The New York Times produced a series of charts for the shift in Texas. The multi-line chart above shows the changes over time for each county. All the lines take a sharp left at the bottom.

You’ve seen maps of the results and maps of the shifts. For the Wall Street Journal, Chiqui Esteban and Anthony DeBarros combined results and shifts into a single map using an interesting aesthetic that looks like strands of hair sprouting off the United States.

The base map shows county-by-county results for the predominant party between Democrat and Republican. The hair strands start as spikes pointing straight up, as we’ve grown used to over the years. The height of a spike represents the margin of victory versus the opposing party.

Scroll to the next step. The spikes bend to represent the swing, in comparison to the previous election, like strands of hair that blow in the wind. A deeper bend represents a greater swing for the county.

There’s a lot going on but it works. I approve of this hairy metaphor.

Randy Au on the manual process of exploratory data analysis that is still irreplaceable:

While I’m fine offloading specific tasks to an LLM, I find they’re just really terrible when given any leeway with decision-making. Since there’s no absolute positive/negative feedback in an analysis, typical coding agent harnesses have no way to tell if their analysis has completely flown off the rails. While toying with the idea, I’ve seen Claude get a peculiar idea that obviously takes assumptions that doesn’t apply to the data and attempt to build a giant testing harness around that analysis idea. I watched it run to completion and asked some pointed questions about why it was doing that and it did the “oh! my bad!” routine. You can jump through all sorts of hoops and harnesses to try to get a handle on things, but the only creativity I’ve encountered with this process is how it comes up with new and unimaginable ways to do the wrong thing.

When you know exactly what you want, generative AI is useful for joining that dataset or making that one chart. Exploratory data analysis relies on not having an exact answer and relying on clues during the process to guide you.

This will evolve as the LLMs grow more extensive, but for now, analysis output from the chatbots converge towards generic results without outside guidance.

In this week’s The Process, we look at the ones that highlight the big shifts and see how changing focus can change the story.

Bloomberg used mountain peaks to show the shifts towards Democrats and Republicans. Taller peaks represent greater shifts, and blue and red indicate Democratic and Republican shifts, respectively.

I prefer this map to the NYT map that uses arrows. It’s easier to spot the handful of Republican shifts without zooming in. The transparency helps too.

The New York Times brings back the angled arrows to map the shift in voting margins between parties, compared to the 2024 presidential margins in each county. A larger blue arrow means more Democratic, and a larger red arrow means more Republican.

It was almost all blue arrows across the country, save a few small red ones in Maine and Minnesota.

James Birdle described his experiment using AI to assist in chair design:

What we have here is, quite literally, a chair designed by someone who has read a lot about chairs but never actually sat in one. They know what a chair should look like, what it should do, the mechanics of how one is put together, the physics of holding you up. But they have no embodied experience of the world, no history with chairs or any other object, no physical memory. They don’t know how it feels, not merely to sit in a chair, but to encounter one in the world, to place it among other things in a home, to live with it, to be around it every day as one goes about the mundane business of life.

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For the Washington Post, Dylan Moriarty has a live tracker for the percentage of votes counted. There is still a ways to go.

If feeling uncertain is your thing, the Needle from the New York Times is live to show the chances of each party winning Senate and House control. Views for individual races are also available.

I suggest waiting until later in the evening before you watch the swings. Do it for your heart.

As midterm election results come in tonight, percentages can flip in favor of a candidate in a way that seems artificial if you don’t know what you’re watching. For the New York Times, Christine Zhang describes election night “mirages” that explain the sudden changes.

This is especially important tonight while watching the needle of uncertainty. It’s coming back tonight.

During the World Series, Google ran an ad for Gemini that noted several correlations between home runs and full moons. Marcos Huerta analyzed data on full moons and home runs to debunk each statement.

I had assumed that some Google marketing types asked some MLB data team members8 for some lunar factoids and some poor analyst when probably knew better sent them whatever they could find: error bars be damned.

But maybe they really did use Gemini. The ad included 3 stats that are all a little true – if you ignore the error bars, and the home run stat in particular has some enormous error bars. That feels like what might happen if you ask an LLM to do data analysis without babysitting it carefully. Agentic p-hacking? Yet, not really p-hacking since it still couldn’t find a statistically significant result.

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Evan Reynolds and Christine Zhang, for the New York Times, have quick tabulations for early midterm voting. Turnout is up by a lot compared to the last midterm election.

Elon Musk has donated $446 million for the midterm elections, because sure why not. The Guardian used a Sankey diagram that flows onto a state map to show where the money goes.

For the Washington Sun, Andrew Van Dam and Manuela Silva note the uptick in Trump comments about butts and bodies.

Trump has long had a penchant for complimenting — or disparaging — the physical appearances of his allies and enemies alike. But a new analysis by The Washington Sun shows that Trump has referenced physical appearance almost three times as much in the past year as he did during the same period in the 2016 campaign.

The president of the United States, ladies and gentlemen.

With more demonstrations and protests in India, Reuters charts and illustrates current tactics police use and the conflict with public safety.

Although Indian law constitutionally protects the right to assembly, a report authored by human rights lawyer Vrinda Grover for International Center for Not-for-Profit Law (ICNL) finds this protection often fails in practice.

Ultimately, the decision to intervene rests with executive magistrates or police.

A majority of surveyed officers believe they should be allowed to use force without fear of punishment, according to the Status of Policing in India Report 2025. Delhi Police and the Home Ministry did not immediately respond to Reuters request for comments.

Last month, the Wealth Inequality in America video by Politizane got an update after thirteen years. The gap between the richest and the normals has only grown. This interactive version uses the very tall chart from the video to show the scale of wealth for the top 0.1% of people.

Set your expectations for how long it will take to scroll from the bottom to the top. Then reset expectations as it becomes clear that it’s annoyingly tall. Keep going. Reset again. Keep going, change the scale, reset. There is an “I quit!” button for a reason.

Every month, I gather tools and resources to make better charts that communicate and reveal. Here is what happened in October.

For the New York Times, Arfa Momin and Ron Lieber charted median income of graduates, by college and major.

Your school can make a big difference — or matter very little — depending on the major. A graduate who studied mechanical engineering at Carnegie Mellon University earned roughly $10,000 more than a graduate from the University of Alabama. That difference is over $150,000 if those same graduates studied computer science.

You can also search for your college and major to compare.

There are correlation and causation considerations to make here, but the data from the Department of Education exists to hold schools accountable. If a school charges a high tuition, but graduates earn lower incomes, then the program is worth a closer look.

The charts in the NYT article are based on an analysis by the HEA Group, who have made their data available for download. [Thanks, Charlotte]

For the Pudding, Owen Lacey analyzed the network of Hollywood actors connected by movies they appeared together in. The goal is to find the “real” Kevin Bacon.

Lacey uses remoteness, or the average number of movies it takes for an actor to connect to all other actors in the network. Lower remoteness means a more connected actor. So to find the most connected, you find the actor with the lowest remoteness.

Kevin Bacon has a lot of acting credits, but there are many actors who are more connected.

The visual essay has a nice a structure that walks through past, present, and future with a well-made line chart.

I also appreciate the classic analysis on degrees of separation. It’s been a while. See also the six degrees of Francis Bacon, four degrees through Facebook, and the six degrees of Kevin Garnett (might be defunct).