yes, I think this is the bug that will be fixed in the next release :
http://github.com/hadley/ggplot2/tree/master/NEWS

2008/12/3 ONKELINX, Thierry <Thierry.ONKELINX@inbo.be>

> 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@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@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@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@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
> >
> > ______________________________________________
> > R-help@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > 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
> 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.
> >
>
>
>
> --
> 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.
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

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