https://github.com/dalmolingroup/easylayout

interactive force-directed graph layouts within RStudio

https://github.com/dalmolingroup/easylayout

Keywords

Repository

interactive force-directed graph layouts within RStudio

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# easylayout: an R package for interactive force-directed layouts within RStudio 


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


easylayout is an R package that leverages interactive force simulations within
the IDE itself (e.g., RStudio, VSCode). It is **not** yet another visualization
library, but instead aims to interconnect existing libraries and streamline
their usage into the R ecosystem.

![](https://github.com/user-attachments/assets/1b91cb11-77ef-47a5-b529-3805a9785a76)

easylayout takes an igraph object and serializes it into a web application 
integrated with the IDE's interface through a Shiny server. The web application
lays out the network by simulating attraction and repulsion forces. Simulation
parameters can be adjusted in real-time. An editing mode allows moving and
rotating nodes. The implementation aims for performance, so that even lower-end
devices are able to work with relatively large networks. Once the user finishes
tinkering the layout, it is sent back to the R session to be plotted through
popular libraries like ggplot2 or even the base package itself.

## Installation

You can install the development version of easylayout from [GitHub](https://github.com/) with:

```r
# install.packages("devtools")
devtools::install_github("dalmolingroup/easylayout")
```

## Example

This is a basic example which shows you how to solve a common problem:

```{r example, eval = FALSE}
library(easylayout)
library(igraph)

g <- igraph::erdos.renyi.game(n = 5000, p.or.m = 10000, type = "gnm")

number_of_vertices <- igraph::vcount(g)

igraph::V(g)$label <- NA
igraph::V(g)$size <- sample(1:5, number_of_vertices, replace = TRUE)
igraph::V(g)$color <- sample(rainbow(5), number_of_vertices, replace = TRUE)

plot(g, layout = easylayout, vertex.label = NA, margin = 0)
```

You can also run easylayout as a standalone function, attributing its output
to a variable in your R environment. The output is just a 2-column matrix:

```{r layout_func, eval = FALSE}
layout <- easylayout(g)
```

Once you store the final coordinates to a variable, use any plotting package to
display the network. In the example below, we plot the network using ggraph.

```{r ggraph, eval = FALSE}
V(g)$x <- layout[, 1]
V(g)$y <- layout[, 2]

ggraph::ggraph(g, layout = layout) +
  ggraph::geom_edge_link() +
  ggraph::geom_node_point(aes(color = color)) +
  ggplot2::coord_fixed() +
  ggplot2::theme_void()
```


## Future work

The current implementation focuses on the R ecosystem, but using web
technologies makes it easily portable to similar environments, like Jupyter
Notebooks. We expect this tool to reduce the time spent tweaking network
layouts, allowing researches to generate more compelling figures.

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