Hadley,
I have counts ranging over 4-6 orders of magnitude with peaks
occurring at various 'magic' values. Using a log scale for the
y-axis enables the smaller peaks, which would otherwise
be almost invisible bumps along the x-axis, to be seen
That doesn't justify the use of a _histogram_ - and regardless of
The usage highlights meaningful characteristics of the data.
What better justification for any method of analysis and display is
there?
what distributional display you use, logging the counts imposes some
pretty heavy restrictions on the shape of the distribution (e.g. that
it must not drop to zero).
Does there have to be a recognized statistical distribution to use R?
In my case I am using R for all of the analysis and graphics in a
new book. This means that sometimes I have to deal with data sets
that are more or less a jumble of numbers with patterns in a few
places. For instance, the numeric value of integer constants
appearing as one operand of the binary bitwise-AND operator (see
figure 1224.1 of www.knosof.co.uk/cbook/usefigtab.pdf, raw data
at: www.knosof.co.uk/cbook/bandcons.hist.gz)
qplot(band, binwidth=8, geom="histogram") + scale_y_log()
does a good job of highlighting the peaks.
It may be useful for your purposes, but that doesn't necessarily make
it a meaningful graphic.
Doesn't being useful for my purpose make it meaningful, at least for me
and I hope my readers?
--
Derek M. Jones tel: +44 (0) 1252 520 667
Knowledge Software Ltd mailto:de...@knosof.co.uk
Source code analysis http://www.knosof.co.uk
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