* Solved the issue related to exclusion of the ggnetwork package from…

… CRAN by removing the dependence from the package

DESCRIPTION

@@ -25,7 +25,6 @@ Imports: stats,
 		 factoextra,
 		 FactoMineR,
 		 forcats,
-		 ggnetwork,
 		 ggplot2,
 		 ggrepel,
 		 igraph,

NAMESPACE

@@ -49,7 +49,6 @@ export(trimES)
 import(bibliometrixData)
 import(dimensionsR)
 import(forcats)
-import(ggnetwork)
 import(ggplot2)
 import(ggrepel)
 import(pubmedR)
@@ -181,16 +180,19 @@ importFrom(dplyr,as_tibble)
 importFrom(dplyr,between)
 importFrom(dplyr,cummean)
 importFrom(dplyr,desc)
+importFrom(dplyr,do)
 importFrom(dplyr,group_by)
 importFrom(dplyr,if_all)
 importFrom(dplyr,inner_join)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
+importFrom(dplyr,n)
 importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
 importFrom(dplyr,right_join)
 importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
+importFrom(dplyr,slice)
 importFrom(dplyr,slice_head)
 importFrom(dplyr,slice_tail)
 importFrom(dplyr,summarise)
@@ -296,6 +298,7 @@ importFrom(tidyr,separate)
 importFrom(tidyr,spread)
 importFrom(tidyr,starts_with)
 importFrom(tidyr,unite)
+importFrom(tidyr,unnest)
 importFrom(utils,adist)
 importFrom(utils,capture.output)
 importFrom(utils,data)

NEWS

@@ -1,8 +1,10 @@
-bibliometrix v3.1.4 (Release date: ????-??-??)
+bibliometrix v3.1.4 (Release date: 2021-07-05)
 
 Changes:
 * Added the possibily to calculate all vertex centrality measures setting the argument type="all"
-* Solved the issue with extracting cited sources from scopus collections (function metaTagExtraction)
+* Solved the issue with extracting cited sources from Scopus collections (function metaTagExtraction)
+* Solved the issue related to exclusion of the ggnetwork package from CRAN by removing the dependence from the package 
+
 
 bibliometrix v3.1.3 (Release date: 2021-05-25)
 

R/couplingMap.R

@@ -306,15 +306,43 @@ labeling <- function(M, df_lab, term, n, n.labels, analysis, ngrams){
            df <- left_join(df_lab,M, by=c("sources" = "SO" ))
          })
 
-  clusters <- unique(df$Cluster)
-  w <- character(length(clusters))
-
-
-  for (i in 1:length(clusters)){
-    ind <- which(df$Cluster == clusters[i])
-    tab <- round((tableTag(df[ind,], term)[1:n.labels])/length(ind)*100,1)
-    w[i]=tolower(paste(names(tab)," ",as.numeric(tab),"%",sep="", collapse="\n"))
-  }
-  return(w)
+  #clusters <- unique(df$Cluster)
+  #w <- character(length(clusters))
+
+  tab_global <- tableTag(df, term)
+  tab_global <- data.frame(label=names(tab_global),tot=as.numeric(tab_global), n=nrow(M),stringsAsFactors = FALSE)
+
+  df <- df %>% 
+    group_by(.data$Cluster) %>% 
+    do(w = best_lab(.data,tab_global, n.labels, term)) %>% 
+    unnest(.data$w) %>% 
+    as.data.frame()
+
+  # for (i in 1:length(clusters)){
+  #   ind <- which(df$Cluster == clusters[i])
+  #   tab <- round((tableTag(df[ind,], term)[1:n.labels])/length(ind),1)
+  #   tab <- data.frame(label=names(tab), value=as.numeric(tab),stringsAsFactors = FALSE)
+  #   tab <- tab %>% 
+  #     left_join(tab_global, by = "label") %>% 
+  #     mutate(freq = .data$value/.data$tot*100) 
+  #   
+  #   w[i]=tolower(paste(tab$label," ",round(tab$freq,1),"%",sep="", collapse="\n"))
+  # }
+  return(df$w)
 
 }
+
+best_lab <- function(d, tab_global, n.labels, term){
+  tab <- tableTag(d, term)
+  tab <- data.frame(label=names(tab), value=as.numeric(tab), stringsAsFactors = FALSE)
+  tab <- tab %>% 
+    left_join(tab_global, by = "label") %>% 
+    mutate(conf = round(.data$value/.data$tot*100,1),
+           supp = round(.data$tot/n*100,1),
+           relevance = round(.data$conf*.data$supp/100,1)) %>% 
+    arrange(desc(.data$relevance)) %>% 
+    slice(1:n.labels) 
+
+  #tolower(paste(tab$label," Supp ",tab$supp,"% - Conf ", tab$conf,"%", sep="", collapse="\n"))
+  tolower(paste(tab$label," - Conf ", tab$conf,"%", sep="", collapse="\n"))
+}

R/histPlot.R

@@ -109,7 +109,7 @@ histPlot<-function(histResults, n=20, size = 5, labelsize = 5, title_as_label =
   layout_m$y <- (diff(range(layout_m$x))/diff(range(layout_m$y)))*layout_m$y
 
   ################
-  df_net <- ggnetwork::ggnetwork(bsk.network, layout=as.matrix(layout_m[c("x","y")]), niter=50000, arrow.gap=0)
+  df_net <- dataFromIgraph(bsk.network, layout=as.matrix(layout_m[c("x","y")]), niter=50000, arrow.gap=0)
   df_net$color <- "slategray1"
   df_net <- left_join(df_net,layout_m[c("name","color")], by = "name")
   names(df_net)[10:11] <- c("color", "color_v")
@@ -133,22 +133,22 @@ histPlot<-function(histResults, n=20, size = 5, labelsize = 5, title_as_label =
                         .data$GCS, sep=""))
 
   g <- ggplot(df_net, aes(x = .data$x, y = .data$y, xend = .data$xend, yend = .data$yend, text=.data$text)) +
-    geom_edges(color = "grey", size=0.4, alpha=0.4) +
-    geom_nodes(aes(color = .data$color_v), size = size, alpha=0.5) +
+    geom_network_edges(color = "grey", size=0.4, alpha=0.4) +
+    geom_network_nodes(aes(color = .data$color_v), size = size, alpha=0.5) +
     geom_text(aes(label=.data$id, color=.data$color_v),  size=labelsize,
               nudge_x = 0,
               nudge_y = 0.02,
               check_overlap = FALSE,alpha=0.7)+
     scale_x_continuous(labels=Ylabel,breaks=Breaks)+
-    guides(size=FALSE, color=FALSE) +
+    guides(size="none", color="none") +
     theme_minimal()+
     theme(legend.position='none', panel.background = element_rect(fill='white', color='white'),
           axis.line.y = element_blank(), axis.text.y = element_blank(),axis.ticks.y=element_blank(),
           axis.title.y = element_blank(), axis.title.x = element_blank(),
           panel.grid.minor.y = element_blank(), panel.grid.major.y = element_blank(),
           panel.grid.major.x = element_line(adjustcolor(col="grey", alpha.f = 0.2), linetype = 2, size = 0.5),
           panel.grid.minor.x = element_blank(), 
-          axis.text.x=element_text(face="bold", angle = 90, size=labelsize+3)) +
+          axis.text.x=element_text(face="bold", angle = 90, size=labelsize+4)) +
     labs(title = "Historical Direct Citation Network") 
 
   ### logo coordinates
@@ -226,3 +226,5 @@ delete.isolates <- function(graph, mode = 'all') {
   isolates <- which(degree(graph, mode = mode) == 0) - 1
   delete.vertices(graph, names(isolates))
 }
+
+