For Scientific American, Jack Murtagh describes the importance of visualization in understanding the roughness of data:

One maxim that should be etched into the walls of all scientific institutions is to visualize your data. Statistics specializes in applying objective quantitative measures to understand data, but there is no substitute for actually graphing it out and getting a look at its shape and structure with one’s own eyeballs. In 1973 statistician Francis Anscombe feared that others in his field were losing sight of the value of visualization. “Few of us escape being indoctrinated” with the notion that “numerical calculations are exact, but graphs are rough,” he wrote. To quash this myth, Anscombe devised an ingenious demonstration known as Anscombe’s quartet. Together with its wacky successor, the datasaurus dozen, nothing more dramatically communicates the primacy of visualization in data analysis.

In the Very Expensive Maps podcast, cartographer Evan Applegate interviews other cartographers about how they got into the field, the thoughts behind their design choices, and the more technical bits behind their process. With cartographers and chart makers sharing many of the same guides, this podcast, which is 33 episodes in, should be worth a listen.

Sidebar: When I read the title of Very Expensive Maps, I immediately thought of Justin Bieber talking about “very expensive” sounds at the 30-second mark of a NYT song breakdown. That’s probably not what Applegate was thinking, but it’s the same sentiment.

For their research on tattoos and choice, Bradley Ruffle and Anne Wilson provide the dataset for perusal:

Survey and experimental evidence documents discrimination against tattooed individuals in the labor market and in commercial transactions. Thus, individuals’ decision to get tattooed may reflect short-sighted time preferences. We show that, according to numerous measures, those with tattoos, especially visible ones, are more short-sighted and impulsive than the non-tattooed. Almost nothing mitigates these results, neither the motive for the tattoo, the time contemplated before getting tattooed nor the time elapsed since the last tattoo. Even the expressed intention to get a(nother) tattoo predicts increased short-sightedness and helps establish the direction of causality between tattoos and short-sightedness.

It’s a couple of hundred responses from the tattooed individuals, but maybe there’s something there. The dataset is linked towards the bottom of the page.

Saloni Dattani, for Our World in Data, used a set of heatmaps to show how causes of death changed by time (on the horizontal axis) and age (on the vertical axis) in France. Each panel represents a cause category.

The code is on GitHub, in case you want to make similar charts for your own country.

Welcome to The Process, where we look closer at how the charts get…

Rotten Tomatoes aggregates movie reviews to spit out a freshness score for each film. There’s a problem though. For Vulture, Lane Brown reports on the flawed system:

But despite Rotten Tomatoes’ reputed importance, it’s worth a reminder: Its math stinks. Scores are calculated by classifying each review as either positive or negative and then dividing the number of positives by the total. That’s the whole formula. Every review carries the same weight whether it runs in a major newspaper or a Substack with a dozen subscribers.

If a review straddles positive and negative, too bad. “I read some reviews of my own films where the writer might say that he doesn’t think that I pull something off, but, boy, is it interesting in the way that I don’t pull it off,” says Schrader, a former critic. “To me, that’s a good review, but it would count as negative on Rotten Tomatoes.”

Studios have of course learned how to game the system, not to mention most of the site is now owned by movie ticket seller Fandango.

The New York Times analyzed water levels across the country since 1920. In more recent years, the levels aren’t looking great if we want to keep growing crops. An animated map using angled lines shows the fluctuations and decline.

Locating the best pizza depends on where you are and what kind of pizza you’re looking for. The best-of lists that favor New York-style pizza and the east coast aren’t much good when you want Chicago-style pizza on the west coast. So The Washington Post parsed Yelp reviews to find the best pizza places for a selected style and state.

For The Pudding, Russell Samora pulled songs via the Spotify API and made a unique kind of clock:

Every minute, random songs are played that contain the time in the title (e.g., 6:47 or 6:47 from Central Station). If there are at least two songs with the correct am/pm (or it is absent), then the incorrect ones will be excluded.

This is the first clock in a working series of four. The second one uses time mentioned in YouTube videos.

Here’s the good stuff for August.

It’s straightforward to share a static SVG online, but maybe you want tooltips or for elements to highlight when you hover over them. Flourish has a new template to provide the interactions easier. Seems promising.

Most of Apple’s suppliers and manufacturing happen outside the United States and in China. But because of tensions between the U.S. and China, Apple has tried to shift to other countries. Bloomberg provides the breakdowns over time, showing the biggest increases in India and Vietnam.

Infinity is an abstraction of endlessness, which seems to suggest that it cannot be measured with finite units or occur in the real world. With a fun visual project, Dea Bankova wonders otherwise.

If you want to maximize fun, a mixed toolbox is still best.

Instead of using a bunch of equations to memorize, Yi Zhe Ang visually explains matrix transformations to provide some intuition behind the math. Make it to the end so that you can transform a 3-D image of a cat.

Excel is getting a bump in capabilities with Python integration. From Microsoft:

Excel users now have access to powerful analytics via Python for visualizations, cleaning data, machine learning, predictive analytics, and more. Users can now create end to end solutions that seamlessly combine Excel and Python – all within Excel. Using Excel’s built-in connectors and Power Query, users can easily bring external data into Python in Excel workflows. Python in Excel is compatible with the tools users already know and love, such as formulas, PivotTables, and Excel charts.

Sounds fun for both Excel users and Python developers.

It’s headed to the Beta Channel in Excel for Windows and then Excel for Windows proper. They didn’t announce a timeline for Mac.