Biden’s Cabinet

Note: I wanted this to go up on Inauguration Day, but I had some server issues last week. And while I got everything back for Friday and Monday, I didn’t want to wait too long to post this. You’ll note at the end that I have questions about General Austin and whether he could be confirmed as Defence Secretary. Spoiler: He was.

Today is Inauguration Day and at noon, President Trump returns to being a citizen and Joe Biden assumes the office of the presidency. He comes to office with arguably the most diverse cabinet in American history supporting him and his agenda.

CNN took a look at that diversity with this piece, which uses an interactive, animated stacked bar chart.

The proposed cabinet vs. the US ethnic breakdown

I took a screenshot at the ethnic/racial diversity. At the top, each bar represents one member of cabinet who you can reveal after mousing over the bar. Below is a stacked bar chart showing the racial makeup of the United States. You can see how it does resemble, and in some cases exceeds, the diversity of the broader United States.

One thing to note, however, is that we see 26 members of Cabinet. Some of those are the heads of the big executive departments like Treasury and Defence. But I’m not certain everyone is technically a cabinet-level position, e.g. Celia Rouse, Chair of the Council of Economic Advisors. It could be that the position is being elevated to cabinet level like John Kerry’s role as climate envoy. And if I just missed the press announcement, that’s on me. But that could affect the overall numbers.

Regardless, the nominated cabinet is more diverse than the previous two administrations as the CNN piece also shows.

The proposed cabinet vs. the preceding inaugural cabinets

I should point out that usually an incoming administration usually has a few of its national security positions already confirmed or confirmed on the first day, e.g. Defence and State. However, the Republican Senate, obsessed with the lie of a fraudulent election, has only just begun to start the confirmation process. In fact, as of late last night, only Avril Haines has been confirmed by the Senate (84–10) for Director of National Intelligence.

Furthermore, almost every administration has one or two nominations that fail to pass the Senate. George W Bush had Linda Chavez, Barack Obama had Tom Daschle, and Donald Trump had Andrew Puzder, just to give one from each of the last three administrations.

With a 50–50 Senate, I would expect there to be a few nominees who fail to make it over the line. Austin could be one, there appears to be some bipartisan agreement that we ought not nominate recent military officials as civilian heads of said military. Another to keep an eye out for is Neera Tanden. She riles conservatives and angers Bernie Sanders supporters, so whether the Senate will confirm her as Director of the Office of Management and Budget remains an open question in my mind.

Credit for the piece goes to Priya Krishnakumar, Catherine E. Shoichet, Janie Boschma and Kenneth Uzquiano.

2020 Election Results…

Via xkcd.

It’s Friday and we’ve made it all to the end of the week. A little while back xkcd posted about the 2020 US election, showing where the votes for both candidates are approximately located.

This isn’t quite funny like I normally might post on a Friday, but it felt appropriate after this week we had with the impeachment.

Lot of blue in the Northeast

Credit for the piece goes to Randall Munroe.

Impeachment 2: The Insurrection

Like many Americans I closely followed the outcome of yesterday’s historic vote by the House of Representatives to impeach President Trump for his incitement of an insurrection at the US Capitol in a failed coup attempt to overturn the 2020 election.

Words I still never thought I’d write describing an American election.

So at the end of the vote, I created this first graphic to capture the bipartisan nature of the impeachment. Ten Republicans broke ranks and voted with the Democrats. Keep in mind that in 2020, zero Republicans did the same. Justin Amash had by then resigned from the Republican Party and sat as an independent.

But I was also interested in how “courageous” these votes could be seen. Trump remains immensely popular with his base despite his attempt to overthrow the US government and keep himself in power. Did the Republicans who supported impeachment sit in districts won by Biden?

The answer? Not really. Two did: congressmen from New York and California. But a look at the other eight reveals they represent Trump-supporting districts.

To be fair, there are probably three tiers of seats in that group. Liz Cheney, the No. 3 Republican in the House, is in her own Trump-supporting seat as Wyoming’s at large representative. But four other Republicans have seats where Trump won by more than 10 points.

Three more Republicans are in seats I’d label competitive, but lean Republican.

Clearly the argument can be made that for most of these Republicans, it was not a politically safe choice to vote for impeachment. House seats will be redistricted this year for the 2022 midterms, but I’ll be curious to see how these Republicans fare in those redistricting proceedings and then in the ultimate elections thereafter.

Credit for the piece is mine.

Red Shift, Blue Shift

Last night I published a graphic on Instagram that I think people may find helpful if they try to follow Election Day results on Tuesday. I wanted to explain the concept of a red shift or blue shift. (I’ve also seen it described as states having a red mirage or a blue mirage.)

For my non-American readers, it’s important to understand that while this is a national election, the United States’ federal system means that each state runs its own election with its own rules and they can vary some state to state. For example, early or mail-in voting can vary significantly from state to state with some states allowing it only in emergencies (and some of those this cycle will not allow people to cite COVID-19 as an emergency).

Another factor for everyone to consider is that polling indicates President Trump’s fraudulent messaging about, well, voting fraud has shifted a normally split use of early/mail-in voting to a Democratic advantage. In other words, Democrats are far more likely to vote early, either in person or by post. Republicans are far more likely to vote on Election Day.

Combine those two factors and we get Red Shift vs. Blue Shift.

Some states allow election officials to begin counting their early votes prior to Election Day. Other states forbid counting until Election Day morning, or in some cases until after the polls close.

In states where early votes can be counted—the swing states Arizona, Florida, and North Carolina are among this group—it is possible that when the polls close, or shortly thereafter, we will see an instant and enormous lead for Joe Biden. But, as the states begin to count in-person day-of votes, which again favour Republicans, Trump may begin to eat into those margins. The question will be, can Trump’s numbers eat in so much that when the final counts are complete, he can overtake those Biden numbers? This is the Red Shift.

Conversely we have the Blue Shift. In these states—swing states like Georgia, Michigan, Pennsylvania, Texas, and Wisconsin are in this group—election officials cannot begin to count early votes either until the morning or when the polls close. In these states we may see the in-person day-of votes, largely expected to be for Republicans, run up to high totals fairly quickly. At that time, Trump may have a significant lead. Then when officials pivot to counting the early votes, Biden will begin to eat into those margins. And again, the question will be, can Biden eat into those margins sufficiently to shift the outcome after all the votes are counted?

Be prepared to hear about these scenarios Tuesday night.

Credit for the piece is mine.

Choose Your Own FiveThirtyEight Adventure

In case you weren’t aware, the US election is in less than a week, five days. I had written a long list of issues on the ballot, but it kept getting longer and longer so I cut it. Suffice it to say, Americans are voting on a lot of issues this year. But a US presidential election is not like many other countries’ elections in that we use the Electoral College.

For my non-American readers, the Electoral College, very briefly, was created by the country’s founding fathers (Washington, Jefferson, Adams, Franklin, et al.) to do two things. One, restrict selection of the American president to a class of individuals who theoretically had a broader/deeper understanding of the issues—but who also had vested interests in the outcome. The founders did not intend for the American people to elect the president. The second feature of the Electoral College was to prevent the largest states from dominating smaller states in elections. Why else would Delaware and Rhode Island surrender their sovereignty to join the new United States if Virginia, Pennsylvania, and New York make all the decisions? (The founders went a step further and added the infamous 3/5 clause, but that’s another post.)

So Americans don’t elect the president directly and larger states like California, New York, and Texas, have slightly less impact than smaller states like Wyoming, Vermont, and Delaware. Each state is allotted a number of Electoral College votes and the key is to reach 270. (Maybe another time I’ll get into the details of what happens in a 269–269 tie.) Many Americans are probably familiar with sites like 270 To Win, where you can determine the outcome of the election by saying who won each state. But, even though the US election is really 50 different state elections, common threads and themes run through all those states and if one candidate or another wins one state, it makes winning or losing other states more or less likely. FiveThirtyEight released a piece that attempts to link those probabilities and help reveal how decisions voters in one state make may reflect on how other voters decide.

The interface is fairly straightforward—I’m looking at this on a desktop, though it does work on mobile—with a bunch of choices at the top and a choropleth map below. There we have a continually divergent gradient, meaning the states aren’t grouped into like bins but we have incredibly subtle differences between similar states. (I should also point out that Maine and Nebraska are the two exceptions to my above description of the Electoral College. They divide their votes by congressional district, whoever wins the district gets that Electoral College vote and then the state overall winner receives the remaining two votes.)

Below that we have a bar chart, showing each state, its more/less likely winner state and the 270 threshold. Below that, we have what I’ve read/heard described as a ball plot. It represents runs of the simulation. As of Thursday morning, the current FiveThirtyEight model says Trump has an 11 in 100 chance of winning, Biden, conversely, an 89-in-100 chance.

But what happens when we start determining the winners of states?

Well, for my non-American readers, this election will feature a large number of voters casting their ballots early. (I voted early by mail, and dropped my ballot off at the county election office.) That’s not normal. And I cannot emphasise this next point enough. We may not know who wins the election Tuesday night or by the time Americans wake up on Wednesday. (Assuming they’re not like me and up until Alaska and Hawaii close their polls. Pro-tip, there’s a potentially competitive Senate race in Alaska, though it’s definitely leaning Republican.)

But, some states vote early and/or by mail every year and have built the infrastructure to count those votes, or the vast majority of them, on or even before Election Day. Three battleground states are in that group: Arizona, Florida, and North Carolina. We could well know the result in those states by midnight on Election Day—though Florida is probably going to Florida.

So what happens with this FiveThirtyEight model if we determine the winners of those three states? All three voted for Trump in 2016, so let’s say he wins them again next week.

We see that the states we’ve decided are now outlined in black. The remainder of the states have seen their colours change as their odds reflect the set electoral choice of our three states. We also now have a rest button that appears only once we’ve modified the map. I’m also thinking that I like FiveyFox, the site’s new mascot? He provides a succinct, plain language summary of what the user is looking at. At the bottom we see what the model projects if Arizona, Florida, and North Caroline vote for Trump. And in that scenario, Trump wins in 58 out of 100 elections, Biden in only 41. Still, it’s a fairly competitive election.

So what happens if by midnight we have results from those three states that Biden has managed to flip them? And as of Thursday morning, he’s leading very narrowly in the opinion polls.

Well, the interface hasn’t really changed. Though I should add below this screenshot there is a button to copy the link to this outcome to your clipboard if, like me, you want to share it with the world or my readers.

As to the results, if Biden wins those three states, Trump has less than a 1-in-100 chance of winning and Biden a greater than 99-in-100.

This is a really strong piece from FiveThirtyEight and it does a great job to show how states are subtly linked in terms of their likelihood to vote one way or the other.

Credit for the piece goes to Ryan Best, Jay Boice, Aaron Bycoffe and Nate Silver.

Cheesesteaks and Politics

For those unaware, Pennsylvania matters in the 2020 election. And it has mattered for years as a perennial swing state. There are of course the visits to steel mill cities like Pittsburgh, deindustrialised places like Johnstown, and unions love visits to places in Lackawanna and Luzerne. (You can read more about Pennsylvania as a swing state in my latest analysis here.)

But I want to focus on visits to Philadelphia. Because they inevitably involve the candidate consuming a cheesesteak. The Economist’s sister magazine, 1843, recently published an article on this very subject. And the whole thing is worth a read.

How have I managed to find this relevant to a blog about data visualisation? Well, they included a recipe to help people understand just what goes into the traditional Philadelphia dish.

Personally, I always have to confess, I’ve never been a huge fan. But, I’ll take provolone over whiz any day.

Credit for the piece goes to Jake Read.

Trumpsylvania

After working pretty much non-stop all spring and summer, your humble author finally took a few days off and throw in a bank holiday and you are looking at a five-day weekend. But, because this is 2020 travelling was out of the question and so instead I hunkered down to finish writing/designing an article I have been working on for the last several weeks/few months.

The main write-up—it is a lengthy-ish read so you may want to brew a cup of tea—is over at my data projects site. This is the first project I have really written about for that since spring/summer 2016. Some of my longer-listening readers may recall that the penultimate piece there I wrote about Pennsyltucky was inspired by work I did here at Coffeespoons.

To an extent, so is this piece. I wrote about Trumpsylvania, the political realignment of the state of Pennsylvania. 2016 and the state’s vote for Donald Trump was less an aberration than many think. It was the near-end result of a decades-long transformation of the state’s political geography. And so I looked at the data underlying the shift and how and where it occurred.

And originally, I had a slightly different conclusion as to how this related to Pennsylvania in the upcoming 2020 election. But, the whole 2020 thing made me shift my thinking slightly. But you’ll have to read the whole thing to understand what I’m talking about. I will leave you with one of the graphics I made for the piece. It looks at who won each county in the state, but also whether or not the candidate was able to flip the county. In other words, was Clinton able to flip a Republican county? Was Trump able to flip a Democratic county?

Who won what? Who flipped what?

Let me know what you think.

And of course, many, many thanks to all the people who suffered my ideas, thoughts, and early drafts over the last several weeks. And even more thanks to those who edited it. Any and all mistakes or errors in the piece are all mine and not theirs.

Credit for the piece is mine.

Parties in Pennsylvania

This is from a social media post I made a few days ago, but think it may be of some relevance/interest to my Coffeespoons followers. I was curious to see at 30+ days from the general election, how has the landscape changed for the two parties since 2016?

Well, this project has driven me to a related, but slightly different project that has been consuming my non-work time. Hopefully I will have more on that in the coming days. Without further ado, the post:

Pennsylvania will likely be one of the more critical battleground swing states in this year’s election. In 2016, then candidate Trump won the state by less than one percentage point. But four years is a long time and I was curious to see how things have changed.

In the first chart on the right we see counties won by Trump and on the left, Clinton. The further from the centre, the greater the candidate’s margin of victory over the other. The top half plots registered Republicans’ margin over Democrats as a percentage of all registered voters in the county (including independents and third party) and the bottom half does the same for Democrats. Closer to the centre, the more competitive, further away, less so.

Trump’s key to victory was the white, working class voter clustered in the west and the northeast of the state–old mining and steel towns. There Democrats normally counted on organised labour support as registered Democrats. That all but collapsed in 2016. The bottom right shows a number of nominally Democratic counties Trump won, whereas Clinton only picked up one Republican county, Chester.

But what are PA’s battlegrounds?

In the second chart we ignore places like Philly and Fulton County and zoom in on more competitive counties within 20 point margins. Polls presently point to a Biden lead of about 5 points in PA. If every dot moved left by 5 points (it doesn’t really work like that), we only see Erie and Northampton with potential to flip.

But Trump’s realignment of politics is accelerating (more on this another day) a realignment of PA’s political geography.

In the fourth chart, neither Erie nor Northampton show any real movement via party registration back to Democrats. Erie may flip, but Northampton’s likely a stretch. Places like Cumberland and Lancaster counties are too solidly Republican to flip this year. Instead Trump is more likely to flip counties like Monroe and Lehigh red, even if he loses the state.

Because, not shown, the key to a Biden victory will be running up the margins in Philly & Pittsburgh, and to a lesser extent Philly’s four collar counties, including Chester, which appears to be rapidly shifting in Democrats’ favour.

Credit for the piece is mine.

Positioning Is Important

Yesterday Pew Research released the results of a survey of how the rest of the world views select countries throughout the world. The Washington Post covered it in an article and created some graphics to support the text. The text, of course, was no big surprise in that the rest of the world views the United States poorly compared to just several years ago and that, in particular, President Trump is a leader in whom the world has no confidence.

But that’s not what I want to talk about. Instead, I want to address a design element in the one of their graphics. (But you should go ahead and read about the survey results.)

The issue here is the positioning of the labels for each bar, representing a world leader. At the very top of the graphic, things are in a good way. We have Merkel with a small space beneath that text then another label, “No confidence, 19 percent”, and then a connecting line to a dot to the blue bar. We then have a small space and the label Macron, meaning we have moved on and are on the next world leader.

But what if the reader sees the title and starts towards the bottom? They want to see the leaders in whom the world has no confidence. Now look at the bottom of the chart and the positioning of the labels for Trump, and above him, Xi, Putin, and maybe even Johnson. Because the “No confidence, x percent” labels have moved further to the right, there is an enormous space between the leader’s name and their coloured bar. Visually, this creates a link between the leader’s name and the preceding bar. For example, Trump appears to have a no confidence value of 78 with an unlabelled bar chart beneath him.

I suggest that there are two easy fixes to better link the labels to the data. The first is to move the leaders’ labels down, once the “No confidence” label has moved sufficiently far to the right. Like so.

The leader is now very clearly attached to his or her data with little confusion.

My second option is to fix the “No confidence” labels permanently to the left of the chart so as not to create that visual space in the first place, like so.

Here, after seeing the first option, I wonder if there is enough visual space at all between the leaders. But, this is only a quick Photoshop exercise. If I wanted to really tweak this, I would consider putting the data point or number in bold to the right of the label.That would eliminate an entire line of type that could be repurposed as a visual buffer between leaders.

I think either option would be preferable because of increased clarity for the reader.

Credit for the piece goes to the Washington Post graphics department.