An Updated Network Graph

Andy Grogan-Kaylor

In a previous post, I looked at some ways to get an idea of what scales and demographic variables in my data are related to other scales and demographic variables in my data.

Among several methods, I looked at a network graph using igraph (Csardi et al., 2025; Csardi & Nepusz, 2006). However, the network graph in my previous post was less than satisfactory because of the limited number of scales in my data.

Below, I provide an example of a network graph with data with more scales.

Call Libraries

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library(igraph) # network analysis

library(ggplot2) # beautiful graphs

library(ggraph) # network graphs for ggplot

library(faux) # simulated data

library(pander) # nice tables

library(skimr) # descriptive statistics

Simulated Data

I simulate some data.

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set.seed(54321) # random seed

# simulate data

cmat <- c(1.00, 0.30, 0.25, 0.15, 0.15, 0.15,
          0.30, 1.00, 0.00, 0.50, 0.10, 0.15,
          0.25, 0.00, 1.00, 0.50, 0.50, 0.15,
          0.15, 0.50, 0.50, 1.00, 0.00, 0.15,
          0.15, 0.10, 0.50, 0.00, 1.00, 0.15,
          0.15, 0.15, 0.15, 0.15, 0.15, 1.00)

mydata <- rnorm_multi(n = 100, 
                   mu = c(0, 20, 100, 100, 100, 100),
                   sd = c(1, 5, 5, 5, 5, 5),
                   r = cmat, # correlation matrix
                   varnames = c("scaleA", 
                                "scaleB", 
                                "scaleC", 
                                "scaleD", 
                                "scaleE", 
                                "scaleF"),
                   empirical = FALSE)

Look At The Data

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pander(head(mydata)) # nicely formatted table

scaleA	scaleB	scaleC	scaleD	scaleE	scaleF
1.602	23.34	99.84	97.63	102.1	101.5
1.452	28.85	101.6	99.86	105.9	99.57
0.5092	20.43	105.6	101.7	102.4	101
1.974	21.07	108.3	106.9	102	106.1
-0.9652	17.06	102	106.7	101.5	95.6
0.7906	22.49	104	111.2	96.67	98.47

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skim(mydata) # descriptive statistics

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
scaleA	1	-0.05	1.09	-2.65	-0.80	-0.08	0.65	2.84	▂▆▇▅▁
scaleB	1	20.13	4.70	7.58	16.83	20.32	23.44	29.89	▁▅▇▆▃
scaleC	1	100.11	4.84	88.04	96.77	100.35	102.94	111.84	▂▃▇▃▂
scaleD	1	100.75	4.86	88.29	97.52	100.31	103.65	113.34	▁▆▇▆▁
scaleE	1	100.14	4.95	82.39	97.41	100.81	103.05	112.10	▁▂▅▇▁
scaleF	1	100.24	5.77	86.48	95.77	100.42	103.80	113.96	▁▇▇▅▂

Network Graph

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cor_mat <- cor(mydata, use = "complete.obs")

diag(cor_mat) <- 0 # set diagonal to 0

g <- graph_from_adjacency_matrix(cor_mat, # make network graph
                                 mode = "undirected", 
                                 weighted = TRUE)

coords <- layout_in_circle(g) # get circular layout

par(mar=c(0, 0, 1, 0) + 1.5) # plot margins

absweight <- abs(E(g)$weight) # absolute value of weight

plot(g, 
     margin = 0.5,
     edge.width = absweight * 20, # absolute value
     vertex.size = 15,
     vertex.label.dist = 2.5,
     vertex.shape = "circle",
     vertex.color = "navy",
     edge.label.color = "red",
     edge.label = round(E(g)$weight, 2),
     edge.label.dist = 1, 
     layout = coords, # layout
     edge.curved = 0,
     main = "Correlation of Scales in Our Data")

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ggraph(g, layout = 'linear', circular = TRUE) + 
  geom_edge_link(aes(width = .25 * weight,
                    color = weight,
                    label = round(weight, 2)),
                label_colour = "red",
                strength = .1,
                angle_calc = 'along',
                show.legend = FALSE) +
  geom_node_point() +
  geom_node_label(aes(label = name),
                  nudge_y = .025,
                  fill = "white") +
  scale_edge_color_gradient(name = "weight",
                            low = "lightgrey",
                            high = "darkgrey") +
  labs(title = "Network Graph of Reports") +
  theme_void()

References

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695. https://igraph.org

Csardi, G., Nepusz, T., Traag, V., Horvat, S., Zanini, F., Noom, D., & Müller, K. (2025). igraph: Network analysis and visualization in R. https://doi.org/10.5281/zenodo.7682609