Blog Posts by Andrew B. Collier / @datawookie

Manfred Schroeder’s book Fractals, Chaos, Power Laws: Minutes from an Infinite Paradise is a fruitful source of interesting topics and projects. He gives a thorough description of Diffusion-Limited Aggregation (DLA) as a technique for simulating physical processes which produce intricate branching structures. Examples, as illustrated below, include Lichtenberg Figures, dielectric breakdown, electrodeposition and Hele-Shaw flow.

On 12 November 2014 the Philae lander from the Rosetta robotic space probe successfully landed on comet 67P/Churyumov–Gerasimenko. A couple of great comics to commemorate this momentous achievement.

A few years ago I ordered a copy of the 2005 edition of Creating More Effective Graphs by Naomi Robbins. Somewhat shamefully I admit that the book got buried beneath a deluge of papers and other books and never received the attention it was due. Having recently discovered the R Graph Catalog, which implements many of the plots from the book using ggplot2, I had to dig it out and give it some serious attention.

Recently I was in my local Standard Bank branch. After finally reaching the front of the queue and being helped by a reasonably courteous young man, I was asked if I would mind filling out a survey. Sure. No problem. I had been in the bank for 30 minutes, I could probably afford another 30 seconds.

I have been looking for an intuitive way to plot flows or connections between states in a process. An obvious choice is a Sankey Plot, but I could not find a satisfactory implementation in R… until I read the post by January Weiner. His {riverplot} package does precisely what I am need.

In my previous post I gave some background information on the Commitments of Traders report along with a selection of summary plots.

A paper authored with my very talented student, Claire Antel, entitled “Investigating Dunedin whistlers using volcanic lightning” has just been published in Geophysical Research Letters. The paper looks at electromagnetic signals (“whistlers”) received at Dunedin, New Zealand, caused by lightning over volcanoes thousands of km away near the Aleutian Chain.

It has been suggested that the average Comrades Marathon runner is gradually getting older. As an “average runner” myself, I will not deny that I am personally getting older. But, what I really mean is that the average age of all runners taking part in this great event is gradually increasing. This is not just an idle hypothesis: it is supported by the data. If you’re interested in the technical details of the analysis, these are included at the end, otherwise read on for the results.

If you are creating an EA or indicator from scratch, then the MetaTrader editor places the files in the correct location and the terminal is automatically able to find them. However, if the files originate from a third party then you will need to know where to insert them so that they show up in the terminal. For older builds of MetaTrader 4 the directory structure was fairly simple.

It looks likes one of the suspect runners from my previous posts cheated again in this year’s Comrades Marathon.

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. So 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.

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.