This post was inspired by this competition, hosted by JumpingRivers. The competition asks you to extract data from their GitHub account containing the details of R groups and R ladies groups around the world. With this data it asks you to create a visualisation, of any kind. I’ve been working on my visualisation skills in R so I thought I would have a go at entering.
Back before I moved to London and had to get rid of my car (sob), I used to do some of my best pondering while stuck in traffic. A thought that kept coming back to me was an optimisation problem about when is the best time to take your driving test. Driving lessons are expensive but a driving test is even more expensive - therefore how many lessons should you have before you take your test so as to spend the least amount of money? I always figured there would be some mathematical optimisation technique to come up with an answer that takes the price of driving lessons, the price of a driving test and a function that estimates your likelihood of passing the driving test given the number of lessons you’d had. However this always seemed to hurt my head too much to work out especially given I was trying to concentrate on driving at the same time. However since starting this blog I decided to explore this idea I’ve had for a few years and try to work it out.
Taylor Swift has just released her 6th studio album ‘Reputation’. The old Taylor is dead, and is her place is a new edgier Taylor, toughened from the years of media scrutiny, turbulent relationships and high profile celebrity feuds. As the title suggests, this is an album all about the contrast in how the world sees you to compared to who you really are and how a negative portrayal can affect your relationships. Whether you like the album or not (personally I love it), this post is not really about Taylor swift. This is about my first experience delving into the world of twitter scraping.