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 [EMAIL PROTECTED] 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:[EMAIL PROTECTED] 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 <[EMAIL PROTECTED]> wrote: > Hi Stephen, > > I think you will need to clarify what your problem is with the second plot. > > HTH, > > Thierry > > > -----Oorspronkelijk bericht----- > Van: [EMAIL PROTECTED] 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.
