Code and data can be found on Github Figured I should chime in here since it’s been… let’s see here… ah, 4 months. I had previously promised to write about some NCAA hockey projects I had been working on. That… will have to wait -_-. In the meantime, let’s look instead at some Saturday Night Live data! For those not familiar, Saturday Night Live, commonly abbreviated as ‘SNL’, is a comedy-variety show that airs live on NBC on select Saturday nights over the course of a ‘season’, which runs from September through May.
Code for this worked example can be found on Github While most of my work posted here over the past year has been inspired by hockey statistics, the majority of my time is spent doing actual work on a much more important problem. Let’s say you know someone in need of a kidney transplant, and after careful reflection on the risks, you are willing to donate one of your kidneys to your friend.
Code for the maps can be found on Github Note that some of the maps on this page are static. To interact with the maps, visit the Shiny app Been looking at some NCAA hockey data lately. Have a few ideas kicking around, none of which are really beyond the idea stage at this point… In the meantime, since I haven’t posted anything in a while, I figured I could at least put up something fun.
Code for this analysis is availabile on Github Thought I would offer up a quick post on something that had sidetracked me earlier this week, namely how to extract notes and annotations in Mendeley using R. Basically I was having a similar problem as Daniel Hynk here, which he solved using Python. I too use Mendeley as a reference manager, which also has the handy feature of allowing users to add their own annotations and notes to their saved documents.
Code for this analysis is availabile on Github In the 2016 NHL playoffs, the Eastern Conference champion Pittsburgh Penguins, coached by Mike Sullivan, defeated the Western Conference champion San Jose Sharks, coached by Peter DeBoer, in a best of seven series to win the fourth Stanley Cup in their franchise’s history. In a series like this, with many ups and downs for both teams, the coach, serving as a spokesman for his team to the media, is under especially high pressure.
Code for this analysis is availabile on Github I plan on using this space to post my musings on statistics that, shall we say, fall outside of the hallowed halls of academia. For the most part, this will mean lots of R, and lots of hockey and Twitter data. I hope to share something interesting every few weeks, though with my ultra hectic (wink) grad student life, we’ll see how long I can keep that promise…