tidygraph.Rmd

---
title: "tidygraph and ggraph"
author: "Thomas Lin Pedersen"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
    %\VignetteIndexEntry{tidygraph and ggraph}
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
library(ggraph)
set_graph_style(family = 'Arial', size = 7, foreground = 'lightgrey', plot_margin = margin(0, 0, 0, 0))
set.seed(2022)
```


Following `ggraph` v2.0 the `tidygraph` package has been used as the central 
data structure. The integration goes beyond using it as a simple background 
engine and has deep implications for what you can do and how you can do it when
plotting with `ggraph`. This vignette will go into the details of the 
`ggraph`/`tidygraph` relationship — buckle up...

## Supported data structures
Prior to v2 `ggraph` had two main supported data structures, namely `dendrogram`
and `igraph`. In addition `hclust` and `network` were supported by automatic 
conversion to `dendrogram` and `igraph` respectively. Each of the two data 
structures had their own layouts and under the hood two different set of 
functionality had to be maintained to extract nodes and edges etc. In v2 and 
going forward this has been simplified and `ggraph` now uses only `tbl_graph` as
a graph representation. This does not mean that you're out of luck if you're not
buying into the whole `tidygraph` idea. Every object supported by `tidygraph` is
supported directly in `ggraph` by automatic conversion to `tbl_graph`. This 
means that `igraph`, `dendrogram`, `hclust`, and `network` is still supported in
addition to `data.tree`, `phylo`, and `graph` as well as a number of 
`data.frame`, `matrix`, and `list` representations.

The change has reduced internal code complexity quite a bit which will make it
easier to provide new features in future. From a user point of view it has the
benefit of simplifying the API in that `ggraph` doesn't really care what type of
network object you pass in - every layout and geom just works with every data
structure. Further, it simplifies how to add `ggraph` support to additional data
structures: just write an `as_tbl_graph()` method for the class!. Due to the
large support of classes and data structures in `tidygraph` this should 
relatively straightforward. If you're developer of a package that defines a 
custom network class simply export an `as_tbl_graph()` method for the class to
gain native `ggraph` (and `tidygraph`) support, or add it directly to 
`tidygraph` through a [PR](https://github.com/thomasp85/tidygraph/pulls).

This simplification for both me and the users have really been the motivation
for the integration of `tidygraph` but as it were it has also allowed or 
instigated a number of cool new features that will be explored below.

## NSE in layout specifications
In `ggraph` the initiation will need to specify a layout to use for the 
subsequent node and edge geoms. Many of these layouts use different node and 
edge variables in their calculations e.g. a node size or an edge weight. Prior 
to v2 these arguments would simply take a string naming the respective variable
to use, but following the v2 update these arguments implement Non-Standard 
Evaluation (NSE) in a manner known from both `dplyr` and `ggplot2` where it is
used inside `aes()` calls. Depending on whether the argument refers to a node or
edge value the provided expression will be evaluated in the context of nodes or
edges respectively. The bottomline is that given a network such as this:

```{r, message=FALSE}
library(tidygraph)

graph <- as_tbl_graph(
  data.frame(
    from = sample(5, 20, TRUE),
    to = sample(5, 20, TRUE),
    weight = runif(20)
  )
)
graph
```

Then, instead of writing:

```{r, eval=FALSE}
ggraph(graph, layout = 'fr', weights = "weight") + 
  geom_edge_link() + 
  geom_node_point()
```

You would simply write:

```{r}
ggraph(graph, layout = 'fr', weights = weight) + 
  geom_edge_link() + 
  geom_node_point()
```

This change means that it is much easier to experiment with modifications to
node and edge parameters affecting layouts as it is not necessary to modify the
underlying graph but only the plotting code, e.g.:

```{r}
ggraph(graph, layout = 'fr', weights = exp(weight)) + 
  geom_edge_link() + 
  geom_node_point()
```

## Access to tidygraph algorithms in ggraph code
The most important improvement resulting from the integration of `tidygraph` and
`ggraph` is that `tidygraph` algorithms are now directly usable within `ggraph` 
calls. This means that it is no longer necessary to precompute and store derived
node and edge variables on the graph in order to use them in a plot:

```{r}
graph <- create_notable('zachary')

ggraph(graph, layout = 'fr') + 
  geom_edge_link() + 
  geom_node_point(aes(size = centrality_pagerank())) + 
  theme(legend.position = 'bottom')
```

here it is not necessary to first compute the pagerank centrality and store it
as a node variable in order to plot it, and if you're interested in looking at 
one of the myriad of other centrality measures you simply change the plotting
code. This feature makes it much easier and painfree to investigate the effect
of different graph measures on your plots and is a huge benefit when iterating
on your visualisation.

Access to `tidygraph` is available within `ggraph()` and `aes()` calls, and 
within `facet` formulas. It is thus possible to use algorithms when specifying
layouts, adding aesthetics to geoms and splitting into subplots - all areas were
ease of iteration is vital:

```{r, message=FALSE}
ggraph(graph, 'matrix', sort.by = node_rank_leafsort()) + 
  geom_edge_point(aes(colour = centrality_edge_betweenness()), mirror = TRUE) + 
  theme(legend.position = 'bottom')
```

```{r}
ggraph(graph, 'fr') + 
  geom_edge_link() + 
  geom_node_point() + 
  facet_nodes(~ group_infomap())
```