iqr.R

#returning vector without extreme/outlier values
rm.extreme <- function(v) {
  q3<-quantile(v, .75)
  q1<-quantile(v, .25)
  iqr <- 1.5 * ( q3- q1)
  sapply(v, function(x) {
    x[x > (q1 - iqr) & x < (q3 + iqr)]
  })
}

#returning vector containing lower and upper limit of normal values
extreme.limit <- function(v) {
  q3<-quantile(v, .75)[[1]]
  q1<-quantile(v, .25)[[1]]
  iqr <- 1.5 * ( q3- q1)
  return(c((q1 - iqr),(q3 + iqr)))
}

#returning boolean if x is an extreme value of vector v
is_extreme <- function(v, x){
  lim <- extreme.limit(v)
  if(x<lim[1]){return('extreme_bottom')}
  else if(x>lim[2]){return('extreme_top')}
  else{return('normal')}
}

#testing
tes.data <- rnorm(100)
extreme.limit(tes.data)
is_extreme(tes.data, 3)

rm.extreme <- function(v) {
  q3<-quantile(v, .75)[[1]]
  q1<-quantile(v, .25)[[1]]
  iqr <- IQR(v)
  v[v > (q1 - iqr) & v < (q3 + iqr)]
  }