resolving imports

NAMESPACE

@@ -11,8 +11,6 @@ export(pdf_clean)
 export(textnet_extract)
 export(top_features)
 import(data.table)
-import(dplyr)
-import(igraph)
 importFrom(R.utils,gunzip)
 importFrom(data.table,rbindlist)
 importFrom(data.table,setDT)
@@ -22,6 +20,27 @@ importFrom(dplyr,desc)
 importFrom(dplyr,filter)
 importFrom(dplyr,group_by)
 importFrom(dplyr,left_join)
+importFrom(dplyr,summarize)
+importFrom(igraph,E)
+importFrom(igraph,V)
+importFrom(igraph,as.undirected)
+importFrom(igraph,as_data_frame)
+importFrom(igraph,cluster_louvain)
+importFrom(igraph,degree)
+importFrom(igraph,delete.edges)
+importFrom(igraph,delete.vertices)
+importFrom(igraph,delete_edge_attr)
+importFrom(igraph,edge_attr)
+importFrom(igraph,edge_attr_names)
+importFrom(igraph,graph_from_data_frame)
+importFrom(igraph,head_of)
+importFrom(igraph,is.loop)
+importFrom(igraph,modularity)
+importFrom(igraph,reciprocity)
+importFrom(igraph,simplify)
+importFrom(igraph,strength)
+importFrom(igraph,tail_of)
+importFrom(igraph,vertex_attr)
 importFrom(magrittr,"%>%")
 importFrom(methods,is)
 importFrom(network,network)

R/combine_networks.R

@@ -8,7 +8,12 @@
 #' 
 #' @return Single igraph object that consolidates nodes and edges from input graphs. If there are multiple nodes with the same name and different attributes originating from different graphs, this function preserves the node attributes associated with the version that appears most commonly. Adds a node attribute num_graphs_in, which denotes the number of input graphs each node was found in. For a weighted graph, the weight is equal to the original number of edges between the respective source and target nodes. Edge attributes for a multiplex graph are described in the help file of textnet_extract. 
 #' @importFrom data.table rbindlist
-#' @import igraph
+#' @importFrom igraph as_data_frame
+#' @importFrom igraph graph_from_data_frame
+#' @importFrom igraph edge_attr_names
+#' @importFrom igraph delete_edge_attr
+#' @importFrom igraph E
+#' @importFrom igraph simplify
 #' @importFrom methods is
 #' @export
 #' 
@@ -59,7 +64,7 @@ combine_networks <- function(textnet_igraphs, mode = c('multiplex','weighted')){
   superedges <- vector(mode = "list", length = length(num_graphs))
   for(m in 1:num_graphs){
     single_ig <- textnet_igraphs[[m]]
-    sidf <- get.data.frame(single_ig, what = "both")
+    sidf <- igraph::as_data_frame(single_ig, what = "both")
     supernodes[[m]] <- sidf$vertices
     superedges[[m]] <- sidf$edges
   }

R/export_to_network.R

@@ -31,7 +31,26 @@
 #'    \item num_communities -- number of communities using louvain cluster algorithm on a weighted, undirected, non-multiplex version of the network
 #'    \item percent_vbn, percent_vbg, percent_vpb, percent_vbd, percent_vb, percent_vbz -- percent of edges in the graph that are of the respective verb tense
 #' }
-#' @import igraph
+#' @importFrom igraph delete_edge_attr
+#' @importFrom igraph delete.edges
+#' @importFrom igraph E
+#' @importFrom igraph edge_attr
+#' @importFrom igraph edge_attr_names
+#' @importFrom igraph head_of
+#' @importFrom igraph tail_of
+#' @importFrom igraph strength
+#' @importFrom igraph reciprocity
+#' @importFrom igraph cluster_louvain
+#' @importFrom igraph modularity
+#' @importFrom igraph V
+#' @importFrom igraph delete.vertices
+#' @importFrom igraph vertex_attr
+#' @importFrom igraph graph_from_data_frame
+#' @importFrom igraph is.loop
+#' @importFrom igraph simplify
+#' @importFrom igraph degree
+#' @importFrom igraph as_data_frame
+#' @importFrom igraph as.undirected
 #' @importFrom dplyr filter
 #' @importFrom network network network.size network.edgecount network.density
 #' @importFrom sna connectedness centralization gtrans
@@ -117,7 +136,7 @@ export_to_network <- function(textnet_extract, export_format, keep_isolates=T, c
     }
 
   #network object
-  agency_df <- igraph::get.data.frame(igr, what = "both")
+  agency_df <- igraph::as_data_frame(igr, what = "both")
   if(keep_isolates==T & collapse_edges == F){
     #keep_all
     net <- network::network(x=agency_df$edges, directed = T,

R/top_features.R

@@ -11,8 +11,13 @@
 #' @importFrom network network
 #' @importFrom ohenery normalize
 #' @importFrom tidyr tibble
-#' @import dplyr
-#' @import igraph
+#' @importFrom dplyr group_by
+#' @importFrom dplyr summarize
+#' @importFrom dplyr arrange
+#' @importFrom dplyr desc
+#' @importFrom igraph degree
+#' @importFrom igraph edge_attr
+#' @importFrom igraph as_data_frame
 #' 
 #' @export
 
@@ -36,7 +41,7 @@ top_features <- function(files, from_file=F){
     }else{
       igr <- files[[i]]
     }
-    igr_df <- igraph::get.data.frame(igr, what = "both")
+    igr_df <- igraph::as_data_frame(igr, what = "both")
 
     net <- network::network(x=igr_df$edges[,1:2], directed = T,
                           hyper = F, loops = T, multiple = T, 
@@ -45,7 +50,7 @@ top_features <- function(files, from_file=F){
 
 
     all_entities[[i]] <- sort(igraph::degree(igr),decreasing = T)
-    all_lemmas[[i]] <- sort(table(igraph::get.edge.attribute(
+    all_lemmas[[i]] <- sort(table(igraph::edge_attr(
       igr, "head_verb_lemma")), decreasing = T)
   }
 

vignettes/textNet_vignette_2024.Rmd

@@ -319,8 +319,8 @@ We can now use these data structures to calculate node statistics, as printed be
 ```{r step8b2}
     paths2 <- diag(old_mat %*% old_mat)
     recip <- 2*paths2 / sna::degree(old_net)
-    totalCC <- as.vector(unname(DirectedClustering::ClustF(old_mat, type = "directed",
-                                                isolates="zero")$totalCC))
+    totalCC <- as.vector(unname(DirectedClustering::ClustF(old_mat, 
+            type = "directed", isolates="zero")$totalCC))
     closens <- sna::closeness(old_net, gmode = "graph", cmode="suminvundir")
     between <- sna::betweenness(old_net,gmode = "graph",cmode="undirected")
     deg <- sna::degree(old_net, gmode = "graph", cmode = "undirected")
@@ -337,7 +337,8 @@ We can now use these data structures to calculate node statistics, as printed be
 
     paths2 <- diag(new_mat %*% new_mat)
     recip <- 2*paths2 / sna::degree(new_net)
-    totalCC <- as.vector(unname(DirectedClustering::ClustF(new_mat, type = "directed", isolates="zero")$totalCC))
+    totalCC <- as.vector(unname(DirectedClustering::ClustF(new_mat, 
+                 type = "directed", isolates="zero")$totalCC))
     closens <- sna::closeness(new_net, gmode = "graph", cmode="suminvundir")
     between <- sna::betweenness(new_net,gmode = "graph",cmode="undirected")
     deg <- sna::degree(new_net, gmode = "graph", cmode = "undirected")