Implements the stub edge bundling by Nocaj and Brandes

Usage

edge_bundle_stub(
object,
xy,
alpha = 11,
beta = 75,
gamma = 40,
t = 0.5,
tshift = 0.5
)

Arguments

object

a graph object (igraph/tbl_graph). Does not support network objects

xy

coordinates of vertices

alpha

maximal angle (in degree) between consecutive edges in a bundle

beta

angle (in degree) at which to connect two stubs

gamma

maximal overall angle (in degree) of an edge bundle

t

numeric between 0 and 1. control point location

tshift

numeric between 0 and 1. The closer to one, the longer the bigger bundle

Value

data.frame containing the bundled edges

Details

see online for plotting tips

References

Nocaj, Arlind, and Ulrik Brandes. "Stub bundling and confluent spirals for geographic networks." International Symposium on Graph Drawing. Springer, Cham, 2013.

David Schoch

Examples

library(igraph)
g <- graph.star(10, "undirected")

xy <- matrix(c(
0, 0,
cos(90 * pi / 180), sin(90 * pi / 180),
cos(80 * pi / 180), sin(80 * pi / 180),
cos(70 * pi / 180), sin(70 * pi / 180),
cos(330 * pi / 180), sin(330 * pi / 180),
cos(320 * pi / 180), sin(320 * pi / 180),
cos(310 * pi / 180), sin(310 * pi / 180),
cos(210 * pi / 180), sin(210 * pi / 180),
cos(200 * pi / 180), sin(200 * pi / 180),
cos(190 * pi / 180), sin(190 * pi / 180)
), ncol = 2, byrow = TRUE)

edge_bundle_stub(g, xy)
#>                x          y     index group
#> 1   0.000000e+00  0.0000000 0.0000000   1.1
#> 2   5.430910e-02  0.3080022 0.1428571   1.1
#> 3   1.086182e-01  0.6160044 0.2857143   1.1
#> 4   5.430910e-02  0.6205022 0.4285714   1.1
#> 5   6.123234e-17  1.0000000 0.5714286   1.2
#> 6   4.975128e-17  0.8125000 0.7142857   1.2
#> 7   3.827021e-17  0.6250000 0.8571429   1.2
#> 8   5.430910e-02  0.6205022 1.0000000   1.2
#> 9   0.000000e+00  0.0000000 0.0000000   2.1
#> 10  5.426506e-02  0.3077524 0.1428571   2.1
#> 11  1.085301e-01  0.6155048 0.2857143   2.1
#> 12  1.085301e-01  0.6155048 0.4285714   2.1
#> 13  1.736482e-01  0.9848078 0.5714286   2.2
#> 14  1.410891e-01  0.8001563 0.7142857   2.2
#> 15  1.085301e-01  0.6155048 0.8571429   2.2
#> 16  1.085301e-01  0.6155048 1.0000000   2.2
#> 17  0.000000e+00  0.0000000 0.0000000   3.1
#> 18  5.430910e-02  0.3080022 0.1428571   3.1
#> 19  1.086182e-01  0.6160044 0.2857143   3.1
#> 20  1.611904e-01  0.6016561 0.4285714   3.1
#> 21  3.420201e-01  0.9396926 0.5714286   3.2
#> 22  2.778914e-01  0.7635003 0.7142857   3.2
#> 23  2.137626e-01  0.5873079 0.8571429   3.2
#> 24  1.611904e-01  0.6016561 1.0000000   3.2
#> 25  0.000000e+00  0.0000000 0.0000000   4.1
#> 26  2.395832e-01 -0.2010342 0.1428571   4.1
#> 27  4.791663e-01 -0.4020683 0.2857143   4.1
#> 28  5.102161e-01 -0.3572842 0.4285714   4.1
#> 29  8.660254e-01 -0.5000000 0.5714286   4.2
#> 30  7.036456e-01 -0.4062500 0.7142857   4.2
#> 31  5.412659e-01 -0.3125000 0.8571429   4.2
#> 32  5.102161e-01 -0.3572842 1.0000000   4.2
#> 33  0.000000e+00  0.0000000 0.0000000   5.1
#> 34  2.393889e-01 -0.2008711 0.1428571   5.1
#> 35  4.787778e-01 -0.4017423 0.2857143   5.1
#> 36  4.787778e-01 -0.4017423 0.4285714   5.1
#> 37  7.660444e-01 -0.6427876 0.5714286   5.2
#> 38  6.224111e-01 -0.5222649 0.7142857   5.2
#> 39  4.787778e-01 -0.4017423 0.8571429   5.2
#> 40  4.787778e-01 -0.4017423 1.0000000   5.2
#> 41  0.000000e+00  0.0000000 0.0000000   6.1
#> 42  2.395832e-01 -0.2010342 0.1428571   6.1
#> 43  4.791663e-01 -0.4020683 0.2857143   6.1
#> 44  4.404543e-01 -0.4404230 0.4285714   6.1
#> 45  6.427876e-01 -0.7660444 0.5714286   6.2
#> 46  5.222649e-01 -0.6224111 0.7142857   6.2
#> 47  4.017423e-01 -0.4787778 0.8571429   6.2
#> 48  4.404543e-01 -0.4404230 1.0000000   6.2
#> 49  0.000000e+00  0.0000000 0.0000000   7.1
#> 50 -2.938923e-01 -0.1069680 0.1428571   7.1
#> 51 -5.877845e-01 -0.2139361 0.2857143   7.1
#> 52 -5.645252e-01 -0.2632180 0.4285714   7.1
#> 53 -8.660254e-01 -0.5000000 0.5714286   7.2
#> 54 -7.036456e-01 -0.4062500 0.7142857   7.2
#> 55 -5.412659e-01 -0.3125000 0.8571429   7.2
#> 56 -5.645252e-01 -0.2632180 1.0000000   7.2
#> 57  0.000000e+00  0.0000000 0.0000000   8.1
#> 58 -2.936539e-01 -0.1068813 0.1428571   8.1
#> 59 -5.873079e-01 -0.2137626 0.2857143   8.1
#> 60 -5.873079e-01 -0.2137626 0.4285714   8.1
#> 61 -9.396926e-01 -0.3420201 0.5714286   8.2
#> 62 -7.635003e-01 -0.2778914 0.7142857   8.2
#> 63 -5.873079e-01 -0.2137626 0.8571429   8.2
#> 64 -5.873079e-01 -0.2137626 1.0000000   8.2
#> 65  0.000000e+00  0.0000000 0.0000000   9.1
#> 66 -2.938923e-01 -0.1069680 0.1428571   9.1
#> 67 -5.877845e-01 -0.2139361 0.2857143   9.1
#> 68 -6.016447e-01 -0.1612331 0.4285714   9.1
#> 69 -9.848078e-01 -0.1736482 0.5714286   9.2
#> 70 -8.001563e-01 -0.1410891 0.7142857   9.2
#> 71 -6.155048e-01 -0.1085301 0.8571429   9.2
#> 72 -6.016447e-01 -0.1612331 1.0000000   9.2
# use ggforce::geom_bezier for plotting