ggraph tricks for common problems

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In this post I am going to share some bits of code for some common problems that I encountered
with {{ggraph}}. By problems, I do not mean problems in the package. Rather, I mean
the things you want to do, that require going a little beyond the standard use cases.

Some of my proposed solutions may seem a little “hacky” and there may well be more simple and
elegant ways of achieving the same goal. Please do share them in the comments!

Besides pure {{ggraph}} snippets, I’ll also highlight some other packages that play well with it
(e.g. {{ggforce}} and {scatterpie}}).

library(tidyverse)
library(igraph)
library(ggraph)
library(graphlayouts)
library(ggforce)
library(scatterpie)

Arrow heads and node size

“How can I achieve that my directed edges stop at the node border, independent from the node size?”

This one has given me headaches for the longest time. No matter what I tried, I always ended up with
something like the below plot.

# create a random network
set.seed(1071)
g <- sample_pa(30, 1)
V(g)$degree <- degree(g, mode = "in")

ggraph(g, "stress") +
  geom_edge_link(
    aes(end_cap = circle(node2.degree + 2, "pt")),
    edge_colour = "black",
    arrow = arrow(
      angle = 10,
      length = unit(0.15, "inches"),
      ends = "last",
      type = "closed"
    )
  ) +
  geom_node_point(aes(size = degree), col = "grey66", show.legend = FALSE) +
  scale_size(range = c(3, 11)) +
  theme_graph()

The overlap can be avoided by using the I() function from base R, which
treats the entries of a vector “as is”. So we know that if a node has degree 5, it will be mapped to
a circle with radius (or diameter?) “5pt”. Since this means, that you have no control over the scaling,
you need to do that beforehand.

# this function is borrowed from the ambient package
normalise <- function (x, from = range(x), to = c(0, 1)) {
  x <- (x - from[1])/(from[2] - from[1])
  if (!identical(to, c(0, 1))) {
    x <- x * (to[2] - to[1]) + to[1]
  }
  x
}

# map to the range you want
V(g)$degree <- normalise(V(g)$degree, to = c(3, 11))

ggraph(g, "stress") +
  geom_edge_link(
    aes(end_cap = circle(node2.degree + 2, "pt")),
    edge_colour = "grey25",
    arrow = arrow(
      angle = 10,
      length = unit(0.15, "inches"),
      ends = "last",
      type = "closed"
    )
  ) +
  geom_node_point(aes(size = I(degree)), col = "grey66") +
  theme_graph()

I would not be surprised though if there is an even easier fix for this problem…

low opacity nodes

“How can I lower the opacity of nodes without making edges visible underneath?”

One of the rules I try to follow is that edges should not be visible on top of nodes.
Usually that is easy to achieve by drawing the edges before the nodes. But if you
want to lower the opacity of nodes, they do become visible again.

g <- sample_gnp(20, 0.5)
V(g)$degree <- degree(g)

ggraph(g, "stress") +
  geom_edge_link(edge_colour = "grey66") +
  geom_node_point(
    size = 8,
    aes(alpha = degree),
    col = "red",
    show.legend = FALSE
  ) +
  theme_graph()

The solution is rather simple. Just add a node layer with the same aesthetics below with
alpha=1 (default) and color="white" (or the background color of the plot).

ggraph(g, "stress") +
  geom_edge_link(edge_colour = "grey66") +
  geom_node_point(size = 8, col = "white") +
  geom_node_point(
    aes(alpha = degree),
    size = 8,
    col = "red",
    show.legend = FALSE
  ) +
  theme_graph()

of course you could also use start_cap and end_cap here, but you may have to fiddle again as in the last example.

use the ggforce

The {{ggforce}} package works pretty nicely with {{ggraph}}. You can, for instance, use
the geom_mark_*() functions to highlight clusters.

set.seed(665)

#create network with a group structure
g <- sample_islands(9, 40, 0.4, 15)
g <- igraph::simplify(g)
V(g)$grp <- as.character(rep(1:9, each = 40))

bb <- layout_as_backbone(g, keep = 0.4)
E(g)$col <- F
E(g)$col[bb$backbone] <- T
ggraph(g,
       layout = "manual",
       x = bb$xy[, 1],
       y = bb$xy[, 2]) +
  geom_edge_link0(aes(col = col), width = 0.2) +
  geom_node_point(aes(fill = grp), shape = 21, size = 3) +
  geom_mark_hull(
    aes(x, y, group = grp, fill = grp),
    concavity = 4,
    expand = unit(2, "mm"),
    alpha = 0.25
  ) +
  scale_color_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Set1") +
  scale_edge_color_manual(values = c(rgb(0, 0, 0, 0.3), rgb(0, 0, 0, 1))) +
  theme_graph()+
  theme(legend.position = "none")

Of course you can also add a label to your clusters.

ggraph(g,
       layout = "manual",
       x = bb$xy[, 1],
       y = bb$xy[, 2]) +
  geom_edge_link0(aes(col = col), width = 0.2) +
  geom_node_point(aes(fill = grp), shape = 21, size = 3) +
  geom_mark_hull(
    aes(x, y, group = grp, fill = grp, label=grp),
    concavity = 4,
    expand = unit(2, "mm"),
    alpha = 0.25
  ) +
  scale_color_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Set1") +
  scale_edge_color_manual(values = c(rgb(0, 0, 0, 0.3), rgb(0, 0, 0, 1))) +
  theme_graph()+
  theme(legend.position = "none")

If you want to avoid node overlaps, you can use geom_node_voronoi().
So this is actually already implemented in {{ggraph}}, but originates from geom_voronoi_tile().

ggraph(g,
       layout = "manual",
       x = bb$xy[, 1],
       y = bb$xy[, 2]) +
  geom_edge_link0(aes(filter = !col, col = col), width = 0.2) +
  geom_node_voronoi(
    aes(x, y, fill = grp),
    max.radius = 0.4,
    expand = unit(-0.5, 'mm'),
    colour = 'black'
  ) +
  scale_color_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Set1") +
  scale_edge_color_manual(values = c(rgb(0, 0, 0, 0.3), rgb(0, 0, 0, 1))) +
  theme(
    legend.position = "none",
    panel.grid = element_blank(),
    axis.ticks = element_blank(),
    axis.text = element_blank()
  ) +
  theme_graph() +
  theme(legend.position = "none")

new node shapes with scatterpie

How can I make my nodes into a pie chart?

I have seen this pop up on twitter from time to time. Disregarding the whole discussion on the
usefulness of pie chart, a solution to this is to use the {{scatterpie}} package.

set.seed(1439)
# create a random graph with three random attributes
g <- sample_pa(20, 1)
V(g)$A <- abs(rnorm(20, sd = 1))
V(g)$B <- abs(rnorm(20, sd = 2))
V(g)$C <- abs(rnorm(20, sd = 3))

#precompute the layout
xy <- layout_with_stress(g)
V(g)$x <- xy[, 1]
V(g)$y <- xy[, 2]

ggraph(g, "manual", x = V(g)$x, y = V(g)$y) +
  geom_edge_link0() +
  geom_scatterpie(
    cols = c("A", "B", "C"),
    data = as_data_frame(g, "vertices"),
    colour = NA,
    pie_scale = 2
  ) +
  coord_fixed() +
  theme_graph() +
  theme(legend.position = "bottom")

This could be useful in many ways. One use-case that comes to mind is for reduced
cluster graphs. That is a new network is created from a given clustering, where each node represents
a cluster. The pie chart can then show the composition of the nodes within the cluster.

#create network with a group structure
g <- sample_islands(9, 40, 0.4, 15)
g <- igraph::simplify(g)
V(g)$grp <- as.character(rep(1:9, each = 40))

# create a random categorical variable
V(g)$cat <- sample(c("A", "B", "C"), vcount(g), replace = T)

# contract the network based on the groups
g_clu <- contract(g, V(g)$grp, vertex.attr.comb = "concat")
E(g_clu)$weight <- 1
g_clu <- simplify(g_clu, edge.attr.comb = "sum")

# calculate the number of occurrences of each attribute in each cluster
V(g_clu)$A <- sapply(V(g_clu)$cat, function(x)
  sum(x == "A"))
V(g_clu)$B <- sapply(V(g_clu)$cat, function(x)
  sum(x == "B"))
V(g_clu)$C <- sapply(V(g_clu)$cat, function(x)
  sum(x == "C"))

# precompute layout
xy <- layout_with_stress(g_clu)
V(g_clu)$x <- xy[, 1]
V(g_clu)$y <- xy[, 2]

ggraph(g_clu, "manual", x = V(g_clu)$x, y = V(g_clu)$y) +
  geom_edge_link0() +
  geom_scatterpie(
    cols = c("A", "B", "C"),
    data = as_data_frame(g_clu, "vertices"),
    colour = "white",
    pie_scale = 3
  ) +
  scale_fill_manual(values = c("goldenrod", "black", "darkorchid2")) +
  coord_fixed() +
  theme_graph() +
  theme(legend.position = "bottom")

Can I do something else besides pie charts?

If you do not like pie charts (who does these days?), you can also create bar charts, but this takes
a bit more effort (the following is adapted from stackoverflow).

#create data frames for nodes (wide and long)
nodes_wide <- igraph::as_data_frame(g_clu, "vertices")

nodes_long <- nodes_wide %>% select(A:C) %>%
  mutate(id = 1:nrow(nodes_wide)) %>%
  gather("attr", "value", A:C)

# create the bar charts
bar_list <- lapply(1:vcount(g_clu), function(i) {
  gt_plot <- ggplotGrob(
    ggplot(nodes_long[nodes_long$id == i, ]) +
      geom_col(
        aes(factor(id), value, group = id, fill = attr),
        position = 'dodge2',
        color = NA
      ) +
      scale_fill_manual(values = c("goldenrod", "black", "darkorchid2")) +
      labs(x = NULL, y = NULL) +
      coord_cartesian(expand = FALSE) +
      theme(
        legend.position = "none",
        panel.background = element_rect(fill = "white", colour = NA),
        line = element_blank(),
        text = element_blank()
      )
  )
  panel_coords <- gt_plot$layout[gt_plot$layout$name == "panel", ]
  gt_plot[panel_coords$t:panel_coords$b, panel_coords$l:panel_coords$r]
})

# convert to custom annotation
annot_list <- lapply(1:vcount(g_clu), function(i) {
  xmin <- nodes_wide$x[i] - 0.07
  xmax <- nodes_wide$x[i] + 0.07
  ymin <- nodes_wide$y[i] - 0.07
  ymax <- nodes_wide$y[i] + 0.07
  annotation_custom(
    bar_list[[i]],
    xmin = xmin,
    xmax = xmax,
    ymin = ymin,
    ymax = ymax
  )
})

# create basic network
p <- ggraph(g_clu, "manual", x = V(g_clu)$x, y = V(g_clu)$y) +
  geom_edge_link0() +
  theme_graph()+
  coord_fixed()

# put everything together
Reduce("+", annot_list, p)

This hack can be used with all kinds of geoms. Here is another example with geom_waffle() from the
{{waffle}} package.

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