查看我最复杂的功能。它具有三种方法（z mad，iqr），以及对异常值的不同处理（删除，或替换）。可以使用绘图并且可以进行黑客攻击（尝试不同的方法或阈值）
看例子：
set.seed(1234)
x = rnorm(10)
ez.outlier(iris,'Sepal.Length',fill='null',hack=T,cutoff=c(1,2,3),plot=T)
#' univariate outlier cleanup
#' @description univariate outlier cleanup
#' @param x a data frame or a vector
#' @param col colwise processing
#' \cr col name
#' \cr if x is not a data frame, col is ignored
#' \cr could be multiple cols
#' @param method z score, mad, or IQR (John Tukey)
#' @param cutoff abs() > cutoff will be treated as outliers. Default/auto values (i.e. if NA):
#' \cr z 95% of values fall within 1.96, qnorm(0.025,lower.tail=F), or 3
#' \cr mad 2.5, which is the standard recommendation, or 5.2
#' \cr iqr 1.5
#' \cr if multiple values specified, use the first one (an exception is hack=T, during which method and cutoff same length or scalar)
#' @param hack call mapply to try all method and cutoff (same length or scalar, ie, different methods with
#' corresponding cutoff, or same method with different cutoff).
#' @param plot boxplot and hist before and after outlier processing.
#' @param fillout how to process outlier, fill with na, mean, median (columnwise for data frame), or
#' null --> remove outlier (only for vector or df with single col specified)
#' @return returns a new data frame or vector. If hack=T, returns nothings
#' @note univariate outlier approach
#' The Z-score method relies on the mean and standard deviation of a group of data to measure central
#' tendency and dispersion. This is troublesome, because the mean and standard deviation are highly
#' affected by outliers – they are not robust. In fact, the skewing that outliers bring is one of the
#' biggest reasons for finding and removing outliers from a dataset!
#' Another drawback of the Z-score method is that it behaves strangely in small datasets – in fact,
#' the Z-score method will never detect an outlier if the dataset has fewer than 12 items in it.
#' \cr
#' \cr
#' Median absolute deviation, modified z-score. The median and MAD are robust measures of central tendency and dispersion, respectively.
#' \cr
#' \cr
#' Interquartile range method is that, like the modified Z-score method, it uses a robust measure of dispersion.
#' \cr
#' @examples
#' set.seed(1234)
#' x = rnorm(10)
#' iris %>% ez.outlier('Sepal.Length',fill='null',hack=T,plot=T)
#' @export
ez.outlier = function(x, col=NULL, method=c('z','mad','iqr'), cutoff=NA, fillout=c('na','null','mean','median'), hack=FALSE, plot=FALSE, na.rm=TRUE, print2scr=TRUE) {
# https://datascienceplus.com/rscript/outlier.R
# https://cran.r-project.org/web/packages/outliers/index.html
# https://rpubs.com/hauselin/outliersDetect
if (hack==T){
# here for programming reason, for mapply,
# cutoff could not be NULL, use NA, because length(NULL)=0, but length(NA)=1
mapply(ez.outlier,method=method,cutoff=cutoff,MoreArgs=list(x=x,col=col,hack=F,plot=plot,fillout=fillout,na.rm=na.rm,print2scr=print2scr),SIMPLIFY=F,USE.NAMES=F)
cat('Hack done! No actual data returned.\n')
return(invisible(NULL))
}
method = match.arg(method); fillout =fillout[1]; cutoff=cutoff[1]
if (!is.data.frame(x)) {
# todropna is a workaround for data frame with single col passed in
x.bak.plot = x; x.replace.na = x; oldNAs = sum(is.na(x.replace.na))
if (fillout=='na' | fillout=='todropna') {
replacement = NA
} else if (fillout=='mean') {
replacement = mean(x, na.rm=na.rm)
} else if (fillout=='median') {
replacement = median(x, na.rm=na.rm)
} else if (fillout=='null') {
replacement = NULL
}
if (method=='z'){
if(is.na(cutoff)) cutoff = qnorm(0.025,lower.tail=F)
absz = abs((x - mean(x, na.rm=na.rm))/sd(x, na.rm=na.rm))
if (!is.null(replacement)) {
x[absz > cutoff] <- replacement
} else {
# if nothing above cutoff, x is untouched
if (length(which(absz > cutoff)) > 0) {
x = x[-which(absz > cutoff)]
}
}
x.replace.na[absz > cutoff] <- NA
} else if (method=='mad'){
if(is.na(cutoff)) cutoff = 2.5
absmad <- abs((x - median(x, na.rm=na.rm))/mad(x, na.rm=na.rm))
if (!is.null(replacement)) {
x[absmad > cutoff] <- replacement
} else {
if (length(which(absmad > cutoff)) > 0) {
x = x[-which(absmad > cutoff)]
}
}
x.replace.na[absmad > cutoff] <- NA
} else if (method=='iqr'){
# https://stackoverflow.com/a/4788102/2292993
if(is.na(cutoff)) cutoff = 1.5
q1 <- quantile(x, 0.25, na.rm=na.rm)
q3 <- quantile(x, 0.75, na.rm=na.rm)
# alternatively iqr = q3-q1
iqr = IQR(x, na.rm = na.rm)
lower_bound = q1 - (iqr * cutoff)
upper_bound = q3 + (iqr * cutoff)
if (!is.null(replacement)) {
x[(x > upper_bound) | (x < lower_bound)] <- replacement
} else {
if (length(which((x > upper_bound) | (x < lower_bound))) > 0) {
x = x[-which((x > upper_bound) | (x < lower_bound))]
}
}
x.replace.na[(x.replace.na > upper_bound) | (x.replace.na < lower_bound)] <- NA
}
newNAs = sum(is.na(x.replace.na)) - oldNAs
if (print2scr) {
if (!is.null(col)) {
cat(sprintf('%-15s %5s(%.2f): %3d outliers found and %s.\n', toString(col), toupper(method), cutoff, newNAs, ifelse((is.null(replacement)|fillout=='todropna'),'REMOVED','REPLACED')))
} else {
cat(sprintf('%5s(%.2f): %3d outliers found and %s.\n', toupper(method), cutoff, newNAs, ifelse((is.null(replacement)|fillout=='todropna'),'REMOVED','REPLACED')))
}
}
if (plot){
# mar controls margin size for individual plot it goes c(bottom, left, top, right)
# oma is margin for the whole?
opar = par(mfrow=c(2, 2), oma=c(0,0,1.5,0), mar = c(2,2,1.5,0.5))
on.exit(par(opar))
boxplot(x.bak.plot, main=sprintf("With outliers (n=%d)",length(x.bak.plot)))
hist(x.bak.plot, main=sprintf("With outliers (n=%d)",length(x.bak.plot)), xlab=NULL, ylab=NULL)
boxplot(x, main=sprintf("With outliers (n=%d)",length(x.bak.plot)-newNAs))
hist(x, main=sprintf("With outliers (n=%d)",length(x.bak.plot)-newNAs), xlab=NULL, ylab=NULL)
title(sprintf("%s Outlier Check: %s(%.2f)",toString(col), toupper(method), cutoff), outer=TRUE)
}
} else if (is.data.frame(x)) {
if (length(col)>1 & fillout=='null') {
cat('I do not know how to remove univariate outliers in multiple cols. fillout: null --> na ...\n')
fillout='na'
} else if (fillout=='null') {
fillout='todropna'
}
# trick to pass actual col name
x[col] = lapply(1:length(col), function(j) {ez.outlier(x=x[col][[j]],col=col[j],method=method,cutoff=cutoff,plot=plot,hack=hack,fillout=fillout,na.rm=na.rm,print2scr=print2scr)})
if (fillout=='todropna') x=x[complete.cases(x[,col,drop=FALSE]),,drop=FALSE]
} # end if
return(invisible(x))
}