[R] Generating a vector for breaks in a histogram

Prof Brian Ripley ripley at stats.ox.ac.uk
Fri Jul 4 08:43:47 CEST 2003

Things have moved on since the ASH work too, but I would agree that
density estimation is often a better way than histograms.  However, close 
to state-of-the-art density estimation is built into R (?density) and
packages `polspline', `KernSmooth' and `sm' are also much more advanced 
than `ash'. 

It was the advent of enough computing power that changed this, and the S 
language has been in the forefront of making the state of the art 
available.  You'll see that MASS (the book) covers histograms and 
alternatives in its chapter on Univariate Distributions, and it has since 
its 1994 first edition (when did you go to `school'?)

One often overlooked alternative is plot ECDFs.

If distributions are not really continuous other techniques may be 
appropriate -- such as dotplots.

On Fri, 4 Jul 2003, Mulholland, Tom wrote:

> One of my discoveries while learning the art of R, is that time has
> moved on since I did my basic statistics in school (although to my
> dismay the teaching of statistics in school appears also to have not
> noticed the movement.) I have seen a few references when people want to
> pie chart something, for the advice to be "find a better way." I've been
> reading some of the ash work (see package of same name and loads of
> papers on the web), also some interesting work on dot plots as an
> alternative to histograms. They make me feel that unless the data that
> you have in both histograms accidentally works well with the same set of
> bins you may not get the comparative assessment that you think you are
> getting.
> I am beginning to form the opinion that in most cases (if not all) there
> are better alternatives to histograms.
> _________________________________________________
> Tom Mulholland
> Senior Policy Officer
> WA Country Health Service
> 189 Royal St, East Perth, WA, 6004
> Tel: (08) 9222 4062
> e-mail: Tom.Mulholland at health.wa.gov.au
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Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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