in JD Flagsotre","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis dataset is about daily price data for a toys brand called 'V-tech' in JD.com in 2018 for three months between 6/8 and 9/10.\n\nThis data comes from price monitor project of Rocabin Trade Co., Ltd in [JD Toys Price Trend](https://goo.gl/a5HaQ6).\n\nThe data is a set incremental file uploaded periodically by Rocabin Trade Co., Ltd.","lastUpdatedTimestamp":1536784723,"imagesUpdatedTimestamp":1541539057},{"id":"8ecad95a4c7e4281936186819cefa158","documentType":"visualization","owner":"3117142","title":"Borehole Dummy Data - Areachart ","description":"This is dummy data and will be used as a template to represent GSI Borehole data in the future.","lastUpdatedTimestamp":1551195197,"height":500,"imagesUpdatedTimestamp":1551195206},{"id":"e9f334c345514ab6a6af93f5996227e8","documentType":"visualization","owner":"3117142","title":"Color and Size legends","description":"","lastUpdatedTimestamp":1538839792,"height":500,"imagesUpdatedTimestamp":1541539079},{"id":"4a56bb0b1a644a9e94d2016c2b3577ee","documentType":"visualization","owner":"1944891","title":"Curved Lines","description":"Inspired by a figure from chapter 5 of Tamara Munzner's excellent book \"Visualization Analysis and Design\".\n\nPlayed around with [d3indepth's](http://bl.ocks.org/d3indepth/b6d4845973089bc1012dec1674d3aff8) D3 curve explorer to until I got lines that were similar in curvature to what I was reproducing (and took some inspiration from the source code too :) )\n","lastUpdatedTimestamp":1541729967,"height":500,"imagesUpdatedTimestamp":1541729977},{"id":"29e5798bf18a43c294e7c3937b117be6","documentType":"visualization","owner":"5385678","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"00e08dddf8924abda6d5bba2e370e2f5","documentType":"visualization","owner":"5385678","title":"Let's make a face with D3.js!","description":"","lastUpdatedTimestamp":1536770789,"imagesUpdatedTimestamp":1541539113},{"id":"1d163b4f8d224e63a362c2d8ef89e6c8","documentType":"visualization","owner":"19274272","title":"Graph of bars  with one rotated","description":"Recreation of a visualization graphic from chapter 5 of Visualization Analysis & Design by Tamara Munzner (2014).","lastUpdatedTimestamp":1541646716,"height":500,"imagesUpdatedTimestamp":1541646724},{"id":"e2d0b33f91fd48e9b47b13b982612734","documentType":"visualization","owner":"222798","title":"An Exmple for D3.scaleSequential","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis is an example for D3.scaleSequential. The code is inspired by the [scaleSequential example](https://bl.ocks.org/d3indepth/de07fcf34538cd6f8459e17038563ed3) in GitHub - d3/d3-scale","lastUpdatedTimestamp":1536784742,"imagesUpdatedTimestamp":1541539135},{"id":"22b432bf9cbf4234b711c43058ec40c2","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536781243,"imagesUpdatedTimestamp":1541539157},{"id":"7663dc7d731644d48d50306231e898a6","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about top 5 best-selling Mario series video game of all time.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536782130,"imagesUpdatedTimestamp":1541539168},{"id":"de8f8ff7d7dc437f81aec5bc55772523","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about video game franchises that have sold or shipped at least 100 million copies.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1536782383,"imagesUpdatedTimestamp":1541539179},{"id":"fd27f185570448f4b191aa91135d464a","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about self-reported life satisfaction over the world from 2008 to 2016\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538004377,"height":500,"imagesUpdatedTimestamp":1541539190},{"id":"b6e83c0e0df848d0aeb92976b2298198","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about share of people who say they are 'very happy' or 'rather happy'.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/)\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","imagesUpdatedTimestamp":100},{"id":"5a93ea13a1f64f4f8f69123545da636c","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about share of people who say they are 'very happy' or 'rather happy'.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/)\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537846721,"height":500,"imagesUpdatedTimestamp":1541539201},{"id":"41859dbf6af847baa65ea98c83d93f19","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about evolution of happiness inequality within countries during periods ofuninterrupted economic growth.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541650368,"height":500,"imagesUpdatedTimestamp":1541650372},{"id":"2d08d71f717f4b41b576834505873ea2","documentType":"visualization","owner":"43082707","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about share of people who say they are 'very happy' or 'rather happy'.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/)\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541643722,"height":500,"imagesUpdatedTimestamp":1541643727},{"id":"7a97b22973ad4725984afb6959a905ae","documentType":"visualization","owner":"68416","title":"Dishonest Bar Chart","description":"This bar charts does not use a zero baseline. It's an example of how _not_ to use area.\n\nThis bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/c3MCROTNN8g?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538737018,"height":500,"imagesUpdatedTimestamp":1541539223},{"id":"08545c706a8c41bab86c31b80a158ad0","documentType":"visualization","owner":"13540669","title":"Making a Bar Chart","description":"","lastUpdatedTimestamp":1544487566,"height":500,"imagesUpdatedTimestamp":1544487576},{"id":"646998bfa3c74310bac399d2c4eb06b5","documentType":"visualization","owner":"222798","title":"B.Toys Sales: Profit v.s Convert Ratio of Visits","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis scatter plot shows data about the relationship between gross profit v.s. convert ratio of user visits for B.toys sales on JD.com in july\nThis data is from [Daily GMV data for B. Toys on JD.com in 2018](https://goo.gl/z218i5) for research purpose.","lastUpdatedTimestamp":1542701920,"height":500,"imagesUpdatedTimestamp":1542701920},{"id":"ddf959ee4ddf466dbc74233e8c4e4b74","documentType":"visualization","owner":"19274272","title":"Bowl of Fruit Special Cases and Transitions","description":"","lastUpdatedTimestamp":1541646734,"height":500,"imagesUpdatedTimestamp":1541646735},{"id":"df23fb7f6a4644e49c7131bcb89c3d1b","documentType":"visualization","owner":"34223598","title":"Data Table Summary","description":"This data is about crime in chicago - filtered to Dec 2017 (due to size contraints)\n\nThis data comes from the year 2017 from chicago public record:\nhttps://data.cityofchicago.org/Public-Safety/Crimes-2017/d62x-nvdr\n","lastUpdatedTimestamp":1540418746,"height":500,"imagesUpdatedTimestamp":1541539280},{"id":"07c2481a1ba44dfc8b18fb9ca763edaa","documentType":"visualization","owner":"13540669","title":"transitions","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n","lastUpdatedTimestamp":1537038901,"imagesUpdatedTimestamp":1541539291},{"id":"2920b79966c141e7988e3e175b9d6e54","documentType":"visualization","owner":"40272594","title":"Shapes with SVG and CSS","description":"","lastUpdatedTimestamp":1536960392,"imagesUpdatedTimestamp":1541539302},{"id":"59f2534baa13425e96ae969f4d91c24e","documentType":"visualization","owner":"16951176","title":"Temperature in San Francisco Area Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","imagesUpdatedTimestamp":100},{"id":"3e505f559e414d9f9d865cc5c5ba6097","documentType":"visualization","owner":"16951176","title":"Temperature in San Francisco Area Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","imagesUpdatedTimestamp":100},{"id":"fd7d0cd28cd54b128e07328f37587612","documentType":"visualization","owner":"16951176","title":"Ebola victims","description":"This line chart shows ebola victims head count over a year 2015: \nhttps://data.humdata.org/dataset/ebola-cases-2014\n","lastUpdatedTimestamp":1537052098,"imagesUpdatedTimestamp":1541539313},{"id":"2ce03364331845d4952421d7190c8a22","documentType":"visualization","owner":"13914434","title":"Bowl of Fruit - General Update Pattern Special Cases","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IyIAR65G-GQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537314001,"imagesUpdatedTimestamp":1541539325},{"id":"f26d83673fca4d17a7579f3fdba400d6","documentType":"visualization","owner":"68416","title":"Topologica Layers Experiment","description":"An experiment exploring how one might go about optimizing D3 rendering when using unidirectional data flow, using [Topologica.js](https://github.com/datavis-tech/topologica).","lastUpdatedTimestamp":1542568780,"height":500,"imagesUpdatedTimestamp":1542568780},{"id":"65377b92874145e4a7e6acc72decabca","documentType":"visualization","owner":"13540669","title":"transitions","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n","lastUpdatedTimestamp":1537180253,"imagesUpdatedTimestamp":1541539349},{"id":"d0028b53356f4540b4f6afed5d6f40c5","documentType":"visualization","owner":"13914434","title":"Fortune 500 Area Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1537041625,"imagesUpdatedTimestamp":1541539359},{"id":"7bbde87b2061426b94a24432fa584a5f","documentType":"visualization","owner":"13914434","title":"Total Salary in MLB from Season 13~16","description":"This line chart shows total salaries from all the teams in MLB from 2013 ~ 2016. The data comes from [Statcrunch - MLB Team Stats 2013-2016](https://www.statcrunch.com/app/index.php?dataid=1953762).","lastUpdatedTimestamp":1537203253,"imagesUpdatedTimestamp":1541539371},{"id":"1227f0088a2b49d698ddfcd61e47bda2","documentType":"visualization","owner":"40272594","title":"Face With D3","description":"","lastUpdatedTimestamp":1537211047,"imagesUpdatedTimestamp":1541539383},{"id":"8e372133d8bf4d1cbe1084626e79dabd","documentType":"visualization","owner":"40272594","title":"Hello VizHub!","description":"","imagesUpdatedTimestamp":100},{"id":"3d3f9e1739a248e8a63dace2b2126e67","documentType":"visualization","owner":"13180246","title":"Arsenal monthly PPG Line Chart","description":"This line chart shows points per game earned by the Arsenal in the English Premier League over the last 5 seasons. The PPG was averaged over each month. The data comes from [DataHub](https://datahub.io/sports-data/english-premier-league). Code for Legend borrowed from Jon Swanton.","lastUpdatedTimestamp":1554698633,"height":500,"imagesUpdatedTimestamp":1554698634},{"id":"07c37e261ce9497c9eb13043ac95c5f9","documentType":"visualization","owner":"43082707","title":"The Best-selling Video Games of All Time","description":"This bar chart shows the top 10 best-selling video games until now. \nThe data are collected from Wikipedia: [List of best-selling video games](https://en.wikipedia.org/wiki/List_of_best-selling_video_games).","lastUpdatedTimestamp":1537126307,"imagesUpdatedTimestamp":1541539431},{"id":"5726b6ee04114f5d9774f7366aea3b58","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537129598,"imagesUpdatedTimestamp":1541539453},{"id":"43efee6df3124b179762dfdb310890f0","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1537134564,"imagesUpdatedTimestamp":1541539464},{"id":"5ab2069de7504bf8a4d36da0b58fac6c","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537132489,"imagesUpdatedTimestamp":1541539476},{"id":"0aeea8917d6441358d4ad8cfb41e6658","documentType":"visualization","owner":"42813309","title":"Temperature in San Francisco Line Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","imagesUpdatedTimestamp":100},{"id":"e62c642ce33a4df288c13be974a200e0","documentType":"visualization","owner":"42813309","title":"Daily Stock Price for RIG","description":"This area chart shows Highest and Lowest daily stock price for RIG [Yahoo Finance](https://finance.yahoo.com/).","lastUpdatedTimestamp":1541040556,"height":500,"imagesUpdatedTimestamp":1541539487},{"id":"a4184cf7ab674d9da4af4746921f1b19","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537134299,"imagesUpdatedTimestamp":1541539510},{"id":"f20622421a144dcd865b3b1b1635ca52","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","imagesUpdatedTimestamp":100},{"id":"2e5278b1c8d641d1aa26d25c6fead702","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","imagesUpdatedTimestamp":100},{"id":"7d22e2f03d0d4fcbac1ab46df3f0e77a","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","imagesUpdatedTimestamp":100},{"id":"5550469ef310478e8bdb915fd9a42d3d","documentType":"visualization","owner":"43082707","title":"Cars Scatter Plot","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1539214557,"height":500,"imagesUpdatedTimestamp":1541539521},{"id":"bd4a5a446f4b46068e5d33fc1a5d200c","documentType":"visualization","owner":"43082707","title":"Happiness Level Plot","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1541539525,"height":500,"imagesUpdatedTimestamp":1541539533},{"id":"bb484a4860114a09998c8e824572a1a9","documentType":"visualization","owner":"8374102","title":"Total Plays for Top 10 Artists, By Year","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data includes a date (specified as a year and a month) and a play count for each artists in the Top 10. All of the play counts were totalled into a \"total plays\" number for each month, which is what I chose for the visualization. Additionally, the data is shown without any curve fitting, because the rapid fluctuations caused the data to look incorrect (for example, at certain times the data would go below 0 plays).","lastUpdatedTimestamp":1538342448,"height":500,"imagesUpdatedTimestamp":1541539545},{"id":"def5bad02c53459e95bbc5a5f6054ea7","documentType":"visualization","owner":"43082707","title":"Happiness Level Plot","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1541650430,"height":500,"imagesUpdatedTimestamp":1541650437},{"id":"0f9bfce4b18b45c587dbff0c7956aa66","documentType":"visualization","owner":"43082707","title":"Circles and Ellipse","description":"A discrete distribution of circle and ellipse by html.","lastUpdatedTimestamp":1537500401,"height":500,"imagesUpdatedTimestamp":1541539569},{"id":"9d9e94d6dbc14a73abda9c7d4bea3b00","documentType":"visualization","owner":"26421182","title":"Song's Note Scatter Plot","description":"This scatter plot shows the music notes from a MIDI song with filename <1a2d8218a9e42cb6b1cdabd5f54b5c56.mid>, track number #12. Also in [Notes Frequency of a Song](https://vizhub.com/rubens2005/7b2156a5442449b0bc7e68938abedd4f).\n\nThe data comes from the [Million Song Dataset](https://labrosa.ee.columbia.edu/millionsong/).\n\nThis song is 'Poor Little Fool'. More information: [Wikipedia, the free encyclopedia](https://en.wikipedia.org/wiki/Poor_Little_Fool).","lastUpdatedTimestamp":1542220380,"height":500,"imagesUpdatedTimestamp":1542220402},{"id":"5f1c278913e7489baa9bcdfadc91f44d","documentType":"visualization","owner":"43082707","title":"Circles and Ellipse","description":"A discrete distribution of circle and ellipse by css.","lastUpdatedTimestamp":1537139133,"imagesUpdatedTimestamp":1541539591},{"id":"f326529bf98a43c0adf4747f2afb46eb","documentType":"visualization","owner":"43082707","title":"Circles and Ellipse","description":"A discrete distribution of circle and ellipse by css.","lastUpdatedTimestamp":1541650473,"height":500,"imagesUpdatedTimestamp":1541650481},{"id":"2253b8427f484780a95ef2a69696b759","documentType":"visualization","owner":"43082707","title":"Circle and Ellipse","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; 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The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1537496056,"forkedFrom":"ea34ac7eb99343ab87e03c11d6b7ba3f","height":500,"imagesUpdatedTimestamp":1541541106},{"id":"144af1e71ab14e37934242b766c0ca35","documentType":"visualization","owner":"43082707","title":"Beer Consumption in U.S.","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1537496408,"forkedFrom":"3afb5ec83c5940379e2255bab5f19623","height":500,"imagesUpdatedTimestamp":1541541118},{"id":"f82068ea09134a918cda1b1a7756a846","documentType":"visualization","owner":"43082707","title":"Beer Consumption in U.S.","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1537496402,"forkedFrom":"144af1e71ab14e37934242b766c0ca35","height":500,"imagesUpdatedTimestamp":1541541130},{"id":"a9d4b0a32e0e4c2f826b2f5f3ac6083b","documentType":"visualization","owner":"43082707","title":"Beer Consumption in U.S.","description":"This line chart shows Beer consumption per person, measured in litres of ethanol per person per year. (From 1920-1933 there was a ban on the sale of alcoholic beverages (prohibition) in the United States.) \n\nThe data comes from [Our World in Data](https://ourworldindata.org/alcohol-consumption).","lastUpdatedTimestamp":1537496318,"forkedFrom":"37a76ebb0d164838a8667e86b86fc626","height":500,"imagesUpdatedTimestamp":1541541141},{"id":"86c173c9e7574f83b50eb4c91c64f548","documentType":"visualization","owner":"43082707","title":"Beer Consumption in U.S.","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1537496543,"forkedFrom":"f82068ea09134a918cda1b1a7756a846","height":500,"imagesUpdatedTimestamp":1541541152},{"id":"23cda5f3a13b4c05a44e342d8d8e0e5b","documentType":"visualization","owner":"43082707","title":"Beer Consumption in U.S.","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1537496867,"forkedFrom":"86c173c9e7574f83b50eb4c91c64f548","height":500,"imagesUpdatedTimestamp":1541541163},{"id":"e00f7a825b3a49429771cd155d113ffb","documentType":"visualization","owner":"43082707","title":"Beer Consumption in U.S.","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1537496995,"forkedFrom":"23cda5f3a13b4c05a44e342d8d8e0e5b","height":500,"imagesUpdatedTimestamp":1541541174},{"id":"c9a5b4d07a4e4fb2ac7fae8ffebec211","documentType":"visualization","owner":"43082707","title":"Beer Consumption in U.S.","description":"This line chart shows Beer consumption per person, measured in litres of ethanol per person per year. (From 1920-1933 there was a ban on the sale of alcoholic beverages (prohibition) in the United States.) \n\nThe data comes from [Our World in Data](https://ourworldindata.org/alcohol-consumption).","lastUpdatedTimestamp":1541650504,"forkedFrom":"fa2cc204d5204080a6fb4efba34268e2","height":500,"imagesUpdatedTimestamp":1541650515},{"id":"d012722df3394b7db5ef508463c33761","documentType":"visualization","owner":"43082707","title":"Breakfast Bowl - Wish You a Perfect 100 Score","description":"","lastUpdatedTimestamp":1537501221,"forkedFrom":"7f4137a77b564607ae2791ab1e49cf7e","height":500,"imagesUpdatedTimestamp":1541541208},{"id":"0526637fed704440ad7d45a61286239b","documentType":"visualization","owner":"43082707","title":"Breakfast Bowl - Wish You a Perfect 100 Score","description":"","lastUpdatedTimestamp":1537501657,"forkedFrom":"d012722df3394b7db5ef508463c33761","height":500,"imagesUpdatedTimestamp":1541541219},{"id":"057852b510e144a487987bb1b92f3c16","documentType":"visualization","owner":"43082707","title":"Breakfast Bowl - Wish You a Perfect 100 Score","description":"You waked up later than usual, since you studied a long time for the exam today.\n\nWhen you came in to the dining room, you found a dish full of bacon.\n\n\"Hey mom, did you just buy too much bacon yesterday?\"\n\nAnd your mom smiled to you:\"Surprise! Wish you have a 100% score today!\"","lastUpdatedTimestamp":1537502641,"forkedFrom":"0526637fed704440ad7d45a61286239b","height":500,"imagesUpdatedTimestamp":1541541230},{"id":"e61975ad79e2486cb382485608bd465d","documentType":"visualization","owner":"43082707","title":"Breakfast Bowl - Wish You a Perfect 100 Score","description":"You waked up later than usual, since you studied a long time for the exam today.\n\nWhen you came in to the dining room, you found a dish full of bacon.\n\n\"Hey mom, did you just buy too much bacon yesterday?\"\n\nAnd your mom smiled to you:\"Surprise! Wish you have a 100% score today!\"\n\n\"Bacon, avocado, egg -- Bae!\"","lastUpdatedTimestamp":1537502744,"forkedFrom":"057852b510e144a487987bb1b92f3c16","height":500,"imagesUpdatedTimestamp":1541541241},{"id":"f83464dc41514f08930aebeaae6c0e91","documentType":"visualization","owner":"43082707","title":"Breakfast Bowl - Wish You a Perfect 100 Score","description":"You waked up later than usual, since you studied a long time for the exam today.\n\nWhen you came in to the dining room, you found a dish full of bacon.\n\n\"Hey mom, did you just buy too much bacon yesterday?\"\n\nAnd your mom smiled to you:\"Surprise! Wish you have a 100% score today!\"\n\n\"Bacon, avocado, egg -- Bae!\"","lastUpdatedTimestamp":1541650563,"forkedFrom":"e61975ad79e2486cb382485608bd465d","height":500,"imagesUpdatedTimestamp":1541650569},{"id":"99c7f8a2369944dbbbd94ed33f129d5c","documentType":"visualization","owner":"13540669","title":"Circles with for loop","description":"","lastUpdatedTimestamp":1540475054,"forkedFrom":"3e0be7ed387e4028bd623410cddce49a","height":500,"imagesUpdatedTimestamp":1541541263},{"id":"e68b8b2fc7f2462ea577062a4ef22bb0","documentType":"visualization","owner":"40272594","title":"Scatter Plot","description":"","lastUpdatedTimestamp":1537975796,"forkedFrom":"9a5c7c76c43540a2bbc5270edce233de","height":500,"imagesUpdatedTimestamp":1541541274},{"id":"9b218854844b49799683376222747d1d","documentType":"visualization","owner":"40272594","title":"Temperature in Sanfracisco scatter plot","description":"","lastUpdatedTimestamp":1539011100,"forkedFrom":"e68b8b2fc7f2462ea577062a4ef22bb0","height":500,"imagesUpdatedTimestamp":1541541285},{"id":"58b566db03964c0c921ddc2938ab5b13","documentType":"visualization","owner":"27960935","title":"Bowl of Fruit - Click to Select","description":"","lastUpdatedTimestamp":1538430597,"forkedFrom":"9857017449ed40688201d91d79814a6d","height":500,"imagesUpdatedTimestamp":1541541296},{"id":"b0c1dc63e50241518107b28af1957dac","documentType":"visualization","owner":"27960935","title":"Bowl of Fruit - Hover to Select","description":"","lastUpdatedTimestamp":1537559308,"forkedFrom":"58b566db03964c0c921ddc2938ab5b13","height":500,"imagesUpdatedTimestamp":1541541307},{"id":"fcd5c8f344c94d5cafa801e4f8ec8996","documentType":"visualization","owner":"27960935","title":"Let's make a map wit D3.js","description":"","lastUpdatedTimestamp":1537562342,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1541541318},{"id":"7dcce24f007545aea5b0e660dfca2129","documentType":"visualization","owner":"1542597","title":"Bowl of Fruit - General Update Pattern Special Cases","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/IyIAR65G-GQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537567978,"forkedFrom":"7f4137a77b564607ae2791ab1e49cf7e","height":500,"imagesUpdatedTimestamp":1541541329},{"id":"26347901d1f5461c837d086041c877e5","documentType":"visualization","owner":"30349340","title":"Map D3","description":"\n","lastUpdatedTimestamp":1537804158,"forkedFrom":"7cb0852301234723b72c15199c2dc1db","height":500,"imagesUpdatedTimestamp":1541541340},{"id":"f49e13889110456fbe7a22eeebccb121","documentType":"visualization","owner":"30349340","title":"Map D3 Cheap Tricks","description":"\n","lastUpdatedTimestamp":1537731751,"forkedFrom":"26347901d1f5461c837d086041c877e5","height":500,"imagesUpdatedTimestamp":1541541351},{"id":"d3c44789f8454c09ad16a09409e0bd70","documentType":"visualization","owner":"27960935","title":"Cheap Tricks for Interaction","description":"","lastUpdatedTimestamp":1537584326,"forkedFrom":"fcd5c8f344c94d5cafa801e4f8ec8996","height":500,"imagesUpdatedTimestamp":1541541362},{"id":"6cdce7727ece4200a677917dc00fc6bd","documentType":"visualization","owner":"26421182","title":"Song's Note Zoom Scatter Plot","description":"This scatter plot shows the music notes from a MIDI song with filename <1a2d8218a9e42cb6b1cdabd5f54b5c56.mid>, track number #12. Also in [Notes Frequency of a Song](https://vizhub.com/rubens2005/7b2156a5442449b0bc7e68938abedd4f).\n\nThe data comes from the [Million Song Dataset](https://labrosa.ee.columbia.edu/millionsong/).\n\nThis song is 'Poor Little Fool'. More information: [Wikipedia, the free encyclopedia](https://en.wikipedia.org/wiki/Poor_Little_Fool).","lastUpdatedTimestamp":1542220104,"forkedFrom":"9d9e94d6dbc14a73abda9c7d4bea3b00","height":500,"imagesUpdatedTimestamp":1542220104},{"id":"96dbcf6590844f9aa9bc3576ae2b354b","documentType":"visualization","owner":"15847634","title":"Bar Chart with Beautified Axes","description":"","lastUpdatedTimestamp":1538149733,"forkedFrom":"68d2557b5824473196c04e532729a517","height":500,"imagesUpdatedTimestamp":1541541384},{"id":"26c7e3709ee940ce8d1d7fa1827d51fd","documentType":"visualization","owner":"26421182","title":"Song's Note: Start & End","description":"This scatter plot shows the music notes from a MIDI song with filename <1a2d8218a9e42cb6b1cdabd5f54b5c56.mid>, track number #12. Also in [Notes Frequency of a Song](https://vizhub.com/rubens2005/7b2156a5442449b0bc7e68938abedd4f).\n\nThe data comes from the [Million Song Dataset](https://labrosa.ee.columbia.edu/millionsong/).\n\nThis song is 'Poor Little Fool'. More information: [Wikipedia, the free encyclopedia](https://en.wikipedia.org/wiki/Poor_Little_Fool).","lastUpdatedTimestamp":1542219651,"forkedFrom":"6cdce7727ece4200a677917dc00fc6bd","height":500,"imagesUpdatedTimestamp":1542219653},{"id":"64f5eaef03be4a98980382b7cb0bf170","documentType":"visualization","owner":"27960935","title":"HW Map with Selectable Countries","description":"The map visualization has been edited to allow each country to be highlighted and its name to be displayed upon click.\n\n","lastUpdatedTimestamp":1541646381,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541646386},{"id":"135547791fb84088851052e8557d7fd0","documentType":"visualization","owner":"1944891","title":"D3.js Map","description":"","lastUpdatedTimestamp":1541730274,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1541730275},{"id":"322b56ff1c6a49e0b5d312dbf4096a6e","documentType":"visualization","owner":"13914434","title":"Cheap Tricks for Interaction","description":"I have made the minimum function with highligh after clicking. Another extra is the showing the selected/hover country on the text label above the map.","lastUpdatedTimestamp":1541560760,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541560763},{"id":"f6740fefa07d44259408b821a70462e0","documentType":"visualization","owner":"8374102","title":"An Interactive Map with a Dynamic Title","description":"This map allows for multiple countries to be selected (which will be highlighted in red). Hovering over any country with the mouse will show the name of that country above the earth. ","lastUpdatedTimestamp":1538000161,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541451},{"id":"a460e0459c9e40b183ebe7eaffc96452","documentType":"visualization","owner":"16951176","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537641212,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541462},{"id":"74b4fa93693f4698b1f0698c06a4294e","documentType":"visualization","owner":"16951176","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537679051,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541473},{"id":"e11ef65d04084652aae2029e68bddd4b","documentType":"visualization","owner":"43082707","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538004009,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541484},{"id":"e1bdb9b00e3546cda12dba026be3969e","documentType":"visualization","owner":"43082707","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538002738,"forkedFrom":"e11ef65d04084652aae2029e68bddd4b","height":500,"imagesUpdatedTimestamp":1541541495},{"id":"3d2cb687e3424a2dbae4a62b723334d3","documentType":"visualization","owner":"934088","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537647545,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541541506},{"id":"3df37a8fea7541a6b3d9473d7b61b3b3","documentType":"visualization","owner":"43082707","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538002762,"forkedFrom":"e1bdb9b00e3546cda12dba026be3969e","height":500,"imagesUpdatedTimestamp":1541541517},{"id":"e3a7a4e645f14f369c6d612346c162e8","documentType":"visualization","owner":"36131688","title":"Interaction","description":"When hovering on countries, they will all be selected, while when click a specific country, then there will be only one country being selected.\n\nI actually don't tackle the task. I have tried the method in  Bowl of Fruit - Click to Select (https://vizhub.com/NTCHANG1289/17953e2e1e864c4e8ab5a637553dec49), but I didn't successfully get any result from the map.\n\nThe method I used in this one is found on the website [Making a map using D3.js](https://medium.com/@andybarefoot/making-a-map-using-d3-js-8aa3637304ee)\n\nBut I am not really sure: is such method more like the cheap tricks example?\n\nI would say this is the most difficult assignment as to now~~~\n","lastUpdatedTimestamp":1541624955,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541624957},{"id":"dd9638e549644ceb8d9d57397a810829","documentType":"visualization","owner":"26421182","title":"Song List","description":"This program prints a summary of a data table.\n\nThis data comes from [Million Song Dataset](https://labrosa.ee.columbia.edu/millionsong/).","lastUpdatedTimestamp":1540578015,"forkedFrom":"0841328a93af402b8b35414fb3f1731f","height":960,"imagesUpdatedTimestamp":1541541539},{"id":"e903a88155f14823b6475a930d4bb0ea","documentType":"visualization","owner":"13719929","title":"Let's make a face!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538440409,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541541551},{"id":"9c3a641c96304f9294849213e6cabf28","documentType":"visualization","owner":"222798","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537700712,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541562},{"id":"9866a31383aa4898ba8cc3e6f2dffe49","documentType":"visualization","owner":"222798","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538582672,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541573},{"id":"385c2cf720b6421c92b6554b04705abd","documentType":"visualization","owner":"222798","title":"Bowl of Fruit - Click to Select","description":"","lastUpdatedTimestamp":1541033548,"forkedFrom":"c2274b1dfe914115bac48f437b3c104e","height":500,"imagesUpdatedTimestamp":1541541584},{"id":"675fcb96cc7349e0a2659d74e9cd9ae3","documentType":"visualization","owner":"31802591","title":"Bowl of Fruit - Click to Select","description":"","lastUpdatedTimestamp":1537723829,"forkedFrom":"c2274b1dfe914115bac48f437b3c104e","height":500,"imagesUpdatedTimestamp":1541541595},{"id":"bc58487c0aa04118b908df5b883d7c43","documentType":"visualization","owner":"31802591","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537723988,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541606},{"id":"cc53f6a5053a4d02bee73bb13ebe5e3b","documentType":"visualization","owner":"13719929","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1537964886,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541541617},{"id":"2e9dde5d8b3f46e1a664a5aa30da83fa","documentType":"visualization","owner":"42821433","title":"Cheap Tricks for Interaction","description":"","lastUpdatedTimestamp":1540656674,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541541628},{"id":"c110283067634cfb9d51e4938179db61","documentType":"visualization","owner":"34223598","title":"Cheap Tricks for Interaction","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; 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Actual Supply Rate for B.toys sales on JD.com between 7/01 to 9/13/2018 on JD.com\nThis data is from [Daily GMV data for B. Toys on JD.com in 2018](https://drive.google.com/drive/folders/13qN8SCzG8nZijD_kAwTKtZEjELqNpZOF?usp=sharing) for research purpose.","lastUpdatedTimestamp":1538590550,"forkedFrom":"646998bfa3c74310bac399d2c4eb06b5","height":500,"imagesUpdatedTimestamp":1541543448},{"id":"e7b90ff946cd420f88782396a4cd19e5","documentType":"visualization","owner":"4325658","title":"World Countries Tree!","description":"","lastUpdatedTimestamp":1538518392,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541543472},{"id":"2e23341a462045c19a77f74e36eb3610","documentType":"visualization","owner":"42813309","title":" Scatter plot with Interactive Filtering","description":"Kathleen Cachel\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. ","lastUpdatedTimestamp":1538517329,"forkedFrom":"8a2efc7cda684580a881a40ff24ccc53","height":500,"imagesUpdatedTimestamp":1541543483},{"id":"47edf1cbfe174cb9aa1981f3ca6e8179","documentType":"visualization","owner":"31802591","title":"GDP Variation over time plot-2","description":"","lastUpdatedTimestamp":1538520889,"forkedFrom":"f7170a8e02e44d7ca48cbe3ecebc18fb","height":500,"imagesUpdatedTimestamp":1541543494},{"id":"649faf7e28674bc0ac92f58b0f2bc298","documentType":"visualization","owner":"31802591","title":"Tax revenue over time plot","description":"This scatter plot shows the revenue generated through taxes by the United States government from 1800 to 2011. The y-axis reports the revenue as a percentage of GDP and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1538521126,"forkedFrom":"ad919ee2b4fd49ceae6d3b071cbc336f","height":500,"imagesUpdatedTimestamp":1541543506},{"id":"20df7b314fe949558939538351dc9363","documentType":"visualization","owner":"31802591","title":"Tax revenue over time plot","description":"This scatter plot shows the revenue generated through taxes by the United States government from 1800 to 2011. The y-axis reports the revenue as a percentage of GDP and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1538522004,"forkedFrom":"ad919ee2b4fd49ceae6d3b071cbc336f","height":500,"imagesUpdatedTimestamp":1541543518},{"id":"7dc9e70d6b6c472b834d07b10f848cf6","documentType":"visualization","owner":"31802591","title":"Plays over Time","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data is parsed such that there are 10 data points per month, one for each artist in the top 10. Each point represents the number of plays for that artist in that month. The legend (which is sorted from most played to least played, top to bottom) can be clicked on to view only the data points corresponding to that artist.","lastUpdatedTimestamp":1538660735,"forkedFrom":"03645b3956de42e2ad3ff87a2147a0d8","height":500,"imagesUpdatedTimestamp":1541543529},{"id":"974d31f9b4c0410089a91bb132b44dd8","documentType":"visualization","owner":"31802591","title":"Variation of GDP over time for USA and Germany","description":"","lastUpdatedTimestamp":1541356212,"forkedFrom":"731074f540d7474fb47049403d3bf1ef","height":500,"imagesUpdatedTimestamp":1541543541},{"id":"035c782673894127880938dac0016672","documentType":"visualization","owner":"31802591","title":"Plays over Time","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data is parsed such that there are 10 data points per month, one for each artist in the top 10. Each point represents the number of plays for that artist in that month. The legend (which is sorted from most played to least played, top to bottom) can be clicked on to view only the data points corresponding to that artist.","lastUpdatedTimestamp":1538662198,"forkedFrom":"731074f540d7474fb47049403d3bf1ef","height":500,"imagesUpdatedTimestamp":1541543554},{"id":"46ed72df492443098d0ac249e9e6c508","documentType":"visualization","owner":"27960935","title":"prototype scatterplot","description":"This scatterplot shows the correlation between the total graudates and the median salary of 173 graduate programs, grouped by the program category. The data comes from [The Economic Guide To Picking A College Major](https://github.com/fivethirtyeight/data/tree/master/college-majors) by FiveThirtyEight.","lastUpdatedTimestamp":1541640683,"forkedFrom":"03ba87367b344669913828e987c680e0","height":500,"imagesUpdatedTimestamp":1541640691},{"id":"88e3e7cd523e4af191f7c059fb756b26","documentType":"visualization","owner":"1944891","title":"Chloropleth Map","description":"","lastUpdatedTimestamp":1552333910,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1552333918},{"id":"d2a75059640d421799bd360f45fb6e63","documentType":"visualization","owner":"31802591","title":"Plays over Time","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data is parsed such that there are 10 data points per month, one for each artist in the top 10. Each point represents the number of plays for that artist in that month. The legend (which is sorted from most played to least played, top to bottom) can be clicked on to view only the data points corresponding to that artist.","lastUpdatedTimestamp":1538660953,"forkedFrom":"974d31f9b4c0410089a91bb132b44dd8","height":500,"imagesUpdatedTimestamp":1541543589},{"id":"76bd7527cd734cf69fb4b81d4f7a4156","documentType":"visualization","owner":"31802591","title":"Variation of GDP over time for USA and Germany-test3","description":"Data loading concept in index.js used from Nick Lima","lastUpdatedTimestamp":1538661397,"forkedFrom":"974d31f9b4c0410089a91bb132b44dd8","height":500,"imagesUpdatedTimestamp":1541543601},{"id":"526129e9ae3846a8ae99e7bf0e9dbe12","documentType":"visualization","owner":"34223598","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars","lastUpdatedTimestamp":1541562601,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541562663},{"id":"9b2fe8e272c5471885b57ef10e5d08ac","documentType":"visualization","owner":"34223598","title":"Choropleth Map","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OoZ0LWD9KUs?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540441207,"forkedFrom":"d5ad96d1fe8148bd827a25230cc0f083","height":500,"imagesUpdatedTimestamp":1541543625},{"id":"23835c6e20e44a7cbcfe6924af71434d","documentType":"visualization","owner":"34223598","title":"Cars Scatter Plot with horsepower and weight against cylinders","description":"This scatter plot shows data about cars, ","lastUpdatedTimestamp":1541562616,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541562673},{"id":"d6a4486e610b4c25b5982f6abd9e848d","documentType":"visualization","owner":"16951176","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539089264,"forkedFrom":"23835c6e20e44a7cbcfe6924af71434d","height":500,"imagesUpdatedTimestamp":1541543647},{"id":"f1ac62d8b6844fb7915f6ecbe40edfd8","documentType":"visualization","owner":"222798","title":"B.Toys Sales: GMV v.s Actual Supply Rate","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis scatter plot shows data about the relationship between GMV(Gross Merchandise Value) v.s. Actual Supply Rate for B.toys sales on JD.com between 7/01 to 9/13/2018 on JD.com\nThis data is from [Daily GMV data for B. Toys on JD.com in 2018](https://drive.google.com/drive/folders/13qN8SCzG8nZijD_kAwTKtZEjELqNpZOF?usp=sharing) for research purpose.","lastUpdatedTimestamp":1538590561,"forkedFrom":"81b13449ea6a4914ba438ab99a0b0711","height":500,"imagesUpdatedTimestamp":1541543659},{"id":"bcb0355d102a42cabaa383907a2e1b88","documentType":"visualization","owner":"13180246","title":"Pokemon: Special Attack vs Special Defence, by Type","description":"Interactive scatterplot for Special Attack vs. Special Defence of Pokemon from the first three generations, color coded by Pokemon type.\n\nSome data preprocssing code borrowed from Nick Lima.\nCode for title group borrowed from Kathleen Cachel.","lastUpdatedTimestamp":1554699382,"forkedFrom":"5c907e49d0294538aad03ad1f41e1e28","height":500,"imagesUpdatedTimestamp":1554699384},{"id":"c3b300c6fdb944d584d562353f866c01","documentType":"visualization","owner":"36131688","title":"Cars colorlegend","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n","lastUpdatedTimestamp":1540998337,"forkedFrom":"6ed74211f8b142d6933873e2a818bc58","height":500,"imagesUpdatedTimestamp":1541543739},{"id":"83c8f3b752b346a1b621ee333977f224","documentType":"visualization","owner":"830054","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538647440,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541543752},{"id":"7556874c01e843be853e965839c79e79","documentType":"visualization","owner":"68416","title":"Color by Month Example","description":"Kathleen Cachel\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. ","lastUpdatedTimestamp":1543934196,"forkedFrom":"8a2efc7cda684580a881a40ff24ccc53","height":500,"imagesUpdatedTimestamp":1543934203},{"id":"0ed9bc8f58524d99ab2f098902f1e801","documentType":"visualization","owner":"222798","title":"B.Toys Sales: GMV v.s Net Renvenu Rate","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis scatter plot shows data about the relationship between GMV(Gross Merchandise Value) v.s. Net Revenue Rate for B.toys sales between 7/01 to 9/13/2018 on JD.com, one of the biggest customer for Rocabin Trade Co., Ltd who have dedicated to market the US toys brand \"B.\" in China.<br>\nThis data is from [Daily GMV data for B. Toys on JD.com in 2018](https://drive.google.com/drive/folders/13qN8SCzG8nZijD_kAwTKtZEjELqNpZOF?usp=sharing) released by Rocabin Trade Co., Ltd for research purpose.\n<br>\n<br>\n<b>Please note that:</b>\n  <li>Each of the dots represents one product, there are 108 SKU. When you hover the dot, it will display a tooltips containing the product name.</li>\n  <li>All products are <font colour ='red'> categorised into four bands</font>, from D to Z, ordered by the sales performances of the product, which are decided by the overall performance of Rocabin's all customers besides JD.com.(The algorithm how a product falls into one band is confidential).</li>\n\n<b>Acronym</b>\n<li><b>GMV</b>: Gross Merchandise Value, it's a number represents sales performance for the product on JD.com. </li>\n<li><b>MSRP</b>: Manufacturer's Suggested Retail Price, this is usually the full price listed in JD.com. <br>\n<li><b>Net Revenue Rate</b>: This is a rate of MSRP divided by actual supply price, this reflects how much profit the product can bring.  Substracted all the fee, operation cost and promotion charge, this is an actual rate of revenue against MSRP Rocabin receive from JD.com, while the GMV just represents how much front page price the product has been sold in that platform itself.\n","lastUpdatedTimestamp":1541051101,"forkedFrom":"f1ac62d8b6844fb7915f6ecbe40edfd8","height":500,"imagesUpdatedTimestamp":1541543786},{"id":"0052d71ec13a4919b0c818b735f0b107","documentType":"visualization","owner":"31802591","title":"Variation of GDP over time for USA and Germany-test4","description":"","lastUpdatedTimestamp":1538622097,"forkedFrom":"76bd7527cd734cf69fb4b81d4f7a4156","height":500,"imagesUpdatedTimestamp":1541543798},{"id":"41037cb752f8418ebc9ffc81177284b9","documentType":"visualization","owner":"31802591","title":"Variation of GDP over time for USA and Germany-test5","description":"Data loading concept in index.js used from Nick Lima","lastUpdatedTimestamp":1538622098,"forkedFrom":"76bd7527cd734cf69fb4b81d4f7a4156","height":500,"imagesUpdatedTimestamp":1541543809},{"id":"fa2cbe19fcaf4eba9792544bd849853b","documentType":"visualization","owner":"31802591","title":"Plays over Time-test","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data is parsed such that there are 10 data points per month, one for each artist in the top 10. Each point represents the number of plays for that artist in that month. The legend (which is sorted from most played to least played, top to bottom) can be clicked on to view only the data points corresponding to that artist.","lastUpdatedTimestamp":1538621882,"forkedFrom":"731074f540d7474fb47049403d3bf1ef","height":500,"imagesUpdatedTimestamp":1541543820},{"id":"a95e9ce70bcb4b6b911c16d6fd2ebe50","documentType":"visualization","owner":"31802591","title":"Almost Final","description":"","lastUpdatedTimestamp":1538621931,"forkedFrom":"731074f540d7474fb47049403d3bf1ef","height":500,"imagesUpdatedTimestamp":1541543833},{"id":"121d398f924a49709bdcc40514d982bf","documentType":"visualization","owner":"31802591","title":"Almost Final-1","description":"","lastUpdatedTimestamp":1538660049,"forkedFrom":"a95e9ce70bcb4b6b911c16d6fd2ebe50","height":500,"imagesUpdatedTimestamp":1541543845},{"id":"cc8711809cf143698dd9bad3da2f6a50","documentType":"visualization","owner":"31802591","title":"Almost Final-2","description":"","lastUpdatedTimestamp":1538591040,"forkedFrom":"a95e9ce70bcb4b6b911c16d6fd2ebe50","height":500,"imagesUpdatedTimestamp":1541543857},{"id":"a9ac4d06ee074c6f8b4e7fec561dab91","documentType":"visualization","owner":"31802591","title":"Almost Final-4","description":"","lastUpdatedTimestamp":1538592009,"forkedFrom":"a95e9ce70bcb4b6b911c16d6fd2ebe50","height":500,"imagesUpdatedTimestamp":1541543870},{"id":"ab68100aecee4faa934c1373a0e2f9a1","documentType":"visualization","owner":"31802591","title":"Almost Final-5","description":"","lastUpdatedTimestamp":1538621185,"forkedFrom":"a9ac4d06ee074c6f8b4e7fec561dab91","height":500,"imagesUpdatedTimestamp":1541543882},{"id":"ff32329d94544d17b2e3611fc0fcccc7","documentType":"visualization","owner":"1944891","title":"Choropleth Map with Interactive Filtering","description":"","lastUpdatedTimestamp":1549376511,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1549376528},{"id":"1623bbdb17ca483dac822e87b22c75f2","documentType":"visualization","owner":"31802591","title":"Almost Final-6","description":"","lastUpdatedTimestamp":1538622552,"forkedFrom":"ab68100aecee4faa934c1373a0e2f9a1","height":500,"imagesUpdatedTimestamp":1541543905},{"id":"7f9930362cc04ea2bb3be44083a6f00b","documentType":"visualization","owner":"31802591","title":"Almost Final-7","description":"","lastUpdatedTimestamp":1538621123,"forkedFrom":"ab68100aecee4faa934c1373a0e2f9a1","height":500,"imagesUpdatedTimestamp":1541543917},{"id":"18da5212fece42319f9145ad16799aa3","documentType":"visualization","owner":"31802591","title":"Almost Final-8","description":"","lastUpdatedTimestamp":1538660067,"forkedFrom":"7f9930362cc04ea2bb3be44083a6f00b","height":500,"imagesUpdatedTimestamp":1541543929},{"id":"53638af0ef89483b966895ccff2f2c6d","documentType":"visualization","owner":"31802591","title":"Almost Final-9","description":"","lastUpdatedTimestamp":1538622606,"forkedFrom":"18da5212fece42319f9145ad16799aa3","height":500,"imagesUpdatedTimestamp":1541543942},{"id":"ed0aa33225fd48c181d027db1f52d9b3","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1538602841,"forkedFrom":"7f1a219686084bfcbc4f0ddfae5014f7","height":500,"imagesUpdatedTimestamp":1541543954},{"id":"50a9e930472149d6889f27881e622b5d","documentType":"visualization","owner":"1944891","title":"Cars Scatter Plot with Interactive Filtering","description":"This is a fork of Curran's[\"Cars Scatter Plot\"](https://vizhub.com/curran/9247d4d42df74185980f7b1f7504dcc5), modified to add the number of cylinders each car has using color. The legend on the right is interactive. I found that a white background made the middle color spectrum hard to see with the opacity, so I opted for a black background.","lastUpdatedTimestamp":1549376871,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1549376880},{"id":"f95799f2e4bf4456b7e9842818625a17","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1538611483,"forkedFrom":"8542981a662547d394bddced771b37f8","height":500,"imagesUpdatedTimestamp":1541543988},{"id":"50ceea3913ad4665bec0eeaad7868102","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1538617723,"forkedFrom":"f95799f2e4bf4456b7e9842818625a17","height":500,"imagesUpdatedTimestamp":1541543999},{"id":"92e53171bcc94797878e06f5f98654c9","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data is parsed such that there are 10 data points per month, one for each artist in the top 10. Each point represents the number of plays for that artist in that month. The legend (which is sorted from most played to least played, top to bottom) can be clicked on to view only the data points corresponding to that artist.","lastUpdatedTimestamp":1538621996,"forkedFrom":"03645b3956de42e2ad3ff87a2147a0d8","height":500,"imagesUpdatedTimestamp":1541544010},{"id":"55b571cb8584418ab6b49a1b4e0a9925","documentType":"visualization","owner":"31802591","title":"Almost Final-10","description":"","lastUpdatedTimestamp":1538613821,"forkedFrom":"53638af0ef89483b966895ccff2f2c6d","height":500,"imagesUpdatedTimestamp":1541544044},{"id":"40d756749e4044a993ab8599a3e2a83c","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538621663,"forkedFrom":"50aef4494c8a4550a3827ffbfd5f0112","height":800,"imagesUpdatedTimestamp":1541544056},{"id":"b0fbe8e33b5a4e75abb4ca7df3ec4294","documentType":"visualization","owner":"31802591","title":"Almost Final-11","description":"","lastUpdatedTimestamp":1538614760,"forkedFrom":"55b571cb8584418ab6b49a1b4e0a9925","height":500,"imagesUpdatedTimestamp":1541544067},{"id":"464161e761c6496daece739dee2168bd","documentType":"visualization","owner":"31802591","title":"GDP changes: Post World War II","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept (index.js) and update pattern ordering (scatterplot.js) used from Nick Lima's visualization.","lastUpdatedTimestamp":1538688815,"forkedFrom":"b0fbe8e33b5a4e75abb4ca7df3ec4294","height":500,"imagesUpdatedTimestamp":1541544080},{"id":"5ed589c84e90483c94b74b52febaea84","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538616879,"forkedFrom":"e6e1782e79f34e75898c49d4ed50abea","height":500,"imagesUpdatedTimestamp":1541544091},{"id":"0fcb042b65784607b0a08234faf9ec62","documentType":"visualization","owner":"19274272","title":"Cars Scatter Plot 2","description":"This scatter plot shows data about cars with a selectable legend for filtering, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n","lastUpdatedTimestamp":1541646787,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541646802},{"id":"40ee796729c240d39e5a372cda8de0b3","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This program prints a summary of a data table.\n\nThis data is about population of the most populous countries in 2018.\n\nThis data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\nThe data is a \"file\" here, but the recommended approach is to upload the data seprately, as in the video below.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538620808,"forkedFrom":"e6e1782e79f34e75898c49d4ed50abea","height":500,"imagesUpdatedTimestamp":1541544113},{"id":"bb0033f180ed451ab99b51f5d574ea3e","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about deaths from natural disaster over years. The data is from [Our World in Data: Natural Catastrophe](https://ourworldindata.org/natural-catastrophes).\n\nThanks to idea from [Jonathan Wang](https://vizhub.com/wangjonathan/a5b316ad7e34497b86bfb185b5b2549a).","lastUpdatedTimestamp":1541644806,"forkedFrom":"3e4ea32252b641ab98c1864cfd135dc7","height":800,"imagesUpdatedTimestamp":1541644809},{"id":"857cded4540c42248e8cc7c652ddcbed","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538622430,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541544158},{"id":"b6396acca73446f5a8cb1f16b19a307c","documentType":"visualization","owner":"42813309","title":"Exports to US and China ($)","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1541001624,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541544170},{"id":"148ef82385d6469f914b72d653480dbc","documentType":"visualization","owner":"31802591","title":"GDP changes: Post World War II","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept in index.js used from Nick Lima's visualization.","lastUpdatedTimestamp":1538654548,"forkedFrom":"464161e761c6496daece739dee2168bd","height":500,"imagesUpdatedTimestamp":1541544181},{"id":"b49bc53ef5c04a0c9ae2d2f13535b1fa","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about deaths from natural disaster over years. The data is from [Our World in Data: Natural Catastrophe](https://ourworldindata.org/natural-catastrophes).\n","lastUpdatedTimestamp":1541020497,"forkedFrom":"bb0033f180ed451ab99b51f5d574ea3e","height":500,"imagesUpdatedTimestamp":1541544192},{"id":"89b975173ee74343adb76579c2e41918","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about deaths from natural disaster over years. The data is from [Our World in Data: Natural Catastrophe](https://ourworldindata.org/natural-catastrophes).\n\nThanks to idea from [Jonathan Wang](https://vizhub.com/wangjonathan/a5b316ad7e34497b86bfb185b5b2549a).","lastUpdatedTimestamp":1541644717,"forkedFrom":"bb0033f180ed451ab99b51f5d574ea3e","height":800,"imagesUpdatedTimestamp":1541644729},{"id":"b19f28c1eb7543eb9b44b565cabee2b8","documentType":"visualization","owner":"830054","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538652423,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541544227},{"id":"66627dd239e2438cb5bc5794b8a89b20","documentType":"visualization","owner":"31802591","title":"GDP changes: Post World War II","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept in index.js used from Nick Lima's visualization.","lastUpdatedTimestamp":1540909057,"forkedFrom":"464161e761c6496daece739dee2168bd","height":500,"imagesUpdatedTimestamp":1541544238},{"id":"8839906f9f0a448d85f427a8df31c801","documentType":"visualization","owner":"31802591","title":"Choropleth Map with Interactive Filtering","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/E9PhCimWSVQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538653547,"forkedFrom":"5c907e49d0294538aad03ad1f41e1e28","height":500,"imagesUpdatedTimestamp":1541544249},{"id":"85b1fc0bf2644b79b16c848d8b70ee6c","documentType":"visualization","owner":"31802591","title":"Choropleth Map with Interactive Filtering","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/E9PhCimWSVQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538653715,"forkedFrom":"5c907e49d0294538aad03ad1f41e1e28","height":500,"imagesUpdatedTimestamp":1541544260},{"id":"ae2245e6c6cf424bb246616981921172","documentType":"visualization","owner":"31802591","title":"Choropleth Map with Interactive Filtering-1","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/E9PhCimWSVQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538655912,"forkedFrom":"5c907e49d0294538aad03ad1f41e1e28","height":500,"imagesUpdatedTimestamp":1541544271},{"id":"fb264451a6f747768924a1e55ad00b8e","documentType":"visualization","owner":"31802591","title":"New-1","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept and function ordering in index.js used from Nick Lima's visualization. ","lastUpdatedTimestamp":1538687140,"forkedFrom":"035c782673894127880938dac0016672","height":500,"imagesUpdatedTimestamp":1541544282},{"id":"53717dbc17944ef3a108766ca436b43e","documentType":"visualization","owner":"31802591","title":"Tax revenue over time plot","description":"This scatter plot shows the revenue generated through taxes by the United States government from 1800 to 2011. The y-axis reports the revenue as a percentage of GDP and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1538692770,"forkedFrom":"ad919ee2b4fd49ceae6d3b071cbc336f","height":500,"imagesUpdatedTimestamp":1541544294},{"id":"b41b737d3bee413b877d88625020a02a","documentType":"visualization","owner":"31802591","title":"New-2","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept and function ordering in index.js used from Nick Lima's visualization. ","lastUpdatedTimestamp":1538668483,"forkedFrom":"fb264451a6f747768924a1e55ad00b8e","height":500,"imagesUpdatedTimestamp":1541544306},{"id":"fa049d5175c84a269d013afb554ff209","documentType":"visualization","owner":"31802591","title":"New-3","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept and function ordering in index.js used from Nick Lima's visualization. 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","lastUpdatedTimestamp":1538666791,"forkedFrom":"fa049d5175c84a269d013afb554ff209","height":500,"imagesUpdatedTimestamp":1541544329},{"id":"ed6537578b28446a917c4506400168d7","documentType":"visualization","owner":"31802591","title":"New-5","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept and function ordering in index.js used from Nick Lima's visualization. ","lastUpdatedTimestamp":1538687263,"forkedFrom":"fa049d5175c84a269d013afb554ff209","height":500,"imagesUpdatedTimestamp":1541544342},{"id":"0826763d613b4f06824f065b6f611ab0","documentType":"visualization","owner":"31802591","title":"New-6","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept and function ordering in index.js used from Nick Lima's visualization. ","lastUpdatedTimestamp":1538670152,"forkedFrom":"ed6537578b28446a917c4506400168d7","height":500,"imagesUpdatedTimestamp":1541544354},{"id":"6b039d68853f4f289de2f37296a154bb","documentType":"visualization","owner":"1462109","title":"Bowl of Fruit","description":"T","lastUpdatedTimestamp":1538670330,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541544367},{"id":"77b41f8812764b53926f9cb97c875e14","documentType":"visualization","owner":"1462109","title":"Bowl of Fruit","description":"","lastUpdatedTimestamp":1538670375,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1541544378},{"id":"a168cdf9cf9d42d3b339266780461721","documentType":"visualization","owner":"31802591","title":"Version_2_SP","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept (index.js) and function ordering (scatterplot.js) used from Nick Lima's visualization. ","lastUpdatedTimestamp":1538687032,"forkedFrom":"0826763d613b4f06824f065b6f611ab0","height":500,"imagesUpdatedTimestamp":1541544389},{"id":"dd3272d247d843348a6e8023a7c32b9d","documentType":"visualization","owner":"31802591","title":"Version_3_SP","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept (index.js) and function ordering (scatterplot.js) used from Nick Lima's visualization. ","lastUpdatedTimestamp":1538687403,"forkedFrom":"a168cdf9cf9d42d3b339266780461721","height":500,"imagesUpdatedTimestamp":1541544401},{"id":"9e2490c899b947a6b045770752e14bd0","documentType":"visualization","owner":"31802591","title":"Change in GDP: Post World War II","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept (index.js) and update pattern ordering (scatterplot.js) used from Nick Lima's visualization. ","lastUpdatedTimestamp":1538693696,"forkedFrom":"dd3272d247d843348a6e8023a7c32b9d","height":500,"imagesUpdatedTimestamp":1541544414},{"id":"91e71929ad404816b77bdf41137db93c","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1538690603,"forkedFrom":"a5b316ad7e34497b86bfb185b5b2549a","height":500,"imagesUpdatedTimestamp":1541544426},{"id":"c683f9230c27420397394580ee5215d3","documentType":"visualization","owner":"31802591","title":"Change in GDP: Post World War II","description":"This visualization shows the changes in GDP after World War II for US and Germany. \nThe y-axis reports the GDP growth (in percent) and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n\nData loading concept (index.js) and update pattern ordering (scatterplot.js) used from [Nick Lima's visualization.](https://vizhub.com/OxfordComma/03645b3956de42e2ad3ff87a2147a0d8) \n\nOther Sources:\n\nhttps://www.investopedia.com/articles/economics/09/german-economic-miracle.asp\nhttps://eh.net/encyclopedia/the-american-economy-during-world-war-ii/\nhttps://www.cato.org/commentary/1920s-income-tax-cuts-sparked-economic-growth-raised-federal-revenues\nhttps://bradfordtaxinstitute.com/Free_Resources/Federal-Income-Tax-Rates.aspx","lastUpdatedTimestamp":1541476275,"forkedFrom":"9e2490c899b947a6b045770752e14bd0","height":500,"imagesUpdatedTimestamp":1541544438},{"id":"768f057b59294798974868b2b0543237","documentType":"visualization","owner":"13180246","title":"Pitch","description":"","lastUpdatedTimestamp":1541648684,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1541648694},{"id":"534f51af756240739b4eb440d3a1f13e","documentType":"visualization","owner":"1944891","title":"idk","description":"","lastUpdatedTimestamp":1538841233,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541544461},{"id":"09fa323b60e245e3b94d8718bcf1b9f1","documentType":"visualization","owner":"27960935","title":"Scatter Plot with Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541191367,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541544473},{"id":"8efa2fdac9f04ce8952b53555212fc47","documentType":"visualization","owner":"1944891","title":"Cars Scatter Plot","description":"","lastUpdatedTimestamp":1541730597,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541730605},{"id":"b83d532d37c446718bdfbcb336a0a942","documentType":"visualization","owner":"407537","title":"Temperature in San Francisco Line Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1538858227,"forkedFrom":"012b5b20ce894b0fa7dc98ef3a0b43a5","height":500,"imagesUpdatedTimestamp":1541544497},{"id":"7b4015dae6ba415d80e11adc0d211bbb","documentType":"visualization","owner":"31802591","title":"Economic factors over time plot with Menus-Final","description":"This scatter plot shows variation of various economic factors since 1800 to 2011 for the United States. The y-axis and x-axis can be chosen from the menus on top of the plot. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1539205443,"forkedFrom":"ad919ee2b4fd49ceae6d3b071cbc336f","height":500,"imagesUpdatedTimestamp":1541544509},{"id":"a6f5bcacdfa0407b90014c40c1a9682d","documentType":"visualization","owner":"43082707","title":"Natural disaster death over years","description":"This scatter plot shows data about deaths from natural disaster over years. The data is from [Our World in Data: Natural Catastrophe](https://ourworldindata.org/natural-catastrophes).\n\nThanks to idea from [Jonathan Wang](https://vizhub.com/wangjonathan/a5b316ad7e34497b86bfb185b5b2549a).","lastUpdatedTimestamp":1541650689,"forkedFrom":"89b975173ee74343adb76579c2e41918","height":800,"imagesUpdatedTimestamp":1541650691},{"id":"5ddbed93ac5742bca010ac82822bf840","documentType":"visualization","owner":"31802591","title":"Debt over time plot - India","description":"This line chart shows the revenue generated through taxes by the United States government from 1800 to 2011. The y-axis reports the revenue as a percentage of GDP and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).","lastUpdatedTimestamp":1538925332,"forkedFrom":"8093e86b369d4c299996806cb30bccb3","height":500,"imagesUpdatedTimestamp":1541544531},{"id":"15f05480e91d422aaf808a7837e62893","documentType":"visualization","owner":"31802591","title":"Debt over time plot - India","description":"This line chart shows the revenue generated through taxes by the United States government from 1800 to 2011. The y-axis reports the revenue as a percentage of GDP and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).","lastUpdatedTimestamp":1541345015,"forkedFrom":"5ddbed93ac5742bca010ac82822bf840","height":500,"imagesUpdatedTimestamp":1541544543},{"id":"2979bdfe1f1a4a8c9972038f100ecb95","documentType":"visualization","owner":"31802591","title":"Temperature in San Francisco Line Chart","description":"This line chart shows one week of temperature (in degrees Celcius) in San Francisco. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1538925426,"forkedFrom":"012b5b20ce894b0fa7dc98ef3a0b43a5","height":500,"imagesUpdatedTimestamp":1541544555},{"id":"26341eb7de554dbdae04f188f319a575","documentType":"visualization","owner":"31802591","title":"Debt over time plot: India","description":" The line chart shows how the debt situation varied for India starting 1913 to 2011. The idea is to look at the cause-effect relationships between any internal/global economic activities and debt spikes.\n The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).","lastUpdatedTimestamp":1541474882,"forkedFrom":"8093e86b369d4c299996806cb30bccb3","height":500,"imagesUpdatedTimestamp":1541544566},{"id":"98b6d7e5bb634a2983ee6d89f1ee0269","documentType":"visualization","owner":"1944891","title":"Cars Scatter Plot with Interactive Filtering","description":"This is a fork of Curran's[\"Cars Scatter Plot\"](https://vizhub.com/curran/9247d4d42df74185980f7b1f7504dcc5), modified to add the number of cylinders each car has using color. The legend on the right is interactive. I found that a white background made the middle color spectrum hard to see with the opacity, so I opted for a black background.","lastUpdatedTimestamp":1552592180,"forkedFrom":"50a9e930472149d6889f27881e622b5d","height":500,"imagesUpdatedTimestamp":1552592188},{"id":"63df2562d3cb4874b86252701f21d7fb","documentType":"visualization","owner":"31802591","title":"Economic factors over time plot with Menus-test1","description":"This scatter plot shows the revenue generated through taxes by the United States government from 1800 to 2011. The y-axis reports the revenue as a percentage of GDP and the x-axis reports the year. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1538927496,"forkedFrom":"7b4015dae6ba415d80e11adc0d211bbb","height":500,"imagesUpdatedTimestamp":1541544590},{"id":"b91acf134e644026af5da5a3eb5b8110","documentType":"visualization","owner":"8374102","title":"Plays over Time with Dropdown","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data is parsed such that there are 10 data points per month, one for each artist in the top 10. Each point represents the number of plays for that artist in that month. The legend (which is sorted from most played to least played, top to bottom) can be clicked on to view only the data points corresponding to that artist.","lastUpdatedTimestamp":1540173408,"forkedFrom":"03645b3956de42e2ad3ff87a2147a0d8","height":500,"imagesUpdatedTimestamp":1541544602},{"id":"6a2192b9f72f47b78ce399f0ccfa8089","documentType":"visualization","owner":"8374102","title":"Plays over Time with Multiple Dropdowns","description":"This dataset is from my [last.fm profile](https://last.fm/user/philosiphicus) (pulled using [this service](https://mainstream.ghan.nl/export.html)). This data was parsed so that it only includes the number of plays for my Top 10 most listened to artists.\n\nThe data is parsed such that there are 10 data points per month, one for each artist in the top 10. Each point represents the number of plays for that artist in that month. The legend (which is sorted from most played to least played, top to bottom) can be clicked on to view only the data points corresponding to that artist.","lastUpdatedTimestamp":1539218592,"forkedFrom":"b91acf134e644026af5da5a3eb5b8110","height":500,"imagesUpdatedTimestamp":1541544614},{"id":"4349341fed2f425b9396035e79e64953","documentType":"visualization","owner":"8374102","title":"Project: Bar Chart","description":"This data is collected by a service called [last.fm](https://last.fm), which has been aggregating my music plays via iTunes and Spotify since 2008. The data shows the top 20 artists by play count, as tracked by the service. The data was accessed from my [profile](https://last.fm/user/philosiphicus) via a service called [last.fm to csv](https://benjaminbenben.com/lastfm-to-csv/), using my username (philosiphicus).","lastUpdatedTimestamp":1539216982,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1541544626},{"id":"6e613faacdaf4cf1aec2c71483d36eee","documentType":"visualization","owner":"43082707","title":"Annual deaths causes in usa","description":"This scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2016.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1541644793,"forkedFrom":"7f1a219686084bfcbc4f0ddfae5014f7","height":500,"imagesUpdatedTimestamp":1541644798},{"id":"51d9bb0c1b7e48c183233ae4d711b2be","documentType":"visualization","owner":"36267844","title":" Scatter plot with Menus - by Year","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). 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The data was accessed from my [profile](https://last.fm/user/philosiphicus) via a service called [last.fm to csv](https://benjaminbenben.com/lastfm-to-csv/), using my username (philosiphicus).","lastUpdatedTimestamp":1546558484,"forkedFrom":"4f92c793909f48d28012e43ddab716df","height":960,"imagesUpdatedTimestamp":1546558485},{"id":"a3e623ebaf5247baba16394b582fa9e1","documentType":"visualization","owner":"42813309","title":"Scatter Plot with Menu","description":"","lastUpdatedTimestamp":1539009390,"forkedFrom":"a7b998b3978448b889c70ccf5555f7e6","height":500,"imagesUpdatedTimestamp":1541544950},{"id":"2517aef4d5d04bddbfca73150dbad4d6","documentType":"visualization","owner":"42813309","title":"Try Menu","description":"","lastUpdatedTimestamp":1539009244,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541544961},{"id":"364d8f4d3f7148ff8e9025e5fdb8558e","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"","lastUpdatedTimestamp":1539009370,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541544973},{"id":"624b8c59caf0453d8b657a0b020a707a","documentType":"visualization","owner":"40272594","title":"Temperature in Sanfracisco scatter plot","description":"","lastUpdatedTimestamp":1539182289,"forkedFrom":"9b218854844b49799683376222747d1d","height":500,"imagesUpdatedTimestamp":1541544984},{"id":"8b9091ae20374f58a83b296d77d58785","documentType":"visualization","owner":"42813309","title":"Scatter Plot with Menu","description":"","lastUpdatedTimestamp":1539014140,"forkedFrom":"a7b998b3978448b889c70ccf5555f7e6","height":500,"imagesUpdatedTimestamp":1541544995},{"id":"28a4e864a1b7470c9bba78be668b71eb","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"","lastUpdatedTimestamp":1539031221,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541545007},{"id":"e4847792be5c470cb618c1922855fdd7","documentType":"visualization","owner":"68416","title":"Scatter Plot with Menu","description":"","lastUpdatedTimestamp":1539095169,"forkedFrom":"a7b998b3978448b889c70ccf5555f7e6","height":500,"imagesUpdatedTimestamp":1541545019},{"id":"a2bcd8ae7ba243c091f7e18bb1358bd8","documentType":"visualization","owner":"1944891","title":"Horizontal Tree","description":"","lastUpdatedTimestamp":1544110398,"forkedFrom":"0c35fdf97a6042188b5550e6712de315","height":960,"imagesUpdatedTimestamp":1544110402},{"id":"50b311cab7c145a8a5a539c60c5c63cc","documentType":"visualization","owner":"30349340","title":"Hello VizHub!","description":"","lastUpdatedTimestamp":1539026791,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541545042},{"id":"24b0e1a00a5145cd9169e05682a10705","documentType":"visualization","owner":"36267844","title":" Scatter Plot Pollutant (Menu) over Time ","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). 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The data was downloaded from the open sourced [PokeAPI](http://pokeapi.co).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554699867,"forkedFrom":"4aac94131bab40d38973ec12cd2b96fe","height":500,"imagesUpdatedTimestamp":1554699868},{"id":"de197218f8824ac08ed9ffa8c5c18cf2","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"","lastUpdatedTimestamp":1539052437,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541545167},{"id":"27710aad067c4fa5bc7995046ab5f94c","documentType":"visualization","owner":"15912112","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539052323,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541545179},{"id":"a2b0bff1a6e44e2c9c81b2d45a18e837","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"","lastUpdatedTimestamp":1539054173,"forkedFrom":"9247d4d42df74185980f7b1f7504dcc5","height":500,"imagesUpdatedTimestamp":1541545190},{"id":"7a58a89418564300950492a1bcbd11bc","documentType":"visualization","owner":"42813309","title":"Cars Scatter Plot","description":"","lastUpdatedTimestamp":1539055083,"forkedFrom":"a2b0bff1a6e44e2c9c81b2d45a18e837","height":500,"imagesUpdatedTimestamp":1541545202},{"id":"9b166fdf11d74193af78943dca7e8317","documentType":"visualization","owner":"42813309","title":"Exports to Seven Countries with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1541040559,"forkedFrom":"7a58a89418564300950492a1bcbd11bc","height":500,"imagesUpdatedTimestamp":1541545214},{"id":"34c5eaef53b648429cf98181fcaa8d1c","documentType":"visualization","owner":"36131688","title":"Life expectancy vs Happiness Score Scatter Plot with menus","description":"This scatter plot shows the relationship between Life Expectancy and Happiness Score in the 2017 World Happiness Report. 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Toys on JD.com in 2018](https://drive.google.com/drive/folders/13qN8SCzG8nZijD_kAwTKtZEjELqNpZOF?usp=sharing) released by Rocabin Trade Co., Ltd for research purpose.\n<br>\n<br>\n\n<b>Acronym</b>\n<li><b>GMV</b>: Gross Merchandise Value, it's a number represents sales performance for the product on JD.com. </li>\n<li><b>MSRP</b>: Manufacturer's Suggested Retail Price, this is usually the full price listed in JD.com. <br>\n<li><b>Net Revenue Rate</b>: This is a rate of MSRP divided by actual supply price, this reflects how much profit the product can bring.  Substracted all the fee, operation cost and promotion charge, this is an actual rate of revenue against MSRP Rocabin receive from JD.com, while the GMV just represents how much front page price the product has been sold in that platform itself.\n\n<li><b>Gross Merchandise Value</b>：it is a number represents sales performance for the product on JD.com.</li>\n<li><b>SKU</b>: unique product ID\nMSRP: Manufacturer's Suggested Retail Price, this is usually the full price listed in JD.com. \n<li><b>Net Revenue Rate</b>: This is a rate of MSRP divided by actual supply price, this reflects how much profit the product can bring. Substracted all the fee, operation cost and promotion charge, this is an actual rate of revenue against MSRP Rocabin receive from JD.com, while the GMV represents how much front page price the product has been sold in that platform itself.\n<li><b>PV</b>: Page Visits, how many visits for that product\n<li><b>UV</b>: Unique Visits, how many unique people visit for that product, different from PV(page visits), if there is 100 customer visit 300 times(PV) of all product page, the UV would be still 100 \n<li><b>CVT</b>: Convert Visit Ratio, the ratio for how many good actual sold divided by UV(how many customer visits) \n\n","lastUpdatedTimestamp":1541529736,"forkedFrom":"9b166fdf11d74193af78943dca7e8317","height":500,"imagesUpdatedTimestamp":1541545317},{"id":"6a5563af1e904ff589019b1dc0de9dd1","documentType":"visualization","owner":"222798","title":"Exports to Seven Countries with Menus","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis scatter plot shows data about the relationship between GMV(Gross Merchandise Value) v.s. Net Revenue Rate for B.toys sales between 7/01 to 9/13/2018 on JD.com, one of the biggest customer for Rocabin Trade Co., Ltd who have dedicated to market the US toys brand \"B.\" in China.<br>\nThis data is from [Daily GMV data for B. Toys on JD.com in 2018](https://drive.google.com/drive/folders/13qN8SCzG8nZijD_kAwTKtZEjELqNpZOF?usp=sharing) released by Rocabin Trade Co., Ltd for research purpose.\n<br>\n<br>\n<b>Please note that:</b>\n  <li>Each of the dots represents one product, there are 108 SKU. When you hover the dot, it will display a tooltips containing the product name.</li>\n  <li>All products are <font colour ='red'> categorised into four bands</font>, from D to Z, ordered by the sales performances of the product, which are decided by the overall performance of Rocabin's all customers besides JD.com.(The algorithm how a product falls into one band is confidential).</li>\n\n<b>Acronym</b>\n<li><b>GMV</b>: Gross Merchandise Value, it's a number represents sales performance for the product on JD.com. </li>\n<li><b>MSRP</b>: Manufacturer's Suggested Retail Price, this is usually the full price listed in JD.com. <br>\n<li><b>Net Revenue Rate</b>: This is a rate of MSRP divided by actual supply price, this reflects how much profit the product can bring.  Substracted all the fee, operation cost and promotion charge, this is an actual rate of revenue against MSRP Rocabin receive from JD.com, while the GMV just represents how much front page price the product has been sold in that platform itself.\n","lastUpdatedTimestamp":1542701934,"forkedFrom":"0ed9bc8f58524d99ab2f098902f1e801","height":500,"imagesUpdatedTimestamp":1542701942},{"id":"0d02dc86b90741c4be8dfd2e0d536474","documentType":"visualization","owner":"222798","title":"B.Toys Sales: GMV v.s Net Renvenu Rate","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis scatter plot shows data about the relationship between GMV(Gross Merchandise Value) v.s. Net Revenue Rate for B.toys sales between 7/01 to 9/13/2018 on JD.com, one of the biggest customer for Rocabin Trade Co., Ltd who have dedicated to market the US toys brand \"B.\" in China.<br>\nThis data is from [Daily GMV data for B. Toys on JD.com in 2018](https://drive.google.com/drive/folders/13qN8SCzG8nZijD_kAwTKtZEjELqNpZOF?usp=sharing) released by Rocabin Trade Co., Ltd for research purpose.\n<br>\n<br>\n<b>Please note that:</b>\n  <li>Each of the dots represents one product, there are 108 SKU. When you hover the dot, it will display a tooltips containing the product name.</li>\n  <li>All products are <font colour ='red'> categorised into four bands</font>, from D to Z, ordered by the sales performances of the product, which are decided by the overall performance of Rocabin's all customers besides JD.com.(The algorithm how a product falls into one band is confidential).</li>\n\n<b>Acronym</b>\n<li><b>GMV</b>: Gross Merchandise Value, it's a number represents sales performance for the product on JD.com. </li>\n<li><b>MSRP</b>: Manufacturer's Suggested Retail Price, this is usually the full price listed in JD.com. <br>\n<li><b>Net Revenue Rate</b>: This is a rate of MSRP divided by actual supply price, this reflects how much profit the product can bring.  Substracted all the fee, operation cost and promotion charge, this is an actual rate of revenue against MSRP Rocabin receive from JD.com, while the GMV just represents how much front page price the product has been sold in that platform itself.\n","lastUpdatedTimestamp":1539105613,"forkedFrom":"0ed9bc8f58524d99ab2f098902f1e801","height":500,"imagesUpdatedTimestamp":1541545341},{"id":"3c8bdd92e63c41d693c183c7cf3a8e3b","documentType":"visualization","owner":"1944891","title":"Scatter Plot with Menus and Categorical Color Coding","description":"This example forks the cars scatter plot with menus exercise and adds a menu for color coding. In this case some categories were too large to properly display a legend and meaningful colors (e.g. name). So I've limited the legend to categories with less than 12 different values. \n\nIf the category has more than 10 values and is a numeric ordinal value, I opted to display a color scale of 15 values from the bottom of the scale to the top. In this case, clicking on a single color value to filter the points is not useful. It would be better to provide a sliding filter. But for categories with less than 12 values, it's still a useful interaction.","lastUpdatedTimestamp":1554314565,"forkedFrom":"98ba4daacc92442f8d9fd7d91bfd712a","height":500,"imagesUpdatedTimestamp":1554314570},{"id":"01a037063c274bb6a4a975f8737605ae","documentType":"visualization","owner":"3939743","title":"World","description":"This program shows a world map.\n\nThis data is about countries.\n\nThis data comes from [https://unpkg.com/world-atlas@1.1.4/world/50m.json](https://unpkg.com/world-atlas@1.1.4/world/110m.json).\n\n","lastUpdatedTimestamp":1539227381,"forkedFrom":"5f89c1c4b9164832ad9982880a9f018c","height":500,"imagesUpdatedTimestamp":1541545366},{"id":"e94eb925b9134c21afbaa73c7936c285","documentType":"visualization","owner":"42821433","title":"Scatter Plot with Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG). The user can select which columns map to X and Y using dropdown menus.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/MjjYLbShFi8?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539125691,"forkedFrom":"98ba4daacc92442f8d9fd7d91bfd712a","height":500,"imagesUpdatedTimestamp":1541545377},{"id":"badcd642a3fd414186900af955fd4312","documentType":"visualization","owner":"42821433","title":"Scatter Plot with Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG). The user can select which columns map to X and Y using dropdown menus.","lastUpdatedTimestamp":1539127128,"forkedFrom":"e94eb925b9134c21afbaa73c7936c285","height":500,"imagesUpdatedTimestamp":1541545389},{"id":"9e7d0f8a323c489fac2bbf7d3b187130","documentType":"visualization","owner":"42821433","title":"Cars Scatter Plot With Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n","lastUpdatedTimestamp":1539128227,"forkedFrom":"4aa8a73d797f46299367917b11d19197","height":500,"imagesUpdatedTimestamp":1541545401},{"id":"0449c55e54dc4f2eaec6a6456a00c072","documentType":"visualization","owner":"42821433","title":"Cars Scatter Plot With Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n","lastUpdatedTimestamp":1540657657,"forkedFrom":"9e7d0f8a323c489fac2bbf7d3b187130","height":500,"imagesUpdatedTimestamp":1541545413},{"id":"2df78366c3324b31b252a4d0cbea0ea6","documentType":"visualization","owner":"42821433","title":"Avocados Scatter Plot With Menus","description":"This scatter plot shows data about Avocados, from [Hass Avocado](https://www.kaggle.com/neuromusic/avocado-prices/home).\n\n","lastUpdatedTimestamp":1539129257,"forkedFrom":"0449c55e54dc4f2eaec6a6456a00c072","height":500,"imagesUpdatedTimestamp":1541545425},{"id":"7650c118b0cd4fc2ab71fae64858e18f","documentType":"visualization","owner":"42821433","title":"Avocados Scatter Plot With Menus","description":"This scatter plot shows data about Avocados, from [Hass Avocado](https://www.kaggle.com/neuromusic/avocado-prices/home).\n\n","lastUpdatedTimestamp":1540660215,"forkedFrom":"2df78366c3324b31b252a4d0cbea0ea6","height":500,"imagesUpdatedTimestamp":1541545436},{"id":"3ce8802ce2de450f93495db2f6cca347","documentType":"visualization","owner":"19274272","title":"Minimum Wage Scatter Plot with Interaction Menus","description":"This scatter plot shows data about Minimum Wages, from [Kaggle: US Minimum Wage by State from 1968 to 2017](https://www.kaggle.com/lislejoem/us-minimum-wage-by-state-from-1968-to-2017/home).","lastUpdatedTimestamp":1541646789,"forkedFrom":"cb64ed8e294b41569bfb49e933e9de96","height":500,"imagesUpdatedTimestamp":1541646813},{"id":"364c64b8e42a42eabd03ac688765c970","documentType":"visualization","owner":"7958306","title":"Cars Scatter Plot with Menu","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).","lastUpdatedTimestamp":1541560321,"forkedFrom":"d68a5b6c60a8424983e87ac65e8b63d8","height":500,"imagesUpdatedTimestamp":1541560330},{"id":"59dd2e82f4d047e69628d4eb5a428721","documentType":"visualization","owner":"34223598","title":"Scatter Plot with Menu by mfr(legends)","description":"This plot gives the info on how cereals with sugar content (sugar being the most important ingredient to cut down for healthy diet) are rated.\nThis scatter plot shows data about cereals, from https://perso.telecom-paristech.fr/eagan/class/igr204/datasets","lastUpdatedTimestamp":1541562618,"forkedFrom":"23835c6e20e44a7cbcfe6924af71434d","height":500,"imagesUpdatedTimestamp":1541562684},{"id":"06a2703ebd6e4cc49cc7fadd463faa1f","documentType":"visualization","owner":"34223598","title":"Let's make a map with D3.js!","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Qw6uAg3EO64?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540443230,"forkedFrom":"c5475d7c95d348d5b8268012fbccb728","height":500,"imagesUpdatedTimestamp":1541545484},{"id":"4ae2f08585944b26947da7955de43c34","documentType":"visualization","owner":"34223598","title":"Let's make a map with D3.js!","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Qw6uAg3EO64?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540443229,"forkedFrom":"06a2703ebd6e4cc49cc7fadd463faa1f","height":500,"imagesUpdatedTimestamp":1541545495},{"id":"e3d79211f1d2476090ae5033b714007c","documentType":"visualization","owner":"19274272","title":"Final Project Week 1","description":"","lastUpdatedTimestamp":1541646825,"forkedFrom":"82a552e5ead14efcbedfb8b58e556e3a","height":500,"imagesUpdatedTimestamp":1541646826},{"id":"ad01c91a872948f08c7f200ff1780eef","documentType":"visualization","owner":"222798","title":"B.Toys Sales on JD.com Between July 1st to Sep 13th","description":"[Ming Zhang](https://vizhub.com/Nikkor)\n<br>\nThis scatter plot shows data about the relationship between all data column submitted in B.toys sales between 7/01 to 9/13/2018 on JD.com, one of the biggest customer for Rocabin Trade Co., Ltd who have dedicated to market the US toys brand \"B.\" in China.<br>\nThis data is from [Daily GMV data for B. Toys on JD.com in 2018](https://drive.google.com/drive/folders/13qN8SCzG8nZijD_kAwTKtZEjELqNpZOF?usp=sharing) released by Rocabin Trade Co., Ltd for research purpose.\n<br>\n<br>\n\n<b>Acronym</b>\n<li><b>GMV</b>: Gross Merchandise Value, it's a number represents sales performance for the product on JD.com. </li>\n<li><b>MSRP</b>: Manufacturer's Suggested Retail Price, this is usually the full price listed in JD.com. <br>\n<li><b>Net Revenue Rate</b>: This is a rate of MSRP divided by actual supply price, this reflects how much profit the product can bring.  Substracted all the fee, operation cost and promotion charge, this is an actual rate of revenue against MSRP Rocabin receive from JD.com, while the GMV just represents how much front page price the product has been sold in that platform itself.\n\n<li><b>Gross Merchandise Value</b>：it is a number represents sales performance for the product on JD.com.</li>\n<li><b>SKU</b>: unique product ID\nMSRP: Manufacturer's Suggested Retail Price, this is usually the full price listed in JD.com. \n<li><b>Net Revenue Rate</b>: This is a rate of MSRP divided by actual supply price, this reflects how much profit the product can bring. Substracted all the fee, operation cost and promotion charge, this is an actual rate of revenue against MSRP Rocabin receive from JD.com, while the GMV represents how much front page price the product has been sold in that platform itself.\n<li><b>PV</b>: Page Visits, how many visits for that product\n<li><b>UV</b>: Unique Visits, how many unique people visit for that product, different from PV(page visits), if there is 100 customer visit 300 times(PV) of all product page, the UV would be still 100 \n<li><b>CVT</b>: Convert Visit Ratio, the ratio for how many good actual sold divided by UV(how many customer visits) \n\n","lastUpdatedTimestamp":1542937943,"forkedFrom":"d8d75559eb794a259d5e50f9c84b9d11","height":500,"imagesUpdatedTimestamp":1542937946},{"id":"c51e94d83048417bb4919c56454c84ba","documentType":"visualization","owner":"3939743","title":"Legend","description":"A legend","lastUpdatedTimestamp":1539636244,"forkedFrom":"7f4137a77b564607ae2791ab1e49cf7e","height":500,"imagesUpdatedTimestamp":1541545529},{"id":"396be57196cf4e3c8c567b3a8a1f4b3d","documentType":"visualization","owner":"30754010","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539193958,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1541545540},{"id":"4c04d16397764fa9a1abd619c953e0e2","documentType":"visualization","owner":"1944891","title":"Event Sequences","description":"Each row represents a sequence of GUI widgets from the root widget to the inner-most child widget. Clicking on a node will highlight the event sequence it belongs to and popup a description of the sequence (IDs starting from root to child nodes). Future versions will need a more diverse dataset and scrolling to allow for hundreds of sequences.","lastUpdatedTimestamp":1549376570,"forkedFrom":"a2bcd8ae7ba243c091f7e18bb1358bd8","height":960,"imagesUpdatedTimestamp":1549376572},{"id":"0f125064a2a1417caca444fbf9f4a250","documentType":"visualization","owner":"31802591","title":"Economic factors over time plot with Menus","description":"This scatter plot shows variation of various economic factors since 1800 to 2011 for the United States. The y-axis and x-axis can be chosen from the menus on top of the plot. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1539204593,"forkedFrom":"7b4015dae6ba415d80e11adc0d211bbb","height":500,"imagesUpdatedTimestamp":1541545562},{"id":"7cdf7937da6a446c9bb583e558951e1e","documentType":"visualization","owner":"31802591","title":"Economic factors over time plot with Menus","description":"This scatter plot shows variation of various economic factors since 1800 to 2011 for the United States. The y-axis and x-axis can be chosen from the menus on top of the plot. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1539205766,"forkedFrom":"0f125064a2a1417caca444fbf9f4a250","height":500,"imagesUpdatedTimestamp":1541545574},{"id":"d058edf930ae49209d94d0fb7b0627fe","documentType":"visualization","owner":"5986793","title":"Scatterplot test","description":"Scatterplot test.\nThis is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).","lastUpdatedTimestamp":1542259942,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":800,"imagesUpdatedTimestamp":1542259950},{"id":"2c8a0e40f8814580889e8b8a4f577d59","documentType":"visualization","owner":"31802591","title":"Economic factors over time for US: Menus","description":"This scatter plot shows variation of various economic factors since 1800 to 2011 for the United States. The y-axis and x-axis can be chosen from the menus on top of the plot. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1541476275,"forkedFrom":"7b4015dae6ba415d80e11adc0d211bbb","height":500,"imagesUpdatedTimestamp":1541545598},{"id":"c8281a0b2bdf4992b12f1da2dfffd6d9","documentType":"visualization","owner":"43082707","title":"Happiness Level Line Chart","description":"Chuchen Dai - forked from [Cars Scatter Plot](https://vizhub.com/curran/9247d4d42df74185980f7b1f7504dcc5) by [Curran Kelleher](https://vizhub.com/curran).\n\nThis scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2017.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life Satisfaction](https://ourworldindata.org/happiness-and-life-satisfaction/).","lastUpdatedTimestamp":1541650860,"forkedFrom":"d54d480c913d40058edac1e4eb072ea6","height":500,"imagesUpdatedTimestamp":1541650867},{"id":"f71c5fb11b074b798e858f0e48f342e2","documentType":"visualization","owner":"42821433","title":"So... here is the Earth","description":"","lastUpdatedTimestamp":1541522400,"forkedFrom":"90fbf3a8da1e4f2c9c149ee135c000cc","height":500,"imagesUpdatedTimestamp":1541545622},{"id":"f57222b98ce844f8aedd0393a24fb664","documentType":"visualization","owner":"42821433","title":"So... here is the Earth","description":"","lastUpdatedTimestamp":1540583780,"forkedFrom":"1fc4ecf7b30d4678a26089818c4eaf47","height":500,"imagesUpdatedTimestamp":1541545633},{"id":"50d4d933d44d496f9e702866ec451302","documentType":"visualization","owner":"42821433","title":"Let's make a map with D3.js!","description":"","lastUpdatedTimestamp":1539215469,"forkedFrom":"ae49aeb6a87c4f60ba5f25f5eed5992d","height":500,"imagesUpdatedTimestamp":1541545644},{"id":"431117a07f644c26a15d8609111f83b1","documentType":"visualization","owner":"42813309","title":"Economic factors over time plot with Menus-Final-test","description":"This scatter plot shows variation of various economic factors since 1800 to 2011 for the United States. The y-axis and x-axis can be chosen from the menus on top of the plot. The dataset comes from the [IMF Website](http://www.imf.org/external/np/fad/histdb/).\n","lastUpdatedTimestamp":1539210309,"forkedFrom":"2c8a0e40f8814580889e8b8a4f577d59","height":500,"imagesUpdatedTimestamp":1541545655},{"id":"fceafcbdf51b4b3b8bc30f54a68f4525","documentType":"visualization","owner":"42813309","title":"Imports with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1539711704,"forkedFrom":"9b166fdf11d74193af78943dca7e8317","height":500,"imagesUpdatedTimestamp":1541545667},{"id":"d3d7c348be2846d68864ef4b4ee6d616","documentType":"visualization","owner":"31802591","title":"Avocados Scatter Plot With Menus","description":"This scatter plot shows data about Avocados, from [Hass Avocado](https://www.kaggle.com/neuromusic/avocado-prices/home).\n\n","lastUpdatedTimestamp":1539212996,"forkedFrom":"7650c118b0cd4fc2ab71fae64858e18f","height":500,"imagesUpdatedTimestamp":1541545679},{"id":"4f6eeed6456f45e7b8ae4d90bd663c8f","documentType":"visualization","owner":"31802591","title":"Exports to Seven Countries with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1539214056,"forkedFrom":"9b166fdf11d74193af78943dca7e8317","height":500,"imagesUpdatedTimestamp":1541545691},{"id":"eacb892f312e40058de96164ca66e50a","documentType":"visualization","owner":"42821433","title":"Let's make a map with D3.js!","description":"","lastUpdatedTimestamp":1539216377,"forkedFrom":"50d4d933d44d496f9e702866ec451302","height":500,"imagesUpdatedTimestamp":1541545703},{"id":"f6aa0ec4c0c74797ada62b8b4fdaa76c","documentType":"visualization","owner":"42813309","title":"Imports with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1539228505,"forkedFrom":"fceafcbdf51b4b3b8bc30f54a68f4525","height":500,"imagesUpdatedTimestamp":1541545714},{"id":"ed497d093b9d4fc5a5772e7d7b326fb9","documentType":"visualization","owner":"42813309","title":"Data Table Summary","description":"This data is about the exports to China, United States, Japan, Brazil, Russia, India and Mexico between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).\n\nThe reporters several continents, important economy groups and countries. The continents reports are Africa, Asia excluding Hong Kong re-exports, Australia and New Zealand, Europe, Middle East, North America, South and Central America and the Caribbean. The economy groups are Commonwealth of Independent States (CIS), including associate and former member States, European Union (28), Four East Asian traders. And country reporters are Brazil, Japan, Mexico, Russian Federation.\n\nThe exports data is divided into six indicators. 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The data is from January 2010 through August 2018. \n\n</n> Thanks to Jonathan for helping me dubug my code.","lastUpdatedTimestamp":1541458979,"forkedFrom":"24b0e1a00a5145cd9169e05682a10705","height":500,"imagesUpdatedTimestamp":1541545814},{"id":"026757bd9705434fbf6e95279fae84d1","documentType":"visualization","owner":"68416","title":" Scatter Plot Convert to Line plot ","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. \n\n</n> Thanks to Jonathan for helping me dubug my code.","lastUpdatedTimestamp":1539415565,"forkedFrom":"82dd576d8a79417ebada48135eb88877","height":500,"imagesUpdatedTimestamp":1541545826},{"id":"c852ba822da7480b8457f24bbe0645d8","documentType":"visualization","owner":"31802591","title":"Choropleth Map with Interactive Filtering","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/E9PhCimWSVQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1541344983,"forkedFrom":"5c907e49d0294538aad03ad1f41e1e28","height":500,"imagesUpdatedTimestamp":1541545849},{"id":"5336d63ed3b34b03b59b0a123dce2934","documentType":"visualization","owner":"31802591","title":"Project","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/9ZB1EgaJnBU?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539541116,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1541545860},{"id":"3fa6151458db468aa78107fad3e9bf58","documentType":"visualization","owner":"31802591","title":"Project: Map Interaction Assignment Final","description":"","lastUpdatedTimestamp":1539780173,"forkedFrom":"687f4f39b5fe4977853a183f1388bb6f","height":500,"imagesUpdatedTimestamp":1541545871},{"id":"28e5a9df05a74bd5aa09880d87223a06","documentType":"visualization","owner":"27960935","title":"Project prototype 10/17","description":"This scatter plot shows basic earning and labor force information about workers graduated from various majors. The data was collected and summarized by [FiveThirtyEight](https://github.com/fivethirtyeight/data/tree/master/college-majors).\n","lastUpdatedTimestamp":1541646462,"forkedFrom":"09fa323b60e245e3b94d8718bcf1b9f1","height":500,"imagesUpdatedTimestamp":1541646463},{"id":"7b43111ad27841bfaaf39eea5177e6bd","documentType":"visualization","owner":"36267844","title":"Multiple Line plot ","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. \n\n</n> Inspiration from Curran and this [Vis](https://twitter.com/ZLabe/status/1050606472361472007).","lastUpdatedTimestamp":1541459019,"forkedFrom":"82dd576d8a79417ebada48135eb88877","height":500,"imagesUpdatedTimestamp":1541545894},{"id":"6a5ae7fecac04e0bb862e6b489520261","documentType":"visualization","owner":"13180246","title":"Pitch","description":"","lastUpdatedTimestamp":1541567614,"forkedFrom":"768f057b59294798974868b2b0543237","height":500,"imagesUpdatedTimestamp":1541567621},{"id":"2546209d161e4294802c4ac0098bebc2","documentType":"visualization","owner":"68416","title":"Line Chart with Multiple Lines","description":"This line chart shows one week of temperature (in degrees Celcius) in cities around the world. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/xFI-us1moj0?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1561714632,"forkedFrom":"012b5b20ce894b0fa7dc98ef3a0b43a5","height":500,"imagesUpdatedTimestamp":1562323041},{"id":"d0b0c26981044b40b63b528dd8cde5f4","documentType":"visualization","owner":"26421182","title":"Blank Canvas","description":"","lastUpdatedTimestamp":1542989491,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":700,"imagesUpdatedTimestamp":1542989493},{"id":"c226083567374542a660fc540de21c39","documentType":"visualization","owner":"26421182","title":"Update and Enter - Another Take","description":"","lastUpdatedTimestamp":1542904712,"forkedFrom":"d0b0c26981044b40b63b528dd8cde5f4","height":500,"imagesUpdatedTimestamp":1542904721},{"id":"cebe2fbb21f348ce8c5f3426a38436ee","documentType":"visualization","owner":"68416","title":"Multiple Line plot ","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. \n\n</n> Inspiration from Curran and this [Vis](https://twitter.com/ZLabe/status/1050606472361472007).","lastUpdatedTimestamp":1539621814,"forkedFrom":"7b43111ad27841bfaaf39eea5177e6bd","height":500,"imagesUpdatedTimestamp":1541545951},{"id":"e91a75d11efc488595c730cdeab93235","documentType":"visualization","owner":"3939743","title":"Circles!","description":"Circles","lastUpdatedTimestamp":1539625554,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":700,"imagesUpdatedTimestamp":1541545962},{"id":"e018aa0596d7412b85151c04ca67841a","documentType":"visualization","owner":"68416","title":"Multiple Line plot (refactored)","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. \n\n</n> Inspiration from Curran and this [Vis](https://twitter.com/ZLabe/status/1050606472361472007).","lastUpdatedTimestamp":1539624877,"forkedFrom":"cebe2fbb21f348ce8c5f3426a38436ee","height":500,"imagesUpdatedTimestamp":1541545973},{"id":"6ea665e254e54279b9e54530a6b5181c","documentType":"visualization","owner":"31802591","title":"Project: Primary-Balance Testing","description":"","lastUpdatedTimestamp":1539627233,"forkedFrom":"3fa6151458db468aa78107fad3e9bf58","height":500,"imagesUpdatedTimestamp":1541545985},{"id":"59d652ba786941048486cd3a929f92af","documentType":"visualization","owner":"42813309","title":"Imports with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1539633773,"forkedFrom":"fceafcbdf51b4b3b8bc30f54a68f4525","height":500,"imagesUpdatedTimestamp":1541545996},{"id":"8abe3aa0cd51443493706c6cf2f657cb","documentType":"visualization","owner":"42813309","title":"World Trades vs Taxes in 2015","description":"This scatter plot shows data about the world trades and taxes for 69 countries in 2015, from [World bank: High-technology exports & Taxes on international trade](https://data.worldbank.org/indicator).\n\nThis graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","lastUpdatedTimestamp":1539631853,"forkedFrom":"5677f4f0e839446ab7dce4fcc43a0fa1","height":0,"imagesUpdatedTimestamp":1541546008},{"id":"cdb5800311a44b5194fad4e52d1f00ca","documentType":"visualization","owner":"42813309","title":"World Trades vs Taxes in 2015","description":"This scatter plot shows data about the world trades and taxes for 69 countries in 2015, from [World bank: High-technology exports & Taxes on international trade](https://data.worldbank.org/indicator).\n\nThis graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","lastUpdatedTimestamp":1539694947,"forkedFrom":"5677f4f0e839446ab7dce4fcc43a0fa1","height":500,"imagesUpdatedTimestamp":1541546020},{"id":"26badaf6efcc45938abd8f1ee9512154","documentType":"visualization","owner":"42813309","title":"Imports with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1539712411,"forkedFrom":"fceafcbdf51b4b3b8bc30f54a68f4525","height":500,"imagesUpdatedTimestamp":1541546031},{"id":"306ba5ce6c4f400d9bbbffea9523cf74","documentType":"visualization","owner":"3939743","title":"City Temperatures","description":"Data\n","lastUpdatedTimestamp":1539726982,"forkedFrom":"2546209d161e4294802c4ac0098bebc2","height":500,"imagesUpdatedTimestamp":1541546043},{"id":"7f5584c5c1cf45cf86c0cc01acf1f875","documentType":"visualization","owner":"5986793","title":"Dirk's Points","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n","lastUpdatedTimestamp":1539800168,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541546055},{"id":"6b200fd09c2342bebc9025a676f8d022","documentType":"visualization","owner":"7958306","title":"Satellites Launches per Year(Final Project)","description":"This line chart the total number of satellites launched per year\n\n\nThis data comes from the Union Of Concerned Scientists [Data](https://www.ucsusa.org/nuclear-weapons/space-weapons/satellite-database#.W5Hbp-hKhaQ)","lastUpdatedTimestamp":1541560349,"forkedFrom":"917e528cabca453b95a4a38b96cb0083","height":500,"imagesUpdatedTimestamp":1541560354},{"id":"844f528247aa443e8224cba12b750245","documentType":"visualization","owner":"34223598","title":"Scatter Plot with Menu by mfr(legends)","description":"This plot gives the info on how cereals with sugar content (sugar being the most important ingredient to cut down for healthy diet) are rated.\nThis scatter plot shows data about cereals, from https://perso.telecom-paristech.fr/eagan/class/igr204/datasets","lastUpdatedTimestamp":1541469815,"forkedFrom":"59dd2e82f4d047e69628d4eb5a428721","height":500,"imagesUpdatedTimestamp":1541546089},{"id":"8f873aa8339f41219e890938103c1045","documentType":"visualization","owner":"43432021","title":"Assignment3","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Qw6uAg3EO64?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539667814,"forkedFrom":"c5475d7c95d348d5b8268012fbccb728","height":500,"imagesUpdatedTimestamp":1541546100},{"id":"4455f021cc194a6ca07c388bce29566d","documentType":"visualization","owner":"43432021","title":"Assignment3","description":"","lastUpdatedTimestamp":1539671427,"forkedFrom":"8f873aa8339f41219e890938103c1045","height":500,"imagesUpdatedTimestamp":1541546111},{"id":"5190e8c46b744882a0751b92b6c8f14a","documentType":"visualization","owner":"1700044","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539841211,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541546122},{"id":"c7488b27438241cb86a6c8fc8ea889ab","documentType":"visualization","owner":"36131688","title":"Project in progress - WEEK 2","description":"The schedule this week is:\nLoad data onto the Vizhub and plot the state map.\nThis week I set up the map base I am going to use in the future.\nAlso tried the tricks zooming/ spanning and hovering on the 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and taxes for 69 countries in 2015, from [World bank: High-technology exports & Taxes on international trade](https://data.worldbank.org/indicator).\n\nThis graph shows the relationship between the High-technology exports (% of manufactured exports) vs the Taxes on international trade (% of revenue) for different income group.","lastUpdatedTimestamp":1539710704,"forkedFrom":"cdb5800311a44b5194fad4e52d1f00ca","height":500,"imagesUpdatedTimestamp":1541546192},{"id":"91439ca403ed4cec9f39fd484fbc362c","documentType":"visualization","owner":"42813309","title":"Imports with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1539711836,"forkedFrom":"26badaf6efcc45938abd8f1ee9512154","height":500,"imagesUpdatedTimestamp":1541546203},{"id":"cf0a555074c84362b31cca87e952e8cc","documentType":"visualization","owner":"42813309","title":"Imports with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1539711929,"forkedFrom":"91439ca403ed4cec9f39fd484fbc362c","height":500,"imagesUpdatedTimestamp":1541546215},{"id":"57e85d282601412ebf61ea56a6f34e5b","documentType":"visualization","owner":"42813309","title":"Imports with Menus","description":"This data is about the exports to China and United States between 2000 and 2016.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1541014093,"forkedFrom":"fceafcbdf51b4b3b8bc30f54a68f4525","height":500,"imagesUpdatedTimestamp":1541546227},{"id":"5a03cfd2ced04e13974dd365cc61e7d2","documentType":"visualization","owner":"42813309","title":"Imports from 2000 to 2016","description":"This data is about the exports to China, United States, Brazil, India, Japan, Mexico and Russian Federation between 2000 and 2016, reporters are Africa, Asia excluding Hong Kong re-exports, Australia and New Zealand, Commonwealth of Independent States (CIS), including associate and former member States, Europe, European Union (28), Four East Asian traders, Middle East, North America, South and Central America and the Caribbean, Brazil, Japan, Mexico, Russian Federation.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1540302728,"forkedFrom":"26badaf6efcc45938abd8f1ee9512154","height":500,"imagesUpdatedTimestamp":1541546239},{"id":"f00eee7c7a214912b39ff68faa07a339","documentType":"visualization","owner":"42813309","title":"Imports from 2000 to 2016","description":"Remark: Hover on the points, Click on the legent\n\nThis data is about the exports to China, United States, Brazil, India, Japan, Mexico and Russian Federation between 2000 and 2016, reporters are Africa, Asia excluding Hong Kong re-exports, Australia and New Zealand, Commonwealth of Independent States (CIS), including associate and former member States, Europe, European Union (28), Four East Asian traders, Middle East, North America, South and Central America and the Caribbean, Brazil, Japan, Mexico, Russian Federation.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1541040559,"forkedFrom":"5a03cfd2ced04e13974dd365cc61e7d2","height":500,"imagesUpdatedTimestamp":1541546251},{"id":"899e03557d9548bca01be986a27a8f72","documentType":"visualization","owner":"19274272","title":"Final Project Week 2","description":"please click on the states","lastUpdatedTimestamp":1541646827,"forkedFrom":"e3d79211f1d2476090ae5033b714007c","height":500,"imagesUpdatedTimestamp":1541646848},{"id":"1e9d50e2da1048a495aabfc1ef1e4db0","documentType":"visualization","owner":"42813309","title":"Imports from 2000 to 2016","description":"This data is about the exports to China, United States, Brazil, India, Japan, Mexico and Russian Federation between 2000 and 2016, reporters are Africa, Asia excluding Hong Kong re-exports, Australia and New Zealand, Commonwealth of Independent States (CIS), including associate and former member States, Europe, European Union (28), Four East Asian traders, Middle East, North America, South and Central America and the Caribbean, Brazil, Japan, Mexico, Russian Federation.\n\nAll data comes from  [World Trade Organization: Network of world merchandise trade](http://stat.wto.org/StatisticalProgram/WSDBViewData.aspx?Language=E).","lastUpdatedTimestamp":1541040560,"forkedFrom":"f00eee7c7a214912b39ff68faa07a339","height":500,"imagesUpdatedTimestamp":1541546275},{"id":"2de2b0f456ad4235b1c5973dc637b56b","documentType":"visualization","owner":"43082707","title":"Happiness Level Line Chart","description":"Chuchen Dai - forked from [Cars Scatter Plot](https://vizhub.com/curran/9247d4d42df74185980f7b1f7504dcc5) by [Curran Kelleher](https://vizhub.com/curran).\n\nThis scatter plot shows data about self-reported life satisfaction over the world from 2004 to 2017.\n\nThis data comes from the World Happiness Report 2017 in [Our World in Data: Happiness and Life 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encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539965065,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1541546641},{"id":"3d2df168aa924ae483ae469a3223a049","documentType":"visualization","owner":"36131688","title":"Project in progress","description":"The schedule this week is:\nLoad data onto the Vizhub and plot the state map.\nThis week I set up the map base I am going to use in the future.\nAlso tried the tricks zooming/ spanning and hovering on the map!\n","lastUpdatedTimestamp":1541236403,"forkedFrom":"c7488b27438241cb86a6c8fc8ea889ab","height":500,"imagesUpdatedTimestamp":1541546652},{"id":"501f3fe24cfb4e6785ac75008b530a83","documentType":"visualization","owner":"68416","title":"Selecting a Year on a Line Chart","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/TGbkLXypPOE?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1542355815,"forkedFrom":"ecb0793c7d674100b3e3133d92cb6957","height":500,"imagesUpdatedTimestamp":1542355825},{"id":"6ba0052c70d34e87b76e3f5f26873b20","documentType":"visualization","owner":"14291661","title":"Cars Report","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1539959749,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541546676},{"id":"91796f58d71e473c966cfd1e4736761d","documentType":"visualization","owner":"14291661","title":"Hello VizHub!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540035712,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541546687},{"id":"b993b2dcd58d4e759e631a4bdf067417","documentType":"visualization","owner":"14291661","title":"Customizing Axes","description":"","lastUpdatedTimestamp":1540038830,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1541546698},{"id":"c68ce9bda2c544a0b11437e8e37046f8","documentType":"visualization","owner":"14291661","title":"Customizing Axes","description":"Data about cars from UCI Machine Learning Repository","lastUpdatedTimestamp":1540040142,"forkedFrom":"b993b2dcd58d4e759e631a4bdf067417","height":500,"imagesUpdatedTimestamp":1541546709},{"id":"5b49061f37d043d586d4840e2a8b6ccd","documentType":"visualization","owner":"36267844","title":"Multiple Line plot with Dropdown ","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. \n\n</n> Inspiration from Curran and this [Vis](https://twitter.com/ZLabe/status/1050606472361472007). This week I spent time munging the data and adding a dropdown menu.","lastUpdatedTimestamp":1541459051,"forkedFrom":"9d912dfaf5e545e5856d42aeb3d1dd9c","height":500,"imagesUpdatedTimestamp":1541546721},{"id":"eb049ed7021a492a8b5929eaadbabd69","documentType":"visualization","owner":"36267844","title":"Multiple Pollutant Line plot Data load","description":"Kathleen Cachel\n\nData in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). The data is from January 2010 through August 2018. \n\n</n> Inspiration from Curran and this [Vis](https://twitter.com/ZLabe/status/1050606472361472007).","lastUpdatedTimestamp":1541459098,"forkedFrom":"9d912dfaf5e545e5856d42aeb3d1dd9c","height":500,"imagesUpdatedTimestamp":1541546732},{"id":"b94eaa0afa4f4bf198a447c033010083","documentType":"visualization","owner":"44307925","title":"HTML Cars Report","description":"","lastUpdatedTimestamp":1540063172,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1541546744},{"id":"17f1850d51b344809a602066a713b243","documentType":"visualization","owner":"44307925","title":"Shapes with SVG and CSS","description":"","lastUpdatedTimestamp":1540064970,"forkedFrom":"b94eaa0afa4f4bf198a447c033010083","height":500,"imagesUpdatedTimestamp":1541546755},{"id":"d39c70bf98ba4783bff607c9920915e4","documentType":"visualization","owner":"44307925","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540072692,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1541546766},{"id":"26b9e73c91ed43449ed5da789133cacf","documentType":"visualization","owner":"44307925","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540093263,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1541546777},{"id":"caa3c87d1314441890659f1fcea69676","documentType":"visualization","owner":"44307925","title":"Auto MPG Summary","description":"This program prints a summary of a data table.\n\nThis data is about cars.\n\nThis data comes from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1540086542,"forkedFrom":"5f89c1c4b9164832ad9982880a9f018c","height":500,"imagesUpdatedTimestamp":1541546788},{"id":"0168f9ed27394eba91011121d8c29859","documentType":"visualization","owner":"44307925","title":"Customizing Axes","description":"This bar chart shows population of the top 10 most populous countries. 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I also added tooltips, shout out to Jonathan who's week 5 scatter plot inspired the tooltips.\n\n</n> Data in this scatter plot is from from the Kings College London Air Quality Dataset in the [London Datastore](https://data.london.gov.uk/dataset/london-average-air-quality-levels). 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This shows a scatterplot.\n\n","lastUpdatedTimestamp":1540475908,"forkedFrom":"4746598efc3540a09cae81aa6d652348","height":850,"imagesUpdatedTimestamp":1541547287},{"id":"8c0b15711fc14fb9911f71f10bca6cc6","documentType":"visualization","owner":"31802591","title":"Project | last part","description":"","lastUpdatedTimestamp":1540435589,"forkedFrom":"2856d7670c264e1dafb8509139b585cb","height":500,"imagesUpdatedTimestamp":1541547299},{"id":"d10bbf36326b4bb3929cccd296b860a3","documentType":"visualization","owner":"34223598","title":"Chicago Crime Data Analysis","description":"Locate high crime regions ","lastUpdatedTimestamp":1541562634,"forkedFrom":"979019ff3f8a4f3b82f51086b5c12e41","height":500,"imagesUpdatedTimestamp":1541562739},{"id":"6d11c064d4b3431981aee5d2f2a46844","documentType":"visualization","owner":"31802591","title":"Project | Interaction","description":"","lastUpdatedTimestamp":1540434906,"forkedFrom":"8c0b15711fc14fb9911f71f10bca6cc6","height":500,"imagesUpdatedTimestamp":1541547321},{"id":"f3f72ba3c2074f8ead535d299bf625a1","documentType":"visualization","owner":"31802591","title":"Project | last part-resubmit","description":"","lastUpdatedTimestamp":1540435553,"forkedFrom":"8c0b15711fc14fb9911f71f10bca6cc6","height":500,"imagesUpdatedTimestamp":1541547333},{"id":"3f6c8c7a927b41c7a0271df4d4c93c45","documentType":"visualization","owner":"31802591","title":"Project | Interaction | Re-submit","description":"","lastUpdatedTimestamp":1540435756,"forkedFrom":"8c0b15711fc14fb9911f71f10bca6cc6","height":500,"imagesUpdatedTimestamp":1541547345},{"id":"d9414309150c4bc18c0f6e9840b35ca6","documentType":"visualization","owner":"31802591","title":"Project | Interaction | Re-submit","description":"","lastUpdatedTimestamp":1541474939,"forkedFrom":"3f6c8c7a927b41c7a0271df4d4c93c45","height":500,"imagesUpdatedTimestamp":1541547357},{"id":"ff22c3c3f63746a5b73ab36b7df26a91","documentType":"visualization","owner":"34223598","title":"Chicago Crime Data Analysis","description":"Chicago Crime Data Analysis","lastUpdatedTimestamp":1541565237,"forkedFrom":"64fb0c9cf25f4a48aef9340c57151a33","height":500,"imagesUpdatedTimestamp":1541565237},{"id":"895cf4155937449b9f0d6d321b630520","documentType":"visualization","owner":"34223598","title":"Starbucks Locations","description":"Starbucks Locations across US\nsource: https://gist.github.com/dankohn/09e5446feb4a8faea24f#file-starbucks_us_locations-csv\n\nDataset contains 3 columns:\nlocation address, longitude and latitude info.","lastUpdatedTimestamp":1541562640,"forkedFrom":"ff22c3c3f63746a5b73ab36b7df26a91","height":500,"imagesUpdatedTimestamp":1541562761},{"id":"9ffdc7eb6d624e93bd3c766eb020e74c","documentType":"visualization","owner":"10935763","title":"Temperature Scatterplot","description":"Testing.   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The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/xFI-us1moj0?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","forkedFrom":"2546209d161e4294802c4ac0098bebc2","height":500,"imagesUpdatedTimestamp":100},{"id":"6b54be1dff8947f0b4e75e9f855c6b86","documentType":"visualization","owner":"40879277","title":"Line Chart with Multiple Lines","description":"This line chart shows one week of temperature (in degrees Celcius) in cities around the world. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/xFI-us1moj0?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","forkedFrom":"2546209d161e4294802c4ac0098bebc2","height":500,"imagesUpdatedTimestamp":100},{"id":"0e22ec42774344cebf86f6a38120820a","documentType":"visualization","owner":"40879277","title":"Line Chart with Multiple Lines","description":"This line chart shows one week of temperature (in degrees Celcius) in cities around the world. 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The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).","lastUpdatedTimestamp":1543680116,"forkedFrom":"012b5b20ce894b0fa7dc98ef3a0b43a5","height":500,"imagesUpdatedTimestamp":1543705995},{"id":"23111bff623b41f0b07a0a6790b0a859","documentType":"visualization","owner":"35175560","title":"Bowl of Fruit, Interaction on Click","description":"# General Update Pattern","lastUpdatedTimestamp":1543755702,"forkedFrom":"487c1e1a753f466e976337c2fb57811e","height":500,"imagesUpdatedTimestamp":1543755709},{"id":"9d384047045646bb86efd5f699014450","documentType":"visualization","owner":"7783512","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1543706017},{"id":"c31a39ccbeb447eeaea7166899cbfda8","documentType":"visualization","owner":"35175560","title":"Nested Elements","description":"# Nested Elements","lastUpdatedTimestamp":1543733191,"forkedFrom":"23111bff623b41f0b07a0a6790b0a859","height":500,"imagesUpdatedTimestamp":1543733201},{"id":"6133326f996644199a1e11b2c1980422","documentType":"visualization","owner":"13540669","title":"Sad Face","description":"","lastUpdatedTimestamp":1543762767,"forkedFrom":"e9ddeb98d91a459780d7c940aa55e2b3","height":500,"imagesUpdatedTimestamp":1543762778},{"id":"82ce55cab9904fd2bcb2b9c67327826e","documentType":"visualization","owner":"13540669","title":"Smiley Face","description":"","lastUpdatedTimestamp":1545165814,"forkedFrom":"6133326f996644199a1e11b2c1980422","height":500,"imagesUpdatedTimestamp":1545165818},{"id":"601e1cd0866f4dc1a31eee301fd6217c","documentType":"visualization","owner":"38759753","title":"Fault Localization 9 load csv","description":"","lastUpdatedTimestamp":1543711300,"forkedFrom":"b7f746bef79f4d0ab3a96d4b0d468d9c","height":500,"imagesUpdatedTimestamp":1543711307},{"id":"4882d3c12184499eb829e688d29e3119","documentType":"visualization","owner":"37315922","title":"Shapes with SVG and CSS","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543719902,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1543719913},{"id":"101ce348d1c7496aa71ee7ad82d486ea","documentType":"visualization","owner":"37315922","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543719333,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1543719341},{"id":"c2095fffcb20448c8ea2432d7b0b2f78","documentType":"visualization","owner":"35175560","title":"Nested Elements with transition","description":"# Nested Elements","lastUpdatedTimestamp":1544006434,"forkedFrom":"c31a39ccbeb447eeaea7166899cbfda8","height":500,"imagesUpdatedTimestamp":1544006436},{"id":"e6798e2ab92b4416b86d9dc132261fb9","documentType":"visualization","owner":"3939743","title":"Understanding the zoom on d3.js","description":"","lastUpdatedTimestamp":1543744874,"forkedFrom":"2442a2e4479f43efbee3ea7bbbf8a11d","height":500,"imagesUpdatedTimestamp":1543744878},{"id":"ae64a8601ad1412c95de88e6f2e5b6ad","documentType":"visualization","owner":"8682525","title":"181202_01_Viewport","description":"This example is used to understand the SVG viewport and viewbox - Ludo;\n\nFor French Speakers, I recommand to read [this article](\"https://la-cascade.io/comprendre-svg-viewbox-et-viewport/\")\n\nImprovements : \n\t- Style of lines are defined in CSS.\n\t- Number of Horizontal and Vertical are parameter\n\t- For automatic Display, number of lines and viewbox dimensions are now linked. \n\t- Viewbox dimensions is not hardcoded anymore\n\n\nThis is based in [SVG Path Tutorial • Easy to Understand!](https://www.youtube.com/watch?v=2IY-xTCFjiM&t=12s), made by [Roberto Matthews](https://www.youtube.com/channel/UCuCSEj8vWEiyz1ZthraU-9g).\n","lastUpdatedTimestamp":1543755515,"forkedFrom":"18951805197b43869b202ad7f14880dc","height":900,"imagesUpdatedTimestamp":1543755517},{"id":"6fa6a6ec74f34cecbd5ec3983f0cc3d1","documentType":"visualization","owner":"8682525","title":"181202_02_ Divide window","description":"Goals : \n\t- Divide a svg element in 4 sub svg elements\n\nElements to grasp :\n\n\nThe D3 [Select module](https://github.com/d3/d3-selection/blob/master/README.md#select) : understand how DOM elements can be created, selected, modified.\n\n\nHow [View port](https://la-cascade.io/comprendre-svg-viewbox-et-viewport/) works for french speakers.\n\n\n\nThis example is used to understand the SVG viewport and viewbox - Ludo;\n\n\nImprovements since the start : \n\t- Style of lines are defined in CSS.\n\t- Number of Horizontal and Vertical are parameter\n\t- For automatic Display, number of lines and viewbox dimensions are now linked. \n\t- Viewbox dimensions is not hardcoded anymore\n\n\nThis is based in [SVG Path Tutorial • Easy to Understand!](https://www.youtube.com/watch?v=2IY-xTCFjiM&t=12s), made by [Roberto Matthews](https://www.youtube.com/channel/UCuCSEj8vWEiyz1ZthraU-9g).\n","lastUpdatedTimestamp":1543759767,"forkedFrom":"ae64a8601ad1412c95de88e6f2e5b6ad","height":600,"imagesUpdatedTimestamp":1543759777},{"id":"71ab237c110e47f1a53cc681da2992cb","documentType":"visualization","owner":"35175560","title":"Bowl of Fruit, Interaction click","description":"# General Update Pattern","lastUpdatedTimestamp":1543756660,"forkedFrom":"23111bff623b41f0b07a0a6790b0a859","height":500,"imagesUpdatedTimestamp":1543756670},{"id":"bd7673d563ea4d148446112816400027","documentType":"visualization","owner":"35175560","title":"Bowl of Fruit, Highlight on Hover","description":"# General Update Pattern","lastUpdatedTimestamp":1543756869,"forkedFrom":"71ab237c110e47f1a53cc681da2992cb","height":500,"imagesUpdatedTimestamp":1543756880},{"id":"c114effc902741f0bb6d43c5cef18d49","documentType":"visualization","owner":"8682525","title":"181202_02_ Divide window_01","description":"Goals : \n\t- Divide a svg element in 4 sub svg elements\n\nElements to grasp :\n\n\nThe D3 [Select module](https://github.com/d3/d3-selection/blob/master/README.md#select) : understand how DOM elements can be created, selected, modified.\n\n\nHow [View port](https://la-cascade.io/comprendre-svg-viewbox-et-viewport/) works for french speakers.\n\n\n\nThis example is used to understand the SVG viewport and viewbox - Ludo;\n\n\nImprovements since the start : \n\t- Style of lines are defined in CSS.\n\t- Number of Horizontal and Vertical are parameter\n\t- For automatic Display, number of lines and viewbox dimensions are now linked. \n\t- Viewbox dimensions is not hardcoded anymore\n\n\nThis is based in [SVG Path Tutorial • Easy to Understand!](https://www.youtube.com/watch?v=2IY-xTCFjiM&t=12s), made by [Roberto Matthews](https://www.youtube.com/channel/UCuCSEj8vWEiyz1ZthraU-9g).\n","lastUpdatedTimestamp":1543762533,"forkedFrom":"6fa6a6ec74f34cecbd5ec3983f0cc3d1","height":600,"imagesUpdatedTimestamp":1543762534},{"id":"c426ddb0f20948cda6934008e62c00fa","documentType":"visualization","owner":"8682525","title":"181202_02_ Put some balls in ","description":"Goals : \n\t- Divide a svg element in 4 sub svg elements\n\nElements to grasp :\n\n\nThe D3 [Select module](https://github.com/d3/d3-selection/blob/master/README.md#select) : understand how DOM elements can be created, selected, modified.\n\n\nHow [View port](https://la-cascade.io/comprendre-svg-viewbox-et-viewport/) works for french speakers.\n\n\n\nThis example is used to understand the SVG viewport and viewbox - Ludo;\n\n\nImprovements since the start : \n\t- Style of lines are defined in CSS.\n\t- Number of Horizontal and Vertical are parameter\n\t- For automatic Display, number of lines and viewbox dimensions are now linked. \n\t- Viewbox dimensions is not hardcoded anymore\n\n\nThis is based in [SVG Path Tutorial • Easy to Understand!](https://www.youtube.com/watch?v=2IY-xTCFjiM&t=12s), made by [Roberto Matthews](https://www.youtube.com/channel/UCuCSEj8vWEiyz1ZthraU-9g).\n","lastUpdatedTimestamp":1543764194,"forkedFrom":"c114effc902741f0bb6d43c5cef18d49","height":600,"imagesUpdatedTimestamp":1543764196},{"id":"aa78098627794f1abf84c52ba3634d9e","documentType":"visualization","owner":"8682525","title":"181202_02_ Put some balls in ","description":"Goals : \n\t- Divide a svg element in 4 sub svg elements\n\nElements to grasp :\n\n\nThe D3 [Select module](https://github.com/d3/d3-selection/blob/master/README.md#select) : understand how DOM elements can be created, selected, modified.\n\n\nHow [View port](https://la-cascade.io/comprendre-svg-viewbox-et-viewport/) works for french speakers.\n\n\n\nThis example is used to understand the SVG viewport and viewbox - Ludo;\n\n\nImprovements since the start : \n\t- Style of lines are defined in CSS.\n\t- Number of Horizontal and Vertical are parameter\n\t- For automatic Display, number of lines and viewbox dimensions are now linked. \n\t- Viewbox dimensions is not hardcoded anymore\n\n\nThis is based in [SVG Path Tutorial • Easy to Understand!](https://www.youtube.com/watch?v=2IY-xTCFjiM&t=12s), made by [Roberto Matthews](https://www.youtube.com/channel/UCuCSEj8vWEiyz1ZthraU-9g).\n","lastUpdatedTimestamp":1543838244,"forkedFrom":"c426ddb0f20948cda6934008e62c00fa","height":600,"imagesUpdatedTimestamp":1543867885},{"id":"a6d1044626164bfbb2eb59be1abc327f","documentType":"visualization","owner":"8682525","title":"181202_04_ Nice hoevering effect","description":"Goals : \n\t- Divide a svg element in 4 sub svg elements\n\nElements to grasp :\n\n\nThe D3 [Select module](https://github.com/d3/d3-selection/blob/master/README.md#select) : understand how DOM elements can be created, selected, modified.\n\n\nHow [View port](https://la-cascade.io/comprendre-svg-viewbox-et-viewport/) works for french speakers.\n\n\n\nThis example is used to understand the SVG viewport and viewbox - Ludo;\n\n\nImprovements since the start : \n\t- Style of lines are defined in CSS.\n\t- Number of Horizontal and Vertical are parameter\n\t- For automatic Display, number of lines and viewbox dimensions are now linked. \n\t- Viewbox dimensions is not hardcoded anymore\n\n\nThis is based in [SVG Path Tutorial • Easy to Understand!](https://www.youtube.com/watch?v=2IY-xTCFjiM&t=12s), made by [Roberto Matthews](https://www.youtube.com/channel/UCuCSEj8vWEiyz1ZthraU-9g).\n","lastUpdatedTimestamp":1543783365,"forkedFrom":"aa78098627794f1abf84c52ba3634d9e","height":600,"imagesUpdatedTimestamp":1543783374},{"id":"8740c80d99ed4e86bc22208571cdc81e","documentType":"visualization","owner":"8682525","title":"181202_05_Nested circles","description":"","lastUpdatedTimestamp":1543828686,"forkedFrom":"a6d1044626164bfbb2eb59be1abc327f","height":600,"imagesUpdatedTimestamp":1543867896},{"id":"ac49db958648416f80bda66b80964311","documentType":"visualization","owner":"68416","title":"Thumbnails Tree","description":"A first test visualization of the forks tree of VizHub, including thumbnails.","lastUpdatedTimestamp":1562156593,"forkedFrom":"4f92c793909f48d28012e43ddab716df","height":500,"imagesUpdatedTimestamp":1562323091},{"id":"e506822f9ad94da9894dc81ff833e07f","documentType":"visualization","owner":"29545122","title":"Fruit: Click to Select ","description":"","lastUpdatedTimestamp":1545241212,"forkedFrom":"9857017449ed40688201d91d79814a6d","height":500,"imagesUpdatedTimestamp":1545241215},{"id":"5e5c65aa0ef84d3abefee0388a8e9b3b","documentType":"visualization","owner":"8682525","title":"Buble game","description":"For the fun of embedding a ve first timeideo for th [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/h2eKImKZviw\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1544000652,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1544000661},{"id":"88ca5b544bd64d5d9b0d8d67b70694b5","documentType":"visualization","owner":"29545122","title":"Fruit: Hover Gets Ya Selected","description":"","lastUpdatedTimestamp":1543872811,"forkedFrom":"e506822f9ad94da9894dc81ff833e07f","height":500,"imagesUpdatedTimestamp":1543872811},{"id":"6e0920c95f3340d9b787aca9bc8ff3f5","documentType":"visualization","owner":"18582087","title":"Scatter Plot with Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG). The user can select which columns map to X and Y using dropdown menus.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/MjjYLbShFi8?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543822343,"forkedFrom":"98ba4daacc92442f8d9fd7d91bfd712a","height":500,"imagesUpdatedTimestamp":1543867953},{"id":"ac2a68f06b0f4742b3f42df236680f0a","documentType":"visualization","owner":"18582087","title":"Scatter Plot with Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG). The user can select which columns map to X and Y using dropdown menus.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/MjjYLbShFi8?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543827732,"forkedFrom":"6e0920c95f3340d9b787aca9bc8ff3f5","height":500,"imagesUpdatedTimestamp":1543867964},{"id":"09567f8e2b634554bdc4fcad3f5e8394","documentType":"visualization","owner":"18582087","title":"Car Sales","description":"This program prints a summary of a data table.\n\nThis data is about cars.\n\nThis data comes from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543828204,"forkedFrom":"639bd95c10c7450fbc56596ea22fce0c","height":500,"imagesUpdatedTimestamp":1543867975},{"id":"cf183817e3c4437d85aa523f1b734de3","documentType":"visualization","owner":"18582087","title":"Mass/Height","description":"This program prints a summary of a data table.\n\nThis data is about cars.\n\nThis data comes from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543827676,"forkedFrom":"175b3fbf2d304e5a99a9a9b0bc6362a5","height":500,"imagesUpdatedTimestamp":1543867986},{"id":"6dbbb551accf4cf8a576af26a3cb5ecb","documentType":"visualization","owner":"18582087","title":"Mass/Height","description":"This program prints a summary of a data table.\n\nThis data Mass and Height of 500 different people with their gender stated.\n\nThis data comes from [Kaggle](https://www.kaggle.com/mustafaali96/weight-height).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543984202,"forkedFrom":"cf183817e3c4437d85aa523f1b734de3","height":500,"imagesUpdatedTimestamp":1543984207},{"id":"542c34a3aaf34c78b70288f2400c3ae1","documentType":"visualization","owner":"18582087","title":"Mass/Height Menu","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG). The user can select which columns map to X and Y using dropdown menus.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/MjjYLbShFi8?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543829510,"forkedFrom":"ac2a68f06b0f4742b3f42df236680f0a","height":500,"imagesUpdatedTimestamp":1543868008},{"id":"d477747d38e14b11a2efd571e77075d0","documentType":"visualization","owner":"18582087","title":"Car Sales","description":"This program prints a summary of a data table.\n\nThis data is about cars.\n\nThis data comes from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1543828212,"forkedFrom":"09567f8e2b634554bdc4fcad3f5e8394","height":500,"imagesUpdatedTimestamp":1543868019},{"id":"7ee6b64766d249e489728d8e4dfbdfa5","documentType":"visualization","owner":"18582087","title":"Mass/Height Menu","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG). 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In this example, I've used the data provided in the class (which is close to the downloaded data).","lastUpdatedTimestamp":1550691486,"forkedFrom":"f5df7d166c3c40e482e747bc3b2743b7","height":500,"imagesUpdatedTimestamp":1550691487},{"id":"bfbc5fcfb9c943808c953399232eb8e2","documentType":"visualization","owner":"47435316","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1549579593,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1549579603},{"id":"72f654e6f9ef40b6843ed2751512c060","documentType":"visualization","owner":"15162825","title":"Cars Report","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1549651950,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1549651979},{"id":"97999a5b60b74631b827d0d85e718b8e","documentType":"visualization","owner":"15162825","title":"Cars Report","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1549652360,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1549652389},{"id":"7ff724ed34c743aabc92da87ab871104","documentType":"visualization","owner":"23043","title":"Salary bullet","description":"Salary bullet","lastUpdatedTimestamp":1549803796,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1549803799},{"id":"049f62a0813d4fb489f4c6cf464f68c8","documentType":"visualization","owner":"23043","title":"Salary scatter plot","description":"Salary scatter plot","lastUpdatedTimestamp":1549808607,"forkedFrom":"7ff724ed34c743aabc92da87ab871104","height":500,"imagesUpdatedTimestamp":1549808615},{"id":"1348f6b4463a4ceba333c5465bb530a0","documentType":"visualization","owner":"2529178","title":"Untitled","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1549814167,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1549814174},{"id":"4524f58d98f948948926d627a9bb0116","documentType":"visualization","owner":"2529178","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1549815791,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1549815792},{"id":"cb59b9852ce64256b51ee69e42c9f8c0","documentType":"visualization","owner":"2529178","title":"HTML Cars Report","description":"","lastUpdatedTimestamp":1549816312,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1549816315},{"id":"9061b979062049889db7a5609dbaea38","documentType":"visualization","owner":"2529178","title":"Shapes with SVG and CSS","description":"","lastUpdatedTimestamp":1549817147,"forkedFrom":"cb59b9852ce64256b51ee69e42c9f8c0","height":500,"imagesUpdatedTimestamp":1549817156},{"id":"ec4582c604c942f1a271a24405cdbc69","documentType":"visualization","owner":"31359262","title":"Line Chart with Multiple Lines","description":"This line chart shows one week of temperature (in degrees Celcius) in cities around the world. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/xFI-us1moj0?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","forkedFrom":"2546209d161e4294802c4ac0098bebc2","height":500,"imagesUpdatedTimestamp":1549846768},{"id":"c595e11d377e4a5ca9a30e4dc4b07073","documentType":"visualization","owner":"5206749","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1549878056,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1549878056},{"id":"84c2d30a1a34436a984df22371644f86","documentType":"visualization","owner":"2529178","title":"Let's make a face with D3.js!","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550674178,"forkedFrom":"366c38ba5ebc4631b4bd936f3b709744","height":500,"imagesUpdatedTimestamp":1550674180},{"id":"4a9083807ea7422aad0db9d54badfbc2","documentType":"visualization","owner":"5206749","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1549894030,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1549894038},{"id":"d3dfc21e0aa64b6d8ae1ade34d648f63","documentType":"visualization","owner":"2529178","title":"Making a Bar Chart","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550029532,"forkedFrom":"84c2d30a1a34436a984df22371644f86","height":500,"imagesUpdatedTimestamp":1550029533},{"id":"a1fe78ced2324022b83adee7459ad52b","documentType":"visualization","owner":"33906711","title":"Let's make a face with D3.js!","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550001229,"forkedFrom":"bd879ba7efff4d01a86915db30c0b653","height":500,"imagesUpdatedTimestamp":1550001235},{"id":"7babe7cceb394cd1bc93157a29f8c161","documentType":"visualization","owner":"10544913","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","lastUpdatedTimestamp":1550051404,"forkedFrom":"f7f12b804bbf41d09cbf83b4229c79a3","height":960,"imagesUpdatedTimestamp":1550051413},{"id":"56b9c311b96f4931800dbb8a37b06fb0","documentType":"visualization","owner":"33906711","title":"Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550029116,"forkedFrom":"0e7690ded837403a9e8c172ee8b5c1b6","height":500,"imagesUpdatedTimestamp":1550029126},{"id":"8b69bf0b69104c31b8720dccd458c936","documentType":"visualization","owner":"2529178","title":"Customizing Axes","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1557725300,"forkedFrom":"d3dfc21e0aa64b6d8ae1ade34d648f63","height":500,"imagesUpdatedTimestamp":1557725304},{"id":"6806e11fc3e149a9a85441d91f6f4481","documentType":"visualization","owner":"2529178","title":"Scatter Plot","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550032297,"forkedFrom":"8b69bf0b69104c31b8720dccd458c936","height":500,"imagesUpdatedTimestamp":1550032298},{"id":"198c09ea1997402db7d055faedc75c13","documentType":"visualization","owner":"2529178","title":"Cars Scatter Plot","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550038506,"forkedFrom":"6806e11fc3e149a9a85441d91f6f4481","height":500,"imagesUpdatedTimestamp":1550038549},{"id":"a3648be250b54ab082c917516527d6ce","documentType":"visualization","owner":"2529178","title":"Temperature in San Francisco Scatter Plot","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550039847,"forkedFrom":"198c09ea1997402db7d055faedc75c13","height":500,"imagesUpdatedTimestamp":1550039856},{"id":"70b3604f678249a4b771d143a6bc1166","documentType":"visualization","owner":"2529178","title":"Temperature in San Francisco Line Chart","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550040261,"forkedFrom":"a3648be250b54ab082c917516527d6ce","height":500,"imagesUpdatedTimestamp":1550040279},{"id":"54a9bea1325044f4ac8e94d7a883eec7","documentType":"visualization","owner":"2529178","title":"Line Chart ","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1554294520,"forkedFrom":"70b3604f678249a4b771d143a6bc1166","height":500,"imagesUpdatedTimestamp":1554294528},{"id":"72728cf6d5574e97aab06a9c5438e1c1","documentType":"visualization","owner":"2529178","title":"Temperature in San Francisco Area Chart","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550041151,"forkedFrom":"54a9bea1325044f4ac8e94d7a883eec7","height":500,"imagesUpdatedTimestamp":1550041158},{"id":"9e79eeaca46e4a63911b9839a52fd582","documentType":"visualization","owner":"2529178","title":"World Population Area Chart","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550041840,"forkedFrom":"72728cf6d5574e97aab06a9c5438e1c1","height":500,"imagesUpdatedTimestamp":1550041848},{"id":"b766e71f845e4b2f990fabc8848a0bb4","documentType":"visualization","owner":"2529178","title":"Bowl of Fruit - General Update Pattern","description":"","lastUpdatedTimestamp":1550644153,"forkedFrom":"84c2d30a1a34436a984df22371644f86","height":500,"imagesUpdatedTimestamp":1550644156},{"id":"59d1c0b5c8934990bb1b5fc0214c8b71","documentType":"visualization","owner":"2529178","title":"Bowl of Fruit - General Update Pattern","description":"","lastUpdatedTimestamp":1550053551,"forkedFrom":"b766e71f845e4b2f990fabc8848a0bb4","height":500,"imagesUpdatedTimestamp":1550053553},{"id":"8823bc3169094eb98538eac7ca64cc89","documentType":"visualization","owner":"2529178","title":"Bowl of Fruit - General Update Pattern","description":"","lastUpdatedTimestamp":1550683516,"forkedFrom":"59d1c0b5c8934990bb1b5fc0214c8b71","height":500,"imagesUpdatedTimestamp":1550683517},{"id":"8c444046bd5241e0bd3f1837608dac7f","documentType":"visualization","owner":"33906711","title":"Cars Scatter Plot","description":"This bar chart shows population of the top 10 most populous countries. 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The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M2s2jowLkUo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550090075,"forkedFrom":"a9ec621b1c36439aa2a65e0c28462d7a","height":500,"imagesUpdatedTimestamp":1550090083},{"id":"850a963255ab4154899ae358e138e96a","documentType":"visualization","owner":"47510034","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550211025,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1550211034},{"id":"c362f7746c7e47d29bd89d6db0ad8559","documentType":"visualization","owner":"47510034","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550209514,"forkedFrom":"850a963255ab4154899ae358e138e96a","height":500,"imagesUpdatedTimestamp":1550209515},{"id":"51ccad262223441eaae3bc1d6e916881","documentType":"visualization","owner":"47510034","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550209645,"forkedFrom":"c362f7746c7e47d29bd89d6db0ad8559","height":500,"imagesUpdatedTimestamp":1550209647},{"id":"02ad53a92b8c48d4b49465cb3b8c8e34","documentType":"visualization","owner":"47510034","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1550212769,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1550212772},{"id":"e26c7de519774527a46f80defd63eb62","documentType":"visualization","owner":"47510034","title":"scatter plot v1","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550215609,"forkedFrom":"d3dfc21e0aa64b6d8ae1ade34d648f63","height":500,"imagesUpdatedTimestamp":1550215615},{"id":"42755d4ea1484e5b9d37f1fa03c29542","documentType":"visualization","owner":"47510034","title":"Q3","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550259731,"forkedFrom":"92c1c444e4a74676aa662e3214a8294b","height":500,"imagesUpdatedTimestamp":1550259739},{"id":"62a7fea9e81d4073a69e21771ed77302","documentType":"visualization","owner":"47510034","title":"Q3","description":"This bar chart...[UN](https://esa.un.org/).","lastUpdatedTimestamp":1550218008,"forkedFrom":"42755d4ea1484e5b9d37f1fa03c29542","height":500,"imagesUpdatedTimestamp":1550218011},{"id":"af11995fb51047cb98c98ec7187d4de5","documentType":"visualization","owner":"68416","title":"JSX Transpilers by Bundle Size","description":"In my quest to make VizHub awesome, one of the forefront features is JSX support. Initially, [Bublé](https://github.com/Rich-Harris/buble) was added, as this seemed the path of least resistance. Unfortunately, this adds quite a bit of heft to the overall VizHub app bundle. This chart is an exploration of comparative bundle sizes for various means of transpiling JSX into JS, for potential use within VizHub as its \"JSX Engine\". [Bundlephobia.com](https://bundlephobia.com) was used to get bundle size (minified, not GZipped) for each package.\n\nThe clear winner looks to be [acorn-jsx](https://github.com/RReverser/acorn-jsx). 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The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553594539,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1553594546},{"id":"67680424e4414adf94e8a89c4b794616","documentType":"visualization","owner":"48942668","title":" Cars Report","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553602008,"forkedFrom":"da80faf6ad464ab49ae42fc1a1795976","height":500,"imagesUpdatedTimestamp":1553602017},{"id":"91a93a1948f94bdfb0a8d0a608e6efcf","documentType":"visualization","owner":"48942668","title":" Cars Report ","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553603196,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1553603205},{"id":"bdb0ed7076c7460c8c08503721c11866","documentType":"visualization","owner":"48942668","title":" Cars Report ","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553604019,"forkedFrom":"91a93a1948f94bdfb0a8d0a608e6efcf","height":500,"imagesUpdatedTimestamp":1553604019},{"id":"313f955cac8f4e87a7f29b5089418d1a","documentType":"visualization","owner":"48942668","title":" Cars Report ","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553604385,"forkedFrom":"bdb0ed7076c7460c8c08503721c11866","height":500,"imagesUpdatedTimestamp":1553604394},{"id":"6cc5c6a4236e45db9ebc1952a44d84d4","documentType":"visualization","owner":"48942668","title":" Cars Report ","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553604714,"forkedFrom":"313f955cac8f4e87a7f29b5089418d1a","height":500,"imagesUpdatedTimestamp":1553604724},{"id":"5eacf1464dbb41c1983b671ccd05754b","documentType":"visualization","owner":"48942668","title":" Cars Report ","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553605544,"forkedFrom":"6cc5c6a4236e45db9ebc1952a44d84d4","height":500,"imagesUpdatedTimestamp":1553605549},{"id":"a3adac86ce2d4d3394149a09f6278d4c","documentType":"visualization","owner":"48942668","title":"Cars Report","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553608700,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1553608707},{"id":"aea5a698b31e4a48be51570bd5cb99f3","documentType":"visualization","owner":"30474195","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553709264,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1553709274},{"id":"fe05aac8a6a146bdbf3f73f9c114356e","documentType":"visualization","owner":"43623067","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553616966,"forkedFrom":"735581f2b12d488ba2d2287a385cb027","height":500,"imagesUpdatedTimestamp":1553616972},{"id":"2e2910397f0747cbb597a1c7cfadc3c1","documentType":"visualization","owner":"43623067","title":"Choropleth Map with Interactive Filtering","description":"A choropleth map where you can hover over items in the color legend items to interactively filter!\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/E9PhCimWSVQ?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553617900,"forkedFrom":"5c907e49d0294538aad03ad1f41e1e28","height":500,"imagesUpdatedTimestamp":1553617907},{"id":"0355cfed38f14562b09fd7d4aa35516e","documentType":"visualization","owner":"43623067","title":"d3 Choropleth Map","description":"","lastUpdatedTimestamp":1553618341,"forkedFrom":"9dfeba2a8cfc45cab9a1f684baebfc96","height":500,"imagesUpdatedTimestamp":1553618347},{"id":"6f48029b6aa84d1ab0c6b4696394b4ec","documentType":"visualization","owner":"43623067","title":"d3 Choropleth Map with Interactive Filtering","description":"","lastUpdatedTimestamp":1553618868,"forkedFrom":"69719c8874f8422fbbbdcc27369a0743","height":500,"imagesUpdatedTimestamp":1553618875},{"id":"1efa314d755546289f4286e2d214be2b","documentType":"visualization","owner":"47285940","title":"Auto MPG Summary","description":"This program prints a summary of a data table.\n\nThis data is about cars.\n\nThis data comes from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/M6g5jKbS2vg?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553634902,"forkedFrom":"5f89c1c4b9164832ad9982880a9f018c","height":500,"imagesUpdatedTimestamp":1553634910},{"id":"fb3b2e661abe48d69d81e7b8834bed4b","documentType":"visualization","owner":"47285940","title":"Massachusetts Towns","description":"","lastUpdatedTimestamp":1553823043,"forkedFrom":"cb4da8ffeff44aa89ce9ff74a85525f5","height":500,"imagesUpdatedTimestamp":1553823052},{"id":"adf12f97d21f4db0b901551b4572d7bb","documentType":"visualization","owner":"47285940","title":"Color and Size Legends - Component Experiment","description":"An experiment in passing parameters into \"components\" by setting properties on the component functions. This is closer to the [Towards Reusable Charts](https://bost.ocks.org/mike/chart/) pattern, in that you can set state once and invoke the component elsewhere. With this approach you can't tap into the getters and setters though.","lastUpdatedTimestamp":1553715598,"forkedFrom":"ac7c11929d91463d94a1410debb08db3","height":500,"imagesUpdatedTimestamp":1553715607},{"id":"808a39d82e854e5ba1e263abd73b3c6f","documentType":"visualization","owner":"43623067","title":"Making a Bar Chart","description":"This bar chart shows population of the thirteen most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://population.un.org/wpp/Download/Standard/Population/).\n\n\nNote: That link doesn't give data for the year 2018 but for the year 2015. In this example, I've used the data provided in the class (which is close to the downloaded data).","lastUpdatedTimestamp":1553892917,"forkedFrom":"55bf6d0ddb1f4500a0eeabda8c6a85ca","height":500,"imagesUpdatedTimestamp":1553892920},{"id":"0255b704e9ed44aeadaeacf49d259b7c","documentType":"visualization","owner":"47285940","title":"d3 Choropleth Map","description":"","lastUpdatedTimestamp":1553745264,"forkedFrom":"0355cfed38f14562b09fd7d4aa35516e","height":500,"imagesUpdatedTimestamp":1553745272},{"id":"16a0b16cbb764529a0a1bf42afe7f851","documentType":"visualization","owner":"47285940","title":"Let's make a map with D3.js!","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Qw6uAg3EO64?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553733472,"forkedFrom":"c5475d7c95d348d5b8268012fbccb728","height":500,"imagesUpdatedTimestamp":1553733489},{"id":"7524acc8dc4444d88f90f6de25b3fe0a","documentType":"visualization","owner":"47285940","title":"Cheap map","description":"","lastUpdatedTimestamp":1553747595,"forkedFrom":"4fb5e4e665474a169325bd18cdc3d0c0","height":500,"imagesUpdatedTimestamp":1553747596},{"id":"74858c85688149a0b048b312295c33ec","documentType":"visualization","owner":"43623067","title":"Making a Bar Chart","description":"This bar chart shows population of the thirteen most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://population.un.org/wpp/Download/Standard/Population/).\n\n\nNote: That link doesn't give data for the year 2018 but for the year 2015. In this example, I've used the data provided in the class (which is close to the downloaded data).","lastUpdatedTimestamp":1553883900,"forkedFrom":"808a39d82e854e5ba1e263abd73b3c6f","height":500,"imagesUpdatedTimestamp":1553883904},{"id":"add49e1c2d534ddeb3b0eb6d003b634b","documentType":"visualization","owner":"48892123","title":"Shapes with SVG and CSS","description":"","lastUpdatedTimestamp":1553880303,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1553880310},{"id":"e7f0e2186d094c34bc74ae1919cefd95","documentType":"visualization","owner":"43623067","title":"Making a Bar Chart","description":"This bar chart shows population of the thirteen most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://population.un.org/wpp/Download/Standard/Population/).\n\n\nNote: That link doesn't give data for the year 2018 but for the year 2015. In this example, I've used the data provided in the class (which is close to the downloaded data).","lastUpdatedTimestamp":1553897681,"forkedFrom":"55bf6d0ddb1f4500a0eeabda8c6a85ca","height":500,"imagesUpdatedTimestamp":1553897687},{"id":"40aff9c2d4b74b4d91b905f82437d9ad","documentType":"visualization","owner":"49018146","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553757116,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1553757125},{"id":"bfde7d18040141d68a86ea4d35632a5e","documentType":"visualization","owner":"8143172","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553782900,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1553782906},{"id":"51e7d84fa1ae4697811dc7df649e3f82","documentType":"visualization","owner":"47285940","title":"d3 Choropleth Map","description":"","lastUpdatedTimestamp":1553813927,"forkedFrom":"0255b704e9ed44aeadaeacf49d259b7c","height":500,"imagesUpdatedTimestamp":1553813936},{"id":"8bfa4df8a6544fd3b180072ea39d22ae","documentType":"visualization","owner":"47285940","title":"d3 Choropleth Map","description":"","lastUpdatedTimestamp":1553815882,"forkedFrom":"51e7d84fa1ae4697811dc7df649e3f82","height":500,"imagesUpdatedTimestamp":1553815884},{"id":"1d4f8534a37a4bc5afc717c7c50e22d0","documentType":"visualization","owner":"47285940","title":"Choropleth Map","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OoZ0LWD9KUs?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553817240,"forkedFrom":"d5ad96d1fe8148bd827a25230cc0f083","height":500,"imagesUpdatedTimestamp":1553817249},{"id":"4dcf2a672ebe4c1487a38039ffbcd4ea","documentType":"visualization","owner":"48892123","title":"Let's make a face with D3.js! ","description":"","lastUpdatedTimestamp":1553975475,"forkedFrom":"add49e1c2d534ddeb3b0eb6d003b634b","height":500,"imagesUpdatedTimestamp":1553975482},{"id":"5a3614f4bb7f43d581b0faf4f2f33d30","documentType":"visualization","owner":"47285940","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553884347,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1553884355},{"id":"dc9b6318b6fe4bdf80d27d64854ac88d","documentType":"visualization","owner":"47285940","title":"Untitled","description":"","lastUpdatedTimestamp":1553884837,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1553884840},{"id":"f8fd13059fb44034bb6aeb325206e883","documentType":"visualization","owner":"43623067","title":"Making a Bar Chart","description":"This bar chart shows population of the thirteen most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://population.un.org/wpp/Download/Standard/Population/).\n\n\nNote: That link doesn't give data for the year 2018 but for the year 2015. In this example, I've used the data provided in the class (which is close to the downloaded data).","lastUpdatedTimestamp":1553898416,"forkedFrom":"e7f0e2186d094c34bc74ae1919cefd95","height":500,"imagesUpdatedTimestamp":1553898426},{"id":"78ef9d13e12b498f9ef973ba244106c4","documentType":"visualization","owner":"48892123","title":"CBH","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1553978237,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1553978245},{"id":"a5d7b11132f346c8a8822730403b9500","documentType":"visualization","owner":"48892123","title":"Making a Bar Chart","description":"","lastUpdatedTimestamp":1553976774,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1553976781},{"id":"5998ccd70f244f2ba24ecf00d84e3818","documentType":"visualization","owner":"48892123","title":"Making a Bar Chart","description":"\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554064493,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1554064499},{"id":"1a6973dc304149ffbf6914b50af5c3ba","documentType":"visualization","owner":"48892123","title":"Making a Bar Chart","description":"\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554065424,"forkedFrom":"5998ccd70f244f2ba24ecf00d84e3818","height":500,"imagesUpdatedTimestamp":1554065434},{"id":"041ff101bb2848da9c84f75603342310","documentType":"visualization","owner":"48892123","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554065574,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1554065577},{"id":"fccb2039b6b54ec7a62817d0dcbdf464","documentType":"visualization","owner":"48892123","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554065639,"forkedFrom":"041ff101bb2848da9c84f75603342310","height":500,"imagesUpdatedTimestamp":1554065645},{"id":"4f0e1f370fbc4cee9289e3255c1fe583","documentType":"visualization","owner":"43699174","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","lastUpdatedTimestamp":1554075010,"forkedFrom":"7babe7cceb394cd1bc93157a29f8c161","height":960,"imagesUpdatedTimestamp":1554075020},{"id":"5322fb8dae2646b4b5e9644311478640","documentType":"visualization","owner":"68416","title":"Victory!","description":"This example shows how to load data from a CSV file using React hooks and visualize the result using [Victory Charts](https://formidable.com/open-source/victory/). Inspired by this [Victory example from Swizec Teller](https://codesandbox.io/s/3v3q013x36?from-embed) in [React for Data Visualization](https://reactfordataviz.com/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OacqgtI30pk\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>","lastUpdatedTimestamp":1559770746,"forkedFrom":"e5df78960d124ee7a6614ae361ddeef3","height":500,"imagesUpdatedTimestamp":1559770750},{"id":"58c4bad471b344dab553530000164d8f","documentType":"visualization","owner":"49163656","title":"Let's make a map with D3.js!","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Qw6uAg3EO64?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554146142,"forkedFrom":"c5475d7c95d348d5b8268012fbccb728","height":500,"imagesUpdatedTimestamp":1554146150},{"id":"7fcc84f68758417a8a1f6076410e98ab","documentType":"visualization","owner":"15616599","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. 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The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/xFI-us1moj0?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554319535,"forkedFrom":"2546209d161e4294802c4ac0098bebc2","height":500,"imagesUpdatedTimestamp":1554319542},{"id":"11f2302b410341a498691c0f2a347290","documentType":"visualization","owner":"23503363","title":"Line Chart with Multiple Lines","description":"This line chart shows one week of temperature (in degrees Celcius) in cities around the world. The data comes from [Data Canvas - Sense Your City](https://grayarea.org/initiative/data-canvas-sense-your-city/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/xFI-us1moj0?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554790609,"forkedFrom":"2546209d161e4294802c4ac0098bebc2","height":500,"imagesUpdatedTimestamp":1554790614},{"id":"9d1f6c8d8775473d8440694972be5a3a","documentType":"visualization","owner":"23503363","title":"Selecting a Year on a Line Chart","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/TGbkLXypPOE?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554788278,"forkedFrom":"94bf5b1182ce40df8de8c9526f4124a4","height":500,"imagesUpdatedTimestamp":1554788282},{"id":"d693d1fd8b6a45dfab94c4ab1a5d05d7","documentType":"visualization","owner":"49045349","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1554332263},{"id":"2f9ef86be63b454dae301bd71ea0be47","documentType":"visualization","owner":"49045349","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554333554,"forkedFrom":"d693d1fd8b6a45dfab94c4ab1a5d05d7","height":500,"imagesUpdatedTimestamp":1554333564},{"id":"2fd9c8d491884c6ba48e79acd3006dde","documentType":"visualization","owner":"20909640","title":"Making a Scatter Plot","description":"This scatterplot shows population of 10 randomly pickedup cities of India. 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The data shows the top 20 artists by play count, as tracked by the service. The data was accessed from my [profile](https://last.fm/user/philosiphicus) via a service called [last.fm to csv](https://benjaminbenben.com/lastfm-to-csv/), using my username (philosiphicus).","lastUpdatedTimestamp":1554429323,"forkedFrom":"de79d5d5bec6405987c8a3c6c1839b0d","height":960,"imagesUpdatedTimestamp":1554429330},{"id":"31920b8097764488ae33360848ca671c","documentType":"visualization","owner":"417978","title":"Shapes with SVG and CSS","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554663720,"forkedFrom":"366c38ba5ebc4631b4bd936f3b709744","height":500,"imagesUpdatedTimestamp":1554663730},{"id":"e67401dfcff4435bab5ade28eb8994db","documentType":"visualization","owner":"40191798","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"460\" height=\"215\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554657455,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1554657456},{"id":"3366760abb4745099e44aa951a4815da","documentType":"visualization","owner":"40191798","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554657522,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1554657522},{"id":"8fc42cfbd4954bb8ad58d37d4f8470bb","documentType":"visualization","owner":"49377820","title":"World Countries Tree","description":"","lastUpdatedTimestamp":1554677536,"forkedFrom":"d92e507f0deb4827a8a6ff0e595496e4","height":960,"imagesUpdatedTimestamp":1554677540},{"id":"c153684859be48fb9cc446b5e11b43f2","documentType":"visualization","owner":"43874770","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554692693,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1554692703},{"id":"78527c05384a4ed788e89303f396dffe","documentType":"visualization","owner":"48216173","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556688580,"forkedFrom":"3366760abb4745099e44aa951a4815da","height":500,"imagesUpdatedTimestamp":1556688584},{"id":"5cfb4a0fbfa94b0abce108b71ca14398","documentType":"visualization","owner":"48216173","title":"Shapes with SVG and CSS","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554719270,"forkedFrom":"31920b8097764488ae33360848ca671c","height":500,"imagesUpdatedTimestamp":1554719279},{"id":"918d39d4645147d0b855ade212b88468","documentType":"visualization","owner":"48216173","title":"Shapes with SVG and CSS","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1554738908,"forkedFrom":"5cfb4a0fbfa94b0abce108b71ca14398","height":500,"imagesUpdatedTimestamp":1554738912},{"id":"b740e5635cdf4f83bd24d30d721be6b8","documentType":"visualization","owner":"49319159","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous cities in the world. 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Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","lastUpdatedTimestamp":1554744510,"forkedFrom":"7babe7cceb394cd1bc93157a29f8c161","height":960,"imagesUpdatedTimestamp":1554744513},{"id":"e1759235762541ea85a885a715c5f88b","documentType":"visualization","owner":"45018040","title":"CA Zip Code Map part II","description":"","lastUpdatedTimestamp":1554985711,"forkedFrom":"a5982b1928294a76851f7dfa571cfbc9","height":500,"imagesUpdatedTimestamp":1554985712},{"id":"180d1e6062714fc1acd0412a80fb7fce","documentType":"visualization","owner":"23503363","title":"Line Chart with Multiple Lines","description":"This line chart shows one week of temperature (in degrees Celcius) in cities around the world. 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Inspired by this [Victory example from Swizec Teller](https://codesandbox.io/s/3v3q013x36?from-embed) in [React for Data Visualization](https://reactfordataviz.com/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OacqgtI30pk\" frameborder=\"0\" allow=\"accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>","lastUpdatedTimestamp":1555870989,"forkedFrom":"5322fb8dae2646b4b5e9644311478640","height":500,"imagesUpdatedTimestamp":1555870999},{"id":"79695d3d671542f99000c44e3aa6e5dc","documentType":"visualization","owner":"43607251","title":"Books with Pages","description":"A fork of [@leorawe: Books with Pages](https://vizhub.com/leorawe/6b49e9b37e9d4449958669ee53341acc) that shows how to get rid of the scroll bars.\n\nThe following CSS was added to the `body` section:\n\n```\n  margin: 0px;\n  overflow: hidden;\n```","lastUpdatedTimestamp":1555484088,"forkedFrom":"48ad67b0d5e04120a9c476cdc2c9ca7b","height":500,"imagesUpdatedTimestamp":1555484094},{"id":"aec947deef534dbe8e80b408964f280e","documentType":"visualization","owner":"3117142","title":"Object assign","description":"An example including some commonly used features of SVG.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/ysG9j4_Uw_g?rel=0&amp;start=765\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1555493244,"forkedFrom":"35388387d4e44f89ba918a07a77047b9","height":500,"imagesUpdatedTimestamp":1555493249},{"id":"9187995c7b0246db8f75858f5b47f0c5","documentType":"visualization","owner":"68416","title":"Multiple Instances","description":"","lastUpdatedTimestamp":1555499907,"forkedFrom":"8a2b57b127b54ad0bcdf904c8155d516","height":500,"imagesUpdatedTimestamp":1555499916},{"id":"88780c7548d448afa730888b2629c2d5","documentType":"visualization","owner":"3117142","title":"Multiple Instances with Chart Brushing","description":"","lastUpdatedTimestamp":1556006858,"forkedFrom":"9187995c7b0246db8f75858f5b47f0c5","height":500,"imagesUpdatedTimestamp":1556006858},{"id":"658a1b473d76449ea532c0914f6a0f64","documentType":"visualization","owner":"11035999","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1555507144,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1555507150},{"id":"6e5fb080fe884908a1a76fa1112aaeda","documentType":"visualization","owner":"11035999","title":"Untitled","description":"","lastUpdatedTimestamp":1555507156,"forkedFrom":"658a1b473d76449ea532c0914f6a0f64","height":500,"imagesUpdatedTimestamp":1555507161},{"id":"97f8ea1c812c40a4bf6252db7e0078c6","documentType":"visualization","owner":"11035999","title":"Untitled","description":"","lastUpdatedTimestamp":1555507321,"forkedFrom":"658a1b473d76449ea532c0914f6a0f64","height":500,"imagesUpdatedTimestamp":1555507326},{"id":"45448afc068c4b39872965536460cdfc","documentType":"visualization","owner":"3117142","title":"Trend line","description":"","lastUpdatedTimestamp":1555602199,"forkedFrom":"88780c7548d448afa730888b2629c2d5","height":500,"imagesUpdatedTimestamp":1555602203},{"id":"fbb66efdd128453eac7730c4ebf32058","documentType":"visualization","owner":"46333228","title":"Scatter Plot with Menus","description":"This scatter plot shows data about cars, from [UCI Machine Learning Repository: Auto MPG Data Set](http://mlr.cs.umass.edu/ml/datasets/Auto+MPG). 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encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1555864076,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1555864080},{"id":"1bde359ef79a405aafd78433d95d3499","documentType":"visualization","owner":"3783676","title":"Cars Report","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1555864107,"forkedFrom":"2805aa06fe8a40ae8fdb53de84d2092c","height":500,"imagesUpdatedTimestamp":1555864113},{"id":"704de1a2509a418795a4c4974a749e53","documentType":"visualization","owner":"3783676","title":"Cars Report","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; 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Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","lastUpdatedTimestamp":1555879637,"forkedFrom":"7fcc84f68758417a8a1f6076410e98ab","height":800,"imagesUpdatedTimestamp":1555879648},{"id":"39931721750844d5824aaebd1213d646","documentType":"visualization","owner":"36172576","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. 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The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556034732,"forkedFrom":"82d6fee6d96f41b2a22cf4a5cba95148","height":500,"imagesUpdatedTimestamp":1556034734},{"id":"4d48cd50a5a9449aa475d2ea5ade5a37","documentType":"visualization","owner":"49739589","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556034823,"forkedFrom":"15c30b3adc6a415b8ac969950df7d33d","height":500,"imagesUpdatedTimestamp":1556034823},{"id":"3881851b84754b15b8000d8de19db891","documentType":"visualization","owner":"49739589","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556034830,"forkedFrom":"4d48cd50a5a9449aa475d2ea5ade5a37","height":500,"imagesUpdatedTimestamp":1556034835},{"id":"4f7272e89b4c4a5e8768dd80018646d0","documentType":"visualization","owner":"49739589","title":"Bar Chart From Memory","description":"attempting to reconstuct the d3 barchart from memory, as an excersize\n","forkedFrom":"193a965ba627426693a520c6c7eea95d","height":500,"imagesUpdatedTimestamp":1556040172},{"id":"2bcf426467024674a1a57252067dc150","documentType":"visualization","owner":"49739589","title":"Making a Bar Chart","description":"This bar chart shows budget and the movietitle with the reference of imdbscore \n\n I've used the data provided in the class \n \n my coding is inspired by vizhub.com with github.","lastUpdatedTimestamp":1556234326,"forkedFrom":"55bf6d0ddb1f4500a0eeabda8c6a85ca","height":500,"imagesUpdatedTimestamp":1556234332},{"id":"bdea7776f6ac4fbf81b429e99e90f3eb","documentType":"visualization","owner":"3783676","title":"Customizing Axis","description":"This is about customizing axis.\n","lastUpdatedTimestamp":1556083113,"forkedFrom":"1d103e13939e40db8e71c3cc627800f5","height":500,"imagesUpdatedTimestamp":1556083118},{"id":"f7538ba38763463bbea5f5cc8347e668","documentType":"visualization","owner":"3783676","title":"Scatter Plot","description":"This is about customizing axis.\n","lastUpdatedTimestamp":1556083617,"forkedFrom":"bdea7776f6ac4fbf81b429e99e90f3eb","height":500,"imagesUpdatedTimestamp":1556083622},{"id":"529f41e258b8470a96f997afb79cb984","documentType":"visualization","owner":"3783676","title":"Cars Scatter Plot","description":"This is a scatter plot for cars.\n","lastUpdatedTimestamp":1556084836,"forkedFrom":"f7538ba38763463bbea5f5cc8347e668","height":500,"imagesUpdatedTimestamp":1556084846},{"id":"d834b142ebc94a6cbfc31fc66ac9e068","documentType":"visualization","owner":"3783676","title":"Temperature in San Francisco Scatter Plot","description":"This is a scatter plot for cars.\n","lastUpdatedTimestamp":1556084998,"forkedFrom":"529f41e258b8470a96f997afb79cb984","height":500,"imagesUpdatedTimestamp":1556085002},{"id":"1ea02bbfe2da4ceeb8773c5553cff2e9","documentType":"visualization","owner":"7525050","title":"Hello VizHub","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556090288,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1556090294},{"id":"ed294cd6a5084bc196c9849cfaac2bcc","documentType":"visualization","owner":"49941349","title":"D3.js collapsible tree with boxes","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556097368,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1556097373},{"id":"3f4e7cdbbea84e8984e2396bafd1eff1","documentType":"visualization","owner":"49941349","title":"D3.js collapsible tree with boxes","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","forkedFrom":"ed294cd6a5084bc196c9849cfaac2bcc","height":500,"imagesUpdatedTimestamp":1556097428},{"id":"57d788f831fd46c08a9697999056f159","documentType":"visualization","owner":"49941349","title":"D3.js collapsibleRRR tree with boxes","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556179550,"forkedFrom":"3f4e7cdbbea84e8984e2396bafd1eff1","height":500,"imagesUpdatedTimestamp":1556179552},{"id":"7dca45887d7a4171a3981aa5a7523f17","documentType":"visualization","owner":"49739589","title":"Making a Bar Chart","description":"This bar chart shows population of the thirteen most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://population.un.org/wpp/Download/Standard/Population/).\n\n\nNote: That link doesn't give data for the year 2018 but for the year 2015. In this example, I've used the data provided in the class (which is close to the downloaded data).","lastUpdatedTimestamp":1556134198,"forkedFrom":"2bcf426467024674a1a57252067dc150","height":500,"imagesUpdatedTimestamp":1556134208},{"id":"3f4f66b068004d149704465375901501","documentType":"visualization","owner":"49739589","title":"Making a Bar Chart","description":"This bar chart shows population of the thirteen most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://population.un.org/wpp/Download/Standard/Population/).\n\n\nNote: That link doesn't give data for the year 2018 but for the year 2015. In this example, I've used the data provided in the class (which is close to the downloaded data).","lastUpdatedTimestamp":1556122505,"forkedFrom":"7dca45887d7a4171a3981aa5a7523f17","height":500,"imagesUpdatedTimestamp":1556122506},{"id":"1d7f916921ca4fdea299c47b5418839f","documentType":"visualization","owner":"49739589","title":"Making a Bar Chart","description":"This bar chart shows budget and the movietitle with the reference of imdbscore \n\n I've used the data provided in the class \n \n my coding is inspired by vizhub.com with github.","lastUpdatedTimestamp":1556141506,"forkedFrom":"2bcf426467024674a1a57252067dc150","height":500,"imagesUpdatedTimestamp":1556141515},{"id":"b84b5dffff384a9e98af78749305070d","documentType":"visualization","owner":"49510791","title":"Choropleth Map","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/OoZ0LWD9KUs?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556507747,"forkedFrom":"ed26e20b55f2480fb48eea0782592d49","height":500,"imagesUpdatedTimestamp":1556507748},{"id":"5a81255d9869427fb5b35f0f3c462f56","documentType":"visualization","owner":"49510791","title":"Map with Selectable Countries","description":"","lastUpdatedTimestamp":1556523750,"forkedFrom":"ed26e20b55f2480fb48eea0782592d49","height":500,"imagesUpdatedTimestamp":1556523750},{"id":"619da1b93b5744cfaa8ed50b991c02ac","documentType":"visualization","owner":"3117142","title":"Brush, Trendline, Legend, Title, Linechart, Barchart","description":"","lastUpdatedTimestamp":1558002101,"forkedFrom":"3e51b3feffc840f89328d39376359cc0","height":600,"imagesUpdatedTimestamp":1558002107},{"id":"85f96cd14d184373b22f9c1c073095e0","documentType":"visualization","owner":"44109369","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556214226,"forkedFrom":"dd44f8fcdc8346ff90bddd63572bf638","height":500,"imagesUpdatedTimestamp":1556214226},{"id":"f3e0b13d554b4dbf979708c6512da9a5","documentType":"visualization","owner":"44109369","title":"Customizing Axes","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/c3MCROTNN8g?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556214513,"forkedFrom":"a44b38541b6e47a4afdd2dfe67a302c5","height":500,"imagesUpdatedTimestamp":1556214513},{"id":"efcdd50f5cb44962b2fd8451b2ad93b4","documentType":"visualization","owner":"44109369","title":"Customizing Axes","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/c3MCROTNN8g?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556214521,"forkedFrom":"f3e0b13d554b4dbf979708c6512da9a5","height":500,"imagesUpdatedTimestamp":1556214524},{"id":"5538f7d8ef40456b8e9021fa3331cee5","documentType":"visualization","owner":"49739589","title":"Making a Bar Chart","description":"This bar chart shows budget and the movietitle with the reference of imdbscore \n\n I've used the data provided in the class \n \n my coding is inspired by vizhub.com with github.","lastUpdatedTimestamp":1556234336,"forkedFrom":"1d7f916921ca4fdea299c47b5418839f","height":500,"imagesUpdatedTimestamp":1556234343},{"id":"74b19c4258c64c60ba53f2f4ff314c40","documentType":"visualization","owner":"49510791","title":"Circles on a Map","description":"Visualizing population by country using circles on a map! The area of each circle corresponds to the population of the country it represents. You can also pan & zoom, and hover over each country for more information.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/c0a02WHjgEs?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556524877,"forkedFrom":"8704c9b7c6df43cabf839aa3f1cb7b70","height":500,"imagesUpdatedTimestamp":1556524885},{"id":"77aafecadde649c1a4ca5fcae92c0f09","documentType":"visualization","owner":"49510791","title":"Cicrcle on Map","description":"","lastUpdatedTimestamp":1556507728,"forkedFrom":"5a81255d9869427fb5b35f0f3c462f56","height":500,"imagesUpdatedTimestamp":1556507759},{"id":"c6aa17110f3346a2b43b648d9a5880d9","documentType":"visualization","owner":"49510791","title":"Let's make a yunnan map with D3.js!","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Qw6uAg3EO64?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556525103,"forkedFrom":"c5475d7c95d348d5b8268012fbccb728","height":500,"imagesUpdatedTimestamp":1556525105},{"id":"416283f22fe94018a0efb6551ae05305","documentType":"visualization","owner":"26042285","title":"HTML Cars Report","description":"","lastUpdatedTimestamp":1556357696,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1556357706},{"id":"3a6f03968d2f479b9433342ce2ed1426","documentType":"visualization","owner":"37874526","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","forkedFrom":"d77c2d3bd9a34d8c98791b89f47517fa","height":800,"imagesUpdatedTimestamp":1556377861},{"id":"e23bf11ce7234df38c3d01eb9439fb32","documentType":"visualization","owner":"37874526","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","forkedFrom":"d77c2d3bd9a34d8c98791b89f47517fa","height":800,"imagesUpdatedTimestamp":1556377872},{"id":"164aeb3a6c24449d8ed0551605b4775b","documentType":"visualization","owner":"37874526","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","forkedFrom":"d77c2d3bd9a34d8c98791b89f47517fa","height":800,"imagesUpdatedTimestamp":1556377883},{"id":"f2fc451aa39d43e49bb4d95fcda000c1","documentType":"visualization","owner":"37874526","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","forkedFrom":"d77c2d3bd9a34d8c98791b89f47517fa","height":800,"imagesUpdatedTimestamp":1556377894},{"id":"9cdbdd32f7c8493aa82e72425085ac60","documentType":"visualization","owner":"37874526","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. 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The data comes ffrom the year 2018 in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).","lastUpdatedTimestamp":1556880293,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1556880295},{"id":"4967cb1383d64b7aab40b182e041a3ac","documentType":"visualization","owner":"32156402","title":"Making a Bar Chart","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556460190,"forkedFrom":"85f96cd14d184373b22f9c1c073095e0","height":500,"imagesUpdatedTimestamp":1556460192},{"id":"ad4eed34585545bdb032cd76ca6b5b4e","documentType":"visualization","owner":"43218114","title":"Let's make a map with D3.js!","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/Qw6uAg3EO64?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556470099,"forkedFrom":"c5475d7c95d348d5b8268012fbccb728","height":500,"imagesUpdatedTimestamp":1556470106},{"id":"50dc765d7f1c4624a472898edd691ecc","documentType":"visualization","owner":"49596291","title":"Habitantes por Distrito en Barcelona","description":"This bar chart shows population of the top 10 most populous countries. The data comes from the year 2018 estimate in [United Nations: World Population Prospects 2017](https://esa.un.org/unpd/wpp/Download/Standard/Population/).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/NlBt-7PuaLk?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556585377,"forkedFrom":"4967cb1383d64b7aab40b182e041a3ac","height":500,"imagesUpdatedTimestamp":1556585387},{"id":"ff5d9bd538ea4169943824f34569a842","documentType":"visualization","owner":"49596291","title":"Saludos Cordiales!","description":"This is a description using [Markdown](https://en.wikipedia.org/wiki/Markdown).\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/qaOzZ7L3dJo?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556584045,"forkedFrom":"a74e18c1f35f46d2b08e4fdd90f6835a","height":500,"imagesUpdatedTimestamp":1556584054},{"id":"4f60c4f5aabb4f12be40b25f6c47964d","documentType":"visualization","owner":"1454462","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556548919,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1556548928},{"id":"02dc49c5f6224dbbb7d828026ca6be3a","documentType":"visualization","owner":"49510791","title":"yunnanMap with Selectable Countries","description":"<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/L2Tj20cGJ_4?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556590509,"forkedFrom":"b6de507a869d4e0581fd8a652b786a7e","height":500,"imagesUpdatedTimestamp":1556590516},{"id":"3cbbcc554d184aae878cdc08815ab5d0","documentType":"visualization","owner":"32156402","title":"HTML Cars Report","description":"","lastUpdatedTimestamp":1556541822,"forkedFrom":"416283f22fe94018a0efb6551ae05305","height":500,"imagesUpdatedTimestamp":1556541828},{"id":"553f1581b1c247f7bde12198c5f52dc1","documentType":"visualization","owner":"43747688","title":"D3 v5 Zoomable Sunburst","description":"Mike Bostock's [D3 Zoomable Sunburst example](https://beta.observablehq.com/@mbostock/d3-zoomable-sunburst)\n* with D3 v5 (based on the [changes in v5](https://github.com/d3/d3/blob/master/CHANGES.md#changes-in-d3-50))\n<br></br>\nPorted from [this Observable notebook](https://beta.observablehq.com/@tasqon/d3-circle-packing) for [this](https://talk.observablehq.com/t/notebook-to-vanilla-javascript-steps/1644/5) forum thread. Many thanks to Bryan Gingechen who answered my question about the necessary steps to get from an (Observable) JavaScript notebook to a vanilla JavaScript index.html. He presented his solution on [blockbuilder.org](https://blockbuilder.org/bryangingechen/ffd619bb5889d146fe6c5d581d3ea00e)","lastUpdatedTimestamp":1556620389,"forkedFrom":"7babe7cceb394cd1bc93157a29f8c161","height":960,"imagesUpdatedTimestamp":1556620393},{"id":"5577dfa6f1564c8cb4e6b5b5efc66291","documentType":"visualization","owner":"68416","title":"WICN Radio Now Playing","description":"An example embed from [Spinitron Web Integration](https://spinitron.com/about/help/web-integration.html).","lastUpdatedTimestamp":1557088170,"forkedFrom":"86a75dc8bdbe4965ba353a79d4bd44c8","height":500,"imagesUpdatedTimestamp":1557088171},{"id":"6cea5978351448d8a457523cf668d9b1","documentType":"visualization","owner":"68416","title":"WICN Radio Now Playing","description":"An example embed from [Spinitron Web Integration](https://spinitron.com/about/help/web-integration.html).","lastUpdatedTimestamp":1557088170,"forkedFrom":"5577dfa6f1564c8cb4e6b5b5efc66291","height":500,"imagesUpdatedTimestamp":1557088182},{"id":"663b03524dc34cc18d1eedc34d1cad1f","documentType":"visualization","owner":"68416","title":"WICN Radio Now Playing IFrame","description":"An example embed from [Spinitron Web Integration](https://spinitron.com/about/help/web-integration.html).","lastUpdatedTimestamp":1556575401,"forkedFrom":"5577dfa6f1564c8cb4e6b5b5efc66291","height":500,"imagesUpdatedTimestamp":1556575409},{"id":"97791b062f694916a08084b719ba86f8","documentType":"visualization","owner":"1454462","title":"Let's make a face with D3.js!","description":"Demonstrates fundamental DOM manipulation using D3.js by making a smiley face whose eyebrows move.\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/-RQWC4I2I1s?rel=0\" frameborder=\"0\" allow=\"autoplay; encrypted-media\" allowfullscreen></iframe>","lastUpdatedTimestamp":1556587501,"forkedFrom":"be771477cb974c938cd8603dd8b59d32","height":500,"imagesUpdatedTimestamp":1556587512},{"id":"0bd785ad24024899a7c58f716aef0ffe","documentType":"visualization","owner":"1454462","title":"Making a Bar Chart","description":"","lastUpdatedTimestamp":1558366384,"forkedFrom":"97791b062f694916a08084b719ba86f8","height":500,"imagesUpdatedTimestamp":1558366387},{"id":"06df022aaf5b48b69cb70f3e646ca003","documentType":"visualization","owne