You missed a few things when copying the example, try ggplot(testdataset2, aes(y=factor(Var2),x=value)) + stat_density(aes(fill=..density..), position="identity", geom="tile") + scale_fill_gradientn(colours=brewer.pal(n=8, name="PuBuGn"))
needed to add tile geom, and factor the correct variable. On Wed, Sep 24, 2014 at 2:14 PM, Federico Lasa <fel...@gmail.com> wrote: > Does this resemble what you're after? > > library(reshape2) > tst <- melt(testdataset) > library(ggplot2) > > ggplot(tst, aes(x=Var1, y=Var2, fill=value)) + > geom_tile() + > scale_fill_gradient2(low="white", > high="white", > mid=scales::muted("blue"), > midpoint=0.6148377) > > On Wed, Sep 24, 2014 at 10:26 AM, <m...@considine.net> wrote: >> Hi, >> I have a matrix of data, with the rows representing observations and the >> columns representing various values that the observation can take on. In >> other words, each row can be thought of as a sampling of the density >> function/histogram associated with the range of values for that observation. >> >> I'd like to graph these with a shaded color, rather than as lines. So a >> given observation would have the darkest shade at the mean and the shading >> would lighten for values that approached the tails. In a sense this is like >> a ribbon chart, but where there are many confidence bands. >> >> I think the example near the bottom of this page >> http://bconnelly.net/2013/10/creating-colorblind-friendly-figures/ >> starts to get at what I want. But when I tried to get a ribbon, I get an >> error message saying that "Error: Aesthetics can not vary with a ribbon" >> >> Can anyone point me to an example that accomplishes my task, or give me some >> ideas as to how to code this? >> >> Below is a reproducible dataset and the code I ran that generated the above >> error. And apologies in advance if I have overlooked some obvious source - >> I'm not exactly sure what keywords to search for. >> >> Regards, >> Matt >> >> testdataset <- structure(c(0.703482475602795, 0.708141442616021, >> 0.696373713631662, >> 0.670284015871304, 0.675183812793659, >> 0.690440437259122, 0.717483375152826, >> 0.775328205198994, 0.848374059782512, >> 0.869939471712489, 0.86329313061477, >> 0.842138830353923, 0.819853961383293, >> 0.808038546509378, 0.826626282345039, >> 0.855428819162732, 0.873943618483253, >> 0.906412218904192, 0.95345525957727, >> 0.941481792397259, 0.923791753474186, >> 0.909206164221341, 0.847283523824235, >> 0.774333551860785, 0.723440114819687, >> 0.653247411286407, 0.585889004137383, >> 0.516531935718585, 0.458855598305008, >> 0.422596378188962, 0.385800210249005, >> 0.363663809831211, 0.703482475602795, >> 0.708055808109959, 0.696379276680681, >> 0.686643131558789, 0.702628930558265, >> 0.736010723583024, 0.790795207667811, >> 0.843997296035071, 0.872447231982615, >> 0.876357159885425, 0.852095141662599, >> 0.815122741092172, 0.759163100114952, >> 0.737079598996168, 0.755626127703219, >> 0.76375495269533, 0.757290640161052, >> 0.754301244147121, 0.738872719902144, >> 0.712590028244082, 0.707690675037336, >> 0.707234385372842, 0.708720518303698, >> 0.723271948541464, 0.738173079905318, >> 0.772161522113349, 0.776237486574842, >> 0.775666977944939, 0.764229462885737, >> 0.758916671383124, 0.742887393474484, >> 0.741362343479079, 0.703482475602795, >> 0.70722192044612, 0.694934601341247, >> 0.675623005679584, 0.67355293987199, >> 0.67514195581405, 0.701338223542176, >> 0.770084545123592, 0.826615555391194, >> 0.815331595124185, 0.801265437257298, >> 0.768736104243487, 0.698903427959817, >> 0.654393072393584, 0.646507677289504, >> 0.606308031283892, 0.574521529688064, >> 0.550931914275617, 0.518538683619987, >> 0.495773346159491, 0.482784058725618, >> 0.473031502762785, 0.462940836756943, >> 0.455472910452526, 0.457374752189383, >> 0.468449683385787, 0.469177346159405, >> 0.47981744053419, 0.500517935694715, >> 0.521161553352487, 0.538278248678118, >> 0.545834896270532, 0.703482475602795, >> 0.707475643569319, 0.695699528962731, >> 0.695460540915422, 0.705063229294573, >> 0.694190083263775, 0.676451221936696, >> 0.661139999162065, 0.627150885842318, >> 0.592467979293877, 0.556197511727567, >> 0.524883713023224, 0.484571801496662, >> 0.427784904562, 0.370137413134906, >> 0.331233866457343, 0.292181528642806, >> 0.265504971226103, 0.239129968439056, >> 0.21258454640671, 0.184521419432522, >> 0.160633576032345, 0.135729972994914, >> 0.115111431576686, 0.0933784744252792, >> 0.0672765522562478, 0.0397992726679255, >> 0.0118179662548541, NA, NA, NA, NA, >> 0.703482475602795, 0.70791132542366, >> 0.696508162877812, 0.672357035115831, >> 0.679831378223931, 0.702075998432084, >> 0.736057349706643, 0.759252979404642, >> 0.739391321260192, 0.706608353324493, >> 0.653481111693474, 0.607986236497692, >> 0.600942686427268, 0.602450590412635, >> 0.594096281507138, 0.598414292518021, >> 0.570859444977738, 0.50462737404968, >> 0.441225469913529, 0.37010584373766, >> 0.299554326292306, 0.250957120974181, >> 0.231147047662909, 0.218081437060998, >> 0.209354124252359, 0.212236940966109, >> 0.213898409405384, 0.200693009702681, >> 0.189880443626695, 0.175663436717225, >> 0.160910269517771, 0.14423774751828, >> 0.703482475602795, 0.707648742091919, >> 0.696092540716741, 0.656540853310246, >> 0.6051367218461, 0.591695064299013, >> 0.596015810648035, 0.603800534715597, >> 0.630728546677123, 0.658732672149451, >> 0.660216960664134, 0.675188962779905, >> 0.680940355728037, 0.677371529164165, >> 0.678862966955137, 0.706307948099043, >> 0.72103331978575, 0.71201796002067, >> 0.695266699409018, 0.685297231486624, >> 0.661576951062559, 0.643301885602357, >> 0.619830526453808, 0.608129873046412, >> 0.594283830084397, 0.562317115717112, >> 0.530350595536459, 0.506782526041746, >> 0.485311767855001, 0.473335687828819, >> 0.47767850215973, 0.47780651839627, >> 0.703482475602795, 0.708970958906805, >> 0.697551150784402, 0.661080016538957, >> 0.61232038566617, 0.587787379274781, >> 0.593431699246192, 0.585207322444761, >> 0.568559116457046, 0.548048255621954, >> 0.530114701694855, 0.534118004338362, >> 0.551070523651917, 0.575923824458594, >> 0.601811119913484, 0.599679943614892, >> 0.571040665848032, 0.537593347467274, >> 0.517448133546794, 0.509401266935977, >> 0.506839184709813, 0.513795796376072, >> 0.538416419444161, 0.54431580354862, >> 0.533416582151419, 0.536830327067308, >> 0.536462655629334, 0.513629165635279, >> 0.478995218355945, 0.438690505845544, >> 0.382567436102273, 0.34023179757446, >> 0.703482475602795, 0.708469004078862, >> 0.697978508894162, 0.676172179276398, >> 0.653999136795877, 0.621901773418305, >> 0.611086726778221, 0.593857278888487, >> 0.59772372401201, 0.615523307201519, >> 0.639972290824288, 0.642201950188424, >> 0.640462288885887, 0.599634050862654, >> 0.556922658075191, 0.51984992524725, >> 0.500551878868105, 0.481116358620079, >> 0.46280476578578, 0.439079916934238, >> 0.426481043460678, 0.4046073777228, >> 0.384894885963074, 0.387418227322783, >> 0.397735327855843, 0.382421337902373, >> 0.364609477937235, 0.351141834403433, >> 0.326023299419572, 0.298998310511409, >> 0.279388702747555, 0.265729633371279, >> 0.703482475602795, 0.708084110634261, >> 0.695308105608089, 0.680512620916581, >> 0.674409687648891, 0.64479397663385, >> 0.611185461407328, 0.574633943767944, >> 0.531967082066137, 0.509238294683539, >> 0.539380037981121, 0.605934156503129, >> 0.67139250700667, 0.692111261722321, >> 0.681033453581129, 0.649259774238939, >> 0.61340010828123, 0.601868728090964, >> 0.626623248125788, 0.630474601620122, >> 0.632877115180945, 0.622655315896617, >> 0.602457206226614, 0.577755838513823, >> 0.575613491077747, 0.56693811567409, >> 0.538199408312755, 0.508731518312269, >> 0.488361667141151, 0.462965384850233, >> 0.438776128537772, 0.433205440295598, >> 0.703482475602795, 0.708955081210283, >> 0.696337276771488, 0.675657944024044, >> 0.682199731001736, 0.713302322144747, >> 0.780735831443758, 0.836330924875064, >> 0.920042864888473, 0.993749005071184, >> 1.09383447176433, 1.13919188883409, >> 1.17120759930688, 1.20149479327377, >> 1.21627978424244, 1.16585978639785, >> 1.11758399725948, 1.04238685207932, >> 0.937208207066276, 0.862384770704829, >> 0.774099878399983, 0.701556273005073, >> 0.670583202198987, 0.686968300073611, >> 0.732378201300416, 0.820191766252091, >> 0.855392362283012, 0.845365464014818, >> 0.803317719342587, 0.757849795095728, >> 0.711624608771727, 0.674398052370244), .Dim = >> c(32L, 10L)) >> matplot(testdataset, type = 'l', las = 1, xlab = 'x values', >> ylab = 'y values', main = 'title - testdataset') >> library(ggplot2,RColorBrewer,reshape2) >> testdataset[ is.na(testdataset) ] <- 0 >> testdataset2 <- melt(testdataset) >> ggplot(testdataset2, aes(x=Var1,y=factor(value))) + >> stat_density(aes(fill=..density..), position="identity") + >> scale_fill_gradientn(colours=brewer.pal(n=8, name="PuBuGn")) >> >> ______________________________________________ >> 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. ______________________________________________ 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.