R/missing_elimination.R

In ayhandis/creditR: A Credit Risk Scoring and Validation Package

#' @title Missing Elimination
#'
#' @description
#' This function allows you to eliminate variables which have missing ratios greater than a given threshold for a given data set.
#'
#' @param data A data set needs to be defined.
#' @param missing_ratio_threshold The missing ratio threshold must be specified in order to make an elimination.
#' @keywords creditR
#' @export
#' @examples
#' name <- c('John Doe','Peter Gynn','Jolie Hope')
#' birth_year <- c(1980, 1985, 1971)
#' salary <- c(20000,NA,10000)
#' example_data <- data.frame(name,birth_year,salary)
#' missing_elimination(example_data, 0.10)

missing_elimination <- function(data, missing_ratio_threshold){
  missing_ratio <- function(data){
    column_names <- matrix(data=NA,nrow=1,ncol=length(data))
    missing_ratio <- matrix(data=NA,nrow=1,ncol=length(data))
    for(i in 1:length(data)){
      column_names[,i]<-colnames(data[i])
      missing_ratio[,i]<-mean(!complete.cases(data[,i]))
    }
    missing_ratio<-as.data.frame(cbind(t(column_names), t(missing_ratio)))
    colnames(missing_ratio) <- c("Variable","Missing_Ratio")
    missing_ratio$Missing_Ratio <- as.numeric(as.character(missing_ratio$Missing_Ratio))
    missing_ratio$Completeness <- 1-missing_ratio$Missing_Ratio
    return(missing_ratio)
  }
  missing_table <- missing_ratio(data)
  elimination_list <- as.vector(subset(missing_table, Missing_Ratio > missing_ratio_threshold)$Variable)
  eliminated_data <- data[,!names(data) %in% elimination_list]

  return(eliminated_data)
}