Unlocking the power of relational data visualization with ggraph
I am absolutely thrilled to announce that ggraph has been released on CRAN. This is my most ambitious package to date, designed to solve a problem that plagues data scientists everywhere: visualizing complex networks without creating a mess.
If ggraph is new to you, think of it as an extension of the ggplot2 API tailored for relational data, such as networks, graphs, and trees. If you love the layered philosophy of ggplot2, you are going to feel right at home.
In this post, I’ll explore the philosophy behind the package and walk you through how to build your first clean, interpretable network visualization.
The Philosophy: Death to Hairballs
There is no shortage of software for creating network visualizations. However, these visualizations often prioritize "impressiveness" over information. We’ve all seen them: dense, unintelligible clusters of nodes and edges that look more like a cat’s hairball than a data insight.
It doesn't have to be this way.
The greatness of ggplot2 lies in its iterative power. It allows users to quickly swap geometries and aesthetics to find the best way to tell a story with data. My belief is that if we extend this "Grammar of Graphics" to relational data, we can move away from hairballs and toward interpretability.
Why learn 7 different visualization packages with different syntaxes when you can just mix and match layouts and geoms in a workflow you already know?
Getting Started
The goal of ggraph is to lessen the cognitive load of experimenting with network visuals. Let’s dive into how it works.
First, ensure you have the package installed from CRAN:
```install.packages("ggraph")
install.packages("tidygraph")``` # We'll use this for data manipulation
1. The Setup
ggraph relies on three main concepts: Layouts, Nodes, and Edges.
Let's create a simple graph object using tidygraph to demonstrate.
```library(ggraph)
library(tidygraph)
# Create a random graph for demonstration
graph <- play_erdos_renyi(n = 10, p = 0.2) %>%
mutate(group = sample(c("A", "B"), 10, replace = TRUE))```
2. Building the Plot
If you know ggplot(), the syntax below will look familiar. Instead of ggplot(), we use ggraph(), and then we add layers.
```ggraph(graph, layout = 'kk') +
geom_edge_link() +
geom_node_point(aes(color = group), size = 5)```
layout: Defines where the nodes sitgeom_edge_link: Draws the lines connecting nodes.geom_node_point: Draws the nodes themselves.
Feature Spotlight: theme_graph()
Understanding layouts and geoms gets you 90% of the way there, but ggraph has specialized features to make your plots publication-ready.
One of the most useful is theme_graph(). Network plots rarely need the standard axes, gridlines, or gray backgrounds found in standard charts. theme_graph() strips these away automatically, giving you a clean canvas.
Consider the following plot:
ggraph(graph, layout = 'kk') +
geom_edge_link(alpha = 0.8, colour = "lightgray") +
geom_node_point(aes(color = group), size = 5) +
theme_graph() +
labs(title = "A Clean Network Visualization")
Note how the distraction of axes and grids is removed, focusing the viewer entirely on the structure of the data.
The Future of ggraph
This release represents a solid foundation—a point where I believe the "chains have come off" for users. However, development is far from done.
I am keeping my development focus open on GitHub. Upcoming features on the roadmap include:
Matrix layouts for dense graphs.
Sankey diagrams for flow data.
Edge routing to avoid node collisions.
Textbox nodes for better labeling.
The world of network visualization moves fast. I encourage you to try out ggraph, break it, and share your feedback. Let’s make network data beautiful and interpretable, together.
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