Tag Archives: infographics

Dataviz of the week: 15/3/17

This front page graphic in the Arizona Republic, by Aviva Loeb was spotted and blogged by Michael Sandberg.


I’m a fan of old school pictograms, and there’s something of the shock tactic of sheer scale here. Of course, a newspaper does not permit such space as xkcd’s global warming or CarbonVisuals’ mountain of CO2, but this is a good compromise in the space available. Kudos to the editors for running with such a bold idea.

The text is really good too, mixing numbers with individual stories and then bringing in the more subtle facts as you get in. “A swath of deadly violence snakes from Interstate 10 north to Bethany Home Road…” is crying out for a map.

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We were there when they made Dear Data


Easy links: dear-data.com deardata-deliveries.tumblr.com









Procedural notes that can be skipped:

I had previously intended to write something about the shapes employed by Giorgia Lupi and the Accurat studio – and indeed I still will. But that takes some time and it got leapfrogged by Dear Data. This post came at a good time because I didn’t get around to it straight away (we’re now at week 35 of the project) and by the time I did, some other ideas had bubbled up in conversations, focussing my attention on the process of design, critique and refinement (which is getting added to my reading pile for the summer). These ideas are so alien to statisticians that I am not sure any of them will have read this far into this post, but they (we) are the ones that need to up their (our) game in communication. Nobody else will do it for us! The other building block that came along in time was finally finding really nice writing paper and resolving to draft everything by hand from now on, preferably in time when I’m physically away from a computer. It has already proven very productive. People seem to have different approaches that work (like starting with bullet points, or cutting out phrases, or mind maps), but mine is to start writing at sentence one, like Evelyn Waugh, and just carry straight on. There is no draft; why should there be? Finding that technique and place to write is really valuable; don’t devalue it and try to squeeze it into a train journey or between phone calls. It’s the principal way in which you communicate your work, and probably the most overlooked.


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Curling spirals of springiness

Last week Andrew Gelman picked up on a couple of graphs of extremely long time periods. Here they are again for your convenience (when one mentions a subject such as climate change, it’s like a magnet for time-wasters, so I’ll spare you from reading through the explosion of comments at Gelman’s blog)

What’s going on in that x-axis?!

Gelman liked the spirals within spirals; not everyone did. It put me in mind of two examples I saw recently when reading Isabel Meirelles’s book “Design For Information” (which is excellent!). The first is not good, in my humble opinion:

10 years of Wikipedia” is a series of line graphs that are bent round into a spiral. You are supposed to compare the position of the line to the ideal spiral in grey. What this adds above and beyond the area chart on the left is questionable. I find it impossible to see the patterns, and I imagine that is something to do with how our brains perceive position radiating out from a central point.


The better use is when the spiralling is metaphorical. In this image from National Geographic, the number of space exploration missions that have flown by and visited different planets and moons are shown as concentric rings. One gets an immediate feel for the number of rings.




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Visualizations on the Monopoly board

Two items of post from utility companies that recently dropped through our door included little graphics. There was a degree of innovation in them both. The first, from British Gas, is technically OK but probably bad on perceptual grounds:


I got a tape measure out and starting checking that they had scaled the flames and light bulbs by their area. (Sad, I know, but this is the fate that befalls all statisticians in the end.) And yes, it seemed they had – if you included the space to the little shadow underneath. In fact, someone had clearly been very careful to scale it just right, but the gap of clear space and the indistinct shadow are probably not perceived as part of the icon. I think they’re cute, but not so easy to derive facts from.

Next up from Thames Water:


This looks like a really bad idea. As if pies weren’t hard enough to judge anyway, making it into a drop is completely confusing. The categories at the top are possibly expanded in size just for aesthetic reasons. I thought I would check how much the area occupied by “day-to-day running” differed from the nominal 38/125=30%. First, to avoid confusion of colors, I brought out the GIMP and made a simplified version:


and then read it into R and counted the blue and black pixels:


and that turns out to have 37% of the drop allocated to “day-to-day running”. Bad, bad bad…


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Staggering to work

This morning I heard an unusual announcement as I arrived at Balham (“gateway to the South”) railway station. The trains going into London are busiest, the man said, between 8:15 and 8:30, so travelling before or after this would make our journeys quicker and more comfortable.

I immediately thought this would make a good excuse to post this old London Transport poster, with its clever design and charming pictograms (and questionable math):

So, in there seemed to be 35,000 people on the tube between 5:00 and 5:30 pm. According to 2009/10’s London Travel Demand Survey, there were about 125,000 (roughly eyeballing Figure 4.1) crammed in cheek by jowl.


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Data viz comes to Errol Street

That sounds a little unfair. It’s not that the Royal Statistical Society is inimical to visualization, just that they don’t keep an eye on it in the way that a certain zone of the blogosphere does. My own Stat Comp committee’s session on visualization at the conference in Newcastle last month was the most overcrowded room of the whole four days. We brought in double the chairs and still people stood out into the corridor and sat on the floor, which was pleasing but not all that surprising.

Last Thursday they held a joint meeting with the Association for Survey Computing on data viz with speakers from the Guardian Digital Agency and the Office for National Statistics. There were a lot of people in the room, and apparently a waiting list for cancellations to attend.

The speakers from Guardian Digital described a process comprising data – story – chart – design, with quite a long time spent getting the data right and looking for the potential stories. (You have to remember, these guys typically start with an interesting dataset gathered for its own merits, not a study with a pre-specified hypothesis.) If a barrier is encountered at any point, you have to start again to ensure integrity.

They cited the case of Ivan Cash’s infographic of infographics, which excels in all aspects except the data, being based on a small number of cases from a single website. The lack of data integrity makes the whole thing collapse. Not that it was ever intended as more than a bit of fun.

Alan Smith from the ONS had a bunch of interactive graphics to share, some already published, some new. My favourite was the internal migration map of the UK, which reduces an intractable (for human brains) 350×350 transition matrix to a clickable map showing where people move home from and to.

Two good questions from the audience which I paraphrase:

Q: Where do your data viz people sit within the organisation to achieve this level of integration and output?
A: Part of methodology, so we are seen as working on best practice; early discussion saw some managers suggesting we fall under the IT department because we would be doing things with computers [cue widespread mirth].

Q: Will there still be a future for static images, printed or otherwise,  or will everything have to be interactive?
A: Static images remain very important. We can’t expect all information to be absorbed online and through no other medium, and also a lot of animations are really just the precursor to a static image that can be considered in depth [e.g. Every Drone Strike]. But it is quite simple to translate a static image into a clickable one for online consumption using some of the newer JavaScript libraries like D3. The idea of Data Driven Documents should appeal to statisticians. The RSS Centre for Statistical Education have used interactive graphics as a stimulus to learning about statistical thinking, although most interest from academics has come from web design / computer science departments, not statisticians. This should act as a wake-up call to statisticians to get involved and acquire these new skills.
[My emphasis]

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Easy pictograms using R

I have been amazed for a while that there is no major stats software offering pictograms. You know the sort of classic infographic I mean:

Isotype’s classic design

Well, I have been working on an R function to help with this. It’s at Github here and below. Here’s an example:




Simple, huh? You can also have more than one icon, although it’s up to you to keep them a sensible height:width ratio or ‘aspect’ to avoid distorting impressions.



Edit 30 July 2013: Thank you to Paul Murrell, who wrote to tell me that you can do the same thing with vector images rather than raster using the grImport package and its function grid.symbols(). The advantage of vector images is that they don’t get pixellated and grainy as you zoom in on them.

Also, if you want to know more about how R handles raster images, you should check out Paul’s R Journal article from 2011.

Suggestions? e-mail me or better still, pull them on Github. Happy pictogramming!

# requires image to be read in by readPNG or similar and supplied as "icon"
# To do: allow for non-integer n
hicons=20,vspace=0.5,labprop=0.2,labelcex=1) {
if(is.list(icon)) {
} else {
for (i in 2:length(n)) {
# dim[1] is the height, dim[2] the width:
# get dims of all elements of licon, find greatest aspect and set ylength
getdim<-function(z) {
if(devaspect*hicons<allv) warning("Icons may extend above the top of the graph")
# vector of icons per row
# vector for how many times to repeat elements of iconrow
for (i in 1:(length(n))) {
# there are more elegant ways to make x0, but for now...
for (i in 1:(length(perrow))) {
for (i in 1:sumn) {
# find positions for labels
for (i in 1:length(n)) {


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