Merge pull request #407 from massimoaria/develop

CRAN Release 4.1.4

Author: massimoaria

.Rbuildignore

@@ -22,3 +22,5 @@ Rubbish
 ^inst/biblioshiny/rsconnect
 ^rsconnect
 
+^CRAN-SUBMISSION$
+^cran-comments\.md$

NAMESPACE

@@ -81,7 +81,6 @@ importFrom(Matrix,Matrix)
 importFrom(Matrix,MatrixClass)
 importFrom(Matrix,Ops)
 importFrom(Matrix,Summary)
-importFrom(Matrix,T2graph)
 importFrom(Matrix,abIseq)
 importFrom(Matrix,abIseq1)
 importFrom(Matrix,all.equal)
@@ -115,7 +114,6 @@ importFrom(Matrix,forceSymmetric)
 importFrom(Matrix,format)
 importFrom(Matrix,formatSpMatrix)
 importFrom(Matrix,formatSparseM)
-importFrom(Matrix,graph2T)
 importFrom(Matrix,head)
 importFrom(Matrix,image)
 importFrom(Matrix,invPerm)
@@ -186,15 +184,18 @@ importFrom(dplyr,bind_rows)
 importFrom(dplyr,count)
 importFrom(dplyr,cummean)
 importFrom(dplyr,desc)
+importFrom(dplyr,distinct)
 importFrom(dplyr,do)
 importFrom(dplyr,group_by)
 importFrom(dplyr,if_all)
 importFrom(dplyr,inner_join)
+importFrom(dplyr,join_by)
 importFrom(dplyr,left_join)
 importFrom(dplyr,mutate)
 importFrom(dplyr,mutate_at)
 importFrom(dplyr,mutate_if)
 importFrom(dplyr,n)
+importFrom(dplyr,reframe)
 importFrom(dplyr,relocate)
 importFrom(dplyr,rename)
 importFrom(dplyr,rename_with)

NEWS

@@ -1,13 +1,16 @@
-bibliometrix v4.1.4 (Release date: )
+bibliometrix v4.1.4 (Release date: 2023-11-29)
 
 Changes:
+* Biblioshiny: After the data quality check, the menus of the analyses that cannot be performed are hidden
 * Start to adding support to OpenAlex data
 * Improvements in historiograph: 
    - Improved Scopus algorithm to match local citations
    - Added OpenAlex algorithm to match local citations
    - Added Lens.org algorithm to match local citations
 * Improved pajek export in biblioshiny
 * Computational speed improvements
+* Solved issue with igraph 1.6.0 release canddate 
+* Solved issue with Matrix package
 
 bibliometrix v4.1.2 (Release date: 2023-03-06)
 

R/Hindex.R

@@ -85,7 +85,7 @@ Hindex <- function(M, field="author", elements=NULL, sep = ";",years=Inf){
 
   H <- df %>% 
     group_by(.data$AUs) %>% 
-    summarize(#Element = .data$AUs[1],
+    reframe(#Element = .data$AUs[1],
       h_index = h_calc(.data$TC),
       g_index = g_calc(.data$TC),
       PY_start = min(.data$PY),

R/couplingMap.R

@@ -22,7 +22,7 @@
 #' @param impact.measure is a character. It indicates the impact measure used to rank cluster elements (documents, authors or sources).
 #' It can be \code{impact.measure = c("local", "global")}.\\
 #' With \code{impact.measure = "local"}, \link{couplingMap} calculates elements impact using the Normalized Local Citation Score while 
-#' using code{impact.measure = "global"}, the function uses the Normalized Global Citation Score to measure elements impact. 
+#' using \code{impact.measure = "global"}, the function uses the Normalized Global Citation Score to measure elements impact. 
 #' @param minfreq is a integer. It indicates the minimum frequency (per thousand) of a cluster. It is a number in the range (0,1000).
 #' @param community.repulsion is a real. It indicates the repulsion force among network communities. It is a real number between 0 and 1. Default is \code{community.repulsion = 0.1}.
 #' @param stemming is logical. If it is TRUE the word (from titles or abstracts) will be stemmed (using the Porter's algorithm).

R/csvScopus2df.R

@@ -29,6 +29,10 @@ csvScopus2df <- function(file){
   DATA$AU=gsub("\\.", "", DATA$AU)
   DATA$AU=gsub(",", ";", DATA$AU)
 
+  ### store raw affiliation format to extract link among authors and affiliations
+  DATA$C1raw <- DATA$C1
+  ###
+  
   # Affiliation
   if (!("C1" %in% names(DATA))){DATA$C1 <- NA}else{
     DATA$C1 <- unlist(lapply(strsplit(DATA$C1,";"), function(l){

R/histNetwork.R

@@ -324,7 +324,7 @@ scopus <- function(M, min.citations, sep, network, verbose){
     dplyr::filter(.data$toRemove != TRUE)
 
   LCS <- CR %>% 
-    group_by(SR_cited) %>% 
+    group_by(.data$SR_cited) %>% 
     count(name="LCS")
 
 
@@ -353,7 +353,7 @@ scopus <- function(M, min.citations, sep, network, verbose){
     CRadd <- data.frame(SR_citing=unique(M$SR), SR_cited=unique(M$SR), value=1)
 
     WLCR <- CR %>%
-      select(SR_citing, SR_cited) %>% 
+      select(.data$SR_citing, .data$SR_cited) %>% 
       mutate(value = 1) %>% 
       bind_rows(CRadd) %>% 
       distinct() %>% 

R/isi2df.R

@@ -64,6 +64,10 @@ Please, take a look at the vignettes:
   df <- cbind(df1,df2)
   rm(df1,df2)
 
+  ### store raw affiliation format to extract link among authors and affiliations
+  df$C1raw <- df$C1
+  ###
+  
   df$DB <- "ISI"
 
   # Authors

R/normalizeCItationScore.R

@@ -18,7 +18,7 @@
 #' @param impact.measure is a character. It indicates the impact measure used to rank cluster elements (documents, authors or sources).
 #' It can be \code{impact.measure = c("local", "global")}.\\
 #' With \code{impact.measure = "local"}, \link{normalizeCitationScore} calculates elements impact using the Normalized Local Citation Score while 
-#' using code{impact.measure = "global"}, the function uses the Normalized Global Citation Score to measure elements impact. 
+#' using \code{impact.measure = "global"}, the function uses the Normalized Global Citation Score to measure elements impact. 
 #' @return a dataframe.
 #'
 #' 

R/zzz.R

@@ -25,6 +25,8 @@
 ## @importFrom dplyr filter
 #' @importFrom dplyr arrange
 #' @importFrom dplyr do
+#' @importFrom dplyr distinct
+#' @importFrom dplyr join_by
 #' @importFrom dplyr n
 #' @importFrom dplyr slice
 #' @importFrom dplyr slice_max
@@ -53,6 +55,7 @@
 #' @importFrom dplyr relocate
 #' @importFrom dplyr slice_head
 #' @importFrom dplyr slice_tail
+#' @importFrom dplyr reframe
 #' @importFrom plotly add_annotations
 #' @importFrom plotly add_lines
 #' @importFrom plotly config
@@ -196,7 +199,6 @@
 #' @importFrom Matrix format
 #' @importFrom Matrix formatSparseM
 #' @importFrom Matrix formatSpMatrix
-#' @importFrom Matrix graph2T
 #' @importFrom Matrix head
 #' @importFrom Matrix image
 #' @importFrom Matrix invPerm
@@ -250,7 +252,6 @@
 #' @importFrom Matrix Summary
 #' @importFrom Matrix symmpart
 #' @importFrom Matrix t
-#' @importFrom Matrix T2graph
 #' @importFrom Matrix tail
 #' @importFrom Matrix tcrossprod
 #' @importFrom Matrix tril