Nothing
utils::globalVariables(c("PY", "LCS", "TC", "SR", "NGCS", "NLCS", "Author",
"year", "SO"))
#' Calculate the normalized citation score metric
#'
#' It calculates the normalized citation score for documents, authors and sources using both global and local citations.
#'
#' The document Normalized Citation Score (NCS) of a document is calculated by dividing the actual count of citing items by the expected
#' citation rate for documents with the same year of publication.
#'
#' The MNCS of a set of documents, for example the collected works of an individual, or published on a journal, is the average of the NCS values for all the documents in the set.
#'
#' The NGCS is the NCS calculated using the global citations (total citations that a document received considering the whole bibliographic database).
#'
#' The NLCS is the NCS calculated using the local citations (total citations that a document received from a set of documents included in the same collection).
#'
#'
#'
#' @param M is a bibliographic data frame obtained by \code{\link{convert2df}} function.
#' @param field is a character. It indicates the unit of analysis on which calculate the NCS. It can be equal to \code{field = c("documents", "authors", "sources")}. Default is \code{field = "documents"}.
#' @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.
#' @return a dataframe.
#'
#'
#'
#' @examples
#'
#' \dontrun{
#' data(management, package = "bibliometrixData")
#' NCS <- normalizeCitationScore(management, field = "authors", impact.measure = "local")
#' }
#'
#' @export
normalizeCitationScore <- function(M,field="documents", impact.measure = "local"){
if (!(field %in% c("documents", "authors", "sources"))) {
cat('\nfield argument is incorrect.\n\nPlease select one of the following choices: "documents", "authors", "sources"\n\n')
return(NA)
}
M$TC=as.numeric(M$TC)
M$PY=as.numeric(M$PY)
if (impact.measure=="local"){
log <- capture.output(M <- localCitations(M, fast.search = FALSE, sep = ";")$M)
} else {
M$LCS <- 0
}
M <- M %>%
group_by(PY) %>%
mutate(LCS = replace(LCS, LCS==0, 1),
NGCS = TC/mean(TC,na.rm=TRUE),
NLCS = LCS/mean(LCS,na.rm=TRUE)) %>%
ungroup() %>% as.data.frame()
switch(field,
documents={
#### Documents Impact by Normalized Local Citations ####
NCS <- M %>%
select(SR, PY, NGCS, NLCS, TC, LCS) %>%
rename(MNGCS = NGCS,
MNLCS = NLCS,
LC = LCS
) %>%
rename(documents=SR) %>% as.data.frame()
},
authors={
#### Authors Impact by Normalized Local Citations ####
AU=names(tableTag(M,"AU"))
df=data.frame("Author"="NA","SR"=NA,"year"=NA, "TC"=NA,"LCS"=NA,"NGCS"=NA,"NLCS"=NA)
if (!("DI" %in% names(M))){M$DI="NA"}
for (i in 1:length(AU)){
ind=which(regexpr(AU[i],M$AU)>-1)
dfAU=data.frame("Author"=rep(AU[i],length(ind)), "SR"=M$PY[ind], "year"=M$PY[ind],
"TC"=M$TC[ind], "LCS"=M$LCS[ind],"NGCS"=M$NGCS[ind],
"NLCS"=M$NLCS[ind])
df=rbind(df,dfAU)
}
df=df[-1,]
NCS <- df %>%
group_by(Author) %>%
summarize(NP = length(year),
MNGCS = mean(NGCS,na.rm=TRUE),
MNLCS = mean(NLCS,na.rm=TRUE),
TC = mean(TC,na.rm=TRUE),
LC = mean(LCS,na.rm=TRUE)
) %>%
rename(authors=Author) %>% as.data.frame()
},
sources={
#### Source Impact by Normalized Local Citations ####
NCS <- M %>%
group_by(SO) %>%
summarize(NP = length(PY),
MNGCS = mean(NGCS,na.rm=TRUE),
MNLCS = mean(NLCS,na.rm=TRUE),
TC = mean(TC,na.rm=TRUE),
LC = mean(LCS,na.rm=TRUE)
) %>%
rename(sources=SO) %>% as.data.frame()
})
if (impact.measure == "global"){
NCS <- NCS[,-c(4,6)]
}else{
NCS$MNLCS[is.na(NCS$MNLCS)] <- 0
}
return(NCS)
}
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