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.
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.
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.
- The elements of graphing data
- Visualizing data
Tukey was one of Tufte's mentors.
It’s very interesting and relevant, just not the final word on the subject.
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