Blog Posts by Andrew B. Collier / @datawookie

I’ve added mortality data to the lifespan package. A result that immediately emerges from these data is that average life expectancy is steadily climbing.

Based on some feedback to a previous post I normalised the birth counts by the (average) number of days in each month. As pointed out by a reader, the results indicate a gradual increase in the number of conceptions during (northern hemisphere) Autumn and Winter, roughly up to the end of December. Normalising the data to give births per day also shifts the peak from August to September.

In a previous post I showed that the data from www.baseball-reference.com support Malcolm Gladwell’s contention that more professional baseball players are born in August than any other month. Although this might be explained by the 31 July cutoff for admission to baseball leagues, it was suggested that it could also be linked to a larger proportion of babies being born in August.

I’ve got a massive bunch of zip archives, each of which contains only a single file. And the name of the enclosed file varies. Dealing with these data is painful.

It’d be a lot more convenient if the files were compressed with gzip or bzip2 and had a consistent naming convention. How would you go about making that conversion without actually unpacking the zip archive, finding the name of the enclosed file and then recompressing? Enter funzip.

The cutoff date for almost all nonschool baseball leagues in the United States is July 31, with the result that more major league players are born in August than in any other month. Malcolm Gladwell, Outliers

A quick analysis to confirm Gladwell’s assertion above. Used data scraped from www.baseball-reference.com.

We are planning to host one of the three inaugural satRday conferences in Cape Town during 2017. The [R Consortium](https://www.r-consortium.org/) has committed to funding three of these events: one will be in Hungary, another will be somewhere in the USA and the third will be at an international destination. At present Cape Town is dicing it out with Monterrey (Mexico) for the third location.

Creating The Next Rembrandt: using data to touch the human soul. How a team from ING, Microsoft, TU Delft, Mauritshuis and Rembrandthuis used technology to synthesise a painting in the style of the Dutch master, Rembrandt, almost 350 years after his death.

As part of International Open Data Day we spent the morning with a bunch of like minded people poring over some open Census South Africa data. Excellent initiative, @opendatadurban, I’m very excited to see where this is all going and look forward to contributing to the journey!

I used to spend an inordinate amount of time digging through lightning data. These data came from a number of sources, the World Wide Lightning Location Network (WWLLN) and LIS/OTD being the most common. I recently needed to work with some Hierarchical Data Format (HDF) data. HDF is something of a niche format and, since that was the format used for the LIS/OTD data, I went to review those old scripts. It was very pleasant rediscovering work I did some time ago.

Setting up an automated job under Linux is a cinch thanks to cron. Doing the same under Windows is a little more tricky, but still eminently doable.

I previously wrote about some R code for downloading Option Chain data from Google Finance. I finally wrapped it up into a package called flipsideR, which is now available via GitHub. Since I last wrote on this topic I’ve also added support for downloading option data from the Australian Securities Exchange (ASX).

This morning I read Wendy Kan’s interesting post on Creating Santa’s Stolen Sleigh. I hadn’t really thought too much about the process of constructing an optimisation competition, but Wendy gave some interesting insights on the considerations involved in designing a competition which was both fun and challenging but still computationally feasible without military grade hardware.

This seems like an opportune time to jot down some of my personal notes and also take a look at the results. I know that this sort of discussion is normally the prerogative of the winners and I trust that my ramblings won’t be viewed as presumptuous.

I routinely use melt() and cast() from the reshape2 package as part of my data munging workflow. Recently I’ve noticed that the data frames I’ve been casting are often extremely sparse. Stashing these in a dense data structure just feels wasteful. And the dismal drone of page thrashing is unpleasant.

I had a look around for an alternative. As it turns out, it’s remarkably easy to cast a sparse matrix using sparseMatrix() from the Matrix package. Here’s an example.

Walmart Trip Type Classification was my first real foray into the world of Kaggle and I’m hooked. I previously dabbled in What’s Cooking but that was as part of a team and the team didn’t work out particularly well. As a learning experience the competition was second to none. My final entry put me at position 155 out of 1061 entries which, although not a stellar performance by any means, is just inside the top 15% and I’m pretty happy with that. Below are a few notes on the competition.

It’s not my personal choice, but I have to spend a lot of my time working under Windows. Installing MongoDB under Ubuntu is a snap. Getting it going under Windows seems to require jumping through a few more hoops. Here are my notes. I hope that somebody will find them useful.

I was asked to review Learning Shiny (Hernán G. Resnizky, Packt Publishing, 2015). I found the book to be useful, motivating and generally easy to read. I’d already spent some time dabbling with Shiny, but the book helped me graduate from paddling in the shallows to wading out into the Shiny sea.