[R] ggplot2 facet_wrap problem

ONKELINX, Thierry Thierry.ONKELINX at inbo.be
Wed Dec 3 13:12:02 CET 2008 

Hi Stephen,

It looks like a bug in facet_wrap which seems to mix up the factor
labels (by sorting only the labels but not the plots?).

HTH,

Thierry


------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature
and Forest
Cel biometrie, methodologie en kwaliteitszorg / Section biometrics,
methodology and quality assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium 
tel. + 32 54/436 185
Thierry.Onkelinx op inbo.be 
www.inbo.be 

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: stephen sefick [mailto:ssefick op gmail.com] 
Verzonden: woensdag 3 december 2008 1:09
Aan: ONKELINX, Thierry
CC: hadley wickham; R-help
Onderwerp: Re: [R] ggplot2 facet_wrap problem

If you look at the TSS graph in the faceted example and then look at
the plot of just the GPP vs. TSS.  They are different graphs all
together.  The one that is not faceted is correct.

On Tue, Dec 2, 2008 at 6:36 PM, ONKELINX, Thierry
<Thierry.ONKELINX op inbo.be> wrote:
> Hi Stephen,
>
> I think you will need to clarify what your problem is with the second
plot.
>
> HTH,
>
> Thierry
>
>
> -----Oorspronkelijk bericht-----
> Van: r-help-bounces op r-project.org namens stephen sefick
> Verzonden: di 2-12-2008 22:52
> Aan: hadley wickham; R-help
> Onderwerp: [R] ggplot2 facet_wrap problem
>
> Hadley,
> I don't know if I am doing something wrong or if it is ggplot please
> see the two graphs at the bottom of the page (code).
>
> melt.nut <- (structure(list(RiverMile = c(119L, 119L, 119L, 119L,
119L, 119L,
> 119L, 119L, 119L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 179L,
> 179L, 179L, 179L, 179L, 179L, 179L, 185L, 185L, 185L, 185L, 185L,
> 185L, 185L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L,
> 190L, 190L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L,
> 198L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L,
> 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
> 215L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 119L, 119L,
> 119L, 119L, 119L, 119L, 119L, 119L, 119L, 148L, 148L, 148L, 148L,
> 148L, 148L, 148L, 179L, 179L, 179L, 179L, 179L, 179L, 179L, 185L,
> 185L, 185L, 185L, 185L, 185L, 185L, 190L, 190L, 190L, 190L, 190L,
> 190L, 190L, 190L, 190L, 190L, 190L, 198L, 198L, 198L, 198L, 198L,
> 198L, 198L, 198L, 198L, 198L, 202L, 202L, 202L, 202L, 202L, 202L,
> 202L, 202L, 202L, 202L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
> 215L, 215L, 215L, 215L, 215L, 61L, 61L, 61L, 61L, 61L, 61L, 61L,
> 61L, 61L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L,
> 148L, 148L, 148L, 148L, 148L, 148L, 148L, 179L, 179L, 179L, 179L,
> 179L, 179L, 179L, 185L, 185L, 185L, 185L, 185L, 185L, 185L, 190L,
> 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 198L,
> 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 202L, 202L,
> 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 215L, 215L, 215L,
> 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 61L, 61L,
> 61L, 61L, 61L, 61L, 61L, 61L, 61L, 119L, 119L, 119L, 119L, 119L,
> 119L, 119L, 119L, 119L, 148L, 148L, 148L, 148L, 148L, 148L, 148L,
> 179L, 179L, 179L, 179L, 179L, 179L, 179L, 185L, 185L, 185L, 185L,
> 185L, 185L, 185L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L,
> 190L, 190L, 190L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L,
> 198L, 198L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L,
> 202L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
> 215L, 215L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 119L,
> 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 148L, 148L, 148L,
> 148L, 148L, 148L, 148L, 179L, 179L, 179L, 179L, 179L, 179L, 179L,
> 185L, 185L, 185L, 185L, 185L, 185L, 185L, 190L, 190L, 190L, 190L,
> 190L, 190L, 190L, 190L, 190L, 190L, 190L, 198L, 198L, 198L, 198L,
> 198L, 198L, 198L, 198L, 198L, 198L, 202L, 202L, 202L, 202L, 202L,
> 202L, 202L, 202L, 202L, 202L, 215L, 215L, 215L, 215L, 215L, 215L,
> 215L, 215L, 215L, 215L, 215L, 215L, 61L, 61L, 61L, 61L, 61L,
> 61L, 61L, 61L, 61L, 119L, 119L, 119L, 119L, 119L, 119L, 119L,
> 119L, 119L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 179L, 179L,
> 179L, 179L, 179L, 179L, 179L, 185L, 185L, 185L, 185L, 185L, 185L,
> 185L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L,
> 190L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L,
> 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 215L,
> 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
> 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 119L, 119L, 119L,
> 119L, 119L, 119L, 119L, 119L, 119L, 148L, 148L, 148L, 148L, 148L,
> 148L, 148L, 179L, 179L, 179L, 179L, 179L, 179L, 179L, 185L, 185L,
> 185L, 185L, 185L, 185L, 185L, 190L, 190L, 190L, 190L, 190L, 190L,
> 190L, 190L, 190L, 190L, 190L, 198L, 198L, 198L, 198L, 198L, 198L,
> 198L, 198L, 198L, 198L, 202L, 202L, 202L, 202L, 202L, 202L, 202L,
> 202L, 202L, 202L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
> 215L, 215L, 215L, 215L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L,
> 61L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 119L, 148L,
> 148L, 148L, 148L, 148L, 148L, 148L, 179L, 179L, 179L, 179L, 179L,
> 179L, 179L, 185L, 185L, 185L, 185L, 185L, 185L, 185L, 190L, 190L,
> 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 198L, 198L,
> 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 202L, 202L, 202L,
> 202L, 202L, 202L, 202L, 202L, 202L, 202L, 215L, 215L, 215L, 215L,
> 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 61L, 61L, 61L,
> 61L, 61L, 61L, 61L, 61L, 61L, 119L, 119L, 119L, 119L, 119L, 119L,
> 119L, 119L, 119L, 148L, 148L, 148L, 148L, 148L, 148L, 148L, 179L,
> 179L, 179L, 179L, 179L, 179L, 179L, 185L, 185L, 185L, 185L, 185L,
> 185L, 185L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L, 190L,
> 190L, 190L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L, 198L,
> 198L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L, 202L,
> 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
> 215L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 61L, 119L, 119L,
> 119L, 119L, 119L, 119L, 119L, 119L, 119L, 148L, 148L, 148L, 148L,
> 148L, 148L, 148L, 179L, 179L, 179L, 179L, 179L, 179L, 179L, 185L,
> 185L, 185L, 185L, 185L, 185L, 185L, 190L, 190L, 190L, 190L, 190L,
> 190L, 190L, 190L, 190L, 190L, 190L, 198L, 198L, 198L, 198L, 198L,
> 198L, 198L, 198L, 198L, 198L, 202L, 202L, 202L, 202L, 202L, 202L,
> 202L, 202L, 202L, 202L, 215L, 215L, 215L, 215L, 215L, 215L, 215L,
> 215L, 215L, 215L, 215L, 215L, 61L, 61L, 61L, 61L, 61L, 61L, 61L,
> 61L, 61L), variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
> 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
> 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
> 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
> 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
> 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
> 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
> 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
> 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
> 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
> 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
> 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
> 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
> 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
> 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
> 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
> 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
> 7L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
> 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
> 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
> 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
> 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L,
> 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
> 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
> 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
> 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
> 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L,
> 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L,
> 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L
> ), .Label = c("P.R", "GPP", "NDM", "CR24", "abs.CR24", "TSS",
> "TIN", "Phosphorus", "TIN.TP", "Kdm"), class = "factor"), value =
c(NA,
> NA, NA, NA, -0.066915449, -0.093917018, NA, 1.019951293, NA,
> NA, 2.017149918, NA, 0.189592164, -0.234196581, 0.269013732,
> NA, NA, 0.748103002, 7.894158712, NA, 0.9479659, NA, NA, NA,
> 3.154523416, 1.548924774, 2.112652562, 2.232891361, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, 2.910836928, NA, NA, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 1.156422043,
> 1.073329968, 0.675283717, 0.919190889, 0.975135008, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -0.116329637,
> 0.623416534, 0.11154653, NA, NA, NA, NA, NA, NA, NA, -0.253939188,
> -0.259694431, NA, 0.303075477, NA, NA, 1.223052413, NA, 0.595659466,
> -0.415908847, 0.130062254, NA, NA, 2.361170968, 2.518121521,
> NA, 2.67636584, NA, NA, NA, 1.173056254, 2.442185134, 1.159948001,
> 4.703411567, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.649974627,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, 11.85338495, 10.04657895, 4.995851341, 4.426742631,
4.700996957,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
-0.314806304,
> 1.165099135, 0.207974813, NA, NA, NA, NA, NA, NA, NA, -4.048865363,
> -3.02484218, NA, 0.005928467, NA, NA, 0.616725435, NA, -2.546134227,
> -2.191805175, -0.353415867, NA, NA, -0.795040091, 2.199136102,
> NA, -0.146906432, NA, NA, NA, 0.801191443, 0.865488076, 0.610899842,
> 2.596989531, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.426679869,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, 1.603333926, 0.686382879, -2.402300298, -0.389169586,
> -0.119870838, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, NA, -3.020963667, -0.703794408, -1.656492078, NA, NA,
> NA, NA, NA, NA, NA, -3.794926175, -2.765147748, NA, -0.297147009,
> NA, NA, 0.606326977, NA, -3.141793693, -1.775896327, -0.48347812,
> NA, NA, -3.156211059, -0.318985419, NA, -2.823272272, NA, NA,
> NA, 0.371864811, -1.576697058, -0.54904816, 2.106422036, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, 0.223294758, NA, NA, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -10.25005103,
> -9.360196068, -7.398151639, -4.815912217, -4.820867795, NA, NA,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, -2.706157363,
> -1.868893543, -1.864466891, NA, NA, NA, NA, NA, NA, NA, 3.794926175,
> 2.765147748, NA, 0.297147009, NA, NA, 0.606326977, NA, 3.141793693,
> 1.775896327, 0.48347812, NA, NA, 3.156211059, 0.318985419, NA,
> 2.823272272, NA, NA, NA, 0.371864811, 1.576697058, 0.54904816,
> 2.106422036, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.223294758,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
> NA, NA, 10.25005103, 9.360196068, 7.398151639, 4.815912217,
4.820867795,
> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
2.706157363,
> 1.868893543, 1.864466891, NA, NA, NA, 8.5, 12, 19, 11, 14, 24,
> 9.3, 6.1, 9.5, 5.4, 11, 9.7, 8.2, 9.6, 6.1, 4.4, 6.2, 8.4, 4.4,
> 5.6, 3.1, 3.1, 3.1, 2.9, 11, 1.4, 1.4, 1.8, 1.4, 0.8, 0, 0, 1,
> 4.4, 5.8, 1.5, 2, 2.1, 0.4, 0.9, 1.2, 2.4, 5.8, 0.5, 0.8, 0.5,
> 1.7, 0.4, 0.6, 0.7, 1.4, 1.9, 1.2, 2.6, 2.1, 8.5, 7, 0.9, 1.4,
> 1.5, 1.6, 0.77, 1.1, 1.1, 0.8, 1, 1.4, 1.1, 1, 0.8, 2.6, 0.8,
> 5.7, 16, 23, 27, 25, 24, 19, 10, 9.8, 14, 0.45, 0.362, 0.51,
> 0.43, 0.29, 0.44, 0.432, 0.52, 0.55, 0.27, 0.345, 0.46, 0.23,
> 0.38, 0.333, 0.408, 0.38, 0.52, 0.34, 0.38, 0.308, 0.37, 0.42,
> 0.35, 0.446, 0.31, 0.3, 0.3, 0.36, 0.403, 0.35, 0.22, 0.2, 0.26,
> 0.34, 0.2, 0.38, 0.35, 0.2, 0.288, 0.36, 0.278, 0.238, 0.204,
> 0.203, 0.12, 0.205, 0.14, 0.124, 0.06, 0.2, 0.205, 0.054, 0.13,
> 0.13, 0.217, 0.228, 0.1, 0.105, 0.22, 0.168, 0.27, 0.069, 0.134,
> 0.092, 0.14, 0.32, 0.29, 0.148, 0.272, 0.141, 0.116, 0.255, 0.44,
> 0.336, 0.491, 0.41, 0.27, 0.37, 0.444, 0.509, 0.49, 0.092, 0.11,
> 0.17, 0.15, 0.13, 0.15, 0.12, 0.16, 0.18, 0.092, 0.1, 0.081,
> 0.13, 0.15, 0.12, 0.13, 0.1, 0.19, 0.099, 0.12, 0.13, 0.12, 0.14,
> 0.1, 0.2, 0.088, 0.099, 0.12, 0.12, 0.15, 0.039, 0.031, 0.09,
> 0.082, 0.038, 0.025, 0.062, 0.036, 0.038, 0.048, 0.037, 0.013,
> 0.021, 0.017, 0.014, 0.012, 0.016, 0.014, 0.012, 0.015, 0.015,
> 0.018, 0, 0.013, 0.019, 0.021, 0.01, 0.013, 0.011, 0.014, 0.0076,
> 0.0085, 0.0072, 0, 0, 0.013, 0.0085, 0.01, 0.01, 0.0086, 0.014,
> 0.011, 0.011, 0.098, 0.11, 0.15, 0.15, 0.15, 0.14, 0.12, 0.15,
> 0.18, 4.89130434782609, 3.29090909090909, 3, 2.86666666666667,
> 2.23076923076923, 2.93333333333333, 3.6, 3.25, 3.05555555555556,
> 2.93478260869565, 3.45, 5.67901234567901, 1.76923076923077,
2.53333333333333,
> 2.775, 3.13846153846154, 3.8, 2.73684210526316, 3.43434343434343,
> 3.16666666666667, 2.36923076923077, 3.08333333333333, 3, 3.5,
> 2.23, 3.52272727272727, 3.03030303030303, 2.5, 3, 2.68666666666667,
> 8.97435897435897, 7.09677419354839, 2.22222222222222,
3.17073170731707,
> 8.94736842105263, 8, 6.12903225806452, 9.72222222222222,
5.26315789473684,
> 6, 9.72972972972973, 21.3846153846154, 11.3333333333333, 12,
> 14.5, 10, 12.8125, 10, 10.3333333333333, 4, 13.3333333333333,
> 11.3888888888889, Inf, 10, 6.8421052631579, 10.3333333333333,
> 22.8, 7.6923076923077, 9.54545454545455, 15.7142857142857,
22.1052631578947,
> 31.7647058823529, 9.58333333333333, Inf, Inf, 10.7692307692308,
> 37.6470588235294, 29, 14.8, 31.6279069767442, 10.0714285714286,
> 10.5454545454545, 23.1818181818182, 4.48979591836735,
3.05454545454545,
> 3.27333333333333, 2.73333333333333, 1.8, 2.64285714285714, 3.7,
> 3.39333333333333, 2.72222222222222, 0.166033006, 0.215899925,
> 0.176977629, 0.177570956, 0.167407343, 0.185127929, 0.153395289,
> 0.13973999, 0.25665936, 0.091509134, 0.226090397, 0.124200915,
> 0.146869715, 0.170982018, 0.154434917, 0.133633404, 0.135307727,
> 0.139343913, 0.108326016, 0.134110718, 0.126367069, 0.119798374,
> 0.178783418, 0.083451416, 0.12388756, 0.183948454, 0.040627567,
> 0.068071771, 0.068648292, 0.074866202, 0.082090337, 0.017929514,
> 0.066658756, 0.076520048, 0.116723214, 0.068629892, 0.053861204,
> 0.071557357, 0.045859125, 0.050126618, 0.054556049, 0.077883942,
> 0.095663423, 0.05493292, 0.036506399, 0.060465605, 0.073304875,
> 0.079904335, 0.08271105, 0.06188989, 0.091794153, 0.050197784,
> 0.035391028, 0.106448921, 0.111450402, 0.111953522, 0.077441789,
> 0.060014159, 0.119853983, 0.107380923, 0.073198622, 0.061834447,
> 0.036280692, 0.034339524, -0.005907224, 0.072871058, 0.053312613,
> 0.096638058, 0.016316281, 0.035933732, 0.122054269, 0.072184127,
> 0.294119459, 0.175691138, 0.189686669, 0.301685969, 0.168586598,
> 0.198433519, 0.192239821, 0.229356909, 0.184770061, 0.072518799
> )), .Names = c("RiverMile", "variable", "value"), row.names = c(NA,
> -820L), class = "data.frame"))
>
> #this plots fine
> qplot(RiverMile, value, data=subset(melt.nut, variable=="TSS"))
>
> #this does not
> qplot(RiverMile, value, data=melt.nut)+facet_wrap(~variable,
> scales="free")+scale_x_reverse(breaks=unique(melt.nut[,"RiverMile"]))
>
>
>
> --
> Stephen Sefick
>
> Let's not spend our time and resources thinking about things that are
> so little or so large that all they really do for us is puff us up and
> make us feel like gods.  We are mammals, and have not exhausted the
> annoying little problems of being mammals.
>
>                                                                -K.
Mullis
>
> ______________________________________________
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
>
> Dit bericht en eventuele bijlagen geven enkel de visie van de
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> en binden het INBO onder geen enkel beding, zolang dit bericht niet
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> door een geldig ondertekend document. The views expressed in  this
message
> and any annex are purely those of the writer and may not be regarded
as stating
> an official position of INBO, as long as the message is not confirmed
by a duly
> signed document.
>



-- 
Stephen Sefick

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

								-K.
Mullis

Dit bericht en eventuele bijlagen geven enkel de visie van de schrijver weer 
en binden het INBO onder geen enkel beding, zolang dit bericht niet bevestigd is
door een geldig ondertekend document. The views expressed in  this message 
and any annex are purely those of the writer and may not be regarded as stating 
an official position of INBO, as long as the message is not confirmed by a duly 
signed document.

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