R/biblioAnalysis.R

In bibliometrix: Comprehensive Science Mapping Analysis

utils::globalVariables(c("PY", "TC"))

#' Bibliometric Analysis
#'
#' It performs a bibliometric analysis of a dataset imported from SCOPUS and Clarivate Analytics Web of Science databases.
#'
#' @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 Web of Science file.
#' @param sep is the field separator character. This character separates strings in each column of the data frame. The default is \code{sep = ";"}.
#' @return \code{biblioAnalysis} returns an object of \code{class} "bibliometrix".
#'
#' The functions \code{\link{summary}} and \code{\link{plot}} are used to obtain or print a summary and some useful plots of the results.
#'
#' An object of \code{class} "bibliometrix" is a list containing the following components:
#'
#' \tabular{lll}{
#' Articles \tab  \tab the total number of manuscripts\cr
#' Authors \tab       \tab the authors' frequency distribution\cr
#' AuthorsFrac \tab      \tab the authors' frequency distribution (fractionalized)\cr
#' FirstAuthors \tab      \tab corresponding author of each manuscript\cr
#' nAUperPaper \tab      \tab the number of authors per manuscript\cr
#' Appearances \tab      \tab the number of author appearances\cr
#' nAuthors \tab       \tab the number of authors\cr
#' AuMultiAuthoredArt \tab      \tab the number of authors of multi-authored articles\cr
#' MostCitedPapers \tab      \tab the list of manuscripts sorted by citations\cr
#' Years \tab      \tab publication year of each manuscript\cr
#' FirstAffiliation \tab      \tab the affiliation of the first author\cr
#' Affiliations \tab      \tab the frequency distribution of affiliations (of all co-authors for each paper)\cr
#' Aff_frac \tab      \tab the fractionalized frequency distribution of affiliations (of all co-authors for each paper)\cr
#' CO \tab      \tab the affiliation country of the first author\cr
#' Countries \tab      \tab the affiliation countries' frequency distribution\cr
#' CountryCollaboration \tab      \tab Intra-country (SCP) and intercountry (MCP) collaboration indices\cr
#' TotalCitation \tab      \tab the number of times each manuscript has been cited\cr
#' TCperYear \tab      \tab the yearly average number of times each manuscript has been cited\cr
#' Sources \tab      \tab the frequency distribution of sources (journals, books, etc.)\cr
#' DE \tab      \tab the frequency distribution of authors' keywords\cr
#' ID \tab      \tab the frequency distribution of keywords associated to the manuscript by SCOPUS and Clarivate Analytics Web of Science database}
#'
#'
#' @examples
#' \dontrun{
#' data(management, package = "bibliometrixData")
#'
#' results <- biblioAnalysis(management)
#'
#' summary(results, k = 10, pause = FALSE)
#' }
#'
#' @seealso \code{\link{convert2df}} to import and convert an WoS or SCOPUS 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.
#'
#' @export

biblioAnalysis <- function(M, sep = ";") {
  # initialize variables
  Authors <- NULL
  Authors_frac <- NULL
  FirstAuthors <- NULL
  PY <- NULL
  FAffiliation <- NULL
  Affiliation <- NULL
  Affiliation_frac <- NULL
  CO <- rep(NA, dim(M)[1])
  TC <- NULL
  TCperYear <- NULL
  SO <- NULL
  Country <- NULL
  DE <- NULL
  ID <- NULL
  MostCitedPapers <- NULL





  # M is the bibliographic dataframe
  Tags <- names(M)

  if (!("SR" %in% Tags)) {
    M <- metaTagExtraction(M, "SR")
  }

  # temporal analyis

  if ("PY" %in% Tags) {
    PY <- as.numeric(M$PY)
  }

  # Author's distribution

  if ("AU" %in% Tags) {
    listAU <- strsplit(as.character(M$AU), sep)
    listAU <- lapply(listAU, function(l) trim(l))
    # nAU=unlist(lapply(listAU,length))  # num. of authors per paper
    nAU <- lengths(listAU)
    # fracAU=unlist(lapply(nAU,function(x){rep(1/x,x)}))  # fractional frequencies
    fracAU <- rep(1 / nAU, nAU)
    AU <- unlist(listAU)

    Authors <- sort(table(AU), decreasing = TRUE)
    Authors_frac <- aggregate(fracAU, by = list(AU), "sum")
    names(Authors_frac) <- c("Author", "Frequency")
    Authors_frac <- Authors_frac[order(-Authors_frac$Frequency), ]
    FirstAuthors <- unlist(lapply(listAU, function(l) {
      if (length(l) > 0) {
        l <- l[[1]]
      } else {
        l <- NA
      }
      return(l)
    }))

    AuSingleAuthoredArt <- length(unique(FirstAuthors[nAU == 1]))
    AuMultiAuthoredArt <- length(Authors) - AuSingleAuthoredArt
  }
  # PY=as.numeric(M$PY)

  # Total Citation Distribution
  if ("TC" %in% Tags) {
    TC <- as.numeric(M$TC)
    CurrentYear <- as.numeric(format(Sys.Date(), "%Y"))
    TCperYear <- TC / (CurrentYear - PY + 1)
    if (!("DI" %in% names(M))) M$DI <- ""
    MostCitedPapers <- data.frame(M$SR, M$DI, TC, TCperYear, PY) %>%
      group_by(PY) %>%
      mutate(NTC = TC / mean(TC)) %>%
      ungroup() %>%
      select(-PY) %>%
      arrange(desc(TC)) %>%
      as.data.frame()

    names(MostCitedPapers) <- c("Paper         ", "DOI", "TC", "TCperYear", "NTC")
  }

  # References
  nReferences <- 0
  if ("CR" %in% Tags) {
    CR <- tableTag(M, "CR", sep)
    nReferences <- length(CR)
  }

  # ID Keywords
  if ("ID" %in% Tags) {
    ID <- tableTag(M, "ID", sep)
  }

  # DE Keywords
  if ("DE" %in% Tags) {
    DE <- tableTag(M, "DE", sep)
  }

  # Sources
  if ("SO" %in% Tags) {
    SO <- gsub(",", "", M$SO, fixed = TRUE)
    SO <- sort(table(SO), decreasing = TRUE)
  }

  # All Affiliations, First Affiliation and Countries
  if (("C1" %in% Tags) & (sum(!is.na(M$C1)) > 0)) {
    if (!("AU_UN" %in% Tags)) {
      M <- metaTagExtraction(M, Field = "AU_UN")
    }
    AFF <- M$AU_UN
    listAFF <- strsplit(AFF, sep, fixed = TRUE)
    nAFF <- unlist(lapply(listAFF, length))
    listAFF[nAFF == 0] <- "NA"
    fracAFF <- unlist(sapply(nAFF, function(x) {
      if (x > 0) {
        x <- rep(1 / x, x)
      } else {
        x <- 0
      }
    })) # fractional frequencies
    AFF <- trim.leading(unlist(listAFF)) # delete spaces
    Affiliation <- sort(table(AFF), decreasing = TRUE)
    Affiliation_frac <- aggregate(fracAFF, by = list(AFF), "sum")
    names(Affiliation_frac) <- c("Affiliation", "Frequency")
    Affiliation_frac <- Affiliation_frac[order(-Affiliation_frac$Frequency), ]

    # First Affiliation
    FAffiliation <- lapply(listAFF, function(l) l[1])

    # Countries
    data("countries", envir = environment())
    countries <- as.character(countries[[1]])

    ### new code{
    if (!("AU1_CO" %in% names(M))) {
      M <- metaTagExtraction(M, Field = "AU1_CO", sep)
    }
    CO <- M$AU1_CO

    Country <- tableTag(M, "AU1_CO")

    SCP_MCP <- countryCollaboration(M, Country, k = length(Country), sep)
  } else {
    M$AU1_CO <- NA
    SCP_MCP <- data.frame(Country = rep(NA, 1), SCP = rep(NA, 1))
  }
  if ("DT" %in% names(M)) {
    Documents <- table(M$DT)
    n <- max(nchar(names(Documents)))
    names(Documents) <- substr(paste(names(Documents), "                                              ", sep = ""), 1, n + 5)
  } else {
    Documents <- NA
  }

  # international collaboration
  if (!("AU_CO" %in% names(M))) {
    M <- metaTagExtraction(M, Field = "AU_CO", sep)
  }
  Coll <- unlist(lapply(strsplit(M$AU_CO, sep), function(l) {
    length(unique(l)) > 1
  }))

  results <- list(
    Articles = dim(M)[1], # Articles
    Authors = Authors, # Authors' frequency distribution
    AuthorsFrac = Authors_frac, # Authors' frequency distribution (fractionalized)
    FirstAuthors = FirstAuthors, # First Author's list
    nAUperPaper = nAU, # N. Authors per Paper
    Appearances = sum(nAU), # Author appearances
    nAuthors = dim(Authors), # N. of Authors
    AuMultiAuthoredArt = AuMultiAuthoredArt, # N. of Authors of multi-authored articles
    AuSingleAuthoredArt = AuSingleAuthoredArt, # N. of Authors of single-authored articles
    MostCitedPapers = MostCitedPapers, # Papers sorted by citations
    Years = PY, # Years
    FirstAffiliation = unlist(FAffiliation), # Affiliation of First Author
    Affiliations = Affiliation, # Affiliations of all authors
    Aff_frac = Affiliation_frac, # Affiliations of all authors (fractionalized)
    CO = CO, # Country of each paper
    Countries = Country, # Countries' frequency distribution
    CountryCollaboration = SCP_MCP, # Intracountry (SCP) and intercountry (MCP) collaboration
    TotalCitation = TC, # Total Citations
    TCperYear = TCperYear, # Total Citations per year
    Sources = SO, # Sources
    DE = DE, # Keywords
    ID = ID, # Authors' keywords
    Documents = Documents,
    IntColl = sum(Coll) / nrow(M) * 100,
    nReferences = nReferences, # N. of References
    DB = M$DB[1]
  )
  class(results) <- "bibliometrix"

  return(results)
}
countryCollaboration <- function(M, Country, k, sep) {
  if (!("AU_CO" %in% names(M))) {
    M <- metaTagExtraction(M, Field = "AU_CO", sep)
  }
  M$SCP <- 0
  M$SCP_CO <- NA
  for (i in 1:dim(M)[1]) {
    if (!is.na(M$AU_CO[i])) {
      co <- M$AU_CO[i]
      co <- table(unlist(strsplit(co, ";")))
      if (length(co) == 1) {
        M$SCP[i] <- 1
      }
      M$SCP_CO[i] <- M$AU1_CO[i]
    } else {
      M$SCP[i] <- NA
    }
  }

  CO <- names(Country)[1:k]

  df <- data.frame(Country = rep(NA, k), SCP = rep(0, k))
  for (i in 1:length(CO)) {
    co <- CO[i]
    df$Country[i] <- co
    df$SCP[i] <- sum(M$SCP[M$SCP_CO == co], na.rm = T)
  }
  df$MCP <- as.numeric(tableTag(M, "AU1_CO")[1:k]) - df$SCP
  return(df)
}