Dataviz of the week, 24/5/2017

On Twitter, @SirSandGoblin is tracking polls before the UK general election in the medium of cross-stitch.

You just have to look. This is clearly the work of a dataviz genius. I have nothing more to say.

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Two great skills to leverage best-in-class big data science analytics

This came up on Twitter and lots of people were outraged, as you see in the replies and retweets.

Let’s unpack a couple of things.

  • appreciate – it’s not clear what he means by this. It could mean “Many software engineers will never be really good at data science using modern machine learning”, which seems like tautology (same goes for estate agents), but see software engineers below. It could mean “Many software engineers will never truly have an intuitive attraction to the elegant mathematical underpinnings of modern machine learning”, and in that case it is true that there is a connection between maths and, er, maths, but that’s not very interesting. Appreciating in this sense is an ivory tower luxury.
  • love – lord above, are you trying to fool me in love? I think high-pressure rote learning in the Asian mould would do the trick too. It seems irrelevant.


    Victorian Dad (c) Viz

  • as a teen – this is what most people hated about it, the gatekeeping and stereotype-enforcement. It’s clearly bollocks, so let’s not waste time on Someone Said Something Wrong On The Internet. If you want to learn now, here’s my reading page.
  • software engineers – if he really is talking about software engineers (isn’t that term, like, a bit 1990s?), then it sounds fair enough despite the inaccuracies and tautologies. Why would they want to or need to have anything to do with modern ML? I’m a statistician, but do enough programming to grasp what it is like to be a day-in, day-out coder. You just grab something that someone wrote — a random forests library perhaps — and plug it in. Why would you appreciate its theory? That’s a waste of time. You don’t go round appreciating the hell out of fibre broadband cables.
  • modern machine learning – I don’t know what is meant by this, but it’s interesting to me that there are some things in ML and stats like logistic regression, which have strong, mathematical underpinnings, which is to say that their asymptotics are understood, and other things in ML and not stats, like deep learning with backprop, which are kind of greedy, heuristic and do not have guaranteed or even understood asymptotics. Depending on what he means by this phrase, there might be nothing to appreciate. If there is something to appreciate, then it might not be that modern — logistic regression was pretty much finished theoretically in the 70s, PCA in the 30s.
  • math – this is the really interesting thing. Do you need maths to do data science well? It certainly helps with reading those tortuous theory papers (but they’re not that useful compared to messing about with software). It is not as useful as programming (hi, software engineers!) skills. The reason a lot of people get caught out is because they have done some analysis that ran, produced no error messages, but led to the wrong answer, and they had no mental tools to spot it. Maths will not give you that tool; you need to think about data and have messed around getting your hands dirty. I studied maths and enjoyed it and did pretty well, if I say so myself, but that has been of very little use to me. I’ve forgotten most of it.

    A page of my A-level maths revision notes. I have never had to do partial fractions. Ever.

    If you really do intend to be a methodological stats prof, then you’d better get good with the old x’s and y’s, but otherwise, install R and play.

Perhaps the one really useful skill I acquired is imagining data as points in space, rotating, distorting, projecting. I had to do a lot of that when doing a Masters dissertation project with PCA, MCA, etc. That has genuinely helped me to develop ideas and think about where things are going wrong.

The other important thing to think about is metrics – different ways of quantifying the distance from this data point to that one, because that underpins a lot of stuff that follows, whether stats or ML (notably loss / log-likelihood functions). And I have another blog post on this very topic coming up.

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Dataviz of the week, 17/5/2017 is a website that offers real-time tracking of pathogens as they evolve (flu, ebola, dengue, all your favourites are here). Data gets pulled in from various monitoring systems worldwide and represented with interactive content in several pretty ways:

Screen Shot 2017-05-16 at 15.24.19Screen Shot 2017-05-16 at 15.25.02Screen Shot 2017-05-16 at 15.24.37Screen Shot 2017-05-16 at 15.25.19Screen Shot 2017-05-16 at 15.25.36

They have their own libraries called fauna, augur and auspice, the last of these doing the dataviz stuff, and as far as I could tell built on D3. I don’t pretend to understand the genetic and genomic work that has to go on to process the raw data but that is clearly substantial.

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Dataviz of the week, 10/5/17

Font Map is an interactive website by designers Ideo which aims to represent typefaces in 2 dimensions so you can eyeball similar ones. They make a big deal out of “leveraging AI and convolutional neural networks to draw higher-vision pattern recognition”. I’m not sure what that sentence means, though I conclude they got a thrill out of it. (I refer to the opaque boardroom talk; I know perfectly well what these techniques are.) What we see on the screen is a classic horseshoe shape of dimension reduction that happens when you have an underlying continuum that mostly lies along one axis. You see this with principal components analysis, multiple correspondence analysis, multidimensional scaling, whatever. t-SNE screws around with it (read: anisotropically transforms the projected space) to straighten out that hoof.

Screen Shot 2017-05-09 at 13.45.14

On this basis, we seem to have one overarching scale from italic to bold. That’s not much of a breakthrough, and although there certainly is merit in a list of similar fonts, you don’t need a whizzy graphic for it. It would also be better done by humans, as some of the fonts are misplaced to my eye. But that’s CNNs for ya; I’d also like some exploration of what features are detected. In a blog post, Ideo’s project lead Kevin Ho explains the method. I don’t know to what extent the number of training images mattered, but that is something to think about if you are doing this sort of thing. Then there’s an image of “early results” through t-SNE that, to my mind, looks better than the final results, because more clusters emerge that way. It’s not clear how he then got to the final result, though it looks like maybe he just spared the t-SNE special sauce, or took the k-D (k>2) projection and then smacked it down further through PCA (ML people love PCA, they think it has magical powers). I don’t know. (You should check out this page on t-SNE, once you understand the principle, by those ninjas of interactivity Viegas & Wattenberg, plus Ian Johnson of Google Cloud).

All in all, you know, it’s fun, and it’s important to experiment (as my grandad said about tasting his own urine), but if you talk up the AI angle too much, people who know about it will start to doubt the quality of your work. That’s a pity but it can be guarded against by providing lots of details of your method and viewing it as an ongoing exploration, not a done deal. I say this as advice to young people, not criticism of Kevin Ho’s work because I just don’t know what he did.

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Dataviz of the week, 3/5/17

I’ve occasionally asked myself odd superimpose-geographies questions like “how far is it from A to B if they were in Winchester?” (because I can feel those distances better) or “would the West Kennet Long Barrow fit inside the Broadgate Centre?” (I’m sure we’ve all thought that). Hans Hack has made an online map like that, with a serious purpose, which superimposes Aleppo and the destroyed parts onto London.

Screen Shot 2017-05-03 at 10.07.10

It’s all done in leaflet.js and weighs in at 800 lines of code with a lot of generous — luxurious one might say — spacing, so it is well with your grasp to do something like this. It’s also just pretty, with sparing colour and layering of information with simple controls. There is also a Berlin version. I suppose you have to know the host city for it to hit home but then it’s a powerful message about the scale of it all.

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The peer-review log

As an academic, I started a page on this blog site that documented each peer review I did for a journal. I never quite got round to going back in time from the start, but there isn’t much of interest there that you won’t get from the stuff I did capture. Now that I am hanging up my mortarboard, it doesn’t make sense to be a page any more so I am moving it here. Enjoy the schadenfreude if nothing else.

Statisticians are in short supply, so scientific journals find it hard to get one of us to review the papers that have been submitted to them. And yet the huge majority of these papers rely heavily on stats for their conclusions. As a reviewer, I see the same problems appearing over and over, but I know how hard it is for most scientists to find a friendly statistician to help them make it better. So, I present this log of all the papers I have reviewed, anonymised, giving the month of review, study design and broad outline of what was good or bad from a stats point of view. I hope this helps some authors improve the presentation of their work and avoid the most common problems.

I started this in November 2013, and am working backwards as well as recording new reviews, although the retrospective information might be patchy.

  • November 2012, randomised controlled trial, recommended rejection. Sample size was based on an unrealistic Minimum Clinically Important Difference from prior research uncharacteristic of the primary outcome, and thus the study was unable to demonstrate benefit, and unethical because the primary outcome was about efficiency of the health system while benefit to patients had already been demonstrated, yet the intervention was withheld in the control group. Power to detect adverse events was even lower as a result, yet bold statements about safety were made. A flawed piece of work that put hospital patients at risk with no chance of ever demonstrating anything, this study should never have been approved in the first place. Of interest to scholars of evidence-based medicine, this study has now been printed by Elsevier in a lesser journal, unchanged from the version I reviewed. Such is life; I only hope the authors learnt something from the review to outweigh the reward they felt at finally getting it published.
  • November 2013, cross-sectional survey, recommended rejection. Estimates were adjusted for covariates (not confounders) when it was not relevant to do so, grammar was poor and confusing in places, odds ratios were used when relative risks would be clearer, t-tests and chi-squareds were carried out and reported without any hypothesis being clearly stated or justified
  • November 2013, exploratory / correlation study, recommended major revision then rejection when authors declined to revise the analysis. Ordinal data analysed as nominal, causing an error crossing p=0.05.
  • March 2014, randomised controlled trial, recommended rejection. Estimates were adjusted for covariates when it was not relevant to do so, bold conclusions are made without justification.
  • April 2014, mixed methods systematic review, recommended minor changes around clarity of writing and details of one calculation.
  • May 2014, meta-analysis, recommended acceptance – conducted to current best practice, clearly written and on a useful topic.
  • July 2014, ecological analysis, recommended major revision. Pretty ropy on several fronts, but perhaps most importantly that any variables the authors could find had been thrown into an “adjusted” analysis with clearly no concept of what that meant or was supposed to do. Wildly optimistic conclusions too. Came back for re-review in September 2014 with toned-down conclusions and clarity about what had been included as covariates but the same issue of throwing the kitchen sink in. More “major revisions”; and don’t even think about sending it voetstoots to a lesser journal because I’ll be watching for it! (As of September 2015, I find no sign of it online)
  • July 2014, some other study I can’t find right now…
  • September 2014, cohort study. Clear, appropriate, important. Just a couple of minor additions to the discussion requested.
  • February 2015, secondary analysis of routine data, no clear question, no clear methods, no justification of adjustment, doesn’t contribute anything that we haven’t already known for 20 years and more. Reject.
  • February 2015, revision of some previously rejected paper where the authors try to wriggle out of any work by refuting basic statistical facts. Straight to the 5th circle of hell.
  • March 2015, statistical methods paper. Helpful, practical, clearly written. Only the very merest of amendments.
  • April 2015, secondary analysis of public-domain data. Inappropriate analysis, leading to meaningless conclusions. Reject.
  • April 2015, retrospective cohort study, can’t find the comments any more… but I think I recommended some level of revisions
  • September 2015, survey of a specific health service in a hard-to-reach population. Appropriate to the question, novel and important. Some amendments to graphics and tables were suggested. Minor revisions.
  • March 2016, case series developing a prognostic score. Nice analysis, written very well, and a really important topic. My only quibbles were about assuming linear effects. Accept subject to discretionary changes.
  • October 2016, cohort study. Adjusted for stuff that probably isn’t confounding, and adjusting (Cox regression) for competing risks when they should be recognised as such. Various facts about the participants that are not declared. Major revisions.
  • October 2016 diagnostic study meta-analysis. Well done, clearly explained. A few things could be spelled out more. Minor revisions.
  • November 2016, kind of a diagnostic study…, well-done, well-written, but very limited in scope and hard to tell what the implications for practice might be. Left in the lap of the gods editors.
  • December 2016, observational study of risk factors, using binary outcomes but would be more powerful with time-to-event if possible. Competing risks would have to be used in that case. Otherwise, nice.

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Performance indicators and routine data on child protection services

The parts of social services that do child protection in England get inspected by Ofsted on behalf of the Department for Education (DfE). The process is analogous to the Care Quality Commission inspections of healthcare and adult social care providers, and they both give out ratings of ‘Inadequate’, ‘Requires Improvement’, ‘Good’ or ‘Outstanding’. In the health setting, there’s many years’ experience of quantitative quality (or performance) indicators, often through a local process called clinical audit and sometimes nationally. I’ve been involved with clinical audit for many years. One general trend over that time has been away from de novo data collection and towards recycling routinely collected data. Especially in the era of big data, lots of organisations are very excited about Leveraging Big Data Analytics to discover who’s outstanding, who sucks, and how to save lives all over the place. Now, it may not be that simple, but there is definitely merit in using existing data.

This trend is just appearing on the horizon for social care though, because records are less organised and electronic, and because there just hasn’t been that culture of profession-led audit. Into this scene came my colleagues Rick Hood (complex systems thinker) and Ray Jones (now retired professor and general Colossus of UK social care). They wanted to investigate recently open-sourced data on child protection services and asked if I would be interested to join in. I was – and I wanted to consider this question: could routine data replace Ofsted inspections? I suspected not! But I also suspected that question would soon be asked on the cash-strapped corridors of the DfE, and I wanted to head it off with some facts and some proper analysis.

We hired master data wrangler Allie Goldacre, who combed through, tested and verified and combined together the various sources:

  • Children in Need census, and its predecessor the Child Protection and Referrals returns
  • Children and Family Court Advisory and Support Service records of care proceedings
  • DfE’s Children’s Social Work Workforce statistics
  • SSDA903 records of looked-after children
  • Spending statements from local authorities
  • Local authority statistics on child population, deprivation and urban/rural locations.

Just because the data were ‘open’ didn’t mean they were useable. Each set had its own quirks and each local authority had its own problems and definitions in some cases. The data wrangling was painstaking and painful! As it’s all in the public domain, I’m going to add the data and code to my website here, very soon.

Then, we wrote this paper investigating the system and this paper trying to predict ‘Inadequate’ ratings. The second of these took all the predictors in 2012 (the most complete year for data) and tried to predict Inadequates in 2012 or 2013. We used the marvellous glmnet package in R and got down to three predictors:

  • Initial assessments within the target of 10 days
  • Re-referrals to the service
  • The use of agency workers

Together they get 68% of teams right, and that could not be improved on. We concluded that 68% was not good enough to replace inspection, and called it a day.

But lo! Soon afterwards, the DfE announced that they had devised a new Big Data approach to predict Inadequate Ofsted scores, and that (what a coincidence!) it used the same three indicators. Well I never. We were not credited for this, nor indeed had our conclusion (that it’s a stupid idea) sunk in. Could they have just followed a parallel route to ours? Highly unlikely, unless they had an Allie at work on it, and I get no impression of the nuanced understanding of the data that would result from that.

Ray noticed that the magazine Children and Young People Now were running an article on the DfE prediction, and I got in touch. They asked for a comment and we stuck it in here.

A salutary lesson that cash-strapped Gradgrinds, starry eyed with the promises of big data after reading some half-cocked article in Forbes, will clutch at any positive message that suits them and ignore the rest. This is why careful curation of predictive models matters. The consumer is generally not equipped to make the judgements about using them.

A closing aside: Thomas Dinsmore wrote a while back that a fitted model is intellectual property. I think it would be hard to argue that coefficients from an elastic-net regression are mine and mine only, although the distinction may well be in how they are used, and this will appear in courts around the world now that they are viewed as commercially advantageous.

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