I’ve been playing around with development data lately, trying to sketch together some pieces for an OpenLearn course on *data visualisation for development* (hopefully!), so I thought this would be a good test of how quickly I could find the data and confirm the results.

Working backwards, GDP data (in various adjusted forms) is available from the World Bank API, which I’ve been accessing via the remote data interface calls in *pandas* (for example, Easy Access to World Bank and UN Development Data from IPython Notebooks).

So where do get the aid ranking from?

There are two ways of doing this – one to look for local UK sources (eg from DFID perhaps), the other to look for international sources of data. The advantage of the former is that these are presumably the sources that whoever answered the question went to. The advantage of the latter is that we should be able to generalise the question to query similar rankings for aid distributed by other countries.

“Official Development Assistance” seems to be a key phrase, with a quick websearch for that phrase and the term “data” turning up this Aid statistics – charts, tables and databases resource page, which in turn points to a whole raft of datatables as Excel files detailing statistics on resource flows to developing countries; the International Development Statistics (IDS) online databases page links to several more general online databases. (There’s also a beta data.oecd.org site.)

Forsaking the raw data files for a minute, the site claims that “the Query Wizard for International Development Statistics [QWIDS] is the easiest way to search our database as it automatically extracts the most appropriate dataset from OECD.Stat to match your search” – so let’s try that… QWIDS.

Nice and simple then…?!

A bit of tinkering (setting the donor, unticking recipients so only countries – rather than countries and groupings are included) gives what I think is the data for the aid disbursements from the UK to other countries, data I could export as a CSV file; but there are no tools onsite to help me look at the top 10.

Poking around, it looks like the data’s also there to allow us to look at disbursements (or perhaps just allocations) by donor country and sector into a particular country? Maybe?! This would then let us see how aid was being allocated from the UK to the top 10 recipients, broken down by sector, which might be more illuminating? I also wonder if there are any relationships between aid paid by donors into a particular sector, and imports into the recipient country from the donor country within the same sectors? For this, we need trade data breakdowns. (We can get total flows between countries (I think?!) but I’m not sure how to find the data broken down by sector?)

The stats.oecd.org site does let us sort, but I couldn’t find an easy or clean way to limit results to countries, and exclude groupings:

The order (of aid disbursements from the UK in 2012) has the same rank order as the response to my MP’s question.

For the GDP and GDP per capita data, we can go to the World Bank:

Note a couple of things – units tend to be given in US dollars rather than Sterling; there are all sorts of US dollars… (see for example Accounting for Inflation – Deflators, or “What Does ‘Prices in Real Terms’ Actually Mean?”).

Hmm… maybe it would have been easier to find the data on the DFID site instead…

PS Indeed it was – Statistics on International Development 2013 – Tables has a link to a dataset that contains the league table: “Table 4: Top Twenty Recipients UK Net Bilateral ODA 2010 – 2012”.

]]>However, here’s a neat idea – *data golf* – as described in a post by Bogumił Kamiński (RGolf) that I found via RBloggers:

There are many code golf sites, even some support R. However, most of them are algorithm oriented. A true RGolf competition should involve transforming a source data frame to some target format data frame.

So the challenge today will be to write a shortest code in R that performs a required data transformation

An example is then given of a data reshaping/transformation problem based on a real data task (wrangling survey data, converting it from a long to a wide format in the smallest amount of R.

Of course, R need not be the only language that can be used to play this game. For the course I’m currently writing, I think I’ll pitch *data golf* as a Python/pandas activity in the section on data shaping. OpenRefine also supports a certain number of reshaping transformations, so that’s another possible data golf course(?). As are spreadsheets. And so on…

Hmmm… thinks… pivot table golf?

Also related: string parsing/transformation or partial string extraction using regular expressions; for example, Regex Tuesday, or how about Regex Crossword.

]]>In a paper that may or may not have been presented at the First European Congress of Mathematics in Paris, July, 1992, Prof. David Singmaster reflected on “The Unreasonable Utility of Recreational Mathematics”.

To begin with, it is worth considering what is meant by recreational mathematics.

…

First, recreational mathematics is mathematics that is fun and popular – that is, the problems should be understandable to the interested layman, though the solutions may be harder. (However, if the solution is too hard, this may shift the topic from recreational toward the serious – e.g. Fermat’s Last Theorem, the Four Colour Theorem or the Mandelbrot Set.)Secondly, recreational mathematics is mathematics that is fun and used as either as a diversion from serious mathematics or as a way of making serious mathematics understandable or palatable. These are the pedagogic uses of recreational mathematics. They are already present in the oldest known mathematics and continue to the present day.

…

These two aspects of recreational mathematics – the popular and the pedagogic – overlap considerably and there is no clear boundary between them and “serious” mathematics.

…

How is recreational mathematics useful?Firstly, recreational problems are often the basis of serious mathematics. The most obvious fields are probability and graph theory where popular problems have been a major (or the dominant) stimulus to the creation and evolution of the subject. …

Secondly, recreational mathematics has frequently turned up ideas of genuine but non-obvious utility. …

Anyone who has tried to do anything with “real world” data knows how much of a puzzle it can represent: from finding the data, to getting hold of it, to getting it into a state and a shape where you can actually work with it, to analysing it, charting it, looking for pattern and structure within it, having a conversation with it, getting it to tell you one of the many stories it may represent, there are tricks to be learned and problems to be solved. And they’re fun.

An obvious definition [of recreational mathematics] is that it is mathematics that is fun, but almost any mathematician will say that he enjoys his work, even if he is studying eigenvalues of elliptic differential operators, so this definition would encompass almost all mathematics and hence is too general. There are two, somewhat overlapping, definitions that cover most of what is meant by recreational mathematics.

…the two definitions described above.

So how might we define “recreational data”. For me, recreational data activities are, in who or in part, data investigations, involving one or more steps of the data lifecycle (discovery, acquisition, cleaning, analysis, visualisation, storytelling). They are the activities I engage in when I look for, or behind, the numbers that appear in a news story. They’re the stories I read on FullFact, or listen to on the OU/BBC co-pro More or Less; they’re at the heart of the beautiful little book that is The Tiger That Isn’t; recreational data is what I do in the “Diary of a Data Sleuth” posts on OpenLearn.

*Recreational data* is about the joy of trying to find stories in data.

Recreational data is, or can be, the data journalism you do for yourself or the sense you make of the stats in the sports pages.

Recreational data is a safe place to practice – I tinker with Twitter and formulate charts around Formula One. But remember this: “*recreational problems are often the basis of serious [practice]*“. The “work” I did around Visualising Twitter User Timeline Activity in R? I can (and do) reuse that code as the basis of other timeline analyses. The puzzle of plotting connected concepts on Wikipedia I described in Visualising Related Entries in Wikipedia Using Gephi? It’s a pattern I can keep on playing with.

If you think you might like to do some doodle of your own with some data, why not check out the School Of Data. Or watch out on OpenLearn for some follow up stories from the OU/BBC co-pro of Hans Rosling’s award winning Don’t Panic

]]>