Andrew B. Collier / @datawookie

With the circlize package, putting together a Chord Diagram is simple.

Predicting Comrades Marathon finishing times is challenging and people have tried a few different approaches. Lindsey Parry, for example, suggests that you use two and a half times your recent marathon time. Sports Digest provides a calculator which predicts finishing time using recent times over three distances. I understand that this calculator is based on the work of Norrie Williamson.

Apparently 14 runners have been disqualified for failing to complete the full distance at the 2012 and 2013 Comrades Marathons.

I’m not convinced.

A contribution which I wrote for Antarctica and the Arctic Circle: A Geographic Encyclopedia of the Earth’s Polar Regions.

Following up on my previous post about generating Sankey plots with the riverplot package. It’s also possible to generate plots which have constant coloured edges.

A contribution which Claire and I wrote for Antarctica and the Arctic Circle: A Geographic Encyclopedia of the Earth’s Polar Regions.

I’ve updated my Comrades Marathon pacing chart to include both the Up and Down runs. You can grab it here. The data for this year’s race are not yet finalised (I think we will be running 87 km), but you can make changes when it’s all confirmed.

The cost of fuel in South Africa (and I imagine pretty much everywhere else) is a contentious topic. It varies from month to month and, although it is clearly related to the price of crude oil and the exchange rate, various other forces play an influential role.

A contribution which I wrote for Antarctica and the Arctic Circle: A Geographic Encyclopedia of the Earth’s Polar Regions. As a side note, I am normally quite pedantic about “Earth” and “Sun” being capitalised but the editor of the Encyclopaedia evidently had different views on this point.

A contribution which Claire and I wrote for Antarctica and the Arctic Circle: A Geographic Encyclopedia of the Earth’s Polar Regions.

A contribution which I wrote for Antarctica and the Arctic Circle: A Geographic Encyclopedia of the Earth’s Polar Regions.

I have just been trying to import some data into R. The data were exported from a SQL Server client in tab-separated value (TSV) format. However, reading the data into R the “usual” way produced unexpected results:

The book Graph Databases by Ian Robinson, Jim Webber and Emil Eifrem gives an engaging overview of Graph Databases, describing typical use cases and illustrating the syntax used to construct and query them.

The book R for Business Analytics by Ajay Ohri sets out to look at “some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages.” In my opinion it succeeds in covering an extensive range of topics but fails to provide anything of substantial use to its intended audience. At least, not anything that could not be uncovered by a brief internet search.

I recently read an article (no longer available, unfortunately) which showed how to download Option Chain data from Google Finance using R.

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.

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. 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.

On Friday I received my copy of The Official Results Brochure for the 2013 Comrades Marathon. Always makes for a diverting half an hour’s reading. And the tables at the front provide some very interesting statistics. Seemed like a good opportunity to update my Chart of Comrades Winners.

I am busy working on a project which uses data from the World Wide Lightning Location Network (WWLLN). Specifically, I am trying to reproduce some of the results from Orville, Richard E, Gary R. Huffines, John Nielsen-Gammon, Renyi Zhang, Brandon Ely, Scott Steiger, Stephen Phillips, Steve Allen, and William Read. 2001. “Enhancement of Cloud-to-Ground Lightning over Houston, Texas”. Geophysical Research Letters 28 (13): 2597–2600.

Amy Cuddy gives a great talk. Provided me with lots to think about and I will happily confess that I have struck a few power poses (but only after ensuring that I am quite alone)!

Following up on my recent post about implementing a queue as a reference class, I am going to derive a Priority Queue class.

I am working on a simulation for an Automatic Repeat-reQuest (ARQ) algorithm. After trying various options, I concluded that I would need an implementation of a queue to make this problem tractable. R does not have a native queue data structure, so this seemed like a good opportunity to implement one and learn something about Reference Classes in the process.

The Implementation

We use setRefClass() to create a generator function which will create objects of the Queue class.