Unconstant Conjunction latest posts

Custom Hexbin Functions with ggplot

Recently, I wanted to create a map similar to James Cheshire’s crime map of London, which shows the most common crimes commited in a rectangular grid of points laid over London. Instead of using a rectangular grid, I wanted to use hexbins, but it turns out that ggplot needs a bit of prodding to do anything other than simply count the number of observations in each bin.

At the time I couldn’t find a good tutorial on writing custom hexbin functions, so this post is a reasonably thorough explanation of what I’ve made work.

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Linting Prose in Emacs

    10 February 2016 // tagged

Recently, I came across the Proselint project, which aspires to be something like a “linter” for written English prose. It differs from spelling or grammar checker in that it is primarily focused on logic and style, taking its advice from the work of many prominent writers on writing.

The project seems to be a little rough around the edges (code blocks seem to make it struggle, for example), but it’s also perfectly usable in its current form. It’s available as a Python package, so you can pip install proselint to get the command-line tool, proselint.

Thanks to the Flycheck framework it’s comically easy to get this imported into a Emacs writing environment. The following snippet will get you up and running in text and markdown modes:

(flycheck-define-checker proselint
  "A linter for prose."
  :command ("proselint" source-inplace)
  ((warning line-start (file-name) ":" line ":" column ": "
	    (id (one-or-more (not (any " "))))
	    (message) line-end))
  :modes (text-mode markdown-mode gfm-mode))

(add-to-list 'flycheck-checkers 'proselint)

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Expressing L-systems in Rust

I thought I’d jump on the Rust blogging bandwagon. Specifically, I’ve been toying with a new library to define and iterate over L-systems in a unique and Rust-inspired fashion. It’s been very rewarding to fool around with a type system so foreign to my roots in duck- and weakly-typed languages.

If you haven’t heard of L-systems before, they’re mostly used to make pretty pictures. I was able to create a little animation of the first seven iterations of an L-system that draws Penrose tiles (below).

Penrose Tile L-system

L-systems have put them to a variety of different uses, including modelling plants and trees, buildings and cities, and all manner of interesting geometric constructions. They hold their fascination in part because of the way in which a very small number of basic components and rules can evolve in startling ways over time. In other words, L-systems put emergence on display.

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Not All Population Maps Are Boring

As the venerable xkcd comic points out, unadjusted geographic data often ends up looking like a population map. Normally, this makes it kind of boring, since it doesn’t tell you anything new.

Except, of course, when a visualizing the population is exactly what you had in mind. I discovered recently that the Australian government keeps track of every public toilet in the country, for example — and what better way is there to learn about Australian geography than through such an important public utility?

Australia, In Public Toilets

As usual, the remainder of this post is a technical discussion of how I created the graphic above. It was a neat opportunity to make use of hexbinning, and I’m quite fond of the end result. I haven’t yet figured out a way to add a “shadow” made of hexagons to indicate the overall shape of Australia, though I would like to. The fully reproducible code can be found in my visualization repository on Github.

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Visualizing Health Expenditure using Spie Charts (and R)

Continuing with the theme of my last post, I wanted to put an interesting visualization together using some publically available data. The result is the following (somewhat surprising) graphic:


One of the looming issues in Canadian public policy is how to address the fact that our population is ageing, and that this will mean a larger burden on many of the social services that are more heavily consumed by those who are older. But just how uneven is the consumption of health services? The above should give you some idea of why this is viewed as a looming problem.

The remainder of this post is a technical discussion of how I created the visualization. I’m not quite satisfied with the overall approach (I think it takes quite a while before you can really read the graphic), but it does serve as a good technical demonstration of what can be accomplished in R.

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