R/mar.R

In JerryTucay/mfdata: Creates missing and faulty data.

# we used p=.5 and q=.2
# column = works with an input of c() and of course just a single value

mar <- function(data, p, q, column = NULL){
  MDS <- data
  U  <- runif(NROW(data))
  
  if (is.null(column)) {
    for (i in 1:ncol(MDS)) {
      m.idx <- (rbinom(n=NROW(data), size=1, prob=q*(U > p))==1)
      MDS[m.idx , i] <- NA
    }
      df_wide <- rbind( data, MDS) %>% mutate( ds = c(rep("df", NROW(data)), rep("df_miss", NROW(MDS))  ))
      
      long<- melt(df_wide, id.vars = "ds")
      
      densplot <-ggplot(long, aes(x=value, fill = ds)) + 
              geom_density(alpha=.4) + 
              facet_wrap(~variable, ncol=2, scales = "free")
      
      misspercplot <- gg_miss_var(MDS, show_pct = T)
      
  } else {
    for(i in 1:length(column)){
      m.idx <- (rbinom(n=NROW(data), size=1, prob=q*(U > p))==1)
      MDS[m.idx ,column[i]] <- NA}
      data_par<-data[, column]
      MDS_par<-MDS[, column]
      
      df_wide <- rbind( data_par, MDS_par) %>% mutate( ds = c(rep("df", NROW(data)), rep("df_miss", NROW(MDS))  ))
      
      long<- melt(df_wide, id.vars = "ds")
      
      densplot <-ggplot(long, aes(x=value, fill = ds)) + 
        geom_density(alpha=.4) + 
        facet_wrap(~variable, ncol=2, scales = "free")
      
      misspercplot <- gg_miss_var(MDS, show_pct = T)
        
    }
  
      output <- list(MDS, densplot, misspercplot)
  return(output)
}