I've never seen a data visualization tool that is universally applicable, so a simple edict like "don't use scatterplots" is a bit too simple. This hexagonal plot looks cool for the problem under question but there are obvious cases where it would be unnecessarily complicated and less informative that a scatter plot. There's a reason why all of the various plot types were invented.
Scatterplots are the tools of choice for displaying time-dependent data that doesn't necessarily makes a smooth curve. Often you only have one value per time stamp, so overlap is not a problem.
In fact, the wikipedia article even says "A scatter plot is used when a variable exists that is under the control of the experimenter". https://en.wikipedia.org/wiki/Scatter_plot
If the author had read that part of the wikipedia article, I guess his claim would have been more specific :-)
If your data can be displayed without points overlapping, a scatterplot can display all the information, while a density plot will always display only a summary of the data. The larger your grid size, the greater the loss of information.
And, by adding noise ("jitter" or "dither") to each point, you can still use a plain scatterplot even for many kinds of overlapping data.
It's simple to do and mimics reversing the effect of truncation of the data (at least for continuous quantities). Just use uniformly distributed values that are as wide as one bin width.
For most purposes, I prefer adding dither, and then using transparency, to moving to a density plot, for exactly the reason you mention -- the density plot introduces another parameter, the smoothing method, which puts another layer between you and the data.
Yes, if the data was sparse enough that it could be plotted without overlap and the graph carried an annotation saying so, I could see a scatter plot being better.
With less than 20 non-overlapping points on the graph, I doubt that I would want to use anything else.
Furthermore, if my data has two outliers that are near each other, they may well be indistinguishable in the hexplot from one (or five) clustered outliers.
Your post was very interesting and your examples are great. I'll definitely use hexplots in the future. But I will still default to scatterplots. It's just easier to see if there's something wrong with the data, and they require less interpretation.
Lots... imagine plotting y = f(x) for several different data series on the same axes. A density plot for all the different data series would be really crowded, whereas a scatter plot could (not necessarily would) better show the differences between the series
A density plot would never have revealed the discrete grouping along the x-axis due to rounding. The density plot hides it, but if he started from density plots this might have gotten him into trouble (for example if he would group them in [starttime,stoptime[ partitions it would skew results) .
