A great reference for further reading about data visualization is "The Visual Display of Quantitative Information" by Edward Tufte, a classic book originally published in 1983 with enduring relevance today.
It's an interesting book with some really interesting examples (good and bad). I also recommend looking at it.
But the central premise the text presents is "maximise the information to ink ratio", which sounds very reasonable but is fundamentally flawed. The problem is that quantity of ink (or, on a screen, number of black pixels) is not the same as visual complexity. By the time your brain is interpreting something visual, it has done edge detection, grouping, and other preprocessing.
He gives an example of shortening the axes on a scatter plot to show the range of data, rather than intersecting at the corner. This is win-win because it uses less ink but shows more information. But, when I look at the comparison, it's just obvious that the modified version is visually more complex. It would be especially worse on a complex page with text and a few plots next to each other – the fragments would all visually bleed together.
One mitigation to that sort of complexity is to put boxes around large district elements, like an entire plot. But boxes are Tufte's absolute nemesis, in that book and elsewhere. It's surprising that after so many years looking at visual displays he still has that attitude.
Afraid not. I only found out about Tufte's book due to a mention in xkcd. Until Randall mentions another book about plots I'm not likely to find one :-)
It would be nice to find someone like Tufte that gathered some actual evidence. Like, trying different plots on groups of people to see which find information faster, maybe using eye tracking. Even just subjective surveys might be an improvement on one person's opinion.
I found the book interesting from a historical perspective, but doesn't have any "secret" information or anything to add above the far more extensive resources available online today. I find http://data-to-viz.com excellent for a high-level look at how to match a chart to your data and story for that data, and the different plotting library examples can be great references for inspiration, eg https://matplotlib.org/stable/gallery/index.html
I think he has an insightful framework for thinking about what makes good data visualization
However I feel a lot of people miss the logic behind it all and just straight up copy the "Tuft style" which often is too stylized and iconoclastic. A good example are the plotting defaults in R's ggplot2
