Tag: #rcharts

With under two weeks to go to the 2015 UK general election, there's no better time to take stock of all the voter intention polls being published by the British media. The data has already been aggregated by UKpollingreport—a site I've used before for analysis of the Scottish independence referendum—and there's not much more to be said then is already widely known: it's likely no single party will win an outright majority and we'll be left with a coalition or minority government.

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Arnie 2010 (source)

I recently read Arnie's autobiography (great fun) and in it he writes about the various roles he's had, discussing those movies that flopped or were surprise box office successes, but it's hard to build up an overall picture of his career from these fragments. Similarly the raw filmography lists at IMDb and Wikipedia are pretty uninspiring.

That gave me the idea of charting his movie career over time, attempting to show a lot of information at once about how well the film did at box office relative to its budget, and at what points these successes and failures happened over the last few decades. After some python-powered scraping of IMDb data, this is what I came up with:

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The most popular accounts on twitter have millions of followers, but what are their demographics like? Twitter doesn't collect or release this kind of information, and even things like name and location are only voluntarily added to people's profiles. Unlike Google+ and Facebook, twitter has no real name policy, they don't care what you call yourself, because they can still divine out useful information from your account activity.

For example, you can optionally set your location on your twitter profile. Should you choose not to, twitter can still just geolocate your IP. If you use an anonymiser or VPN, they could use the timing of your account activity to infer a timezone. This could then be refined to a city or town using the topics you tweet about and the locations of friends and services you mention most.

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