Implements the classic edge bundling by Holten.
Usage
edge_bundle_force(
object,
xy,
K = 1,
C = 6,
P = 1,
S = 0.04,
P_rate = 2,
I = 50,
I_rate = 2/3,
compatibility_threshold = 0.6,
eps = 1e-08,
directed = FALSE,
...
)Arguments
- object
a graph object (igraph/network/tbl_graph)
- xy
coordinates of vertices
- K
spring constant
- C
number of iteration cycles
- P
number of initial edge divisions
- S
initial step size
- P_rate
rate of edge divisions
- I
number of initial iterations
- I_rate
rate of iteration decrease per cycle
- compatibility_threshold
threshold for when edges are considered compatible
- eps
accuracy
- directed
logical. If
TRUE, use divided edge bundling (Selassie et al. 2011) which keeps edges running in opposite directions in separate lanes. Requires an igraph/tbl_graph object with edge directions.- ...
arguments passed to the divided bundler when
directed = TRUE:lane_width,k_spring,k_charge,decay,friction,step,passes,iterations,use_connectivity.
Details
This is a re-implementation of https://github.com/upphiminn/d3.ForceBundle. Force directed edge bundling is slow (O(E^2)).
see online for plotting tips
References
Holten, Danny, and Jarke J. Van Wijk. "Force-Directed Edge Bundling for Graph Visualization." Computer Graphics Forum (Blackwell Publishing Ltd) 28, no. 3 (2009): 983-990.
Selassie, David, Brandon Heller, and Jeffrey Heer. "Divided edge bundling for directional network data." IEEE Transactions on Visualization and Computer Graphics 17, no. 12 (2011): 2354-2363.
Examples
library(igraph)
g <- graph_from_edgelist(
matrix(c(
1, 12, 2, 11, 3, 10,
4, 9, 5, 8, 6, 7
), ncol = 2, byrow = TRUE), FALSE
)
xy <- cbind(c(rep(0, 6), rep(1, 6)), c(1:6, 1:6))
edge_bundle_force(g, xy)
#> x y index group
#> 1 0.000000000 1.000000 0.00000000 1
#> 2 0.008647966 1.185137 0.03030303 1
#> 3 0.014879426 1.301964 0.06060606 1
#> 4 0.027136509 1.510348 0.09090909 1
#> 5 0.038181351 1.672843 0.12121212 1
#> 6 0.048136015 1.806628 0.15151515 1
#> 7 0.058286705 1.942714 0.18181818 1
#> 8 0.076570895 2.159926 0.21212121 1
#> 9 0.089507277 2.296006 0.24242424 1
#> 10 0.107794433 2.484172 0.27272727 1
#> 11 0.123932609 2.646244 0.30303030 1
#> 12 0.138893228 2.794076 0.33333333 1
#> 13 0.160706671 2.969781 0.36363636 1
#> 14 0.184114686 3.100309 0.39393939 1
#> 15 0.210712207 3.246366 0.42424242 1
#> 16 0.300016322 3.428936 0.45454545 1
#> 17 0.424797192 3.501619 0.48484848 1
#> 18 0.550181475 3.500919 0.51515152 1
#> 19 0.681988987 3.578892 0.54545455 1
#> 20 0.772008726 3.711603 0.57575758 1
#> 21 0.805646559 3.852816 0.60606061 1
#> 22 0.841016035 4.029786 0.63636364 1
#> 23 0.856873630 4.175313 0.66666667 1
#> 24 0.875719301 4.353678 0.69696970 1
#> 25 0.889282610 4.486283 0.72727273 1
#> 26 0.907750606 4.677837 0.75757576 1
#> 27 0.919511313 4.819154 0.78787879 1
#> 28 0.934304209 5.002496 0.81818182 1
#> 29 0.946512243 5.164910 0.84848485 1
#> 30 0.958332596 5.327352 0.87878788 1
#> 31 0.968731700 5.489332 0.90909091 1
#> 32 0.972317748 5.543579 0.93939394 1
#> 33 0.979247216 5.652067 0.96969697 1
#> 34 1.000000000 6.000000 1.00000000 1
#> 35 0.000000000 2.000000 0.00000000 2
#> 36 0.012483188 2.111069 0.03030303 2
#> 37 0.023071894 2.199039 0.06060606 2
#> 38 0.038486381 2.318076 0.09090909 2
#> 39 0.052530417 2.421444 0.12121212 2
#> 40 0.065954324 2.513160 0.15151515 2
#> 41 0.080995457 2.616051 0.18181818 2
#> 42 0.098862179 2.730940 0.21212121 2
#> 43 0.114156109 2.828123 0.24242424 2
#> 44 0.131954813 2.936929 0.27272727 2
#> 45 0.148830020 3.039865 0.30303030 2
#> 46 0.163030022 3.126668 0.33333333 2
#> 47 0.182632526 3.245728 0.36363636 2
#> 48 0.211371263 3.345556 0.39393939 2
#> 49 0.243392762 3.443319 0.42424242 2
#> 50 0.328403682 3.495960 0.45454545 2
#> 51 0.421779351 3.498344 0.48484848 2
#> 52 0.528429303 3.499147 0.51515152 2
#> 53 0.646198160 3.516107 0.54545455 2
#> 54 0.733635240 3.561518 0.57575758 2
#> 55 0.774820271 3.657489 0.60606061 2
#> 56 0.810968528 3.755175 0.63636364 2
#> 57 0.828159043 3.857603 0.66666667 2
#> 58 0.845568014 3.960892 0.69696970 2
#> 59 0.862314885 4.062295 0.72727273 2
#> 60 0.879556688 4.166727 0.75757576 2
#> 61 0.895060868 4.266615 0.78787879 2
#> 62 0.911477347 4.372896 0.81818182 2
#> 63 0.925693316 4.469107 0.84848485 2
#> 64 0.941956956 4.579289 0.87878788 2
#> 65 0.956948050 4.681183 0.90909091 2
#> 66 0.971409379 4.785817 0.93939394 2
#> 67 0.979255815 4.842598 0.96969697 2
#> 68 1.000000000 5.000000 1.00000000 2
#> 69 0.000000000 3.000000 0.00000000 3
#> 70 0.030303030 3.030303 0.03030303 3
#> 71 0.060605926 3.060606 0.06060606 3
#> 72 0.090073842 3.090074 0.09090909 3
#> 73 0.121211745 3.121212 0.12121212 3
#> 74 0.150010899 3.150011 0.15151515 3
#> 75 0.181817369 3.181817 0.18181818 3
#> 76 0.208569537 3.208570 0.21212121 3
#> 77 0.242423262 3.242423 0.24242424 3
#> 78 0.270175650 3.270176 0.27272727 3
#> 79 0.303029046 3.303029 0.30303030 3
#> 80 0.328060386 3.328060 0.33333333 3
#> 81 0.363635159 3.363635 0.36363636 3
#> 82 0.391719320 3.391719 0.39393939 3
#> 83 0.424240824 3.424241 0.42424242 3
#> 84 0.446807587 3.446808 0.45454545 3
#> 85 0.484846744 3.484847 0.48484848 3
#> 86 0.512057899 3.512058 0.51515152 3
#> 87 0.545453022 3.545453 0.54545455 3
#> 88 0.569373135 3.569373 0.57575758 3
#> 89 0.606059551 3.606060 0.60606061 3
#> 90 0.636183634 3.636184 0.63636364 3
#> 91 0.666665543 3.666666 0.66666667 3
#> 92 0.690160799 3.690161 0.69696970 3
#> 93 0.727270978 3.727271 0.72727273 3
#> 94 0.753500919 3.753501 0.75757576 3
#> 95 0.787877587 3.787878 0.78787879 3
#> 96 0.814783445 3.814783 0.81818182 3
#> 97 0.848484302 3.848484 0.84848485 3
#> 98 0.878787691 3.878788 0.87878788 3
#> 99 0.907922642 3.907923 0.90909091 3
#> 100 0.937589848 3.937590 0.93939394 3
#> 101 0.969696680 3.969697 0.96969697 3
#> 102 1.000000000 4.000000 1.00000000 3
#> 103 0.000000000 4.000000 0.00000000 4
#> 104 0.030303030 3.969697 0.03030303 4
#> 105 0.060605926 3.939394 0.06060606 4
#> 106 0.090073842 3.909926 0.09090909 4
#> 107 0.121211745 3.878788 0.12121212 4
#> 108 0.150010899 3.849989 0.15151515 4
#> 109 0.181817369 3.818183 0.18181818 4
#> 110 0.208569537 3.791430 0.21212121 4
#> 111 0.242423262 3.757577 0.24242424 4
#> 112 0.270175650 3.729824 0.27272727 4
#> 113 0.303029046 3.696971 0.30303030 4
#> 114 0.328060386 3.671940 0.33333333 4
#> 115 0.363635159 3.636365 0.36363636 4
#> 116 0.391719320 3.608281 0.39393939 4
#> 117 0.424240824 3.575759 0.42424242 4
#> 118 0.446807587 3.553192 0.45454545 4
#> 119 0.484846744 3.515153 0.48484848 4
#> 120 0.512057899 3.487942 0.51515152 4
#> 121 0.545453022 3.454547 0.54545455 4
#> 122 0.569373135 3.430627 0.57575758 4
#> 123 0.606059551 3.393940 0.60606061 4
#> 124 0.636183634 3.363816 0.63636364 4
#> 125 0.666665543 3.333334 0.66666667 4
#> 126 0.690160799 3.309839 0.69696970 4
#> 127 0.727270978 3.272729 0.72727273 4
#> 128 0.753500919 3.246499 0.75757576 4
#> 129 0.787877587 3.212122 0.78787879 4
#> 130 0.814783445 3.185217 0.81818182 4
#> 131 0.848484302 3.151516 0.84848485 4
#> 132 0.878787691 3.121212 0.87878788 4
#> 133 0.907922642 3.092077 0.90909091 4
#> 134 0.937589848 3.062410 0.93939394 4
#> 135 0.969696680 3.030303 0.96969697 4
#> 136 1.000000000 3.000000 1.00000000 4
#> 137 0.000000000 5.000000 0.00000000 5
#> 138 0.012483188 4.888931 0.03030303 5
#> 139 0.023071894 4.800961 0.06060606 5
#> 140 0.038486381 4.681924 0.09090909 5
#> 141 0.052530417 4.578556 0.12121212 5
#> 142 0.065954324 4.486840 0.15151515 5
#> 143 0.080995457 4.383949 0.18181818 5
#> 144 0.098862179 4.269060 0.21212121 5
#> 145 0.114156109 4.171877 0.24242424 5
#> 146 0.131954813 4.063071 0.27272727 5
#> 147 0.148830020 3.960135 0.30303030 5
#> 148 0.163030022 3.873332 0.33333333 5
#> 149 0.182632526 3.754272 0.36363636 5
#> 150 0.211371263 3.654444 0.39393939 5
#> 151 0.243392762 3.556681 0.42424242 5
#> 152 0.328403682 3.504040 0.45454545 5
#> 153 0.421779351 3.501656 0.48484848 5
#> 154 0.528429303 3.500853 0.51515152 5
#> 155 0.646198160 3.483893 0.54545455 5
#> 156 0.733635240 3.438482 0.57575758 5
#> 157 0.774820271 3.342511 0.60606061 5
#> 158 0.810968528 3.244825 0.63636364 5
#> 159 0.828159043 3.142397 0.66666667 5
#> 160 0.845568014 3.039108 0.69696970 5
#> 161 0.862314885 2.937705 0.72727273 5
#> 162 0.879556688 2.833273 0.75757576 5
#> 163 0.895060868 2.733385 0.78787879 5
#> 164 0.911477347 2.627104 0.81818182 5
#> 165 0.925693316 2.530893 0.84848485 5
#> 166 0.941956956 2.420711 0.87878788 5
#> 167 0.956948050 2.318817 0.90909091 5
#> 168 0.971409379 2.214183 0.93939394 5
#> 169 0.979255815 2.157402 0.96969697 5
#> 170 1.000000000 2.000000 1.00000000 5
#> 171 0.000000000 6.000000 0.00000000 6
#> 172 0.008647966 5.814863 0.03030303 6
#> 173 0.014879426 5.698036 0.06060606 6
#> 174 0.027136509 5.489652 0.09090909 6
#> 175 0.038181351 5.327157 0.12121212 6
#> 176 0.048136015 5.193372 0.15151515 6
#> 177 0.058286705 5.057286 0.18181818 6
#> 178 0.076570895 4.840074 0.21212121 6
#> 179 0.089507277 4.703994 0.24242424 6
#> 180 0.107794433 4.515828 0.27272727 6
#> 181 0.123932609 4.353756 0.30303030 6
#> 182 0.138893228 4.205924 0.33333333 6
#> 183 0.160706671 4.030219 0.36363636 6
#> 184 0.184114686 3.899691 0.39393939 6
#> 185 0.210712207 3.753634 0.42424242 6
#> 186 0.300016322 3.571064 0.45454545 6
#> 187 0.424797192 3.498381 0.48484848 6
#> 188 0.550181475 3.499081 0.51515152 6
#> 189 0.681988987 3.421108 0.54545455 6
#> 190 0.772008726 3.288397 0.57575758 6
#> 191 0.805646559 3.147184 0.60606061 6
#> 192 0.841016035 2.970214 0.63636364 6
#> 193 0.856873630 2.824687 0.66666667 6
#> 194 0.875719301 2.646322 0.69696970 6
#> 195 0.889282610 2.513717 0.72727273 6
#> 196 0.907750606 2.322163 0.75757576 6
#> 197 0.919511313 2.180846 0.78787879 6
#> 198 0.934304209 1.997504 0.81818182 6
#> 199 0.946512243 1.835090 0.84848485 6
#> 200 0.958332596 1.672648 0.87878788 6
#> 201 0.968731700 1.510668 0.90909091 6
#> 202 0.972317748 1.456421 0.93939394 6
#> 203 0.979247216 1.347933 0.96969697 6
#> 204 1.000000000 1.000000 1.00000000 6