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# Data Journalism 2: Interrogating, Visualising and Mashing

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Session for MA students at City University's Journalism School

Session for MA students at City University's Journalism School

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### Transcript

• 1. Data 2: Interrogating, visualising, mashing Online Journalism City University Paul BradshawMonday, 7 March 2011
• 2. Themes 5 things you need to know about each Data journalism in action WalkthroughMonday, 7 March 2011
• 3. Interrogating data .Monday, 7 March 2011
• 4. Monday, 7 March 2011
• 5. 5 things you need to know about interrogating data 1. Data always needs cleaning up 2. Treat the &#x2018;source&#x2019; like a source 3. Use the right &#x2018;average&#x2019; and percentage 4. Variation over time &amp; space: context 5. Spreadsheet tools are your friend - but always backup copiesMonday, 7 March 2011
• 6. Monday, 7 March 2011
• 7. &#x201C;What the Independent have done is confuse the UK&#x2019;s deficit with our debt [making] the debt problem look around eight times worse than it is. And it used the whole of its front page to do so.&#x201D; - James BallMonday, 7 March 2011
• 8. Monday, 7 March 2011
• 9. What is the data worth? Measurement doesnt answer anything if theres only one variable Statistical significance Sample size and selection Controls and the placebo effect Read up.Monday, 7 March 2011
• 10. 1. Variance is interesting. 2. Variance is different for different variables and in different populations. 3. The amount of variance is easily quantified. - Philip Meyer, Precision JournalismMonday, 7 March 2011
• 11. Getting data in the right form Data &gt; Text to columns Find &amp; replace Conditional formulas: =IF(condition, if met, if not) =COUNTIF(range, test)Monday, 7 March 2011
• 12. Walkthrough: cleaning data in Google Refine Edit cells &gt; common transforms Edit cells &gt; split multi-valued cells Facet &gt; text facet Export...Monday, 7 March 2011
• 13. Visualising data .Monday, 7 March 2011
• 14. 5 things you need to know about visualising data 1. Choose the chart for the purpose 2. It can be used to spot a lead 3. Good design is when there&#x2019;s nothing more to take away 4. It should be self-contained &amp; have refs 5. Be careful with scales and classesMonday, 7 March 2011
• 15. or http://chartchooser.juiceanalytics.com/Monday, 7 March 2011
• 16. Monday, 7 March 2011
• 17. Monday, 7 March 2011
• 18. What is wrong with this picture?Monday, 7 March 2011
• 19. Monday, 7 March 2011
• 20. http://simplecomplexity.net/statistics-without-context/Monday, 7 March 2011
• 21. http://junkcharts.typepad.com/junk_charts/trifecta-checkup/Monday, 7 March 2011
• 22. Visualisation tools ManyEyes Tableau Wordle, Tagxedo BatchGeo Gephi Delicious.com/paulb/visualisation+toolsMonday, 7 March 2011
• 23. Walkthrough: visualising data with Google Gadgets .Monday, 7 March 2011
• 24. Walkthrough: visualising data in ManyEyes .Monday, 7 March 2011
• 25. Mashing data .Monday, 7 March 2011
• 26. 5 things you need to know about mashing data 1. It is what a journalist does best 2. Look for a point of connection: place? Person? Company? Date? 3. What an API can do 4. What APIs there are 5. Mashups can be live, updated or staticMonday, 7 March 2011
• 27. Monday, 7 March 2011
• 28. Monday, 7 March 2011
• 29. Mashup tools Yahoo! Pipes OpenHeatMap Mapalist xFruits Scraperwiki MaptubeMonday, 7 March 2011
• 30. Walkthrough: making mashups with Yahoo! Pipes Inputs - Fetch Feed, CSV, Data, Page, YQL, Flickr, Form Operators - Filter, Sort, Unique, Union, Count, Split, Rename, Regex, Unique, Location extractor, URL Builder Outputs - Map, Gallery, List, XML, KMLMonday, 7 March 2011
• 31. Walkthrough: making mashups with OpenHeatMap Format the spreadsheet Publish it as CSV Copy link Paste it at OpenHeatMap Fix any problemsMonday, 7 March 2011
• 32. Walkthrough: grabbing geo data with Google Refine Edit column &gt; Add column by fetching URLs Use GREL (Google Refine Expression Language) Search web for help &amp; examplesMonday, 7 March 2011
• 33. Questions? .Monday, 7 March 2011
• 34. Links OnlineJournalismClasses.tumblr.com Delicious.com/paulb/cityoj09 Delicious.com/paulb/datajournalism Delicious.com/paulb/visualisation Delicious.com/paulb/statistics Delicious.com/paulb/mashupsMonday, 7 March 2011
• 35. Lab Before the lab: play with these techniques yourself, have problems, find solutions, raise questions. Install Google Refine and Tableau on your laptop to use. - Visualise, interrogate or mash dataMonday, 7 March 2011
• 36. Books Kaiser Fung - Numbers Rule Your World Ben Goldacre - Bad Science Donna Wong - The WSJ Guide to Information Graphics Brian Suda - A Practical Guide to Designing with DataMonday, 7 March 2011