Outliersassignment4

Load Libraries

#install.packages("xlsx")
#install.packages("corrgram")
Sys.setenv(JAVA_HOME='C:\\Program Files\\Java\\jre1.8.0_221')
library(xlsx)
library(ggplot2)

Read xlsx Sheet1

# read xlsx sheet Ex1
df1 <- read.xlsx('E:\\R-Files\\Outliers1.xlsx', sheetName="Sheet1", header=TRUE, colClasses=NA)
head(df1)

##   data
## 1  514
## 2  220
## 3  810
## 4  721
## 5  991
## 6  438

Detect Outliers using IQR

# detect Outliers
outliers_iqr <- function(inp, na.rm=TRUE) {
    i.qnt <- quantile(inp, probs=c(.25, .75), na.rm=na.rm)
    i.max <- 1.5 * IQR(inp, na.rm=na.rm)
    otp <- inp
    otp[inp < (i.qnt[1] - i.max)] <- NA
    otp[inp > (i.qnt[2] + i.max)] <- NA
    inp[is.na(otp)]
}

outliers_iqr(df1$data)

## [1] 2019 1999 1971

Detect Outliers using STD_DEV

# detect Outliers
outliers_sd <- function(inp, na.rm=TRUE) {
    lfence <- mean(inp) - (3 * sd(inp))
    ufence <- mean(inp) + (3 * sd(inp))
    otp <- inp
    otp[inp < lfence] <- NA
    otp[inp > ufence] <- NA
    inp[is.na(otp)]
}

outliers_sd(df1$data)

## [1] 2019 1999

Read xlsx Sheet2

# read xlsx sheet Ex2
df2 <- read.xlsx('E:\\R-Files\\Outliers1.xlsx', sheetName="Sheet2", header=TRUE, colClasses=NA)
head(df2)

##         data
## 1 3.98580730
## 2 0.06947271
## 3 5.93342246
## 4 4.28510776
## 5 3.34469264
## 6 5.61086322

Detect Outliers Using IQR

# detect Outliers
outliers_iqr <- function(inp, na.rm=TRUE) {
    i.qnt <- quantile(inp, probs=c(.25, .75), na.rm=na.rm)
    i.max <- 1.5 * IQR(inp, na.rm=na.rm)
    otp <- inp
    otp[inp < (i.qnt[1] - i.max)] <- NA
    otp[inp > (i.qnt[2] + i.max)] <- NA
    inp[is.na(otp)]
}

outliers_iqr(df2$data)

## [1] 15.93889 18.61065 17.03925

Detect Outliers Using STD_DEV

# detect Outliers
outliers_sd <- function(inp, na.rm=TRUE) {
    lfence <- mean(inp) - (3 * sd(inp))
    ufence <- mean(inp) + (3 * sd(inp))
    otp <- inp
    otp[inp < lfence] <- NA
    otp[inp > ufence] <- NA
    inp[is.na(otp)]
}

outliers_sd(df2$data)

## [1] 18.61065 17.03925