Network graph using R Plotly
2023-10-19
Last updated: 2023-10-19
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | c0079b2 | Dave Tang | 2023-10-19 | Plot networks using Plotly |
Plotly can be used to plot a Network Graph in R. The documentation does not work, so here’s my implementation.
Dependencies
packages <- c('plotly', 'igraph', 'igraphdata')
sapply(packages, function(x){
y <- require(x, character.only = TRUE)
if(y == FALSE){
install.packages(x, quiet = TRUE)
library(x, character.only = TRUE)
}
ver <- as.character(packageVersion(x))
})Loading required package: plotly
Attaching package: 'plotly'The following object is masked from 'package:ggplot2':
last_plotThe following object is masked from 'package:stats':
filterThe following object is masked from 'package:graphics':
layoutLoading required package: igraph
Attaching package: 'igraph'The following object is masked from 'package:plotly':
groupsThe following objects are masked from 'package:lubridate':
%--%, unionThe following objects are masked from 'package:dplyr':
as_data_frame, groups, unionThe following objects are masked from 'package:purrr':
compose, simplifyThe following object is masked from 'package:tidyr':
crossingThe following object is masked from 'package:tibble':
as_data_frameThe following objects are masked from 'package:stats':
decompose, spectrumThe following object is masked from 'package:base':
unionLoading required package: igraphdata plotly igraph igraphdata
"4.10.2" "1.5.1" "1.0.1" Example
Load Zachary’s karate club. Network description from Wikipedia:
A social network of a karate club was studied by Wayne W. Zachary for a period of three years from 1970 to 1972. The network captures 34 members of a karate club, documenting links between pairs of members who interacted outside the club. During the study a conflict arose between the administrator “John A” and instructor “Mr. Hi” (pseudonyms), which led to the split of the club into two. Half of the members formed a new club around Mr. Hi; members from the other part found a new instructor or gave up karate. Based on collected data Zachary correctly assigned all but one member of the club to the groups they actually joined after the split.
data(karate, package="igraphdata")
karateThis graph was created by an old(er) igraph version.
Call upgrade_graph() on it to use with the current igraph version
For now we convert it on the fly...IGRAPH 4b458a1 UNW- 34 78 -- Zachary's karate club network
+ attr: name (g/c), Citation (g/c), Author (g/c), Faction (v/n), name
| (v/c), label (v/c), color (v/n), weight (e/n)
+ edges from 4b458a1 (vertex names):
[1] Mr Hi --Actor 2 Mr Hi --Actor 3 Mr Hi --Actor 4 Mr Hi --Actor 5
[5] Mr Hi --Actor 6 Mr Hi --Actor 7 Mr Hi --Actor 8 Mr Hi --Actor 9
[9] Mr Hi --Actor 11 Mr Hi --Actor 12 Mr Hi --Actor 13 Mr Hi --Actor 14
[13] Mr Hi --Actor 18 Mr Hi --Actor 20 Mr Hi --Actor 22 Mr Hi --Actor 32
[17] Actor 2--Actor 3 Actor 2--Actor 4 Actor 2--Actor 8 Actor 2--Actor 14
[21] Actor 2--Actor 18 Actor 2--Actor 20 Actor 2--Actor 22 Actor 2--Actor 31
[25] Actor 3--Actor 4 Actor 3--Actor 8 Actor 3--Actor 9 Actor 3--Actor 10
+ ... omitted several edgesUpgrade graph.
G <- upgrade_graph(karate)
str(G)Class 'igraph' hidden list of 10
$ : num 34
$ : logi FALSE
$ : num [1:78] 1 2 3 4 5 6 7 8 10 11 ...
$ : num [1:78] 0 0 0 0 0 0 0 0 0 0 ...
$ : num [1:78] 0 1 16 2 17 24 3 4 5 35 ...
$ : num [1:78] 0 1 2 3 4 5 6 7 8 9 ...
$ : num [1:35] 0 0 1 3 6 7 8 11 15 17 ...
$ : num [1:35] 0 16 24 32 35 37 40 41 41 44 ...
$ :List of 4
..$ : num [1:3] 1 0 1
..$ :List of 3
.. ..$ name : chr "Zachary's karate club network"
.. ..$ Citation: chr "Wayne W. Zachary. An Information Flow Model for Conflict and Fission in Small Groups. Journal of Anthropologica"| __truncated__
.. ..$ Author : chr "Wayne W. Zachary"
..$ :List of 4
.. ..$ Faction: num [1:34] 1 1 1 1 1 1 1 1 2 2 ...
.. ..$ name : chr [1:34] "Mr Hi" "Actor 2" "Actor 3" "Actor 4" ...
.. ..$ label : chr [1:34] "H" "2" "3" "4" ...
.. ..$ color : num [1:34] 1 1 1 1 1 1 1 1 2 2 ...
..$ :List of 1
.. ..$ weight: num [1:78] 4 5 3 3 3 3 2 2 2 3 ...
$ :<environment: 0x55e817f73598> This is how the graph is supposed to look, plotted using
igraph.
set.seed(1984)
L <- layout_nicely(G)
plot.igraph(G, layout = L)
The colour can be obtained from vertex_attr, which can
query vertex attributes of a graph.
vertex_attr(G, 'color') [1] 1 1 1 1 1 1 1 1 2 2 1 1 1 1 2 2 1 1 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2The function layout_nicely:
This function tries to choose an appropriate graph layout algorithm for the graph, automatically, based on a simple algorithm. See details below.
head(L) [,1] [,2]
[1,] -2.2255823 0.3194989
[2,] -1.8386306 -0.4652557
[3,] -0.8207017 -0.4476734
[4,] -1.3904744 0.5791814
[5,] -3.2027804 1.2951202
[6,] -3.3451087 1.9517754We can get the name of the vertices using V.
vs <- V(G)
vs+ 34/34 vertices, named, from 4b458a1:
[1] Mr Hi Actor 2 Actor 3 Actor 4 Actor 5 Actor 6 Actor 7 Actor 8
[9] Actor 9 Actor 10 Actor 11 Actor 12 Actor 13 Actor 14 Actor 15 Actor 16
[17] Actor 17 Actor 18 Actor 19 Actor 20 Actor 21 Actor 22 Actor 23 Actor 24
[25] Actor 25 Actor 26 Actor 27 Actor 28 Actor 29 Actor 30 Actor 31 Actor 32
[33] Actor 33 John A The edge list shows the connections.
el <- as.data.frame(get.edgelist(G))
el V1 V2
1 Mr Hi Actor 2
2 Mr Hi Actor 3
3 Mr Hi Actor 4
4 Mr Hi Actor 5
5 Mr Hi Actor 6
6 Mr Hi Actor 7
7 Mr Hi Actor 8
8 Mr Hi Actor 9
9 Mr Hi Actor 11
10 Mr Hi Actor 12
11 Mr Hi Actor 13
12 Mr Hi Actor 14
13 Mr Hi Actor 18
14 Mr Hi Actor 20
15 Mr Hi Actor 22
16 Mr Hi Actor 32
17 Actor 2 Actor 3
18 Actor 2 Actor 4
19 Actor 2 Actor 8
20 Actor 2 Actor 14
21 Actor 2 Actor 18
22 Actor 2 Actor 20
23 Actor 2 Actor 22
24 Actor 2 Actor 31
25 Actor 3 Actor 4
26 Actor 3 Actor 8
27 Actor 3 Actor 9
28 Actor 3 Actor 10
29 Actor 3 Actor 14
30 Actor 3 Actor 28
31 Actor 3 Actor 29
32 Actor 3 Actor 33
33 Actor 4 Actor 8
34 Actor 4 Actor 13
35 Actor 4 Actor 14
36 Actor 5 Actor 7
37 Actor 5 Actor 11
38 Actor 6 Actor 7
39 Actor 6 Actor 11
40 Actor 6 Actor 17
41 Actor 7 Actor 17
42 Actor 9 Actor 31
43 Actor 9 Actor 33
44 Actor 9 John A
45 Actor 10 John A
46 Actor 14 John A
47 Actor 15 Actor 33
48 Actor 15 John A
49 Actor 16 Actor 33
50 Actor 16 John A
51 Actor 19 Actor 33
52 Actor 19 John A
53 Actor 20 John A
54 Actor 21 Actor 33
55 Actor 21 John A
56 Actor 23 Actor 33
57 Actor 23 John A
58 Actor 24 Actor 26
59 Actor 24 Actor 28
60 Actor 24 Actor 30
61 Actor 24 Actor 33
62 Actor 24 John A
63 Actor 25 Actor 26
64 Actor 25 Actor 28
65 Actor 25 Actor 32
66 Actor 26 Actor 32
67 Actor 27 Actor 30
68 Actor 27 John A
69 Actor 28 John A
70 Actor 29 Actor 32
71 Actor 29 John A
72 Actor 30 Actor 33
73 Actor 30 John A
74 Actor 31 Actor 33
75 Actor 31 John A
76 Actor 32 Actor 33
77 Actor 32 John A
78 Actor 33 John ACreate the network with just the nodes using our layout
L.
network <- plot_ly(
x = ~L[, 1],
y = ~L[, 2],
mode = "markers",
text = vs$label,
hoverinfo = "text",
type = "scatter",
size = I(42),
color = as.character(vertex_attr(G, 'color')),
colors = c('orange', 'skyblue'),
showlegend=FALSE
)
networkThe graph above lacks the edges, which we will manually create. For example, these two nodes need to connect.
el[1, ] V1 V2
1 Mr Hi Actor 2The layout contains the coordinates of the nodes but is not named.
tail(L) [,1] [,2]
[29,] 0.3532557 -0.2640971
[30,] 1.4325491 -2.5669892
[31,] -0.8031078 -1.9404862
[32,] 0.2683601 -0.8890504
[33,] 0.3278217 -2.2911416
[34,] 0.2410524 -1.9796959We can get the names using names.
my_layout <- L
row.names(my_layout) <- names(V(G))
tail(my_layout) [,1] [,2]
Actor 29 0.3532557 -0.2640971
Actor 30 1.4325491 -2.5669892
Actor 31 -0.8031078 -1.9404862
Actor 32 0.2683601 -0.8890504
Actor 33 0.3278217 -2.2911416
John A 0.2410524 -1.9796959To get the (x, y) coordinates, we just subset
my_layout.
get_xy <- function(x){
my_layout[x, ]
}
get_xy('Mr Hi')[1] -2.2255823 0.3194989Get the (x, y) coordinates of every node in the edge list.
xy1 <- t(
apply(el, 1, function(x){
get_xy(x[1])
})
)
xy2 <- t(
apply(el, 1, function(x){
get_xy(x[2])
})
)
head(xy1) [,1] [,2]
[1,] -2.225582 0.3194989
[2,] -2.225582 0.3194989
[3,] -2.225582 0.3194989
[4,] -2.225582 0.3194989
[5,] -2.225582 0.3194989
[6,] -2.225582 0.3194989Build the list of lines that will be used to connect the nodes.
line <- list(
type = "line",
line = list(color = "#030303", width = 0.3),
xref = "x",
yref = "y"
)
lines <- lapply(seq_along(xy1[, 1]), function(x){
c(
line,
x0 = xy1[x, 1],
y0 = xy1[x, 2],
x1 = xy2[x, 1],
y1 = xy2[x, 2]
)
})Plot the graph using layout to modify the default
layout.
axis <- list(title = "", showgrid = FALSE, showticklabels = FALSE, zeroline = FALSE)
layout(
network,
title = "Zachary's karate club Network",
shapes = lines,
xaxis = axis,
yaxis = axis
)
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] igraphdata_1.0.1 igraph_1.5.1 plotly_4.10.2 lubridate_1.9.3
[5] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.3 purrr_1.0.2
[9] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 ggplot2_3.4.4
[13] tidyverse_2.0.0 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.4 xfun_0.40 bslib_0.5.1 htmlwidgets_1.6.2
[5] processx_3.8.2 callr_3.7.3 tzdb_0.4.0 vctrs_0.6.4
[9] tools_4.3.1 crosstalk_1.2.0 ps_1.7.5 generics_0.1.3
[13] fansi_1.0.5 pkgconfig_2.0.3 data.table_1.14.8 lifecycle_1.0.3
[17] compiler_4.3.1 farver_2.1.1 git2r_0.32.0 munsell_0.5.0
[21] getPass_0.2-2 httpuv_1.6.11 htmltools_0.5.6.1 sass_0.4.7
[25] yaml_2.3.7 lazyeval_0.2.2 later_1.3.1 pillar_1.9.0
[29] jquerylib_0.1.4 whisker_0.4.1 ellipsis_0.3.2 cachem_1.0.8
[33] tidyselect_1.2.0 digest_0.6.33 stringi_1.7.12 rprojroot_2.0.3
[37] fastmap_1.1.1 grid_4.3.1 colorspace_2.1-0 cli_3.6.1
[41] magrittr_2.0.3 utf8_1.2.3 withr_2.5.1 scales_1.2.1
[45] promises_1.2.1 timechange_0.2.0 rmarkdown_2.25 httr_1.4.7
[49] hms_1.1.3 evaluate_0.22 knitr_1.44 viridisLite_0.4.2
[53] rlang_1.1.1 Rcpp_1.0.11 glue_1.6.2 rstudioapi_0.15.0
[57] jsonlite_1.8.7 R6_2.5.1 fs_1.6.3