I'm glad that it worked! For the record, I'm forwarding this to the mailing list in case someone else is having the same problem.
-- T. ------------------------------------------------------ From: Axel Hille [email protected] Reply: Axel Hille [email protected] Date: 10 June 2014 at 20:26:02 To: Tamás Nepusz [email protected] Subject: Aw: Re: [igraph] igraph_haplotype network_vertex.shape = "pie"_problem > Dear Tamás, > thanks for your reply. I had a similar idea to select the different kinds of > shapes (none > for not-wanted, pie for the wanted). > But made the mistake to generate also a new color vector, that made the pie > proportions > disappear. When only using the vertex.shape-vector everythink work fine. > Thanks again > for your help. The following code worked finally: > > V(GraphEdgesSub)$shape <- as.vector(ifelse(V(GraphEdgesSub) > 104, "none", > "pie")) > V(GraphEdgesSub)$shape > is.vector(V(GraphEdgesSub)$shape) > #V(GraphEdgesSub)$color <- as.vector(ifelse(V(GraphEdgesSub) > 104, "white", > colorcode)) > #V(GraphEdgesSub)$color > #str(V(GraphEdgesSub)$color) > > par(mai=c(0,0,1,0)) > set.seed(12345) > plot.igraph(GraphEdgesSub, layout=layout.kamada.kawai, > #vertex.shape="pie", vertex.pie=row.mat, > vertex.shape=V(GraphEdgesSub)$shape, vertex.pie=row.mat, > vertex.pie.color=list(colorcode), > #vertex.pie.color=V(GraphEdgesSub)$color, > vertex.size=5, vertex.label=V(GraphEdgesSub)$name, > vertex.label.cex=0.6, vertex.label.font=2, > edge.color="black", edge.arrow.size = 0.05) > > > > Best, > Axel > > > > Gesendet: Dienstag, 10. Juni 2014 um 11:22 Uhr > Von: "Tamás Nepusz" > An: "Help for igraph users" , "Axel Hille" > Betreff: Re: [igraph] igraph_haplotype network_vertex.shape = "pie"_problem > Dear Axel, > > If I understand correctly, the gist of you problem is that you need pie > charts for some > of the vertices but you don't need them for others -- you just want these > virtual nodes > to show a label. The trick is that you can pass a vector to the > vertex.shape=... argument, > and the i-th vertex will use the i-th element of this vector to determine the > vertex shape. > Therefore, you simply have to construct a vector where "ordinary" vertices > use "pie" > as the vertex shape and "virtual" vertices use "none". > > All the best, > -- > T. > > ------------------------------------------------------ > From: Axel Hille [email protected] > Reply: Help for igraph users [email protected] > Date: 7 June 2014 at 22:54:41 > To: [email protected] [email protected] > Subject: [igraph] igraph_haplotype network_vertex.shape = "pie"_problem > > > Dear all, > > > > I like to post the following igraph-related problem which might be very > > simple to solve, > > but I didn't manage to do so. > > > > # Shortly, I generate a network of genetic variants, that have > > # defined ancestor-descendant-relationships progressing over time in a > > # coalescent process and spreading over space (directed edges). That process > > # is reconstructed by so-called haplotype-trees or networks. The sampled > > # haplotypes per location were then allocated with certain probabilities to > > an > > # optimal number of genetic clusters that show genetic similarity of > > # haplotypes. These cluster probabilities give the proportions for the > > vertex > > # pies to demonstrate the mixture of genetic variants in sampling sites over > > # space. The network, however can also contain virtually reconstructed > > # haplotypes that were not actually been sampled but inferred by the > > # coalescent as potential intermediate evolutionary steps. Thus these nodes > > # have no geolocation nor a cluster allocation at all by definition. The > > # problem is that these nodes are also labelled but have proportion NA in > > each > > # cluster which is not allowed for the pie argument. I liked to set these > > # virtual nodes to transparency with labels, but did not manage to perform > > # that task. I get the error message " > > # Fehler in seq.int(values[i], values[i + 1], length.out = n) : > > # 'length.out' muss eine nicht-negative Zahl sein > > # ", and in the plot the labels disappear. Any help is highly appreciated. > > # Below you find my basic plotting code. > > > > #sessionInfo() > > #R version 2.15.3 (2013-03-01) > > #Platform: i386-w64-mingw32/i386 (32-bit) > > #locale: > > # [1] LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 > > #[3] LC_MONETARY=German_Germany.1252 LC_NUMERIC=C > > #[5] LC_TIME=German_Germany.1252 > > #attached base packages: > > # [1] grDevices datasets splines graphics stats tcltk utils > > #[8] methods base > > #other attached packages: > > # [1] jpeg_0.1-2 png_0.1-4 XML_3.95-0.2 igraph_0.6.5-1 > > #[5] svSocket_0.9-54 svIDE_0.9-50 TinnR_1.0-5 R2HTML_2.2 > > #[9] Hmisc_3.10-1 survival_2.37-4 > > #loaded via a namespace (and not attached): > > # [1] cluster_1.14.3 grid_2.15.3 lattice_0.20-13 svMisc_0.9-68 > > #[5] tools_2.15.3 > > #> > > > > # load needed library "igraph" > > library("igraph") > > > > # my graph is a haplotype network > > GraphEdgesSub <- > > structure(list(178, TRUE, c(166, 166, 164, 167, 136, 136, 165, > > 94, 130, 163, 163, 75, 75, 155, 159, 129, 129, 151, 128, 128, > > 128, 134, 18, 65, 65, 126, 138, 115, 115, 16, 16, 16, 16, 16, > > 16, 16, 16, 16, 16, 16, 46, 49, 137, 83, 22, 107, 108, 112, 64, > > 48, 48, 80, 80, 45, 135, 67, 119, 0, 0, 0, 0, 0, 0, 0, 0, 0, > > 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 127, 68, 1, 1, 5, 5, 5, 12, > > 13, 26, 98, 106, 106, 110, 110, 111, 148, 58, 58, 58, 58, 10, > > 104, 40, 105, 109, 120, 120, 121, 156, 57, 70, 168, 27, 27, 131, > > 160, 170, 20, 122, 152, 172, 139, 141, 141, 174, 145, 147, 176, > > 142, 144, 175, 61, 61, 61, 61, 61, 61, 61, 61, 61, 173, 87, 90, > > 90, 96, 96, 96, 96, 113, 113, 116, 149, 150, 171, 117, 118, 140, > > 54, 177, 157, 158, 169, 146, 35, 35, 35, 125, 161, 162, 143, > > 114, 123, 55, 153, 154, 132, 124, 133), c(164, 167, 136, 165, > > 94, 130, 163, 93, 75, 155, 159, 95, 129, 100, 151, 128, 134, > > 18, 65, 126, 138, 115, 16, 46, 47, 49, 137, 59, 83, 17, 21, 22, > > 29, 34, 39, 52, 72, 107, 108, 112, 64, 50, 48, 80, 45, 19, 23, > > 102, 135, 66, 67, 81, 119, 0, 127, 68, 82, 1, 2, 3, 5, 11, 12, > > 13, 24, 26, 28, 33, 37, 38, 42, 53, 73, 98, 106, 110, 111, 148, > > 63, 58, 10, 104, 6, 7, 8, 40, 105, 109, 99, 15, 120, 41, 121, > > 51, 156, 56, 57, 70, 168, 9, 4, 27, 14, 25, 31, 131, 44, 160, > > 71, 69, 170, 20, 30, 122, 152, 172, 139, 32, 141, 174, 145, 43, > > 147, 176, 142, 144, 175, 61, 88, 173, 78, 79, 87, 90, 96, 113, > > 116, 149, 150, 171, 117, 92, 118, 86, 97, 103, 140, 54, 177, > > 77, 157, 158, 169, 85, 91, 146, 35, 125, 161, 162, 89, 143, 60, > > 114, 123, 55, 153, 154, 101, 74, 132, 124, 76, 84, 36, 133, 62 > > ), c(57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, > > 71, 72, 73, 74, 75, 76, 77, 80, 81, 82, 83, 84, 99, 85, 86, 29, > > 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 22, 116, 44, 87, 111, > > 112, 162, 163, 164, 101, 53, 40, 49, 50, 41, 156, 171, 108, 95, > > 96, 97, 98, 130, 131, 132, 133, 134, 135, 136, 137, 138, 48, > > 23, 24, 55, 79, 109, 11, 12, 51, 52, 43, 140, 141, 142, 7, 143, > > 144, 145, 146, 88, 100, 102, 89, 90, 45, 46, 103, 91, 92, 93, > > 47, 147, 148, 169, 27, 28, 149, 153, 154, 56, 104, 105, 106, > > 117, 170, 175, 165, 25, 78, 18, 19, 20, 15, 16, 8, 113, 174, > > 176, 21, 54, 4, 5, 42, 26, 120, 155, 121, 122, 127, 168, 128, > > 124, 161, 125, 94, 150, 151, 17, 118, 172, 173, 13, 107, 158, > > 159, 14, 114, 166, 167, 9, 10, 2, 6, 0, 1, 3, 110, 160, 115, > > 152, 119, 139, 123, 129, 126, 157), c(53, 57, 58, 59, 100, 60, > > 82, 83, 84, 99, 80, 61, 62, 63, 102, 89, 22, 29, 17, 45, 111, > > 30, 31, 46, 64, 103, 65, 101, 66, 32, 112, 104, 117, 67, 33, > > 156, 174, 68, 69, 34, 85, 91, 70, 121, 106, 44, 23, 24, 42, 25, > > 41, 93, 35, 71, 147, 165, 95, 96, 79, 27, 162, 127, 176, 78, > > 40, 18, 49, 50, 55, 109, 97, 108, 36, 72, 169, 8, 172, 149, 130, > > 131, 43, 51, 56, 28, 173, 153, 143, 132, 128, 160, 133, 154, > > 141, 7, 4, 11, 134, 144, 73, 88, 13, 168, 47, 145, 81, 86, 74, > > 37, 38, 87, 75, 76, 39, 135, 163, 21, 136, 140, 142, 52, 90, > > 92, 113, 164, 171, 157, 19, 54, 15, 12, 5, 105, 170, 175, 16, > > 48, 2, 26, 20, 116, 146, 118, 124, 161, 125, 120, 155, 122, 77, > > 137, 138, 14, 114, 166, 167, 9, 94, 150, 151, 10, 107, 158, 159, > > 6, 0, 3, 1, 98, 152, 110, 139, 115, 129, 119, 126, 123, 148), > > c(0, 21, 23, 23, 23, 23, 26, 26, 26, 26, 26, 27, 27, 28, > > 29, 29, 29, 40, 40, 41, 41, 42, 42, 43, 43, 43, 43, 44, 46, > > 46, 46, 46, 46, 46, 46, 46, 49, 49, 49, 49, 49, 50, 50, 50, > > 50, 50, 51, 52, 52, 54, 55, 55, 55, 55, 55, 56, 57, 57, 58, > > 62, 62, 62, 71, 71, 71, 72, 74, 74, 75, 76, 76, 77, 77, 77, > > 77, 77, 79, 79, 79, 79, 79, 81, 81, 81, 82, 82, 82, 82, 83, > > 83, 83, 85, 85, 85, 85, 86, 86, 90, 90, 91, 91, 91, 91, 91, > > 91, 92, 93, 95, 96, 97, 98, 100, 101, 102, 104, 105, 107, > > 108, 109, 110, 111, 113, 114, 115, 116, 117, 118, 119, 120, > > 123, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, > > 137, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, > > 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 162, > > 163, 164, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, > > 176, 177), c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, > > 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, > > 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, > > 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, > > 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, > > 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, > > 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, > > 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, > > 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, > > 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, > > 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, > > 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, > > 163, 164, 165, 166, 166, 167, 168, 169, 170, 171, 172, 173, > > 174, 175, 176, 177), list(c(1, 0, 1), structure(list(), .Names = > > character(0)), > > structure(list(name = c("1", "3", "5", "13", "16", "21", > > "24", "25", "36", "38", "41", "43", "47", "49", "52", > > "57", "58", "61", "62", "66", "68", "69", "70", "75", > > "77", "78", "79", "81", "83", "85", "88", "92", "93", > > "98", "100", "102", "103", "109", "111", "114", "118", > > "120", "121", "124", "125", "127", "128", "129", "130", > > "131", "132", "145", "147", "150", "153", "154", "155", > > "156", "157", "162", "167", "169", "171", "173", "174", > > "175", "176", "178", "182", "186", "188", "189", "193", > > "194", "196", "200", "202", "205", "207", "208", "209", > > "211", "212", "213", "215", "216", "217", "218", "219", > > "221", "222", "224", "225", "226", "227", "229", "230", > > "231", "236", "237", "240", "241", "243", "245", "246", > > "247", "248", "249", "250", "251", "252", "253", "254", > > "255", "256", "257", "258", "259", "260", "261", "262", > > "263", "264", "265", "266", "267", "268", "269", "270", > > "271", "272", "274", "275", "276", "277", "278", "279", > > "280", "281", "282", "283", "284", "285", "286", "287", > > "288", "289", "290", "291", "292", "293", "294", "295", > > "296", "297", "298", "299", "300", "301", "302", "303", > > "304", "305", "306", "307", "308", "309", "310", "311", > > "312", "313", "314", "315", "316", "317", "318", "319", > > "320")), .Names = "name"), list())), class = "igraph") > > > > #my proportion list for vertex.shape = "pie" > > # The list- object "row.mat" contains the cluster proportions (summing up to > > # 1000) for 5 genetic clusters for 179 haplotypes in total. The haplotype > > # numbers 1 to 104 have been found in mixed proportions in locations, the > > # haplotypes 105 to 179 are reconstructed and have proportions "NA". > > > > row.mat <- > > list(c(643, 73, 0, 4, 280), c(270, 178, 0, 11, 541), c(268, 178, > > 0, 11, 544), c(268, 178, 0, 11, 544), c(283, 178, 0, 11, 528), > > c(268, 178, 0, 11, 544), c(268, 178, 0, 11, 544), c(268, > > 178, 0, 11, 544), c(268, 178, 0, 11, 544), c(270, 178, 0, > > 11, 541), c(270, 178, 0, 11, 541), c(268, 178, 0, 11, 544 > > ), c(896, 3, 0, 0, 100), c(891, 3, 0, 1, 106), c(891, 3, > > 0, 1, 106), c(786, 52, 0, 4, 159), c(912, 17, 0, 2, 70), > > c(912, 17, 0, 2, 70), c(804, 99, 0, 7, 90), c(912, 17, 0, > > 2, 70), c(927, 3, 0, 0, 70), c(912, 17, 0, 2, 70), c(886, > > 17, 0, 2, 95), c(912, 17, 0, 2, 70), c(892, 3, 0, 0, 106), > > c(889, 6, 0, 0, 106), c(889, 6, 0, 0, 106), c(898, 3, 0, > > 0, 99), c(892, 3, 0, 0, 106), c(912, 17, 0, 2, 70), c(898, > > 3, 0, 0, 99), c(786, 52, 0, 4, 159), c(786, 52, 0, 4, 159 > > ), c(881, 7, 0, 0, 112), c(912, 17, 0, 2, 70), c(686, 310, > > 0, 0, 5), c(686, 310, 0, 0, 5), c(885, 4, 0, 1, 111), c(892, > > 3, 0, 0, 106), c(912, 17, 0, 2, 70), c(897, 3, 0, 0, 100), > > c(715, 55, 0, 4, 227), c(886, 6, 0, 2, 106), c(749, 63, 0, > > 3, 185), c(715, 55, 0, 4, 227), c(880, 17, 0, 2, 101), c(0, > > 46, 887, 59, 8), c(0, 46, 887, 59, 8), c(0, 32, 951, 10, > > 6), c(0, 46, 887, 59, 8), c(0, 46, 887, 59, 8), c(622, 77, > > 0, 6, 294), c(911, 17, 0, 2, 70), c(879, 8, 0, 2, 111), c(686, > > 310, 0, 0, 5), c(697, 303, 0, 0, 0), c(0, 28, 960, 6, 6), > > c(0, 28, 960, 6, 6), c(0, 28, 960, 6, 6), c(0, 209, 691, > > 91, 8), c(686, 310, 0, 0, 5), c(233, 374, 18, 374, 0), c(697, > > 303, 0, 0, 0), c(0, 46, 887, 59, 8), c(0, 46, 887, 59, 8), > > c(0, 46, 887, 59, 8), c(0, 28, 959, 7, 6), c(0, 38, 944, > > 10, 8), c(0, 38, 944, 10, 8), c(0, 28, 960, 6, 6), c(0, 28, > > 960, 6, 6), c(0, 28, 960, 6, 6), c(911, 17, 0, 2, 70), c(268, > > 178, 0, 11, 544), c(686, 271, 0, 1, 42), c(0, 248, 670, 72, > > 10), c(243, 348, 25, 384, 1), c(245, 364, 22, 369, 1), c(62, > > 395, 26, 517, 1), c(63, 395, 25, 516, 1), c(0, 300, 390, > > 306, 3), c(0, 300, 390, 306, 3), c(0, 300, 390, 306, 3), > > c(0, 300, 390, 306, 3), c(304, 349, 17, 330, 0), c(62, 387, > > 26, 525, 1), c(68, 386, 26, 519, 1), c(62, 387, 26, 525, > > 1), c(749, 63, 0, 3, 185), c(0, 28, 960, 6, 6), c(62, 388, > > 26, 525, 1), c(62, 387, 26, 525, 1), c(62, 388, 26, 525, > > 1), c(16, 338, 538, 78, 31), c(16, 338, 540, 77, 30), c(0, > > 249, 652, 89, 10), c(68, 386, 26, 519, 1), c(68, 386, 26, > > 519, 1), c(268, 178, 0, 11, 544), c(266, 178, 0, 11, 545), > > c(404, 135, 365, 63, 33), c(17, 346, 37, 599, 1), c(912, > > 17, 0, 2, 70), c(68, 386, 26, 519, 1), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_ > > ), c(NA_real_, NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, > > NA_real_, NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, > > NA_real_, NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, > > NA_real_, NA_real_), c(NA_real_, NA_real_, NA_real_, NA_real_, > > NA_real_)) > > > > #vertex.pie.color > > colorcode <- c(7, 5, 6, 3, 2, 8) > > # plot.igraph > > set.seed(12345) > > plot.igraph(GraphEdgesSub, layout=layout.kamada.kawai, > > vertex.shape="pie", vertex.pie=row.mat, > > vertex.pie.color=list(colorcode), > > vertex.size=5, vertex.label=V(GraphEdgesSub)$name, > > vertex.label.cex=0.6, vertex.label.font=2, > > edge.color="black", edge.arrow.size = 0.1) > > > > # error message > > #Fehler in seq.int(values[i], values[i + 1], length.out = n) : > > #'length.out' muss eine nicht-negative Zahl sein > > > > > > > > > > > > Diplom-Biologe > > > > _______________________________________________ > > igraph-help mailing list > > [email protected] > > https://lists.nongnu.org/mailman/listinfo/igraph-help > > > _______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
