Initially I wanted today’s piece to be coverage of the apparent coup d’état in Zimbabwe over night. But while I have found some coverage of the event, I have not yet seen a single graphic trying to explain what happened. Maybe if I have time…
In the meantime, we have the Economist with a short little piece about Trump on Twitter and how he has bested his rivals. Well, most of them at least.
The piece uses a nice set of small multiples to compare Trump’s number of followers to those of his rivals. The multiples come into play as the rivals are segmented into three groups: political, sport, and media. (Or is that fake media?)
Small multiples of course prevent spaghetti charts from developing, and you can easily see how that would have occurred had this been one chart. But I like the use of the reddish-orange line for Trump being the consistent line throughout each. And because the colour was consistent, the labelling could disappear after identifying the data series in the first chart.
And worth calling out too the attention to detail. Look at the line breaks in the chart for the labelling of Fox News and NBA. It prevents the line from interfering with and hindering the legibility of the type. Again, a very small point, but one that goes a long way towards helping the reader.
I think the only thing that could have made this a really standout, stellar piece of work is the inclusion of another referenced data series: the followers of Barack Obama. At 97 million followers, Obama dwarfs Trump’s 42.2 million. Would it not be fantastic to see that line soaring upwards, but cutting away towards the side of the graphic would be the text block of the article continuing on? Probably easier for them to do in their print edition.
Regardless, this is another example of doing solid work at small scale. (Because small multiples, get it?)
Credit for the piece goes to the Economist Data Team.
And I’m not talking about walking into a bar late at night. Instead, I am talking about the ratio of likes to retweets to replies, which, for those of you unfamiliar with the service, refers to engagement with a person’s tweets on Twitter.
The Ratio does not come from FiveThirtyEight—read the article for the full background on the concept, it is well worth the read—but they applied it to President Trump, whom we all know has a penchant for tweeting. The basic premise of the ratio is that you want more retweets and likes than replies. Think of it like customer reviews. Rarely do people bother to put the effort in to complement good service, but they will often write scathing reviews if something does not fit their expectations. Same in Twitter. If I do not care for what you say, I will let you know. But if I do, it is easy for me to like it, or even retweet it.
Anyway, the point is they took this and applied it to the tweets of Donald Trump and received this chart.
What I truly enjoy is the interactivity. Each dot reflects a tweet, and you can reveal that tweet by hovering over it. (I would be curious to know if the dots move. That is, do they, say, refresh daily with new tabulations on the updated numbers of likes, retweets, and replies?)
But the post goes on using the same chart form, in both other interactive displays and as static, small multiple pieces, to explore the political realm of previous tweeting presidents and current senators.
A solid article with some really nice graphics to boot.
Credit for the piece goes to Oliver Roeder, Dhrumil Mehta, and Gus Wezerek.
Let’s aim for something a bit lighter today. Well, lighter in all things but calories, perhaps. Today we have a piece from the Wall Street Journal that looks at the social media presence of several large fast food brands. Overall, it has a few too many gimmicky illustrations for my comfort. But, the strength of the piece is that it does look at some real data, e.g. plotted Twitter response rates, and then contextualises it with appropriate callouts.
The illustrations are killing me, though.
Credit for the piece goes to Marcelo Prince and Carlos A. Tovar.
As the title says, baseball is almost back. Red Sox spring training games begin as the Red Sox take on Northeastern today. The off-season is perhaps the hardest part of the year for a fan, because unless you take super interest in trades, there is no baseball. But what about on Twitter? Well, today’s piece is an article from Fangraphs that looks at team-by-team off-season Twitter use.
Personally, I am not really a fan of the graphic. As a static image, it does not allow me to easily compare the different retweets or favourites. But, in the aggregate, you can see that the Seattle Mariners are perhaps the most active Twitter account.
To continue with the sports theme from yesterday, today we have an interactive map from Twitter that looks at NFL team popularity. The methodology is simple, where are the users following the various football teams and map that out by county. The overall blog post features a country-wide map, but then narrows down into a few particular stories. The image below is from the divide in the state of Pennsylvania between Eagles fans and Steelers fans.
Credit for the piece goes to Simon Rogers and Krist Wongsuphasawat.
Today’s piece is hit and miss. It comes from the World Economic Forum and the subject matter is the use of Twitter across Africa. I think the subject matter is interesting; mobile communication technology is changing Africa drastically. The regional trends shown in the map at the core of the piece are also fascinating. Naturally I am left wondering about why certain countries. Does spending on infrastructure, GDP per capita, disposable income levels have any sort of correlation if even only on a national and not city level?
But what really irks me is the content that wraps around the map. First the donut chart, I think my objections to donuts—at least the non-edible kind—are well known. In this case, I would add—or sprinkle on—that the white gaps between the languages are unnecessary and potentially misleading.
Secondly, the cities are eventually displayed upside down. Thankfully the labels are reversed so that city names are legible. However, the continually changing angle of the chart makes it difficult to compare Douala to Luanda to Alexandria. A neatly organised matrix of small multiples would make the data far clearer to read.
In short, I feel this piece is a good step in the right direction. However, it could do with a few more drafts and revisions.