/dʒiː.dʒɪˈrɑːf/ (or g-giraffe)
ggraph is an extension of
ggplot2 aimed at supporting relational data structures such as networks, graphs, and trees. While it builds upon the foundation of
ggplot2 and its API it comes with its own self-contained set of geoms, facets, etc., as well as adding the concept of layouts to the grammar.
library(ggraph) #> Loading required package: ggplot2 library(tidygraph) #> #> Attaching package: 'tidygraph' #> The following object is masked from 'package:stats': #> #> filter # Create graph of highschool friendships graph <- as_tbl_graph(highschool) %>% mutate(Popularity = centrality_degree(mode = 'in')) # plot using ggraph ggraph(graph, layout = 'kk') + geom_edge_fan(aes(alpha = after_stat(index)), show.legend = FALSE) + geom_node_point(aes(size = Popularity)) + facet_edges(~year) + theme_graph(foreground = 'steelblue', fg_text_colour = 'white')
ggraph builds upon three core concepts that are quite easy to understand:
ggraphhas access to all layout functions available in
igraphand furthermore provides a large selection of its own, such as hive plots, treemaps, and circle packing.
geom_node_*()family of geoms. Some node geoms make more sense for certain layouts, e.g.
geom_node_tile()for treemaps and icicle plots, while others are more general purpose, e.g.
geom_edge_*()family of geoms that contain a lot of different edge types for different scenarios. Sometimes the edges are implied by the layout (e.g. with treemaps) and need not be plotted, but often some sort of line is warranted.
All of the tree concepts have been discussed in detail in dedicated blog posts that are also available as vignettes in the package. Please refer to these for more information.
Note: The linked blog posts are based on ggraph v1. After ggraph v1.1 the underlying implementation was moved to tidygraph and cleaned up, but this resulted in some breaking changes in the process. Therefore the vignette versions are generally recommended as they have been updated.
There are many different ways to store and work with relational data in R.
ggraph is built upon
tidygraph and the large swath of data structures it supports are thus natively supported in
ggraph. In order to get a data type supported by
ggraph, simply provide an
as_tbl_graph method for it.
ggraph is available through CRAN and can be installed with
install.packages('ggraph'). The package is under active development though and the latest set of features can be obtained by installing from this repository using
ggraph is not the only package to provide some sort of support for relational data in
ggplot2, though I’m fairly certain that it is the most ambitious.
ggdendro provides support for
hclust objects through conversion of the structures into line segments that can then be plotted with
ggtree provides more extensive support for all things tree-related, though it lacks some of the layouts and edge types that
ggraph offers (it has other features that
ggraph lacks though). For more standard hairball network plots
GGally all provide some functionality though none of them are as extensive in scope as
Please note that the ‘ggraph’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.