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https://github.com/thomasp85/tidygraph

A tidy API for graph manipulation
https://github.com/thomasp85/tidygraph

graph-algorithms graph-manipulation igraph network-analysis r tidyverse

Last synced: 6 days ago
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A tidy API for graph manipulation

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README 

        
---

output: github_document

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "README-"

)

set.seed(1)

```



# tidygraph 



[![R-CMD-check](https://github.com/thomasp85/tidygraph/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/thomasp85/tidygraph/actions/workflows/R-CMD-check.yaml)

[![CRAN_Release_Badge](http://www.r-pkg.org/badges/version-ago/tidygraph)](https://CRAN.R-project.org/package=tidygraph)

[![CRAN_Download_Badge](http://cranlogs.r-pkg.org/badges/tidygraph)](https://CRAN.R-project.org/package=tidygraph)

[![Coverage Status](https://codecov.io/gh/thomasp85/tidygraph/branch/main/graph/badge.svg)](https://app.codecov.io/gh/thomasp85/tidygraph?branch=main)



This package provides a tidy API for graph/network manipulation. While network 

data itself is not tidy, it can be envisioned as two tidy tables, one for node

data and one for edge data. `tidygraph` provides a way to switch between the two

tables and provides `dplyr` verbs for manipulating them. Furthermore it provides

access to a lot of graph algorithms with return values that facilitate their

use in a tidy workflow.



## An example

```{r, message=FALSE}

library(tidygraph)



play_gnp(10, 0.5) %>% 

  activate(nodes) %>% 

  mutate(degree = centrality_degree()) %>% 

  activate(edges) %>% 

  mutate(centrality = centrality_edge_betweenness()) %>% 

  arrange(centrality)

```



## Overview

`tidygraph` is a huge package that exports 280 different functions and methods.

It more or less wraps the full functionality of `igraph` in a tidy API giving 

you access to almost all of the `dplyr` verbs plus a few more, developed for use 

with relational data.



### More verbs

`tidygraph` adds some extra verbs for specific use in network analysis and 

manipulation. The `activate()` function defines whether one is manipulating node or edge

data at the moment as shown in the example above. `bind_edges()`, `bind_nodes()`,

and `bind_graphs()` let you expand the graph structure you're working with,

while `graph_join()` lets you merge two graphs on some node identifier. 

`reroute()`, on the other hand, lets you change the terminal nodes of the edges in

the graph.



### More algorithms

`tidygraph` wraps almost all of the graph algorithms from `igraph` and provides a 

consistent interface and output that always matches the sequence of nodes and 

edges. All `tidygraph` algorithm wrappers are intended for use inside verbs 

where they know the context they are being called in. In the example above it is

not necessary to supply the graph nor the node/edge IDs to `centrality_degree()`

and `centrality_edge_betweenness()` as they are aware of them already. This 

leads to much clearer code and less typing.



### More maps

`tidygraph` goes beyond `dplyr` and also implements graph centric version of the

`purrr` map functions. You can now call a function on the nodes in the order of

a breadth or depth first search while getting access to the result of the 

previous calls.



### More morphs

`tidygraph` lets you temporarily change the representation of your graph, do

some manipulation of the node and edge data, and then change back to the

original graph with the changes being merged in automatically. This is powered

by the new `morph()`/`unmorph()` verbs that let you e.g. contract nodes, work on

the linegraph representation, split communities to separate graphs etc. If you

wish to continue with the morphed version, the `crystallise()` verb lets you

*freeze* the temporary representation into a proper `tbl_graph`.



### More data structure support

While `tidygraph` is powered by igraph underneath it wants everyone to join the

fun. The `as_tbl_graph()` function can easily convert relational data from all

your favourite objects, such as `network`, `phylo`, `dendrogram`, `data.tree`,

`graph`, etc. More conversion will be added in the order I become aware of them.



## Visualisation

`tidygraph` itself does not provide any means of visualisation, but it works

flawlessly with `ggraph`. This division makes it easy to develop the 

visualisation and manipulation code at different speeds depending on where the

needs arise.



## Installation

`tidygraph` is available on CRAN and can be installed simply, using 

`install.packages('tidygraph')`. For the development version available on GitHub,

use the `devtools` package for installation:



```{r, eval=FALSE}

# install.packages('pak')

pak::pak('thomasp85/tidygraph')

```



## Thanks

`tidygraph` stands on the shoulders of particularly the `igraph` and 

`dplyr`/`tidyverse` teams. It would not have happened without them, so thanks so

much to them.



## Code of Conduct

Please note that the tidygraph project is released with a 

[Contributor Code of Conduct](https://tidygraph.data-imaginist.com/CODE_OF_CONDUCT.html). 

By contributing to this project, you agree to abide by its terms.