Two R functions to detect and remove outliers using standard-score or MAD method

Detect Outliers

#for tutorial, see https://rpubs.com/hauselin/outliersDetect
#clean vector using Z-score cut-off method; return a vector with outliers = replace (default is NA)
outliersZ <- function(data, zCutOff = 1.96, replace = NA, values = FALSE, digits = 2) {
    #compute standard deviation (sample version n = n [not n-1])
    stdev <- sqrt(sum((data - mean(data, na.rm = T))^2, na.rm = T) / sum(!is.na(data)))
    #compute absolute Z values for each value
    absZ <- abs(data - mean(data, na.rm = T)) / stdev
    #subset data that has absZ greater than the zCutOff and replace them with replace
    #can also replace with other values (such as max/mean of data)
    data[absZ > zCutOff] <- replace 
    
    if (values == TRUE) {
        return(round(absZ, digits)) #to return absZ values, provide 'absZ' to argument output   
    } else {
        return(round(data, digits)) #otherwise, return values with outliers = replace
    }
}

#clean vector using MAD cut-off method; return a vector with outliers = replace
outliersMAD <- function(data, MADCutOff = 2.5, replace = NA, values = FALSE, bConstant = 1.4826, digits = 2) {
    #compute number of absolute MADs away for each value
    #formula: abs( ( x - median(x) ) )/ mad(x)
    absMADAway <- abs((data - median(data, na.rm = T))/mad(data, constant = bConstant, na.rm = T))
    #subset data that has absMADAway greater than the MADCutOff and replace them with replace
    #can also replace values other than replace
    data[absMADAway > MADCutOff] <- replace
    
    if (values == TRUE) { 
        return(round(absMADAway, digits)) #to return absMADAway, provide 'MADAway' to argument output   
    } else {
        return(round(data, digits)) #otherwise, return values with outliers = replace
    }
}