[igraph] Fw: Aw: Re: igraph_haplotype network_vertex.shape = "pie"_problem

[Tamás Nepusz]

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 address@hidden
Reply: Axel Hille address@hidden
Date: 10 June 2014 at 20:26:02
To: Tamás Nepusz address@hidden
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 address@hidden
> Reply: Help for igraph users address@hidden
> Date: 7 June 2014 at 22:54:41
> To: address@hidden address@hidden
> 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
> > address@hidden
> > https://lists.nongnu.org/mailman/listinfo/igraph-help
> >
>