Nothing
utils::globalVariables(c("nLABEL", "LABEL", "GCS", "CITING", "CIT_PY", "paper", "TC",
"AU", "Page.start", "Page.end", "PP", "SR", "Included",
"PP.y", "PP.x", "toRemove", "SR_cited", "LCS", "SR_FULL",
"TI","DE","ID","DI", "Year", "SR_citing", "ref", "n",
"id_oa", "UT","PY"))
#' Historical co-citation network
#'
#' \code{histNetwork} creates a historical citation network from a bibliographic
#' data frame.
#'
#' @param M is a bibliographic data frame obtained by the converting function
#' \code{\link{convert2df}}. It is a data matrix with cases corresponding to
#' manuscripts and variables to Field Tag in the original SCOPUS, OpenAlex, Lens.org and Clarivate
#' Analytics Web of Science file.
#' @param min.citations DEPRECATED. New algorithm does not use this parameters. It will be remove in the next version of bibliometrix.
#' @param sep is the field separator character. This character separates strings
#' in CR column of the data frame. The default is \code{sep = ";"}.
#' @param network is logical. If TRUE, function calculates and returns also the direct citation network. If FALSE,
#' the function returns only the local citation table.
#' @param verbose is logical. If TRUE, results are printed on screen.
#' @return \code{histNetwork} returns an object of \code{class} "list"
#' containing the following components:
#'
#' \tabular{lll}{ NetMatrix \tab \tab the historical co-citation network
#' matrix\cr histData \tab \tab the set of n most cited references\cr M
#' \tab \tab the bibliographic data frame}
#'
#'
#' @examples
#'
#' \dontrun{
#' data(management, package = "bibliometrixData")
#'
#' histResults <- histNetwork(management, sep = ";")
#' }
#'
#' @seealso \code{\link{convert2df}} to import and convert a supported
#' export file in a bibliographic data frame.
#' @seealso \code{\link{summary}} to obtain a summary of the results.
#' @seealso \code{\link{plot}} to draw some useful plots of the results.
#' @seealso \code{\link{biblioNetwork}} to compute a bibliographic network.
#'
#' @export
histNetwork<-function(M, min.citations, sep = ";", network = TRUE, verbose = TRUE){
min.citations = 0
db <- M$DB[1]
if (!("DI" %in% names(M))){M$DI <- ""}else{M$DI[is.na(M$DI)] <- ""}
if (!("CR" %in% names(M))){
cat("\nYour collection does not contain Cited References metadata (Field CR is missing)\n")
return(NA)
}
M$TC[is.na(M$TC)] <- 0
if (db == "ISI") db <- "WOS"
switch(db,
WOS={
results <- wos(M=M, min.citations=min.citations, sep=sep, network=network, verbose=verbose)
},
SCOPUS={
results <- scopus(M=M, min.citations=min.citations, sep=sep, network=network, verbose=verbose)
},
OPENALEX={
results <- openalex(M=M, min.citations=min.citations, sep=sep, network=network, verbose=verbose)
},
LENS={
results <- lens(M=M, min.citations=min.citations, sep=sep, network=network, verbose=verbose)
},
{cat("\nDatabase not compatible with direct citation analysis\n")})
return(results)
}
wos <- function(M, min.citations, sep, network, verbose){
if (isTRUE(verbose)) {
cat("\nWOS DB:\nSearching local citations (LCS) by reference items (SR) and DOIs...\n")
}
if (!("SR_FULL" %in% names(M))) {
M = metaTagExtraction(M, Field = "SR")
}
M = M[order(M$PY), ]
M$Paper <- 1:nrow(M)
M_orig <- M
M$nLABEL <- 1:nrow(M)
#papers <- M$nLABEL[M$TC >= min.citations]
# Reference list and citing papers
CR <- strsplit(M$CR, sep)
CR <- lapply(seq_along(CR), function(i) {
l <- data.frame(ref = CR[[i]],
paper = i)
})
CR <- (do.call(rbind, CR))
CR$DI <-
trimws(unlist(lapply(
strsplit(CR$ref, 'DOI', fixed = TRUE), '[', 2
)))
CR$DI[is.na(CR$DI) | CR$DI=="NA"] <- ""
CR$AU <-
trimws(gsub("[ ]{2,}", "", (gsub(
"\\.", " ", unlist(lapply(strsplit(CR$ref, ',', fixed = TRUE), '[', 1))
))))
CR$PY <-
trimws(unlist(lapply(strsplit(CR$ref, ',', fixed = TRUE), '[', 2)))
CR$SO <-
trimws(unlist(lapply(strsplit(CR$ref, ',', fixed = TRUE), '[', 3)))
CR$SR <- paste(CR$AU, ", ", CR$PY, ", ", CR$SO, sep = "")
if (isTRUE(verbose)) {
cat("\nAnalyzing", nrow(CR),"reference items...\n")
}
# Local cited documents by DOI and reference item
#M=M[papers,]
M$LABEL <- paste(M$SR_FULL, "DOI", toupper(M$DI))
CR$LABEL <- paste(CR$SR, "DOI",CR$DI)
# By reference
L <- left_join(M,CR,by=c("LABEL"))
L <- L[!is.na(L$paper),]
L$CITING <- M$LABEL[L$paper]
L$nCITING <- M$nLABEL[L$paper]
L$CIT_PY <- M$PY[L$paper]
LCS <- L %>% group_by(nLABEL) %>%
summarize(LABEL = LABEL[1],
n = length(nLABEL)) %>%
as.data.frame()
M$LCS <- 0
M[LCS$nLABEL, "LCS"] <- LCS$n
M_orig$LCS <- M$LCS
histData <- M[c("LABEL","TI","DE","ID","DI","PY","LCS","TC")]
names(histData) <- c("Paper","Title","Author_Keywords","KeywordsPlus", "DOI","Year","LCS","GCS")
histData <- histData %>%
dplyr::filter(GCS>=min.citations)
if (isTRUE(network)){
# Citing data frame
CITING <- L %>%
group_by(CITING) %>%
summarize(
LCR = paste(LABEL, collapse = ";"),
PY = CIT_PY[1],
Paper = paper[1]
) %>%
ungroup() %>%
arrange(PY) %>%
as.data.frame()
M_orig$LCR <- NA
M_orig$LCR[CITING$Paper] <- CITING$LCR
M_orig$LABEL <- M$LABEL
M <- M_orig %>%
dplyr::filter(TC >= min.citations)
## assign an unique name to each document
st<-i<-0
while(st==0){
ind <- which(duplicated(M$LABEL))
if (length(ind)>0){
i <- i+1
M$LABEL[ind]=paste0(M$LABEL[ind],"-",letters[i],sep="")}else{st <- 1}}
row.names(M) <- M$LABEL
# NetMatrix
WLCR <- cocMatrix(M, "LCR", sep = ";")
missingLABEL <- setdiff((M$LABEL), colnames(WLCR))
colLab <- c(colnames(WLCR), missingLABEL)
WLCR <- cbind(WLCR, matrix(0, nrow(WLCR), length(missingLABEL)))
WLCR <- as.data.frame(as.matrix(WLCR))
colnames(WLCR) <- colLab
LABEL <- (row.names(WLCR))
WLCR <- as.matrix(WLCR[LABEL])
#row.names(WLCR) <- LABEL
} else{
WLCR = NULL
}
if (isTRUE(verbose)) {
cat("\nFound",
length(M$LCS[M$LCS > 0]),
"documents with no empty Local Citations (LCS)\n")
}
results <-
list(
NetMatrix = WLCR,
histData = histData,
M = M_orig,
LCS = M$LCS
)
return(results)
}
# New algorithm for Scopus
# Local citation matching is based on First Author, Year and PP
scopus <- function(M, min.citations, sep, network, verbose){
if (!("SR_FULL" %in% names(M))) {
M = metaTagExtraction(M, Field = "SR")
}
CR <- strsplit(M$CR,";")
CR <- data.frame(SR_citing=rep(M$SR,lengths(CR)), ref=trimws(unlist(CR)))
CR$PY <- as.numeric(gsub(".*\\((\\d{4})\\).*", "\\1", CR$ref))
CR$AU <- trimws(gsub("\\.", "", gsub("\\. ", "", gsub("^(.*?),.*$", "\\1", CR$ref))))
CR$PP <- gsub(".*PP\\. ([0-9-]+).*", "\\1", CR$ref)
CR <- CR %>%
dplyr::filter(!is.na(PY), (substr(CR$PP,1,1) %in% 0:9))
M_merge <- M %>%
select(AU,PY,Page.start, Page.end, PP, SR) %>%
mutate(AU = trimws(gsub("\\.", "", gsub("\\. ", "", gsub("^(.*?),.*$", "\\1", SR)))),
Page.start = as.numeric(Page.start),
Page.end = as.numeric(Page.end),
PP = ifelse(!is.na(Page.start), paste0(Page.start,"-",Page.end), NA),
Included = TRUE
) %>%
rename(SR_cited = SR)
CR <- CR %>%
left_join(M_merge, join_by("PY", "AU"), relationship = "many-to-many") %>%
dplyr::filter(!is.na(Included)) %>%
group_by(PY,AU) %>%
mutate(toRemove = ifelse(!is.na(PP.y) & PP.x!=PP.y, TRUE,FALSE)) %>% # to remove FALSE POSITIVE
ungroup() %>%
dplyr::filter(toRemove != TRUE) %>%
mutate(toRemove = ifelse(!is.na(PP.x) & is.na(PP.y),TRUE,FALSE)) %>%
dplyr::filter(toRemove != TRUE)
LCS <- CR %>%
group_by(SR_cited) %>%
count(name="LCS")
M <- M %>%
left_join(LCS, by=c("SR" = "SR_cited")) %>%
mutate(LCS = ifelse(is.na(LCS),0,LCS))
histData <- M %>%
select(SR_FULL, TI,DE,ID,DI, PY, LCS, TC) %>%
rename(
Paper = SR_FULL,
Title = TI,
Author_Keywords = DE,
KeywordsPlus = ID,
DOI = DI,
Year = PY,
GCS = TC
) %>%
arrange(Year) %>%
as.data.frame()
names(histData) <- c("Paper","Title","Author_Keywords","KeywordsPlus", "DOI","Year","LCS","GCS")
if (isTRUE(network)){
CRadd <- data.frame(SR_citing=unique(M$SR), SR_cited=unique(M$SR), value=1)
WLCR <- CR %>%
select(SR_citing, SR_cited) %>%
mutate(value = 1) %>%
bind_rows(CRadd) %>%
distinct() %>%
pivot_wider(names_from = "SR_cited", values_from = "value", values_fill = 0) %>%
dplyr::filter(SR_citing %in% CRadd$SR_cited)
SRrow <- WLCR$SR_citing
SRcol <- colnames(WLCR)[-1]
WLCR <- as.matrix(WLCR %>% select(-1))
row.names(WLCR) <- SRrow
colnames(WLCR) <- SRcol
} else {
WLCR = NULL
}
if (isTRUE(verbose)) {
cat("\nFound",
length(M$LCS[M$LCS > 0]),
"documents with no empty Local Citations (LCS)\n")
}
results <-
list(
NetMatrix = WLCR,
histData = histData,
M = M,
LCS = M$LCS
)
}
openalex <- function(M, min.citations=min.citations, sep=sep, network=network, verbose=verbose){
M$CR[is.na(M$CR) | M$CR==""] <- "none"
ids <- M$id_oa
CR <- strsplit(M$CR, ";")
CR <- data.frame(id_oa = rep(M$id_oa,lengths(CR)), ref = unlist(CR)) %>%
dplyr::filter(ref %in% ids)
LCS <- CR %>%
count(id_oa = ref) %>%
rename(LCS = n)
histData <- M %>%
left_join(LCS, by = c("id_oa")) %>%
mutate(LCS = ifelse(is.na(LCS),0,LCS),
DE = ID) %>%
rename(LABEL = SR,
GCS = TC) %>%
select(c("LABEL","TI","DE","ID","DI","PY","LCS","GCS")) %>%
dplyr::filter(GCS>=min.citations)
names(histData) <- c("Paper","Title","Author_Keywords","KeywordsPlus", "DOI","Year","LCS","GCS")
if (isTRUE(network)){
CRadd <- data.frame(id_oa=unique(M$id_oa), ref=unique(M$id_oa), value=1)
WLCR <- CR %>%
mutate(value = 1) %>%
bind_rows(CRadd) %>%
distinct() %>%
pivot_wider(names_from = "ref", values_from = "value", values_fill = 0) %>%
dplyr::filter(id_oa %in% CRadd$ref)
SRrow <- WLCR %>% select(id_oa) %>%
left_join(M %>%
select(id_oa, SR),
by="id_oa")
SR_col <- data.frame(id_oa = colnames(WLCR)[-1]) %>%
left_join(M %>%
select(id_oa, SR),
by="id_oa")
WLCR <- as.matrix(WLCR %>% select(-1))
row.names(WLCR) <- SRrow$SR
colnames(WLCR) <- SR_col$SR
WLCR <- WLCR[colnames(WLCR),colnames(WLCR)]
} else {
WLCR = NULL
}
results <-
list(
NetMatrix = WLCR,
histData = histData,
M = M %>%
left_join(LCS, by = "id_oa") %>%
replace_na(list(LCS = 0)),
LCS = M$LCS
)
}
lens <- function(M, min.citations=min.citations, sep=sep, network=network, verbose=verbose){
M$CR[is.na(M$CR)] <- "none"
ids <- M$UT
CR <- lapply(strsplit(M$CR, ";"), trimws)
CR <- data.frame(UT = rep(M$UT,lengths(CR)), ref = unlist(CR)) %>%
dplyr::filter(ref %in% ids)
LCS <- CR %>%
count(UT = ref) %>%
rename(LCS = n)
histData <- M %>%
left_join(LCS, by = c("UT")) %>%
mutate(LCS = ifelse(is.na(LCS),0,LCS),
DE = ID) %>%
rename(LABEL = SR,
GCS = TC) %>%
select(c("LABEL","TI","DE","ID","DI","PY","LCS","GCS")) %>%
dplyr::filter(GCS>=min.citations)
names(histData) <- c("Paper","Title","Author_Keywords","KeywordsPlus", "DOI","Year","LCS","GCS")
if (isTRUE(network)){
CRadd <- data.frame(UT=unique(M$UT), ref=unique(M$UT), value=1)
WLCR <- CR %>%
mutate(value = 1) %>%
bind_rows(CRadd) %>%
distinct() %>%
pivot_wider(names_from = "ref", values_from = "value", values_fill = 0) %>%
dplyr::filter(UT %in% CRadd$ref)
SRrow <- WLCR %>% select(UT) %>%
left_join(M %>%
select(UT, SR),
by="UT")
SR_col <- data.frame(UT = colnames(WLCR)[-1]) %>%
left_join(M %>%
select(UT, SR),
by="UT")
WLCR <- as.matrix(WLCR %>% select(-1))
row.names(WLCR) <- SRrow$SR
colnames(WLCR) <- SR_col$SR
WLCR <- WLCR[colnames(WLCR),colnames(WLCR)]
} else {
WLCR = NULL
}
results <-
list(
NetMatrix = WLCR,
histData = histData,
M = M %>%
left_join(LCS, by = "UT") %>%
replace_na(list(LCS = 0)),
LCS = M$LCS
)
}
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