Merge pull request #302 from massimoaria/develop

Develop

NAMESPACE

@@ -197,6 +197,7 @@ importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
 importFrom(dplyr,rename_with)
 importFrom(dplyr,right_join)
+importFrom(dplyr,row_number)
 importFrom(dplyr,rowwise)
 importFrom(dplyr,select)
 importFrom(dplyr,slice)
@@ -233,6 +234,7 @@ importFrom(igraph,E)
 importFrom(igraph,V)
 importFrom(igraph,arpack_defaults)
 importFrom(igraph,as_adjacency_matrix)
+importFrom(igraph,as_long_data_frame)
 importFrom(igraph,authority_score)
 importFrom(igraph,betweenness)
 importFrom(igraph,centr_betw)

R/cocMatrix.R

@@ -1,9 +1,10 @@
-#' Occurrence Matrix
+#' Bibliographic bipartite network matrices
 #'
-#' \code{cocMatrix} computes occurences between elements of a Tag Field from a bibliographic data frame. Manuscript is the unit of analysis.
+#' \code{cocMatrix} computes occurrences between elements of a Tag Field from a bibliographic data frame. Manuscript is the unit of analysis.
 #'
-#' This occurrence matrix can be transformed into a collection of compatible
-#' networks. Through matrix multiplication you can obtain different networks. 
+#' This occurrence matrix represents a bipartite network which can be transformed into a collection of bibliographic
+#' networks such as coupling, co-citation, etc.. 
+#' 
 #' The function follows the approach proposed by Batagelj & Cerinsek (2013) and Aria & cuccurullo (2017).\cr\cr
 #' 
 #' References:\cr
@@ -36,22 +37,22 @@
 #' @param short is a logical. If TRUE all items with frequency<2 are deleted to reduce the matrix size.
 #' @param remove.terms is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is \code{remove.terms = NULL}.
 #' @param synonyms is a character vector. Each element contains a list of synonyms, separated by ";",  that will be merged into a single term (the first word contained in the vector element). The default is \code{synonyms = NULL}.
-#' @return a co-occurrence matrix with cases corresponding to manuscripts and variables to the
+#' @return a bipartite network matrix with cases corresponding to manuscripts and variables to the
 #'   objects extracted from the Tag \code{Field}.
 #'
 #' @examples
-#' # EXAMPLE 1: Articles x Authors co-occurrence matrix
+#' # EXAMPLE 1: Articles x Authors occurrence matrix
 #'
 #' data(scientometrics, package = "bibliometrixData")
 #' WA <- cocMatrix(scientometrics, Field = "AU", type = "sparse", sep = ";")
 #'
-#' # EXAMPLE 2: Articles x Cited References co-occurrence matrix
+#' # EXAMPLE 2: Articles x Cited References occurrence matrix
 #'
 #' # data(scientometrics, package = "bibliometrixData")
 #'
 #' # WCR <- cocMatrix(scientometrics, Field = "CR", type = "sparse", sep = ";")
 #'
-#' # EXAMPLE 3: Articles x Cited First Authors co-occurrence matrix
+#' # EXAMPLE 3: Articles x Cited First Authors occurrence matrix
 #'
 #' # data(scientometrics, package = "bibliometrixData")
 #' # scientometrics <- metaTagExtraction(scientometrics, Field = "CR_AU", sep = ";")
@@ -68,10 +69,10 @@ cocMatrix<-function(M, Field = "AU", type = "sparse", n=NULL, sep = ";",binary=T
   #
   # The function creates occurrences data between Works and Field
   #
-  # type indicates the output format of co-occurrences:
+  # type indicates the output format of occurrences:
   #   "matrix" argument generates a W x Field sparse matrix
   #   "sparse" argument generates a compact representation of the matrix (using the package Matrix)
-  #    it represents a compact representation of a co-occurrences matrix.
+  #    it represents a compact representation of a occurrences matrix.
   # Field indicates the ISI Tag
   # if Field is AU -> WA (Works x Authors)
   # if Field is CR -> WR (Works x References)

R/collabByRegionPlot.R

@@ -1,5 +1,6 @@
-#' A function to plot country collaboration networks by Region
-#' The function \code{\link{networkPlot}} can plot a bibliographic network previously created by \code{\link{biblioNetwork}}.
+#' Country Collaboration Networks by Region
+#' 
+#' A function to create and plot country collaboration networks by Region
 #' 
 #' @param NetMatrix is a country collaboration matrix obtained by the function \code{\link{biblioNetwork}}. 
 #' @param normalize is a character. It can be "association", "jaccard", "inclusion","salton" or "equivalence" to obtain Association Strength, Jaccard, 
@@ -55,7 +56,9 @@
 #' 
 #' NetMatrix <- biblioNetwork(management, analysis = "collaboration", network = "countries") 
 #' 
-#' net <- collabByRegionPlot(NetMatrix, edgesize = 4, label.cex = TRUE, labelsize=2.5, weighted = TRUE, size=0.5, size.cex=TRUE, community.repulsion = 0, verbose=FALSE)
+#' net <- collabByRegionPlot(NetMatrix, edgesize = 4, label.cex = TRUE, labelsize=2.5, 
+#'                           weighted = TRUE, size=0.5, size.cex=TRUE, community.repulsion = 0, 
+#'                          verbose=FALSE)
 #' 
 #' cbind(names(net))
 #' 

R/histPlot.R

@@ -149,14 +149,53 @@ 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 <- dataFromIgraph(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$color <- "slategray1"
+  # df_net <- left_join(df_net,layout_m[c("name","color")], by = "name") %>% 
+  #   rename(
+  #     color = .data$color.x,
+  #     color_v =.data$color.y
+  #   )
+  # #names(df_net)[10:11] <- c("color", "color_v")
+
+  df_net <- igraph::as_long_data_frame(bsk.network) 
   df_net$color <- "slategray1"
-  df_net <- left_join(df_net,layout_m[c("name","color")], by = "name") %>% 
+
+  ID <- setdiff(df_net$to,df_net$from)
+
+  df_from <- df_net %>%   
+    select(.data$from,.data$to,.data$color,.data$from_name, .data$from_title, .data$from_keywords, .data$from_keywordsplus, .data$from_id, .data$from_size, .data$from_years)
+
+  df_to <- df_net %>% dplyr::filter(.data$to %in% ID) %>% 
+    mutate(from2=.data$to) %>% 
+    select(.data$from2,.data$to,.data$color,.data$to_name, .data$to_title, .data$to_keywords, .data$to_keywordsplus, .data$to_id, .data$to_size, .data$to_years)
+
+  df_to <- df_to[!duplicated(df_to$to),]
+
+  label <- c("from", "to", "color","name", "title", "keywords", "keywordsplus", "id", "size", "years")
+
+  names(df_from) <- label
+  names(df_to) <- label
+
+  df_net <- rbind(df_from,df_to)
+
+  layout_norm <- layout_m %>% 
+    mutate(x = (.data$x-min(.data$x))/(max(.data$x)-min(.data$x)),
+           y = (.data$y-min(.data$y))/(max(.data$y)-min(.data$y)))
+  df_net <- left_join(df_net,layout_norm[c("name","color","x","y")], by = c("name" ="name")) %>% 
     rename(
       color = .data$color.x,
       color_v =.data$color.y
-    )
-  #names(df_net)[10:11] <- c("color", "color_v")
+    ) 
+
+  df_coord <- layout_norm %>%
+    mutate(to=row_number()) %>% 
+    select(.data$to,.data$x,.data$y) %>% 
+    rename(xend=.data$x,
+           yend=.data$y)
+
+  df_net <- df_net %>% 
+    left_join(df_coord, by="to") 
 
   ylength <- diff(range(df_net$years))+1
   Ylabel <- (as.character(seq(min(df_net$years),max(df_net$years),length.out=ylength)))