Well, at least over the last three weeks it did. In previous examples of my pub trivia team’s performance, we have had a lacklustre performance. But a few weeks we had an epic collapse. Having been in 4th place out of 10 in the penultimate round we ultimately finished in 8th out of 9—somebody left early—and 14 points out of first place. It probably didn’t help we put Beyonce down as the artist for three different songs. It probably really didn’t help that none of those artists were Beyonce. And it probably definitely didn’t help that we had no idea who those three artists were.
Then after a middling performance two weeks ago, last week we shocked even ourselves with our first victory since last autumn. Just how shocking? 19 points in that oft ill-fated music round. (It’s not really, but I’ll have to make another graphic about that.)
Credit for the piece is mine. Credit for the score goes to my teammates.
Here in the States we are accustomed to unstable governments—the Trump administration has set records for the most departures so early in its term. But the United Kingdom is not to be outdone as Amber Rudd, the Home Secretary, resigned in response to an immigration scandal. She makes six the number of cabinet officials who have left the British government.
The Economist put together a small graphic showing how long it took various governments, British and otherwise, to reach the level of so many departures. May’s government has been the fastest to reach so many departures in recent years.
The key thing to note here is what I pointed out last week, which is the use of a thin white stroke on the outside of the lines being highlighted with the Theresa May government using a bolder weight to make it stand out just a wee bit more. This is a bit different than the Times version which uses the outline approach for only what would here be the May line, but it still works overall to draw attention to the British governments.
Credit for the piece goes to the Economist graphics department.
Last week Angela Merkel, the German chancellor, visited President Trump in Washington. This post comes from the Economist and, while not specifically about that trip, describes Germany in a few different metrics. Back in the day it would be what I called a country-specific datagraphic. That is, it shows metrics not necessarily connected to each other, but all centred around a country. In theory, the framework can then be used to examine a number of different countries.
That sort of works here, except the choropleth is for the Alternative for Germany political party. That only real works as a metric in, you know, Germany.
Overall, I like the piece. The layout works well, but Germany is fortunate in that the geographic shape works here. Try it with Russia and good luck.
First let us dispense with the easy criticism: do we need the box map in the lower right corner to show where in the world Germany is? For Americans, almost certainly yes. But even if you cannot identify where Germany is, I am not certain its location in Europe is terribly important for the data being presented.
But the pie charts. I really wish they had not done that. Despite my well-known hatred of pie charts, they do work in a very few and specific instances. If you want to show a reader 1/4 of something, i.e. a simple fraction, a pie chart works. You could stretch and argue that is the case here: what is the migrant percentage in Bavaria? But the problem is that by having a pie party and throwing pie charts all over the map, the reader will want to compare Bavaria to the Rhineland-Palatinate.
Just try that.
Mentally you have to take out the little red slice from Bavaria and then transpose it to Rhineland-Palatinate. So which slice is larger? Good luck.
Instead, I would have left that little fact out as a separate chart. Basically you need space for 16 lines, presumably ranked, maybe coloured by their location in former East or West Germany, and then set in the graphic. Nudge Germany to the left, and eat up the same portion of Bavaria the box map, cover the Czech Republic, and you can probably fit it.
Or you could place both metrics on a scatter plot and see if there is any correlation. (To the designers’ credit, perhaps they did and found there is none. Although that in and of itself could be a story to tell.)
The point is that I still hate pie charts.
Credit for the piece goes to the Economist’s graphics department.
On Monday I read, in print, part of a page one article in the Times. I ran out of times given the whole new royal baby coverage, and opted to read the rest digitally. Originally, this was just for my own enjoyment as there were no graphics in the article.
But this one appeared online.
I clearly have nothing to compare it to in print, which is a shame because this is a nice graphic with one thing I really wanted to point out. Although, maybe a print version would not have had the thing I will get to. But maybe there just wasn’t space in the print edition or they tried to make it work, but the colours or layout wasn’t working. Who knows.
When I saw the digital version, the line chart struck me as particularly nice. Now, maybe the Times has been doing this for a little while and I have missed it, but notice the highlighted line, Rural public. Yes the line is thicker or bolder than the others, but more importantly it has a thin white stroke attached that helps separate it from the lines behind it. Those lines are important for context, but not necessarily to tell the story of how rural public servant jobs have been hit the hardest.
You often see this kind of approach taken with maps. Don’t believe me? Take a look at Google Maps as one example. Their text often has a thin white outline to make it stand out from the content of the map. I just have never seen the logic applied to a line chart.
I doubt the design would hold up in a number of other scenarios. For example, a straight line chart with no line highlighted in particular, the spaghetti-ness mess would make the above a largely white line chart. Too much overlap. And a simple comparison, say of two lines, probably is clear enough that the approach is not necessary. But in scenarios like these where the highlighted series is important, the choice clearly works.
On a much smaller note, check out the x-axis labels. They are used only once for the first chart. And then because the bar charts and line charts align, they carry through straight down the rest of the piece. Very efficient.
I only wish I knew how this would have appeared in print…
Credit for the piece goes to the New York Times graphics department.
So two weeks ago I posted about the graphics in a BBC article about how London has surpassed New York in terms of murders, due to a spate of stabbings in the British capital. Well, somehow I missed this: an article from the Economist that rebuts that point. And it does it brilliantly.
Lies, damned lies, and statistics.
I think everybody who works with data knows that adage. Now, I am not using it to say that the BBC—or the numerous other media outlets that ran the story—lied. Just that it is easy to change the story based on the data, how it is presented, or which subsets of the data are selected.
The Economist’s article points out that the surpassing of New York is a short term data point, a worrying short term trend, definitely, but they then look at the data. They select two timeframes and look at them side-by-side.
And that is what I love about this piece. It shows the long-term context of New York having a far-higher medium-term history of murder (some 28 years of data is shown). When I was growing up in the 90s, murders in New York—and to be fair almost all large American cities—was just something that was a known fact. During that time, London hovered below 200 or so, compared to the 2000+ in early 90s New York.
But then they also show the short term, which does point to a steady rise in London murders. But, the data could also show a one-time dip in the murders in New York. But they also show that the total number of deaths is still higher in New York than London, despite the three months of data.
Murder is not good. But these graphics are a good example of how selecting different time series for the same data set, and then showing which parts of the data to show. The earlier BBC piece, and my revision of it, did not show the total deaths. Nor did either piece show the longer timeline of data.
Credit for the piece goes to the Economist graphics department.
Yesterday was murders in London and New York. Today, we have a nice article from FiveThirtyEight about deaths more broadly in America. If you recall, my point yesterday was that not all graphics need to be full column width. And this article takes that approach—some graphics are full width whereas others are not.
This screenshot shows a nice line chart that, while the graphic sits in the full column, the actual chart is only about half the width of the graphic. I think the only thing that does not sit well with me is the alignment of the chart below the header. I probably would align the two as it creates an odd spacing to the left of the chart. But I applaud the restraint from making the line charts full width, as it would mask the vertical change in the data set.
Meanwhile, the article’s maps all sit in the full column. But my favourite graphic of the whole set sits at the very end of the piece. It examines respiratory deaths in a tabular format. But it makes a fantastic use of sparklines to show the trend leading towards the final number in the row.
Credit for the piece goes to Ella Koeze and Anna Maria Barry-Jester.
I work with economists. And so I get to see working papers and other technical papers on a rather frequent basis. But I still have no way of verifying this premise. Though I most certainly believe in that dip…
I know I’ve looked at the Times a few times this week, but before we get too far into the next week, I did want to show what they printed on Saturday.
It is not too often we get treated to data on the front page or even the section pages. But last Saturday we got just that in the Business Section. Two very large and prominent charts looked at federal government borrowing and the federal deficit. Both are set to grow in the future, largely due to the recently enacted tax cuts.
The great thing about the graphic is just how in-the-face it puts the data. Do two charts with 14 data points (28 total) need to occupy half the page? No. But there is something about the brashness of the piece that I just love.
And then it continues and the rest of the article points, at more normal sizes, to treasury bill yields and car loan rates. The inside is what you would expect and does it well in single colour.
Last week I covered the Pennsylvania congressional district map changes quite a bit. Consequently I was not able to share a few good pieces of work. Let’s hope nothing goes terribly wrong this week and maybe we can catch up.
From last Friday we have this nice piece from FiveThirtyEight looking at the spread of influenza this season.
The duller blues and greens give way to a bright red from south to north. Very quickly you can see how from, basically, Christmas on, the flu has been storming across the United States. It looks as if your best bets are to head to either Maine or Montana. Maybe DC, it’s too small to tell, but I kind of doubt that.
As you all know, I am a fan of small multiples and so I love this kind of work. To play Devil’s advocate, however, I wonder if an interactive piece that featured one large map could have worked better? Could the ability to select the week and then the state yield information on how the flu has spread across each state? I am always curious what other other forms and options were under consideration before they chose this path.
Credit for the piece goes to the FiveThirtyEight graphics department.