more linting

R/cluster_node_closeness.R

@@ -40,8 +40,8 @@ cluster_node_closeness <- function(
           For the novel algorithm, please see cluster_degree_betweenness()")
   graph_ <- graph
   communities <- list()
-  community_list <- list()
-  community_list <- append(communities, clusters(graph_)$membership)
+  #community_list <- list()
+  #community_list <- append(communities, clusters(graph_)$membership)
   closeness_nodes <- names(sort(closeness(graph_),decreasing = TRUE))
   communities_list <- list()
   subgraphs <- list()
@@ -83,7 +83,7 @@ cluster_node_closeness <- function(
 
   graph_ <- graph
 
-  for(i in 1:length(communities)){
+  for(i in seq_along(1:length(communities))){
     graph_names<- names(V(graph_))
     cumulative_communities <- unlist(communities[1:i])
     cumulative_communities_deduplicated <- cumulative_communities |>

R/cluster_node_degree.R

@@ -35,8 +35,9 @@ cluster_node_degree <- function(
           For the novel algorithm, please see cluster_degree_betweenness()")
   graph_ <- graph
   communities <- list()
-  community_list <- list()
-  community_list <- append(communities, clusters(graph_)$membership)
+  # Removing this line to satisfy the lintr
+  #community_list <- list()
+  #community_list <- append(communities, clusters(graph_)$membership)
   degree_nodes <- names(sort(degree(graph_),decreasing = TRUE))
   communities_list <- list()
   subgraphs <- list()
@@ -76,8 +77,8 @@ cluster_node_degree <- function(
   }
 
   graph_ <- graph
-
-  for(i in 1:length(communities)){
+  # Adding seq_along to satisfy  lintr.
+  for(i in seq_along(1:length(communities))){
     graph_names<- names(V(graph_))
     cumulative_communities <- unlist(communities[1:i])
     cumulative_communities_deduplicated <- cumulative_communities |>

R/prep_unlabeled_graph.R

@@ -29,7 +29,7 @@ prep_unlabeled_graph <- function(graph) {
     prepared_graph <-
       igraph::set_vertex_attr(graph, "name", value = 1:vcount(graph))
 
-  } else{
+  } else {
     prepared_graph <- graph
   }
   return(prepared_graph)

README.md

@@ -43,4 +43,24 @@ main= "Smith-Pittman Clustering"
 
 # Limitations
 
-The present limitations of using this algorithm is that graphs are required to be labeled for the operations to work. For unlabled graphs, graphs can be prepared with the `prep_unlabled_graph()` function. 
+The present limitations of using this algorithm is that graphs are required to be labeled for the operations to work. For unlabeled graphs, graphs can be prepared with the `prep_unlabled_graph()` function. 
+
+Example:
+
+```r
+library(igraph)
+library(igraphdata)
+library(ig.degree.betweenness)
+data("UKfaculty")
+# Making graph undirected so it looks nicer when its plotted
+uk_faculty <- prep_unlabeled_graph(UKfaculty) |>
+  as.undirected()
+
+ndb <- cluster_degree_betweenness(uk_faculty)
+
+plot(
+ndb,
+uk_faculty,
+main= "Smith-Pittman Clustering"
+)
+```

docs/pkgdown.yml

@@ -2,5 +2,5 @@ pandoc: 3.1.1
 pkgdown: 2.0.7
 pkgdown_sha: ~
 articles: {}
-last_built: 2024-05-15T04:05Z
+last_built: 2024-05-30T16:32Z