Blog Posts by Andrew B. Collier / @datawookie

Having picked up a viral infection days before this year’s Comrades Marathon, on 1 June I was left with time on my hands and somewhat desperate for any distraction. I spent some time looking at my archive of Comrades data and considering some new questions. For example, what are the chances of two runners passing through halfway and the finish line at exactly the same time? How likely is it that three runners achieve the same feat?

It’s something that I do surprisingly often: concatenating a list of data frames into a single (possibly quite enormous) data frame. Until now my naive solution worked pretty well. However, today I needed to deal with a list of over 6 million elements. The result was hours of page thrashing before my R session finally surrendered. I suppose I should be happy that my hard disk survived.

Although I have been thinking vaguely about my Plan A goal of a Bill Rowan medal at the Comrades Marathon this year, I have not really put a rigorous pacing plan in place. I know from previous experience that I am likely to be quite a bit slower towards the end of the race. I also know that I am going to lose a few minutes at the start. How fast does this mean I need to run in order to get from Pietermaritzburg to Durban in under 9 hours?

The Commitments of Traders (COT) report is issued weekly by the Commodity Futures Trading Commission (CFTC). It reflects the level of activity in the futures markets. The report, which is issued every Friday, contains the data from the previous Tuesday.

There was some significant bias in the histogram from my previous post: the data from all years were lumped together. This is important because as of 2003 (when the Vic Clapham medal was introduced) the final cutoff for the Comrades Marathon was extended from 11:00 to 12:00. In 2000 they also applied an extended cutoff.

Most novice Comrades Marathon runners finish the race on their first attempt and the majority of them walk (shuffle, crawl?) away with Bronze medals.

In two previous posts in this series I have wrangled NEO orbital data into R and then solved Kepler’s Equation to get the eccentric anomaly for each NEO. The final stage in the visualisation of the NEO orbits will be the transformation of locations from the respective orbital planes into a single reference frame.

I have been thinking a little more about those mysterious negative splits. Not too surprisingly, this thinking happened while I was out running along the Durban beachfront this morning.

Following on from my previous post about Near Earth Objects, today we are going to solve Kepler’s Equation to find the eccentric anomaly, which is the next step towards plotting the positions of these NEOs relative to Earth.

With this year’s Comrades Marathon just less than a month away, I was reminded of a story from earlier in the year. Mark Dowdeswell, a statistician at Wits University, found evidence of cheating by some middle and back of the pack Comrades runners. He identified a group of 20 athletes who had suspicious negative splits: they ran much faster in the second half of the race. There was one runner in particular whose splits were just too good to be true. When the story was publicised, this particular runner claimed that it was a conspiracy.

The recent story about a skydiver nearly being hit by falling meteor got me thinking about all the pieces of rock floating around in near-Earth space. Despite the fact that the supposed meteor was probably just a chunk of rock mistakenly packed in with a parachute, the fact that something like that could actually happen is quite intriguing. And not a little frightening.

Myfxbook provides an interface to your FOREX trading accounts as well as an active trading community.

The next stage in my earthquake analysis project is to partition the events into groups with epicentre over land or water.

Around 199 years ago the largest volcano in recorded history, Mount Tambora, erupted, spewing an enormous volume of molten rock and ash into the atmosphere and onto the surrounding land.

I am working on a project related to secondary effects of earthquakes. To guide me in the analysis I need a chart showing the location, magnitude and depth of recent earthquakes. A host of such charts are already available, but since I had the required data on hand, it seemed like a good idea to take a stab at it myself.

Does the transition to and from Daylight Saving Time (DST) have a (significant) effect on the stock market?

A few days ago I posted about Filtering Data with L2 Regularisation. Today I am going to explore the other filtering technique described in the paper by Tung-Lam Dao.

I have just finished reading Momentum Strategies with L1 Filter by Tung-Lam Dao. The smoothing results presented in this paper are interesting and I thought it would be cool to implement the L1 and L2 filtering schemes in R. We’ll start with the L2 scheme here because it has an exact solution and I will follow up with the L1 scheme later on.

This morning my wife presented me with a rather interesting statistic: a healthy couple has a 25% chance of conception every month [1], and that this should result in a 75% to 85% chance of conception after a year. This sounded rather interesting and it occurred to me that it really can’t be that simple. A lot of variables influence this probability. Certainly age should be a factor and, after a short search, I found some more age-specific information which indicated that for a woman in her thirties, the probability is only around 15% [2,3].

I got quite inspired by the EXIF with R post on the Timely Portfolio blog and decided to do a similar analysis with my photographic database.