R/keywordAssoc.R

In bibliometrix: Comprehensive Science Mapping Analysis

#' ID and DE keyword associations
#'
#' It associates authors' keywords to keywords plus.
#'
#' @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 and Clarivate Analytics WoS file.
#' @param sep is the field separator character. This character separates keywords in each string of ID and DE columns of the bibliographic data frame. The default is \code{sep = ";"}.
#' @param excludeKW is character vector. It contains authors' keywords to exclude from the analysis.
#' @param n is a integer. It indicates the number of authors' keywords to associate to each keyword plus. The default is \code{n = 10}.
#' @return an object of \code{class} "list".
#'
#'
#'
#' @examples
#'
#' data(scientometrics, package = "bibliometrixData")
#'
#' KWlist <- keywordAssoc(scientometrics, sep = ";", n = 10, excludeKW = NA)
#'
#' # list of first 10 Keywords plus
#' names(KWlist)
#'
#' # list of first 10 authors' keywords associated to the first Keyword plus
#' KWlist[[1]][1:10]
#'
#' @seealso \code{\link{convert2df}} to import and convert a WoS or SCOPUS Export file in a bibliographic data frame.
#' @seealso \code{\link{biblioAnalysis}} function for bibliometric analysis.
#' @seealso \code{\link{summary}} to obtain a summary of the results.
#' @seealso \code{\link{plot}} to draw some useful plots of the results.
#'
#' @export
keywordAssoc <- function(M, sep = ";", n = 10, excludeKW = NA) {
  excludeKW <- toupper(excludeKW)

  WDE <- cocMatrix(M, Field = "DE", type = "sparse", sep = sep)
  WID <- cocMatrix(M, Field = "ID", type = "sparse", sep = sep)

  NetMatrix <- Matrix::crossprod(WID, WDE)
  if (!is.na(excludeKW)) {
    NetMatrix <- NetMatrix[!(row.names(NetMatrix) %in% excludeKW), !(colnames(NetMatrix) %in% excludeKW)]
  }
  NetMatrix <- NetMatrix[!is.na(row.names(NetMatrix)), !is.na(colnames(NetMatrix))]
  NetMatrix <- NetMatrix[nchar(row.names(NetMatrix)) > 0, nchar(colnames(NetMatrix)) > 0]
  rS <- Matrix::rowSums(NetMatrix)

  NetDegree <- sort(rS, decreasing = TRUE)[n]
  NET <- NetMatrix[rS >= NetDegree, Matrix::colSums(NetMatrix) > 1]
  KW <- apply(NET, 1, function(x) {
    i <- sort(x, decreasing = TRUE)[n]
    x <- sort(x[x >= i], decreasing = TRUE)
    return(x)
  })

  return(KW)
}