R/R_absorb.R

In Saraghanadian/SNAinSCM: SNA metrics for supply chains

R_absorb <- function(frame){
  colnames(frame) <- c("From", "to", "value")
  colnames(frame) <- toupper(colnames(frame))
  my_data_subset <- subset(frame, frame[,3] != 0, select = c(1:3))
  my_data_subset<- my_data_subset[
    with(my_data_subset, order(my_data_subset$TO,my_data_subset$VALUE)),
    ]
  CD_in <- as.data.frame(table(my_data_subset[,2]))
  colnames(CD_in) <- c("Facility","degree_centrality")
  SD_in<-aggregate(x= my_data_subset$VALUE,by=list(my_data_subset$TO), FUN="sum")
  colnames(SD_in) <- c("Facility","strenght_centrality")
  R <- aggregate(x= my_data_subset$VALUE,by=list(my_data_subset$TO), FUN="cumsum")
  colnames(R) <- c("Facility","cumulativeweight")

  solution <- list()
  Rfac <-matrix()
  for (i in 1:length(R$Facility)){
    degreevec <- as.vector(CD_in$degree_centrality)
    stvect <- as.vector(SD_in$strenght_centrality)
    maxarea <- (degreevec *stvect)/2
    solution[[i]] <- as.numeric((sum(R$cumulativeweight[[i]])*2 -max(R$cumulativeweight[[i]]))/2)
    Rfac[i] <- solution[[i]]/maxarea[i]

  }
  Rfinal <-ifelse(degreevec != 1, Rfac, NA)
  Rfactor <- as.data.frame(cbind(R$Facility,Rfinal))
  colnames(Rfactor) <- c("Facility","R_absorb")
  return(Rfactor)

}