library(networkdata)
library(igraph)
data("got")
gotS1 <- got[[1]]
plot(gotS1)
Network Visualization
Most network analytic tasks are fairly straightforward to do in R. But when it comes to visualizing networks, R may lack behind some standalone software tools. Not because it is not possible to produce nice figures, but rather because it requires some time to obtain pleasing results. Just take a look at the default output when plotting a network with the plot() function.
It is definitely possible to produce nice figures with the igraph package (Check out this wonderful tutorial), yet it may take some time to familiarize yourself with the syntax. Additionally, most of the layout algorithms of igraph are non-deterministic. This means that running the same plot call twice may produce different results.
In this part, you will learn the basics of ggraph, the “ggplot2 of networks”, together with the graphlayouts package, which introduces additional useful layout algorithms to R. Arguably, using ggraph is not really easier than igraph. But once the underlying principle of the grammar of graphics is understood, you’ll see that it is actually quite intuitive to work with.
Required libraries
To run all the code in this tutorial, you need to install and load several packages.
install.packages(c("igraph", "graphlayouts", "ggraph", "ggforce"))
devtools::install_github("schochastics/networkdata")Make sure you have at least the version given below. Some of the examples may not be backward compatible.
packageVersion("igraph")[1] '1.6.0'packageVersion("graphlayouts")[1] '1.1.1'packageVersion("ggraph")[1] '2.2.0'packageVersion("networkdata")[1] '0.2.1'packageVersion("ggforce")[1] '0.4.1'igraph is mostly used for its data structures and graphlayouts and ggraph for visualizations. The networkdata package contains a huge amount of example network data that always comes in handy for learning new visualization techniques.
library(igraph)
library(ggraph)Loading required package: ggplot2library(graphlayouts)
library(ggforce)