R/bradford.R

In bibliometrix: Comprehensive Science Mapping Analysis

utils::globalVariables(c("Rank", "SO", "Freq"))
#' Bradford's law
#'
#' It estimates and draws the Bradford's law source distribution.
#'
#' Bradford's law is a pattern first described by (\cite{Samuel C. Bradford, 1934}) that estimates the exponentially diminishing returns
#' of searching for references in science journals.
#'
#' One formulation is that if journals in a field are sorted by number of articles into three groups, each with about one-third of all articles,
#' then the number of journals in each group will be proportional to 1:n:n2.\cr\cr
#'
#' Reference:\cr
#' Bradford, S. C. (1934). Sources of information on specific subjects. Engineering, 137, 85-86.\cr
#'
#' @param M is a bibliographic dataframe.
#' @return The function \code{bradford} returns a list containing the following objects:
#' \tabular{lll}{
#' \code{table}  \tab   \tab a dataframe with the source distribution partitioned in the three zones\cr
#' \code{graph}   \tab   \tab the source distribution plot in ggplot2 format}
#'
#' @examples
#' \dontrun{
#' data(management, package = "bibliometrixData")
#'
#' BR <- bradford(management)
#' }
#'
#' @seealso \code{\link{biblioAnalysis}} function for bibliometric analysis
#' @seealso \code{\link{summary}} method for class '\code{bibliometrix}'
#'
#' @export

bradford <- function(M) {
  SO <- sort(table(M$SO), decreasing = TRUE)
  n <- sum(SO)
  cumSO <- cumsum(SO)
  cutpoints <- round(c(1, n * 0.33, n * 0.67, Inf))
  groups <- cut(cumSO, breaks = cutpoints, labels = c("Zone 1", "Zone 2", "Zone 3"))
  a <- length(which(cumSO < n * 0.33)) + 1
  b <- length(which(cumSO < n * 0.67)) + 1
  Z <- c(rep("Zone 1", a), rep("Zone 2", b - a), rep("Zone 3", length(cumSO) - b))
  df <- data.frame(SO = names(cumSO), Rank = 1:length(cumSO), Freq = as.numeric(SO), cumFreq = cumSO, Zone = Z)

  x <- c(max(log(df$Rank)) - 0.02 - diff(range(log(df$Rank))) * 0.125, max(log(df$Rank)) - 0.02)
  y <- c(min(df$Freq), min(df$Freq) + diff(range(df$Freq)) * 0.125) + 1
  data("logo", envir = environment())
  logo <- grid::rasterGrob(logo, interpolate = TRUE)

  g <- ggplot2::ggplot(df, aes(x = log(Rank), y = Freq, text = paste("Source: ", SO, "\nN. of Documents: ", Freq))) +
    geom_line(aes(group = "NA")) +
    # geom_area(aes(group="NA"),fill = "gray90", alpha = 0.5) +
    annotate("rect", xmin = 0, xmax = log(df$Rank[a]), ymin = 0, ymax = max(df$Freq), alpha = 0.2) +
    labs(x = "Source log(Rank)", y = "Articles", title = "Core Sources by Bradford's Law") +
    annotate("text", x = log(df$Rank[a]) / 2, y = max(df$Freq) / 2, label = "Core\nSources", fontface = 2, alpha = 0.5, size = 10) +
    scale_x_continuous(breaks = log(df$Rank)[1:a], labels = as.character(substr(df$SO, 1, 25))[1:a]) +
    theme(
      text = element_text(color = "#444444"),
      legend.position = "none",
      panel.background = element_rect(fill = "#FFFFFF"),
      panel.grid.minor = element_blank(),
      panel.grid.major = element_blank(),
      plot.title = element_text(size = 24),
      axis.title = element_text(size = 14, color = "#555555"),
      axis.line.x = element_line(color = "black", linewidth = 0.5),
      axis.line.y = element_line(color = "black", linewidth = 0.5),
      axis.title.y = element_text(vjust = 1, angle = 90),
      axis.title.x = element_text(hjust = 0),
      axis.text.x = element_text(angle = 90, hjust = 1, size = 8, face = "bold")
    ) +
    annotation_custom(logo, xmin = x[1], xmax = x[2], ymin = y[1], ymax = y[2])

  results <- list(table = df, graph = g)
  return(results)
}