---
title: "dendroNetwork: how to use it"
output: rmarkdown::html_vignette
bibliography: references.bib
csl: journal-of-computer-applications-in-archaeology.csl
vignette: >
  %\VignetteIndexEntry{dendroNetwork}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

dendroNetwork is a package to create dendrochronological networks for gaining insight into provenance or other patterns based on the statistical relations between tree ring curves. The code and the functions are based on several published papers [@visser2022; @visser2021; @visser2021].

The package is written for dendrochronologists and have a general knowledge on the discipline and used jargon. There is an excellent website for the introduction of using R in dendrochronology: <https://opendendro.org/r/>. The basics of dendrochronology can be found in handbooks [@speer2010; @cook1990] or on <https://www.dendrohub.com/>.

## Usage {#usage}

The package aims to make the creation of dendrochronological (provenance) networks as easy as possible. To be able to make use of all options, it is assumed that Cytoscape [@shannon2003] is installed (<https://cytoscape.org/>). Cytoscape is open source software and platform independent and provides easy visual access to complex networks, including the attributes of both nodes and edges in a network (see the [Cytoscape-website](https://cytoscape.org/what_is_cytoscape.html) for more information). Some data is included in this package, namely the Roman data published by Hollstein [@hollstein1980].

The first steps are visualized in the flowchart below, including community detection using either (or both) the Girvan-Newman algorithm [@girvan2002] and Clique Percolation Method [@palla2005] for all clique sizes. Both methods are explained very well in the papers, and on wikipedia for both [CPM](https://en.wikipedia.org/wiki/Clique_percolation_method) and the [Girvan-Newman algorithm](https://en.wikipedia.org/wiki/Girvan%E2%80%93Newman_algorithm). More information on the dendrochronological data can be found in a separate [vignette](dendrochronological_data.html).

```{r flowchart_workflow, echo=FALSE, fig.width=5}
DiagrammeR::grViz("
digraph {
  # graph attributes
  graph [overlap = true]
  # node attributes
  node [shape = box,
        fontname = Helvetica,
        color = gray]
  # edge attributes
  edge [color = black]
  # node statements
  1 [label = 'Load data']
  2 [label = 'Create similarity table']
  3 [label = 'Create network using default settings']
  4 [label = 'Detect communities using the \n Girvan-Newman algorithm']
  5 [label = 'Detect communities using the \n Clique Percolation Method']
  # edge statements
  1 ->  2
  2 -> 3
  3 -> 4
  3 -> 5
  }
")
```

```{r network_hollstein_1980, fig.width=5}
library(dendroNetwork)
data(hol_rom) # 1
sim_table_hol <- sim_table(hol_rom) # 2
g_hol <- dendro_network(sim_table_hol) # 3
g_hol_gn <- gn_names(g_hol) # 4
g_hol_cpm <- clique_community_names(g_hol, k=3) # 4
hol_com_cpm_all <- find_all_cpm_com(g_hol) # 5
# plotting the graph in R
plot(g_hol)  
# better readable version
plot(g_hol, vertex.color="deepskyblue", vertex.size=15, vertex.frame.color="gray",
     vertex.label.color="darkslategrey", vertex.label.cex=0.8, vertex.label.dist=2) 
```

For large datasets of tree-ring series see also `vignette("large_datasets_communities")` .

### Visualization in Cytoscape {#visualization_cytoscape}

After creating the network in R, it is possible to visualize the network using Cytoscape. The main advantage is that visualisation in Cytoscape is more easy, intuitive and visual. In addition, it is very easy to automate workflows in Cytoscape with R (using [RCy3](https://bioconductor.org/packages/release/bioc/html/RCy3.html)). For this purpose we need to start Cytoscape firstly. After Cytoscape has completely loaded, the next steps can be taken.

1.  The network can now be loaded in Cytoscape for further visualisation: `cyto_create_graph(g_hol, CPM_table = hol_com_cpm_all, GN_table = g_hol_gn)`
2.  Styles for visualisation can now be generated. However, Cytoscape comes with a lot of default styles that can be confusing. Therefore it is recommended to use: `cyto_clean_styles()` once in a session.
3.  To visualize the styles for CPM with only k=3: `cyto_create_cpm_style(g_hol, k=3, com_k = g_hol_cpm)`
    -   This can be repeated for all possible clique sizes. To find the maximum clique size in a network, please use: `igraph::clique_num(g_hol)`.
    -   To automate this: `for (i in 3:igraph::clique_num(g_hol)) { cyto_create_cpm_style(g_hol, k=i, com_k = g_hol_cpm)}`.
4.  To visualize the styles using the Girvan-Newman algorithm (GN): `cyto_create_gn_style(g_hol)` This would look something like this in Cytoscape:

![The network of Roman sitechronologies with the Girvan-Newman communities visualized.](images/g_hol_GN.png){width="800"}

A more complete description of using Cytoscape with this package can be found here: `vignette("large_datasets_communities")` .

## Citation

If you use this software, please cite this using:

Visser, R. (2024). DendroNetwork: a R-package to create dendrochronological provenance networks (Version 0.5.0) [Computer software]. <https://zenodo.org/doi/10.5281/zenodo.10636310>

## References
