update for new branch and new R version

.r-cache/R/renv/projects

@@ -0,0 +1 @@
+C:/Users/mattp/ArchNetSci

01-data.Rmd

@@ -165,7 +165,7 @@ If you choose to install everything, however, you can simply run the chunk of co
 ```{r, message=F, warning=F, eval=F}
 packages <- c("tidyverse", "ape", "devtools", "igraph", "statnet", "intergraph",
   "tnet", "ggplot2", "rjson", "d3r", "cccd", "networkD3", "visNetwork",
-  "GISTools", "rgeos", "maptools", "sf", "igraphdata", "ggrepel",
+  "GISTools", "sf", "igraphdata", "ggrepel",
   "ggsn", "tidyverse", "superheat", "ggplotify", "ggforce", "colorspace",
   "ggmap", "dplyr", "ggpubr", "ggraph", "reshape2", "multinet",
   "RColorBrewer", "Rcpp", "deldir", "vegan", "geosphere", "networkDynamic",

02-network-data-formats.Rmd

@@ -280,7 +280,7 @@ weighted_net <-
 E(weighted_net)$weight <- cibola_edgelist$weight
 
 # Explore the first few rows and columns of network object
-head(get.data.frame(weighted_net))
+head(igraph::as_data_frame(weighted_net))
 
 # View network as adjacency matrix. Notice the attr="weight" command that
 # indicates which edge attribute to use for values in the matrix

03-exploratory-analysis.Rmd

@@ -125,10 +125,10 @@ igraph::degree(directed_net, mode = "out")[1:5] # out-degree
 igraph::degree(simple_net, normalize = T)[1:5]
 
 # it is also possible to directly plot the degree distribution for
-# a given network using the degree.distribution function.
+# a given network using the degree_distribution function.
 # Here we embed that call directly in a call for a histogram plot
 # using the "hist" function
-hist(igraph::degree.distribution(simple_net))
+hist(igraph::degree_distribution(simple_net))
 
 # graph level centralization
 igraph::centr_degree(simple_net)
@@ -444,10 +444,10 @@ R allows you to use a variety of common cluster detection algorithms to define g
 
 #### Girvan-Newman Clustering{#GirvanNewman}
 
-Girvan-Newman clustering is a divisive algorithm based on betweenness that defines a partition of network that maximizes modularity by removing nodes with high betweenness iteratively (see discussion in Brughmans and Peeples 2023 Chapter 4.6). In R this is referred to as the `igraph::edge.betweenness.community` function. This function can be used on directed or undirected networks with or without edge weights. This function outputs a variety of information including individual edge betweenness scores, modularity information, and partition membership. See the help documents for more information
+Girvan-Newman clustering is a divisive algorithm based on betweenness that defines a partition of network that maximizes modularity by removing nodes with high betweenness iteratively (see discussion in Brughmans and Peeples 2023 Chapter 4.6). In R this is implemented with the `igraph::cluster_edge_betweenness` function. This function can be used on directed or undirected networks with or without edge weights. This function outputs a variety of information including individual edge betweenness scores, modularity information, and partition membership. See the help documents for more information
 
 ```{r}
-gn <- igraph::edge.betweenness.community(simple_net)
+gn <- igraph::cluster_edge_betweenness(simple_net)
 set.seed(4353)
 plot(simple_net, vertex.color = gn$membership)
 ```