Peter--
That's it exactly! Thanks.
--Chris
Christopher W. Ryan, MD
SUNY Upstate Medical University Clinical Campus at Binghamton
425 Robinson Street, Binghamton, NY 13904
cryanatbinghamtondotedu
"Observation is a more powerful force than you could possibly reckon.
The invisible, the overlook
On 2011-03-22 12:12, Christopher W Ryan wrote:
I have a dataframe that looks like this:
> str(chr)
'data.frame': 84 obs. of 7 variables:
$ county: Factor w/ 3 levels "Broome","Nassau",..: 3 3 3 3 3 3 3 3 3 3 ...
$ item : Factor w/ 28 levels "Access to healthy foods",..: 21 19 20
18 16
My apologies. That should have been Paul Murrell.
-- Bert
On Tue, Mar 22, 2011 at 5:07 PM, Tóth Dénes wrote:
>
> You might also consider the Deducer package. You can build up a plot by
> point and click and then have a look at (and amend) the code and learn the
> syntax of ggplot2, which is a ni
IMHO both methods (or languages) have advantages and disadvantages.
Sometimes I even find basic graphics the most useful, it always depends on
a lot of factors. So do not exclude any of them...
> Thanks, the ggplot2 strategy looks promising. For making
> information-dense graphs, I tend to vacil
Thanks, the ggplot2 strategy looks promising. For making
information-dense graphs, I tend to vacillate between lattice and
ggplot2. I should probably settle on one or the other and learn it
better. I'll admit I like the default look of lattice plots better, but
so far custom panel functions sti
You might also consider the Deducer package. You can build up a plot by
point and click and then have a look at (and amend) the code and learn the
syntax of ggplot2, which is a nice alternative to the lattice package.
The website of the Deducer package (www.deducer.org) is a good start.
--
An
Well, a custom panel function is what you need (or one that may
already exist somewhere: try googling on "high low intervals in R
graphs" or some such).
So if you haven;t already done so, try Paul Morrell's Chapter on
lattice plots from his book for how panel functions work:
http://www.stat.auckl
I have a dataframe that looks like this:
> str(chr)
'data.frame': 84 obs. of 7 variables:
$ county: Factor w/ 3 levels "Broome","Nassau",..: 3 3 3 3 3 3 3 3 3 3 ...
$ item : Factor w/ 28 levels "Access to healthy foods",..: 21 19 20
18 16 3 2 6 17 8 ...
$ value : num 8644 15 3.5 3.9 7.7 .
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