R/ECforshiny.R

In Lauren-Eyler/ECforshiny: Population-Specific Economic Groups Defined by Few Asset Variables

ECforshiny<-function(X, Y=rep(NA, nrow(X)), nvars, kmin, kmax, ncores, threshold=NA){
  if (class(X[,1])=="numeric"){
    wt<-X[,1]
    X<-X[,-1]
  } else {
    wt<-rep(1, nrow(X))
  }
  col_cat<-seq(1,ncol(X),1)
  combn<-utils::combn(c(1:ncol(X)),nvars)
  col_indx=matrix(c("NULL"), ncol(combn), ncol(X), byrow=FALSE)
  for (i in 1:ncol(combn)){
    col_indx[i,]<-t(is.element(col_cat, combn[,i]))
    col_indx[i,][col_indx[i,]==FALSE]<-NA
    colnames(col_indx)=colnames(X)
  }
  doParallel::registerDoParallel(cores=ncores)
  ASW<-matrix(nrow=nrow(col_indx), ncol=(kmax-kmin+1))
  #' @import foreach
  #' @import doParallel
  if (!is.na(threshold)) {
    for(n in 1:ncol(ASW)){
      ASW[,n]<-foreach::foreach (i=1:nrow(col_indx), .combine='rbind')  %dopar% {
        combi<-X[,!is.na(col_indx[i,])]
        if(any(!is.na(Y))){
          combi<-cbind(Y, combi)
        }
        dizzy<-cluster::daisy(combi, metric="gower")
        wcKMR<-WeightedCluster::wcKMedRange(dizzy, kvals=(kmin+n-1), weights=wt)
        ASW[i,n]<-wcKMR$stats[,5]
        rm(combi)
        rm(dizzy)
        rm(wcKMR)
        return(ASW[i,n])
      }
      if (any(ASW>threshold, na.rm=TRUE)){
        break}
    }
  } else {
    ASW<-foreach::foreach (i=1:nrow(col_indx), .combine='rbind') %dopar% {
      combi<-X[,!is.na(col_indx[i,])]
      if(any(!is.na(Y))){
        combi<-cbind(Y, combi)
      }
      dizzy<-cluster::daisy(combi, metric="gower")
      wcKMR<-WeightedCluster::wcKMedRange(dizzy, kvals=seq(kmin, kmax, 1), weights=wt)
      ASW[i,]<-wcKMR$stats[,5]
      rm(combi)
      rm(dizzy)
      rm(wcKMR)
      return(ASW[i,])
    }
  }

  ASW_max<-max(ASW, na.rm=TRUE)
  ASW_max_model<-which(ASW == ASW_max, arr.ind=TRUE)
  model<-list()
  Assets<-list()
  clust<-list()
  K<-list()
  for (a in 1:nrow(ASW_max_model)){
    model[[a]]<-ASW_max_model[a,1]
    Assets[[a]]<-which( !is.na(col_indx[model[[a]],]), arr.ind=TRUE)
    Assets[[a]]<-names(Assets[[a]])
    if (any(!is.na(Y))){
      Assets[[a]]<-c(deparse(substitute(Y)), Assets[[a]])
    }
    clust[[a]]<-ASW_max_model[a,2]
    K[[a]]<-2-1+clust[[a]]
  }
  results<-list()
  results[[1]]<-ASW_max
  results[[2]]<-Assets
  results[[3]]<-K
  win_ds<-list()
  win_daisy<-list()
  win_wcKMedRange<-list()
  Group<-list()
  Medoids<-list()
  Medoid_df<-list()
  for (a in 1:length(model)){
    win_ds[[a]]<-X[,!is.na(col_indx[model[[a]],])]
    if(any(!is.na(Y))){
      win_ds[[a]]<-cbind(Y, win_ds[[a]])
    }
    win_daisy[[a]]<-cluster::daisy(win_ds[[a]], metric="gower")
    win_wcKMedRange[[a]]<-WeightedCluster::wcKMedRange(win_daisy[[a]], kvals=K[[a]], weights=wt)
    Group[[a]]<-win_wcKMedRange[[a]]$clustering
    Medoids[[a]]<-c(unique(win_wcKMedRange[[a]]$clustering))
    Medoid_df[[a]]<-X[as.numeric(rownames(X)) %in% unlist(Medoids[[a]],) ==TRUE, !is.na(col_indx[model[[a]],])]
    if (any(!is.na(Y))){
      Medoid_df[[a]]<-cbind(Y[as.numeric(rownames(X)) %in% unlist(Medoids[[a]],) ==TRUE], Medoid_df[[a]])
      colnames(Medoid_df[[a]])<-c(deparse(substitute(Y)), colnames(Medoid_df[[a]][,-1]))
    }
  }
  results[[4]]<-Medoids
  results[[5]]<-Medoid_df
  if (length(results[[5]])==1){
    results[[5]]<-as.data.frame(results[[5]])
  }
  results[[6]]<-Group
  if (length(results[[6]])==1){
    results[[6]]<-unlist(results[[6]])
    results[[6]]<-as.factor(results[[6]])
  }
  class(results)<- "ECshiny"
  return(results)
  #' @export
}