Daniel,
I see your problem, the graph you posted is pretty awful.
The problem you are trying to solve is a common one in the presentation of
survey results. The graph you show looks similar to a series of fourteen
related questions with responses coded on a scale of 1 to 6. Having been
faced with this situation many times, I think the issue is not one of
technology, but rather of making choices about what data to present and what
to suppress. Thus, it would be easier to help if it were clear what the
categories actually are, rather than just providing numeric codes.
Some general points to consider are:
1. Can you combine any of the 6 categories? Your first category is clearly
dominant. The general idea here is to reduce the amount of data "clutter"
and focus on showing the dominant pattern in your data. All those little
bars may just be needless detail (depending on what they represent). If you
need to provide all of that detail, then perhaps you should provide a table
rather than a graph. Remember that graphs are meant to summarize data, and
need not provide all of the gory details.
2. Do all of the fourteen "over" groups need to represented on a single
graph? Do they fall into natural groups that could be split out over a
series of figures?
3. With regard to my earlier point about using a different type of graph: is
it possible to show these results as a series of effects? For example, could
you reduce the amount of data by graphing, say, odds ratios and confidence
intervals rather than the individual data points? If you can do this, then
you will be showing effects directly rather then leaving your reader to
infer the effect from the raw data points.
4. There is a type of graph that represents the contents of an r x c table
as a rectangular array of variously sized boxes (the bigger the cell number,
the larger the box). The idea here is to directly translate tabular data
into a graphical form. As far as I know, this is not implemented in Stata,
but Nick Cox may know better. It is implemented in R and I searched for
about 20 minutes to find it, but can't seem to put my finger on it. Perhaps
someone else knows the name of the command. EpiInfo implements this graph
type for 2 x 2 tables, but strictly, I think, as an aid to the
numerically-challenged trying to solve an outbreak. It probably has some
better use in larger r x c tables, but I'm hesitant to even mention it
because it is probably not really a solution. The fact that such graphs are
rarely published or even implemented is perhaps an indication that they lack
resonance in the scientific community.
That's the best I can do for you without knowing what your data actually
represent.
Regards,
Lee
Lee Sieswerda, Epidemiologist
Thunder Bay District Health Unit
[email protected]
> -----Original Message-----
> From: Daniel R Sabath [mailto:[email protected]]
> Sent: Friday, October 24, 2003 6:23 PM
> To: [email protected]
> Subject: st: RE: RE: Making bar charts readable in a
> grayscale photocopy.
>
>
> Lee wrote:
>
> In most cases, a bar chart should not need more than three
> colours, in which case, you can use white, gray, and black as
> your "colours". If you are showing more than three variables
> over multiple categories of another stratifying variable,
> then the presentation becomes hard to follow and you probably
> want to use a different type of graph.
> ------
> This is exactly what I am doing. What type of graph would you
> suggest? I have been using Nick's -catplot- to render graphs
> similar to: http://staff.washington.edu/sabathd/stata/bargraph.png
> ------
> However, if you do find yourself in need of more than the
> three bar colours, you may have categories that can be
> rationally ordered, in which case you may find that using a
> gradient of a single colour from light to dark is helpful.
> That way, the reader can more easily derive information from
> the order of the bars, and a precise colour or tonal match
> becomes less critical.
> ------
> Yes. This works when there is data in all of the categories.
> When one or more category drops out, it becomes confusing.
>
> Thanks again,
>
> Dan
>
>
>
> *
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> * http://www.ats.ucla.edu/stat/stata/
>
*
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