Most underused data visualization [closed]

Answer 1

Another nice time series visualization that I was just reviewing is the "bump chart" (as featured in this post on the "Learning R" blog). This is very useful for visualizing changes in position over time.

You can read about how to create it on http://learnr.wordpress.com/, but this is what it ends up looking like:

Answer 2

Check out Edward Tufte's work and especially this book

You can also try and catch his travelling presentation. It's quite good and includes a bundle of four of his books. (i swear i don't own his publisher's stock!)

By the way, i like his sparkline data visualization technique. Surprise! Google's already written it and put it out on Google Code

Answer 3

Summary plots? Like mentioned in this page:

Visualizing Summary Statistics and Uncertainty

Answer 4

We shouldn't forget about cute and (historically) important stem-and-leaf plot (that Tufte loves too!). You get a directly numerical overview of you data density and shape (of course if your data set is not larger then about 200 points). In R, the function stem produces your stem-and-leaf dislay (in workspace). I prefer to use gstem function from package fmsb to draw it directly in a graphic device. Below is a beaver body temperature variance (data should be in your default dataset) in a stem-by-leaf display:

  require(fmsb)
  gstem(beaver1$temp)

Answer 5

I really like dotplots and find when I recommend them to others for appropriate data problems they are invariably surprised and delighted. They don't seem to get much use, and I can't figure out why.

Here's an example from Quick-R:

I believe Cleveland is most responsible for the development and promulgation of these, and the example in his book (in which faulty data was easily detected with a dotplot) is a powerful argument for their use. Note that the example above only puts one dot per line, whereas their real power comes with you have multiple dots on each line, with a legend explaining which is which. For instance, you could use different symbols or colors for three different time points, and thence easily get a sense of time patterns in different categories.

In the following example (done in Excel of all things!), you can clearly see which category might have suffered from a label swap.

Answer 6

I also like Tufte's modifications of boxplots which let you do small multiples comparison much more easily because they are very "thin" horizontally and don't clutter up the plot with redundant ink. However, it works best with a fairly large number of categories; if you've only got a few on a plot the regular (Tukey) boxplots look better since they have a bit more heft to them.

library(lattice)
library(taRifx)
compareplot(~weight | Diet * Time * Chick, 
  data.frame=cw , 
  main = "Chick Weights",
  box.show.mean=FALSE,
  box.show.whiskers=FALSE,
  box.show.box=FALSE
  )

Other ways of making these (including the other kind of Tufte boxplot) are discussed in this question.