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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# snahelper <img src="man/figures/logo.png" align="right"/>
[](https://github.com/schochastics/snahelper/actions)
[](https://cran.r-project.org/package=snahelper)
[](https://CRAN.R-project.org/package=snahelper)
`snahelper` provides a set RStudio Addin for social network analysis.
The main addin is the `SNAhelper` which provides a simple GUI to do common network analytic tasks
and visualize a network with [ggraph](https://ggraph.data-imaginist.com/).
The second addin, called `Netbuilder` allows you to quickly build small networks with a small
"canvas" to draw on. The network can be exported as an igraph object at the end of the session by clicking on "Done".
The third addin `Netreader` is meant to facilitated the import of raw network data. It provides a GUI to easily
read network and attribute data and combine them to an igraph object. The underlying code of the import procedure
is shown at the end. This should help users to learn importing data themselves.
The fourth addin `Componentlayouter` allows to layout networks with several components manually by placing them on an empty
canvas. Components can also be rotated and resized. After finishing the session, the layout is saved as x and y vertex attributes.
## Quick look at the SNAhelper
Code to reproduce the used network.
```{r starwars,eval=F}
library(tidyverse)
library(igraph)
url <- "https://raw.githubusercontent.com/evelinag/StarWars-social-network/master/networks/starwars-full-interactions-allCharacters.json"
df <- jsonlite::fromJSON(url)
df$nodes$id <- 1:nrow(df$nodes)
df$nodes$display <- df$nodes$name
df$nodes$name <- df$nodes$id-1
g <- graph_from_data_frame(df$links,directed = F,vertices = df$nodes)
V(g)$name <- V(g)$display
g <- remove.vertex.attribute(g,"id")
g <- remove.vertex.attribute(g,"display")
g <- delete.vertices(g,which(degree(g)==0))
V(g)$display <- ifelse(V(g)$value>75,V(g)$name,"")
V(g)$colour <- ifelse(V(g)$display=="",NA,V(g)$display)
g
```
## Quick look at the Netreader
`Netreader` should be pretty selfexplanatory. The first two tabs allow you to import
raw data (edges and attributes). Make sure to specify file delimiters, etc. according to the shown preview.
Using the `Netreader` should comes with a learning effect (hopefully). The last tab shows the R code
to produce the network with the chosen data **without** using the Addin.
The network will be saved in your global environment once you click "Done".
## Quick look at the Netbuilder
## Quick look at the Componentlayouter
Highlight an igraph object in your script and selcet the `Componentlayouter` from the RStudio addin menu.
Components are added by clicking on the canvas and can be resized/rotated and re-positioned using the buttons at the
bottom. The final layout is stored as x and y coordinates in the igraph object.
## Installation
``` {r install1,eval=FALSE}
# developer version
#install.packages(remotes)
remotes::install_github("schochastics/snahelper")
#CRAN version
install.packages("snahelper")
```
To work properly, you also need [graphlayouts](https://github.com/schochastics/graphlayouts), which adds new
layout algorithms.
```{r install2,eval=FALSE}
install.packages("graphlayouts")
```
## Usage of SNAhelper
To use the main addin, simply highlight a network in your script and select
`SNAhelper` from the Addin dropdown menu.
## Interface
### Layout
The layout tab allows you to choose from all implemented algorithms in `igraph` and
some layouts from `graphlayouts`. The default is a stress based layout and also the recommended choice.
See [my blog](http://blog.schochastics.net/post/stress-based-graph-layouts/) for an
explanation. In the tweak section you can move individual nodes around. Choose the node from
the dropdown menu and click on its new position in the plot.
### Node Attribute Manager
The Node Attribute Manager shows all existing node attributes in a sortable table.
In addition, you can calculate some new ones (centrality and clustering).
The functions automatically choose the right version of indices, depending if
the network is directed/weighted/undirected/unweighted.
### Nodes
This is where you can style your nodes. You can either do it manually, by choosing a
color/size for all nodes together, or based on an attribute.
### Edge Attribute Manager
Same as Node Attribute Manager but for edges. So far only shows existing edge attributes.
### Edges
You can style your edges here. `snahelper` automatically detects if your network
is directed and adds arrows if the network is directed. The other options are similar to the nodes tab.
The snahelper automatically chooses the appropriate edge geom. If multiple edges are present, it uses
`geom_edge_parallel0()`. Otherwise `geom_edge_link0()`.
### Result
The result tab shows the network in its full size. If you are satisfied with the results, hit
the Done button and the R code to produce the plot is automatically inserted in your script.
Or you can directly save the result as a png file.
