Most underused data visualization [closed]

Answer 1

If your scatter plot has so many points that it becomes a complete mess, try a smoothed scatter plot. Here is an example:

library(mlbench) ## this package has a smiley function
n <- 1e5 ## number of points
p <- mlbench.smiley(n,sd1 = 0.4, sd2 = 0.4) ## make a smiley :-)
x <- p$x[,1]; y <- p$x[,2]
par(mfrow = c(1,2)) ## plot side by side
plot(x,y) ## left plot, regular scatter plot
smoothScatter(x,y) ## right plot, smoothed scatter plot

The hexbin package (suggested by @Dirk Eddelbuettel) is used for the same purpose, but smoothScatter() has the advantage that it belongs to the graphics package, and is thus part of the standard R installation.

Answer 2

Violin plots (which combine box plots with kernel density) are relatively exotic and pretty cool. The vioplot package in R allows you to make them pretty easily.

Here's an example (The wikipedia link also shows an example):

Answer 3

Horizon graphs (pdf), for visualising many time series at once.

Parallel coordinates plots (pdf), for multivariate analysis.

Association and mosaic plots, for visualising contingency tables (see the vcd package)

Answer 4

Mosaic plots seem to me to meet all four criteria mentioned. There are examples in r, under mosaicplot.

Answer 5

In addition to Tufte's excellent work, I recommend the books by William S. Cleveland: Visualizing Data and The Elements of Graphing Data. Not only are they excellent, but they were all done in R, and I believe the code is publicly available.

Answer 6

Boxplots! Example from the R help:

boxplot(count ~ spray, data = InsectSprays, col = "lightgray")

In my opinion it is the most handy way to take a quick look at the data or to compare distributions. For more complex distributions there is an extension called vioplot.