R/dominance.R

In massimoaria/bibliometrix: Comprehensive Science Mapping Analysis

#' Authors' dominance ranking
#'
#' It calculates the authors' dominance ranking from an object of the class '\code{bibliometrix}' as proposed by Kumar & Kumar, 2008.
#' @param results is an object of the class '\code{bibliometrix}' for which the analysis of the authors' dominance ranking is desired.
#' @param k is an integer, used for table formatting (number of authors). Default value is 10.
#' @return The function \code{dominance} returns a data frame with cases corresponding to the first \code{k} most productive authors and variables to typical field of a dominance analysis.
#'
#' the data frame variables are:
#' \tabular{lll}{
#' \code{Author} \tab   \tab Author's name\cr
#' \code{Dominance Factor}  \tab   \tab Dominance Factor (DF = FAA / MAA)\cr
#' \code{Tot Articles}   \tab   \tab N. of Authored Articles (TAA)\cr
#' \code{Single Authored}   \tab   \tab N. of Single-Authored Articles (SAA)\cr
#' \code{Multi Authored}   \tab   \tab N. of Multi-Authored Articles (MAA=TAA-SAA)\cr
#' \code{First Authored} \tab   \tab N. of First Authored Articles (FAA)\cr
#' \code{Rank by Articles}    \tab   \tab Author Ranking by N. of Articles\cr
#' \code{Rank by DF}    \tab   \tab Author Ranking by Dominance Factor}
#'
#'
#'
#' @examples
#' data(scientometrics, package = "bibliometrixData")
#' results <- biblioAnalysis(scientometrics)
#' DF <- dominance(results)
#' DF
#'
#' @seealso \code{\link{biblioAnalysis}} function for bibliometric analysis
#' @seealso \code{\link{summary}} method for class '\code{bibliometrix}'
#'
#' @export

dominance <- function(results, k = 10) {
  # Author Rank by Dominance Rank  (Kumar & Kumar, 2008)

  # options(warn=-1)

  if (!inherits(results, "bibliometrix")) {
    cat('\n argument "results" have to be an object of class "bibliometrix"\n')
    return(NA)
  }

  Nmf <- table(results$FirstAuthors[results$nAUperPaper > 1])
  FA <- names(Nmf)
  # FA=gsub(" ", "", FA, fixed = TRUE)  # delete spaces

  AU <- names(results$Authors)


  Tot <- Single <- rep(NA, length(FA))
  for (i in 1:length(FA)) {
    Single[i] <- sum(results$FirstAuthors[results$nAUperPaper == 1] == FA[i])
    Tot[i] <- results$Authors[FA[i] == AU]
  }
  Dominance <- Nmf / (Tot - Single)

  D <- data.frame("Author" = FA, "Dominance Factor" = as.numeric(Dominance), "Articles" = Tot, "Single-Authored" = Single, "Multi-Authored" = Tot - Single, "First-Author" = as.numeric(Nmf))
  D <- D[order(-D[, 3]), ]
  D <- D[1:k, ]
  D$RankbyArticles <- rank(-D$Articles, ties.method = "min")
  D <- D[order(-D$Dominance.Factor), ]
  D$RankDF <- rank(-D$Dominance.Factor, ties.method = "min")
  names(D) <- c("Author", "Dominance Factor", "Tot Articles", "Single-Authored", "Multi-Authored", "First-Authored", "Rank by Articles", "Rank by DF")
  row.names(D) <- 1:k
  return(D)
}