The Ratio

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.

The interactivity makes this chart worth checking out
The interactivity makes this chart worth checking out

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.

Speaking Freely About Free Speech

Last week the Economist published an article looking at the attitudes of the young at university in the United States. The examination was sparked by the recent-ish waves of news about stifled speech on campuses. Thankfully, we have a long-running survey from those on the ground in our universities and it reveals some interesting facts. You should head on over to the article if you want the full set, but in general, to perhaps nobody’s surprise, the media is exaggerating the confrontations we have seen.

You said what?
You said what?

My only quibble with the graphic is the height of the small multiples. I probably would have increased the height a little bit to allow any real fluctuations over the years to show more readily. But, for all I know, that could have been a limitation of the space in which the designers had to work, i.e. converting a print graphic to work on their blog.

Credit for the piece goes to the Economist’s Data Team.

What If Designers Ruled the World? Or At Least the Country

Happy Friday, all. We made it.

So today we enjoy an xkcd post about how graphic designers would change the country if they seized control.

It's a good start, designers.
It’s a good start, designers.

Though to be fair, if this graphic designer seized control of the country, he would not be interested in just adjusting state borders. He’d probably work on the margins and bounds and then establish a whole new baseline grid.

Credit for the goes to Randall Munroe.

Whom the Tax Reforms Will Benefit Most

Yesterday the New York Times published a piece looking at the potential impacts of the proposed tax reforms on Americans. Big caveat, not a lot has been detailed about what the reforms entail. Instead, much remains vague. But using the bits that are clear, the Tax Policy Centre has explored some possible impacts and the Times has visualised them.

RIch do richly
RIch do richly

I like the opening graphic, though all are informative, that cycles through various proposals. It highlights which group benefits most from the proposals. The quick takeaway is that while all would moderately benefit, the rich do really well.

Credit for the piece goes to Ernie Tedeschi.

The Economic Impact of Hurricanes

Yesterday Hurricane Ophelia hit Ireland and the United Kingdom. Yes, the two islands get hit with ferocious storms from time to time, but rarely do they enjoy the hurricanes like we do on the eastern seaboards of Canada, Mexico, and the United States.

Earlier this hurricane season the US had to deal with Harvey, Irma, and Maria. And in early October the Wall Street Journal published a piece that looked at the economic impact of the former two hurricanes as exhibited in economic data.

Overall the piece does a nice job explaining how hurricanes impact different sectors of the economy, well, differently. And wouldn’t you know it that leisure and hospitality is the hardest hit? But then they put together this stacked bar chart showing the impact of the hurricanes on both Florida/Georgia and Texas for Irma and Harvey, respectively.

I just want a common baseline…
I just want a common baseline…

The problem is that the stacked bar chart does not allow us to examine each hurricane as a specific data set. Because the Harvey data set is first, we have the common baseline and can compare the lengths of the magenta-ish bars. But what about the blue sets for Irma? How large is natural resources and mining compared to professional and business services? It is incredibly difficult to tell because neither bar starts at the same point. You must mentally move the bars to the same baseline and then hope your brain can accurately capture the length.

Instead, a split bar chart with each sector having two bars would have been preferable. Or, barring that, two plots under the same title. Then you could even sort the data sets and make it even easier to see which sectors were more important in the impacted areas.

Stacked bar charts work when you are trying to show total magnitude and the breakdowns are incidental to the point. But as soon as the comparison of the breakdowns becomes important, it’s time to make another chart.

Credit for the piece goes to Andrew Van Dam.

The Middle Income Trap?

Last week I covered a lot of Red Sox data. And your feedback has been fantastic. I think you can look forward to more visualisation of sportsball data. But since this is not a sports blog, let us dive back into some other topics. Like today’s piece on economic growth.

It comes from the Economist and explores the development history of national economies relative to that of the United States. The point of the chart was to illustrate what the researchers determined was the middle income trap, a space in which countries develop and become semi-rich, but then can never quite escape.

It's a trap! (Unless it isn't.)
It’s a trap! (Unless it isn’t.)

The Economist makes the point that the definition of middle income matters. The range is enormous and one statistic says that it could take 48 years to graduate at a healthy rate of economic growth. I wonder is this bit, however, could also have been charted. The show don’t tell mantra works well here for setting up the problem, but a chart or two showing that exact range could have supplemented the text and perhaps made it more digestible.

Credit for the piece goes to the Economist Data Team.

Whom to Root for in the Playoffs

This week I covered a lot of Red Sox stuff. (And I received some great feedback from people, so maybe more baseball-related stats things will be forthcoming.) But, since it is Friday, I wanted to keep today late. So over breakfast I worked on a flowchart to help you choose whom to root for in the playoffs now that Boston, Colorado, Arizona, Minnesota, Washington, and Cleveland have all been eliminated.

To be fair, my second choice was good old Terry Francona and the Indians (like last year). But, the Evil Empire is returning.

Happy Friday, all.

But did you catch the overarching theme?
But did you catch the overarching theme?

Power Sapped

Following on yesterday’s post about the Red Sox offence, I wanted to follow up and look into their power numbers. So here we have a smaller scale graphic. Nothing too fancy, but the data backs what my eyes saw all year. A definite power drain up and down the Red Sox lineup in 2017.

Where did all the power go?
Where did all the power go?

The Red Sox Offence in 2017

Like I said yesterday, the Red Sox season is over. And the coverage on offseason needs began in the morning papers. But I wanted to follow up on the data from yesterday and delve a bit more deeply into the offence.

Yes, we know it was roughly league average across the team. And we know it took a hit with David Ortiz’s retirement at the end of last year. But what happened? Well, I took those same OPS+ numbers for the starting nine and compared 2017 to 2016. I then looked further back to see how those same players performed throughout their careers (admittedly I skipped Hanley Ramirez’s 2 plate appearances in 2005.)

You should take a look at the full graphic, but the short version, pretty much everyone had an off year. And when everyone has an off year, it is a pretty safe bet the team will have an off year.

You can't all take a break…
You can’t all take a break…

A Brief Review of the Boston Red Sox Season

Well the 2017 season ended yesterday afternoon for my Boston Red Sox as we lost 5–4 to the Houston Astros and they took Game 4 of the ALDS. So this morning we will surely see the critiques and hot takes on what to do to improve the team begin to make the internet rounds.

But before we get into all of that, I wanted to take a look at the 2017 season from a data perspective. At least, the regular season. After all, we can see how Sale in Game 1 and Kimbrel in Game 4 just had poorly timed bad days. But what about the other 162 games? After all, we will need to win a lot of them if we want to make it back to the playoffs in 2018.

I just pulled a couple quick stats from Baseball Reference. Now we can quibble about which stats are best another time, but from my experience the more sabremetric datapoints are lost on a general audience. So here we are using OPS, basically a hitter’s average combined with his power/slugging ability, and ERA, the amount of runs a pitcher can be expected to allow every nine innings. I also threw in defensive runs saved above average, i.e. is the player saving more runs than an average player.

You can read the graphic for the details, but the takeaway is that Boston, we need not panic. The 2017 Red Sox were a good team. Far from perfect—here is looking at you lack of middle-of-the-order power—but a solid lineup, good rotation, good defence, and a fantastic bullpen. How can we add without subtracting too much?

Overall, not a bad team
Overall, not a bad team