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 (Visser 2021, 2021; Visser and Vorst 2022).
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 (Cook and Kariukstis 1990; Speer 2010) or on https://www.dendrohub.com/.
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 (Shannon et al. 2003) 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 for more information). Some data is included in this package, namely the Roman data published by Hollstein (Hollstein 1980).
The first steps are visualized in the flowchart below, including community detection using either (or both) the Girvan-Newman algorithm (Girvan and Newman 2002) and Clique Percolation Method (Palla et al. 2005) for all clique sizes. Both methods are explained very well in the papers, and on wikipedia for both CPM and the Girvan-Newman algorithm. More information on the dendrochronological data can be found in a separate vignette.
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")
.
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). For this purpose we need to start Cytoscape firstly. After Cytoscape has completely loaded, the next steps can be taken.
cyto_create_graph(g_hol, CPM_table = hol_com_cpm_all, GN_table = g_hol_gn)
cyto_clean_styles()
once in a
session.cyto_create_cpm_style(g_hol, k=3, com_k = g_hol_cpm)
igraph::clique_num(g_hol)
.for (i in 3:igraph::clique_num(g_hol)) { cyto_create_cpm_style(g_hol, k=i, com_k = g_hol_cpm)}
.cyto_create_gn_style(g_hol)
This would look something like
this in Cytoscape:A more complete description of using Cytoscape with this package can
be found here: vignette("large_datasets_communities")
.
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