Just use a scatterplot. Also, Sydney sprawls.

Dual-axes at tipping-point

Sydney’s congestion at ‘tipping point’ blares the headline and to illustrate, an interactive chart with bars for city population densities, points for commute times and of course, dual-axes.

Yuck. OK, I guess it does show that Sydney is one of three cities that are low density, but have comparable average commute times to higher-density cities. But if you’re plotting commute time versus population density…doesn’t a different kind of chart come to mind first? y versus x. C’mon.

Let’s explore.
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Twitter coverage of the Australian Bioinformatics & Computational Biology Society Conference 2017

You know the drill by now. Grab the tweets. Generate the report using RMarkdown. Push to Github. Publish the report.

This time it’s the Australian Bioinformatics & Computational Biology Society Conference 2017, including the COMBINE symposium. Looks like a good time was had by all in Adelaide.

A couple of quirks this time around. First, the rtweet package went through a brief phase of returning lists instead of nice data frames. I hope that’s been discarded as a bad idea :) There also seem to be additional columns, new column names and list-columns in the output from the latest search_tweets(), so there goes my previous code…

Second, given that most Twitter users have had 280 characters since about November 7, is this reflected in the conference tweets?

With thanks to Andrew Lonsdale for clearing up my confusion and pointing me to Twitter extended mode, the answer is “yes, somewhat”. Plenty of tweets are still hitting the 140 limit though: time to update those clients?

Mapping data using R and leaflet

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.

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Feels like a dry winter – but what does the data say?

A reminder that when idle queries pop into your head, the answer can often be found using R + online data. And a brief excursion into accessing the Weather Underground.

One interesting aspect of Australian life, even in coastal urban areas like Sydney, is that sometimes it just stops raining. For weeks or months at a time. The realisation hits slowly: at some point you look around at the yellow-brown lawns, ovals and “nature strips” and say “gee, I don’t remember the last time it rained.”

Thankfully in our data-rich world, it’s relatively easy to find out whether the dry spell is really as long as it feels. In Australia, meteorological data is readily available via the Bureau of Meteorology (known as BoM). Another source is the Weather Underground (WU), which has the benefit that there may be data from a personal weather station much closer to you than the BoM stations.

Here’s how you can access WU data using R and see whether your fuzzy recollection is matched by reality.
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Presentations online for Bioinformatics FOAM 2015

Off to Melbourne tomorrow for perhaps my favourite annual work event: the Bioinformatics FOAM (Focus on Analytical Methods) meeting, organised by CSIRO.

Unfortunately, but for good reasons, it’s an internal event this year, but I’m putting my presentations online. I’ll be speaking twice; the first for Thursday is called “Online bioinformatics forums: why do we keep asking the same questions?” It’s an informal, subjective survey of the questions that come up again and again at bioinformatics Q&A forums such as Biostars and my attempt to understand why this is the case. Of course one simple answer might be selection bias – we don’t observe the users who came, found that their question already had an answer and so did not ask it again. I’ll also try to articulate my concern that many people view bioinformatics as a collection of recipe-style solutions to specific tasks, rather than a philosophy of how to do biological data analysis.

My second talk on Friday is called “Should I be dead? a very personal genomics.” It’s a more practical talk, outlining how I converted my own 23andMe raw data to VCF format, for use with the Ensembl Variant Effect Predictor. The question for the end – which I’ve left open – is this: as personal genomics becomes commonplace, we’re going to need simple but effective reporting tools that patients and their clinicians can use. What are those tools going to look like?

Looking forward to spending some time in Melbourne and hopefully catching up with this awesome lady.

Better living through informatics: in search of koalas

In 2015, I’d like to write, think and do more about things that I care about. One of those things happens to be the koala. Now, this being a blog about bioinformatics and computational biology, I can’t just start writing about any old thing that takes my fancy…I guess. So in this post I’m going to stretch the definition to include ecological informatics and tell you the story of how I achieved a long-held ambition using one of my favourite online resources, The Atlas of Living Australia. And then we’ll wrap up with a quick survey of the (sorry) state of marsupial genomics.
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“Health Hack”: crossing the line between hackfest and unpaid labour

I’ve never attended a hackathon (hack day, hackfest or codefest). My impression of them is that there is, generally, a strong element of “working for the public good”: seeking to use code and data in new ways that maximise benefit and build communities.

Which is why I’m somewhat mystified by the projects on offer at the Sydney HealthHack. They read like tenders for consultants. Unpaid consultants.

The projects – a pedigree drawing tool, a workflow to process microscopy images, a statistical calculator and a mutation discovery pipeline – all describe problems that competent bioinformaticians could solve using existing tools in a relatively short time. For example, off the top of my head, ImageJ or CSIRO’s Workspace might be worth looking at for problem (2). The steps described in problem (4) – copy and paste between spreadsheets, manual inspection and manipulation of sequence data – should be depressingly familiar examples to many bioinformaticians. This project can be summarised simply as “you’re doing it wrong because you don’t know any better.”

The overall tone is “my research group requires this tool, but we’re unable to employ anyone to do it.” There is no sense of anything wider than the immediate needs of individual researchers. This does not seem, to me, what hackfest philosophy is all about.

This raises an issue that I think about a lot: how do we (the science community) best get the people with the expertise (in this case, bioinformaticians) to the people with the problems? In an ideal world the answer would be “everyone should employ at least one.” I wonder about the market (Australian or more generally) for paid consulting “biological data scientists”? We complain that we’re under-valued; well, perhaps it is we who are doing the valuation when we offer our skills for free.

Git for bioinformaticians at the Bioinformatics FOAM meeting

Last week, I attended the annual Computational and Simulation Sciences and eResearch Conference, hosted by CSIRO in Melbourne. The meeting includes a workshop that we call Bioinformatics FOAM (Focus On Analytical Methods). This year it was run over 2.5 days (up from the previous 1.5 by popular request); one day for internal CSIRO stuff and the rest open to external participants.

I had the pleasure of giving a brief presentation on the use of Git in bioinformatics. Nothing startling; aimed squarely at bioinformaticians who may have heard of version control in general and Git in particular but who are yet to employ either. I’m excited because for once I am free to share, resulting in my first upload to Slideshare in almost 4.5 years. You can view it here, or at the Australian Bioinformatics Network Slideshare, or in the embed below.

See the slides…

Lots of “open goodness” in the AU/NZ region

January/February are exciting months for open [data|research|science|access] proponents in our region – by which I mean Australia and New Zealand.

First, we’ve enjoyed a speaking tour by Sir Tim Berners-Lee, during which he discussed the benefits of open data several times. I was able to attend two events in Sydney in person and a third, linux.conf.au, by video stream. The events were the work of many people but in particular, Pia Waugh. Go follow her on Twitter, now.

Next – I wish I had been able to get to this one – the Open Research Conference on February 6-7, University of Auckland. I’m enjoying the high-quality live stream right now. Flying the flag for Sydney are Mat and Alex.

Not strictly under the “open” umbrella but worth a mention anyway: software carpentry is in town, February 7-8, just up the road from me at Macquarie University. Looking forward to hearing some reports from that.

Open Access: sometimes all it takes is the right person

We can debate the economics, complexities, details, implementation… of open access publishing for as long as we like. However, the basic principle: that publicly-funded research should be publicly-accessible seems to me at least, very obviously correct and “the right thing to do”.

So this, from April 2012, was very depressing.

Open access not as simple as it sounds: outgoing ARC boss

For those outside Australia, the ARC is the Australian Research Council. Much debate ensued in which one contributor to the comment thread wrote:

…it is particularly galling that Sheil is projecting her own simplistic understanding of open access onto its advocates. Hopefully she will be replaced at the Australian Research Council by someone who understands and supports open access.

VoilĂ .

The ARC has introduced a new open access policy for ARC funded research which takes effect from 1 January 2013. According to this new policy the ARC requires that any publications arising from an ARC supported research project must be deposited into an open access institutional repository within a twelve (12) month period from the date of publication.

I did giggle at the assumption that the author’s version of their article is by default a Word document, but then I guess that’s true for > 90% of authors.

Outcomes like this come dangerously close to restoring hope.