Anyone that follows US politics will be aware of the tremendous changes and volatility that has struck the US political landscape in the past year. In this post, I leverage third-party data to surface who are the most frequent liars, and show how to build a containerized Shiny app to ... [Read more...]

In this post, I leverage the SportVu data available for all NBA players active during the 2014-2015 season to not only infer players with the most similar playing styles, but also teams with the most similar rosters. http://tlfvincent.github.io/2016/03... [Read more...]

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In this post, I will focus on the analysis of available R packages and the behavior of its users. In essence, this involves looking at the data in two different ways (1) relationships among available R packages in CRAN and (2) tracking the behavior of R users through download logs on CRAN ... [Read more...]

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While the NBA finals are in full swing and the two best teams are battling it out for the ultimate prize, another 28 are now in summer vacation. In order to achieve their goal of still playing this time next year, teams often look to improve their roster through trades, player ... [Read more...]

Unless you’ve been living under a rock for the last few years, you have probably heard of TV shows such as Breaking Bad, Mad Men, How I Met Your Mother or Game of Thrones. While I generally don’t spend a whole lot of time watching TV, I have ... [Read more...]

It’s no secret that I enjoy basketball, but I’ve often wondered about the carbon footprint that can be caused by 30 teams each playing an 82-game season. Ultimately, that’s 2460 air flights across the whole of the USA, each carrying 30+ individuals. For these reasons, I decided to investigate the ... [Read more...]

While good draft picks and deft management can help you win championships, there is no doubt that NBA teams can massively gain, or lose, by trading players with one another. Here, I played around with some publicly available data given at basketball-reference.com, and had a look at the numbers ... [Read more...]

NBA basketball is the one the sports I enjoy watching the most. As I was ordering my (undisclosed amount)th beer while watching a game during after-work hours, it occurred to me how often I had seen sparsely populated arenas during games, with large areas of seats going unoccupied. This ... [Read more...]

This post is going to differ slightly from the data-orientated material that I usually publish. I was recently playing around with the Google trends API and came across some very interesting…well….trends. There has definitely been a huge amount of publicity surrounding “Big Data”, maybe even too much. For ... [Read more...]

I have been watching the awesome Netflix show “House of Cards” and been fascinated by the devious schemes that Underwood is constantly plotting. The show often mentions approval ratings and it got me to wondering what Obama’s ratings currently were, and all other past US president  for that matter. ... [Read more...]

I recently discovered the Capitol Words API and have had some fun playing around with it. One of the categories in the API allows you to search for the words spoken by the senators of each state in the USA, and I was interested in finding out the number of ... [Read more...]

Although I have a heavy background in statistics (and therefore am primarily an R user), I find that my overall knowledge in computer science is generally lacking. Therefore, I have recently delved deeper into learning about data structures, their associated ADT’s and how they can be implemented. At the ... [Read more...]

Python has an extremely intuitive and straightforward way of dealing with APIs, and makes it simple for people like you or me to access and retrieve information from databases. Before I quickly describe how to use APIs in Python, maybe we should begin with: What is an API? API (Application ... [Read more...]

While browsing through different sites, I randomly cam across the ominous-sounding disaster center website. There is a fair amount of data that could be analyzed there, but my attention was caught by an entry stating that they had just updated the “1965 to 2012 State Crime Pages”. From there, I chose the ... [Read more...]

I’ve recently come across the https://www.undata-api.org/ website, which makes available all the great data that has been gathered by the UN. There’s literally a thousand different datasets one could analyze, and I intend on doing just that, but for some reason I opted to look ... [Read more...]

Living in NYC is not good for one’s cooking skills. There are just too many mouth-watering options out there that always convince me to eat out rather that stay in line for two hours at Trader Joe’s. Also, this means that my fridge always has room for life ... [Read more...]

Recently, I came across this interesting blog post http://blog.revolutionanalytics.com/2013/12/k-means-clustering-86-single-malt-scotch-whiskies.html by the Revolutions blog poster Luba Gloukhov. This post initially caught my attention because of the originality of the dataset: 86 scottish whiskeys marked on a scale of 0-4 in 12 different taste profile (source data is ... [Read more...]

A file format that I am seeing more and more often is the JSON (JavaScript Object Notation) format. JSON is an open standard format in human-readable form that is used to transmit data between servers and web applications. Below is a typical example of data in JSON format. {"votes":  {   "funny": 0,   "... [Read more...]