I would dissuade no one from writing drunk, and I'm confident that you too can say that people are penguins! But I'm sorry to report that personally I don't do it by drinking but rather writing a much longer version with all those kinds of clarifications included and then obsessively editing it down.

Do you happen to have any recommended pointers for research on health impacts of processed food? It's pretty easy to turn up a few recent meta reviews, which seems like a decent place to start, but I'd be interested if there were any other sources, particularly influential individual experiments, etc. (It seems like there's a whole lot of observational studies, but many fewer RCTs, for reasons that I guess are pretty understandable.) It seems like some important work here might never use the word "processing".

If I hadn't heard back from them, would you want me to tell you? Or would that be too sad?

Seed oils are usually solvent extracted, which makes me wonder, how thoroughly are they scrubbed of solvent, what stuff in the solvent is absorbed into the oil (also an effective solvent for various things), etc

I looked into this briefly at least for canola oil. There, the typical solvent is hexane. And some hexane does indeed appear to make it into the canola oil that we eat. But hexane apparently has very low toxicity, and—more importantly—the hexane that we get from all food sources apparently makes up less than 2% of our total hexane intake! https://www.hsph.harvard.edu/nutritionsource/2015/04/13/ask-the-expert-concerns-about-canola-oil/ Mostly we get hexane from gasoline fumes, so if hexane is a problem, it's very hard to see how to pin the blame on canola oil.

It's a regression. Just like they extrapolate backwards to (1882+50=1932) using data from 1959, they extrapolate forwards at the end. (This is discussed in the "timelines" section.) This is definitely a valid reason to treat it with suspicion, but nothing's "wrong" exactly.

Many thanks! All fixed (except one that I prefer the old way.)

As the original author of underrated reasons to be thankful (here), I guess I can confirm that tearing apart the sun for raw materials was not an intended implication.

I think matplotlib has way too many ways to do everything to be comprehensive! But I think you could do almost everything with some variants of these.

ax.spines['top'].set_visible(False) # or 'left' / 'right' / 'bottom'
ax.set_xticks([0,50,100],['0%','50%','100%'])
ax.tick_params(axis='x', left=False, right=False) # or 'y'
ax.set_ylim([0,0.30])
ax.set_ylim([0,ax.get_ylim()[1]])

Good point regarding year tick marks! I was thinking think that labeling 0°C would make the most sense when freezing is really important. Say, if you were plotting historical data on temperatures and you were interested in trying to estimate the last frost date in spring or something. Then, 10°C would mean "twice as much margin" as 5°C.

One way you could measure which one is "best" would be to measure how long it takes people to answer certain questions. E.g. "For what fraction of the 1997-2010 period did Japan spend more on healthcare per-capita than the UK?" or "what's the average ratio of healthcare spending in Sweden vs. Greece between 2000 and 2010?" (I think there is an academic literature on these kinds of experiments, though I don't have any references on hand.)

In this case, I think Tufte goes overboard in saying you shouldn't use color. But if the second plot had color, I'd venture it would win most such contests, if only because the y-axis is bigger and it's easier to match the lines with the labels. But even if I don't agree with everything Tufte says, I still find him useful because he suggests different options and different ways to think about things.