This resonated with me since a colleague at work and I were having a debate about whether the plethora of toolsets for visualization are really ‘good’ for the data professionals. My contention is that certain tools just make it easier to make horrible charts. I think my quote was:
“This is like (possibly) giving young children access to chainsaws”
Just because you can use a given type of chart doesn’t mean you should use that type of chart. Tools like this can make it even easier to generate horrible visualizations like: http://junkcharts.typepad.com/junk_charts/2011/04/worst-statistical-graphic-nominated.html
Sometimes making things easier to do is not always the best answer. (“Now you too can run your own nuclear reactor, with three simple controls!”)
But I digressed from Kaiser Fung’s point.. His point is that different information/statistical charts have much different ‘usability’ factors for the reader, along with different levels of effort for the creator. He turned this into a simple quad chart which I think is pretty reasonable (even though I really am not a fan of the Napoleon’s March chart). I think one central theme to most of the complaints about ‘bad graphics’ is either a total lack of a point or where the point is so obscured by the details of the graphic. The one exception to that is really the high effort, high reward space — graphics which try to tell several stories or illuminate several key points often are complex in nature. The exploratory-type analysis graphics need significant skill and background to interpret and find the information in the ‘haystack’ of the graphic.