The ggnetwork package provides a way to build network plots with ggplot2.

Install the stable version from CRAN:

install.packages("ggnetwork")

Or use remotes to install the latest version of the package from GitHub:

remotes::install_github("briatte/ggnetwork")

The package is meant to be used with ggplot2 version 2.0.0 or above, so make sure that you update your version of ggplot2 from CRAN before using ggnetwork:

install.packages("ggplot2")
library(ggplot2)

ggnetwork further requires the network and sna packages for network manipulation, and will also install the ggrepel package for repulsive label drawing.

The ggnetwork package is very much related to the development of geom_net by Samantha C. Tyner and Heike Hoffmann. It also shares some similarity to the ggnet and ggnet2 functions, which are part of the GGally package by Barret Schloerke and others. Each of these projects are extensions to Hadley Wickham’s implementation of Leland Wilkinson’s “grammar of graphics” in ggplot2.

Minimal example

Let’s define a small random graph to illustrate each component of ggnetwork:

library(network)
library(sna)
n <- network(rgraph(10, tprob = 0.2), directed = FALSE)

Let’s now add categorical and continuous attributes for both edges and vertices. We’ll start with nodes, adding a categorical vertex attribute called "family", which is set to either "a", "b" or "c", and a continuous vertex attribute called "importance", which is set to either 1, 2 or 3.

n %v% "family" <- sample(letters[1:3], 10, replace = TRUE)
n %v% "importance" <- sample(1:3, 10, replace = TRUE)

We now add a categorical edge attribute called "type", which is set to either "x", "y" or "z", and a continuous vertex attribute called "day", which is set to either 1, 2 or 3.

e <- network.edgecount(n)
set.edge.attribute(n, "type", sample(letters[24:26], e, replace = TRUE))
set.edge.attribute(n, "day", sample(1:3, e, replace = TRUE))

Last, note that ggnetwork contains a “blank” plot theme that will avoid plotting axes on the sides of the network. We will use that theme in most of the plots:

theme_blank
## function (base_size = 12, base_family = "", ...) 
## {
##     ggplot2::theme_bw(base_size = base_size, base_family = base_family) + 
##         ggplot2::theme(axis.text = ggplot2::element_blank(), 
##             axis.ticks = ggplot2::element_blank(), axis.title = ggplot2::element_blank(), 
##             legend.key = ggplot2::element_blank(), panel.background = ggplot2::element_rect(fill = "white", 
##                 colour = NA), panel.border = ggplot2::element_blank(), 
##             panel.grid = ggplot2::element_blank(), ...)
## }
## <bytecode: 0x7fe81edfade8>
## <environment: namespace:ggnetwork>

Main building blocks

ggnetwork

The ggnetwork package is organised around a ‘workhorse’ function of the same name, which will ‘flatten’ the network object to a data frame that contains the edge list of the network, along with the edge attributes and the vertex attributes of the sender nodes.

The network object referred to above might be an object of class network, or any data structure that can be coerced to it, such as an edge list, an adjacency matrix or an incidence matrix. If the intergraph package is installed, then objects of class igraph can also be used with the ggnetwork package.

The data frame returned by ggnetwork also contains the coordinates needed for node placement as columns "x", "y", "xend" and "yend", which as a consequence are “reserved” names in the context of ggnetwork. If these names show up in the edge or the vertex attributes, the function will simply fail to work.

The default node placement algorithm used by ggnetwork to produce these coordinates is the Fruchterman-Reingold force-directed layout algorithm. All of the placement algorithms implemented in the sna package are available through ggnetwork, which also accepts additional layout parameters:

ggnetwork(n, layout = "fruchtermanreingold", cell.jitter = 0.75)
ggnetwork(n, layout = "target", niter = 100)

The layout argument will also accept user-submitted coordinates as a two-column matrix with as many rows as the number of nodes in the network.

The top of the data frame produced by ggnetwork contains self-loops to force every node to be included in the plot. This explains why the rows shown below have the same values in "x" and "xend" (and in "y" and "yend"), and only missing values in the columns corresponding to the edge attributes:

head(ggnetwork(n))
##            x         y family importance vertex.names       xend      yend day
## 2 0.06988382 0.5310508      b          2            7 0.33260115 0.6985749   2
## 3 0.06988382 0.5310508      b          2            7 0.51253630 0.2101922   2
## 4 0.06988382 0.5310508      b          2            7 0.00000000 1.0000000   2
## 5 0.09663049 0.1981104      b          3           10 0.33260115 0.6985749   3
## 6 0.09663049 0.1981104      b          3           10 0.06988382 0.5310508   1
## 7 0.09663049 0.1981104      b          3           10 0.51253630 0.2101922   2
##   type
## 2    x
## 3    y
## 4    z
## 5    z
## 6    y
## 7    y

The next rows of the data frame contain the actual edges:

tail(ggnetwork(n))
##             x         y family importance vertex.names      xend      yend day
## 51  0.3470495 0.7938100      b          3            5 0.3470495 0.7938100  NA
## 61  0.7147970 0.6360043      a          2            6 0.7147970 0.6360043  NA
## 71  0.1712591 0.4108127      b          2            7 0.1712591 0.4108127  NA
## 81  1.0000000 0.5759609      a          2            8 1.0000000 0.5759609  NA
## 91  0.7239504 0.9909025      c          2            9 0.7239504 0.9909025  NA
## 101 0.3025580 0.0000000      b          3           10 0.3025580 0.0000000  NA
##     type
## 51  <NA>
## 61  <NA>
## 71  <NA>
## 81  <NA>
## 91  <NA>
## 101 <NA>

The data frame returned by ggnetwork has (N + E) rows, where N is the number of nodes of the network, and E its number of edges. This data format is very likely to include duplicate information about the nodes, which is unavoidable.

Note that ggnetwork does not include any safety mechanism against duplicate column names. As a consequence, if there is both a vertex attribute called "na" and an edge attribute called "na", as in the example above, then the vertex attribute will be renamed "na.x" and the edge attribute will be renamed "na.y".

fortify.network and fortify.igraph

The ‘flattening’ process described above is implemented by ggnetwork as fortify methods that are recognised by ggplot2. As a result, ggplot2 will understand the following syntax as long as n is an object of class network or of class igraph:

ggplot(n)

However, if the object n is a matrix or an edge list to be coerced to a network object, you are required to use the ggnetwork function to pass the object to ggplot2:

ggplot(ggnetwork(n))

geom_edges

Let’s now draw the network edges using geom_edges, which is just a lightly hacked version of geom_segment. In the example below, we map the type edge attribute to the linetype of the network edges:

ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_edges(aes(linetype = type), color = "grey50") +
  theme_blank()

The other aesthetics that we mapped are the basic coordinates of the network plot. These might also be set as part of the call to geom_segment, but setting them at the root of the plot avoids having to repeat them in additional geoms.

Note that geom_edges can also produce curved edges by setting its curvature argument to any value above 0 (the default):

ggplot(n, aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_edges(aes(linetype = type), color = "grey50", curvature = 0.1) +
  theme_blank()