#For Reading Raw Data from the created file
  
  #Required Libraries
  library(MASS)
  library(dplyr)
  library(tidyr)
  library(readr)
  library(stringr)
  
  
  #Necessary Functions
  
  #1# Function for discretizing the data 
  dndat <- function(NDATA){
  	rownd <- dim(NDATA)[1]
  	colnd <- dim(NDATA)[2]
  	DDATA <- matrix(0,nrow=rownd,ncol=colnd)
  	colnames(DDATA) <- colnames(NDATA)
  	i = 1
  	for(i in 1:rownd){

  	    j <- 1

  		for(j in 1:colnd){
  			if(is.na(NDATA[i,j])==FALSE){
  			
  				if(NDATA[i,j] < -1){
  					DDATA[i,j]=0L
  				}
  				if(NDATA[i,j] > 1){
  					DDATA[i,j]=2L
  				}
  				if(-1 <= NDATA[i,j] && NDATA[i,j] < 1){
  					DDATA[i,j]=1L
  				}
  			} else{
  				DDATA[i,j] = NDATA[i,j]
  			}
  			j = j + 1			
  		}
  		i = i + 1
  	}
  	DDATA
  }
  
  
  #Bringing in the file
  rawdat <- file.choose()
  RAWDAT <- rawdat %>% 
  	read_delim(delim ="\t",col_names = FALSE,skip=1) %>%
  	filter(.,!grepl("Group|Age|Region|PMI|Title|Sex|Braak",X1))
  attributes(RAWDAT)$names <- RAWDAT[1,]
  
  #Just the clinical data
  RAWWORD <- rawdat %>%
  	read_delim(delim ="\t",col_names = FALSE,skip=1) %>%
  	filter(.,grepl("Group|Age|Region|PMI|Title|Sex|Braak",X1))
  attributes(RAWWORD)$names <- RAWDAT[1,]
  #Add col of NAs to clinical data
  z <- 1
  naroww <- as.data.frame(rep(0,dim(RAWWORD)[1]),stringsAsFactors = FALSE)
  for(z in 1:dim(RAWWORD)[1]){
  	naroww[z,1] <- as.integer(sum(is.na(RAWWORD[z,])))
  	z <- z + 1
  }
  colnames(naroww) <- "ROW_NAs"
  RAWWORD <- bind_cols(RAWWORD,naroww)
  
  
  ##Getting back to the data
  RAWDAT2 <- RAWDAT[-1,] %>%
  	dplyr::arrange(.,ID_REF)
  
  ##Editing the file for R processing
  RAWDATID <- RAWDAT2[,1] %>%
  	as.matrix(.)
  RAWDATNUM <- RAWDAT2[,-1] %>%
  	mapply(.,FUN = as.numeric) %>%
  	t(.)
  
  ##Consolidating genes with the same name
  tabRDATID <- table(RAWDATID)
  NuRDATN <- matrix(0, nrow = dim(RAWDATNUM)[1], ncol = length(tabRDATID))
  j <- 1
  for(j in 1:length(tabRDATID)){
  	##Putting the ones without duplicates in their new homes
  	if(tabRDATID[j] == 1){
  		NuRDATN[,j] <- RAWDATNUM[,which(RAWDATID==rownames(tabRDATID)[j])]
  	}
  	##Averaging duplicates and putting them in their new homes
  	if(tabRDATID[j] > 1){
  		NuRDATN[,j] <- rowMeans(RAWDATNUM[,which(RAWDATID==rownames(tabRDATID)[j])],na.rm = TRUE)
  	}
  	j <- j + 1
  }
  
  
  #Scaling the Data
  scrawdat <- NuRDATN%>%
  	scale()
  attr(scrawdat,"scaled:center") <- NULL
  attr(scrawdat,"scaled:scale") <- NULL
  colnames(scrawdat) <- rownames(tabRDATID)
  
  
  #Discretized the Data
  dialzdat <- scrawdat %>%
  	dndat(.) %>%
  	t()%>%
  	as.data.frame(.)
  colnames(dialzdat) <- rownames(RAWDATNUM)
  
  #gene names
  genena <- as.data.frame(as.matrix(rownames(dialzdat),ncol=1))
  #setting "ID_REF" as a new variable
  colnames(genena) <- "ID_REF"
  rownames(dialzdat) <- NULL
  dialzdat <-bind_cols(genena,dialzdat)
  
  #NAs in a column
  x <- 2
  nacol <- as.data.frame(t(rep(0,dim(dialzdat)[2])),stringsAsFactors = FALSE)
  nacol[1,1] = "COL_NAs"
  for(x in 2:dim(dialzdat)[2]){
  	nacol[1,x] <- as.integer(sum(is.na(dialzdat[,x])))
  	x <- x + 1
  }
  colnames(nacol) <- colnames(dialzdat)
  dialzdat<-bind_rows(dialzdat,nacol)
  
  #NAs in a row
  y <- 1
  narowd <- as.data.frame(rep(0,dim(dialzdat)[1]),stringsAsFactors = FALSE)
  for(y in 1:dim(dialzdat)[1]){
  	narowd[y,1] <- as.integer(sum(is.na(dialzdat[y,])))
  	y <- y + 1
  }
  colnames(narowd) <- "ROW_NAs"
  dialzdat <- bind_cols(dialzdat,narowd)
  
  #converting to character so that the clinical can be brought together with discrete data
  k <- 2
  for(k in 2:dim(dialzdat)[2]-1){
  	dialzdat[,k] <- as.character(dialzdat[,k])
  	k <- k + 1
  }

  #The End the full data

  Fullalzdw <- bind_rows(RAWWORD,dialzdat)
  
  #Create the file
  nfnaex <- strsplit(rawdat,"[\\|/]") %>%
  	.[[1]] %>%
  	.[length(.)] %>%
  	gsub("\\D","",.) %>%
  	c("GSE",.,"dscrt.txt") %>%
  	paste(collapse = "")
  write.table(Fullalzdw, file = nfnaex, sep = "\t",col.names = TRUE,row.names = FALSE)