So you can also use glyphs to do high dimensional visualization.
So in this case this is worlds within worlds,
I've set up horizontal and vertical nominal,
categorical axes and when I'm plotting inside
each of these table cells, is a separate plot, it's a scatter plot.
I just happen to have one data point in the scatter plot, so
I've embedded a two dimensional plot in each element of a two dimensional table.
So that enables you to look at four dimensional data
as basically a two dimensional table of two-dimensional plots.
In this case what I'm doing is I've got a two-dimensional table of glyphs.
Each glyph happens to be a two-dimensional plot.
And so I'm looking in this case at life expectancy, and
I'm looking at the rates of infant mortality inside each year from 2000,
2001, 2002, 2003, 2004.
And then vertically I'm looking at the various continents or regions, and
I'm looking at the life expectancy measured inside each one of those.
So I've got an outer table that's looking at the year and
vertically looking at the region of the world, and
then inside each year and each region of the world,
I'm plotting infant mortality versus life expectancy.
You can see some shifts in the data as we move across different areas of the world.
And you can see some shifts in infant mortality, but
you can see that there aren't as many shifts as you move from year to year.
There's another interesting glyph that you can use called Chernoff faces, and
in this case you're creating a table, some other layout, but
the data points are being plotted with faces, and the attributes of the face
are indicating various measures of some dimension of the data set.
In this case this is 12 state judges as
rated by lawyers and you can map certain features, certain aspects,
of the way the state judges were being measured, for example, the number of
times they were overturned, might change the expression from happy to sad.
And the size of the face may be the number of cases they've seen.
So, you know, this face may look quite large, and this face look
quite small because this state judge has reviewed more cases than has this judge.
And so you can't make value judgments based on the appearance of the faces, but
the appearance of the faces and the expressions that they
have can give you another data channel that you can perceive and remember,
actually quite well as a way of finding interesting aspects of the data,
things in the data that stand out.