The commute to my workplace is 90 minutes each way. Podcasts are my friend. I’m a long-time listener of In Our Time and enjoyed the recent episode about The Danelaw.
Melvyn and I hail from the same part of the world, and I learned as a child that many of the local place names there were derived from Old Norse or Danish. Notably: places ending in -by denote a farmstead, settlement or village; those ending in -thwaite mean a clearing or meadow.
So how local are those names? Time for some quick and dirty maps using R.
The recent ABC News article Australia’s pollution mapped by postcode reveals nation’s dirty truth is interesting. It contains a searchable table, which is useful if you want to look up your own suburb. However, I was left wanting more: specifically, the raw data and some nice maps.
So here’s how I got them, using R.
I love it when researchers take the time to share their knowledge of the computational tools that they use. So first, let me point you at Environmental Computing, a site run by environmental scientists at the University of New South Wales, which has a good selection of R programming tutorials.
One of these is Making maps of your study sites. It was written with the specific purpose of generating simple, clean figures for publications and presentations, which it achieves very nicely.
I’ll be honest: the sole motivator for this post is that I thought it would be fun to generate the map using Leaflet for R as an alternative. You might use Leaflet if you want:
- An interactive map that you can drag, zoom, click for popup information
- A “fancier” static map with geographical features of interest
- concise and clean code which uses pipes and doesn’t require that you process shapefiles
The R language provides many different tools for creating maps and adding data to them. I’ve been using the leaflet package at work recently, so I thought I’d provide a short example here.
Whilst searching for some data that might make a nice map, I came across this article at ABC News. It includes a table containing Australian members of parliament, their electorate and their voting intention regarding legalisation of same-sex marriage. Since I reside in New South Wales, let’s map the data for electorates in that state.
New Zealand earthquake density 2010 – November 2016
Using R to add data to maps has been pretty straightforward for a few years now
. That said, it seems easier than ever to do things like use map APIs (e.g.
Google, Open Street Map), overlay quite complex data visualisations (e.g.
“heatmap-style” densities) and even generate animations.
A couple of key R packages in this space: ggmap and gganimate. To illustrate, I’ve used data from the recent New Zealand earthquake to generate some static maps and an animation. Here’s the Github repository and a report. Thanks to Florian Teschner for a great ggmap tutorial which got me started.
My own work in bioinformatics to date has not (sadly!) required much analysis of geospatial data but I can see use cases in many areas – environmental microbiology, for example.
Can someone please plot the BioStar users on a Google Map?
Sounds like a challenge. Let’s go.
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