Import data:
library(readr)
input = read_csv("person_info.csv")
input# A tibble: 10 x 14
first_name last_name birthday age state address City phone email
<chr> <chr> <chr> <int> <chr> <chr> <chr> <chr> <chr>
1 Carol Davis 9/29/1996 23 Illin… 1674 Ca… Burr … 312-… curt…
2 Bruno Horan 6/11/1995 24 Calif… 1561 St… San D… 858-… guad…
3 William Moody 2/27/1997 22 Illin… 541 Jad… Arlin… 979-… roos…
4 Robin Steel 8/3/1989 57 Texas 1674 Ca… Josep… 214-… lloy…
5 Michelle Roberts 7/17/1995 24 Oregon 1372 Ga… "Port… 503-… ben1…
6 June Sneed 3/27/2000 19 Arizo… 2411 Cl… Phoen… 256-… kath…
7 Curtis Campbell 3/15/1991 28 Idahol 2760 Sc… Pocat… 979-… just…
8 Dorothy Schott 1/2/1997 21 Calif… 2742 Su… Santa… 501-… mega…
9 Mae Skinner 3/16/1995 24 Penns… <NA> Newar… 501-… enri…
10 David Victoria 8/2/1996 23 Maine 3327 Ch… Harps… 207-… caro…
# ... with 5 more variables: car_1 <chr>, gpa <dbl>, year <chr>,
# class_of <int>, online_signiture <chr>Using boxplot to pinpoint the outliers:
A point is defined as an outlier if it is less than Q1 − 1.5 × IQR (interquartile range) or above Q3 + 1.5 × IQR.
# example 1: outliers exist
age = input$age
boxplot(age, xlab = "age", main = "boxplot for the age")
boxplot.stats(age)$out # value of the outlier(s)[1] 57 28outliers = boxplot.stats(age)$out # value of the outlier(s)
out.index = rep(NA, length(outliers)) # index of the outlier(s)
for (i in 1:length(outliers)){
out.index[i] = which(age == outliers[i])
}
age[out.index] <- mean(age, na.rm = T) # impute the outliers with the average value
age [1] 23.0 24.0 22.0 26.5 24.0 19.0 26.5 21.0 24.0 23.0# re-plot the boxplot without outliers
boxplot(age, xlab = "age", main = "boxplot for the age after removing the outliers")
# example 2: no outliers exist
grade = input$gpa
boxplot(grade, xlab = "age", main = "boxplot for the age")
boxplot.stats(grade)$out # value of the outlier(s)numeric(0)outliers = boxplot.stats(grade)$out # value of the outlier(s)
out.index = rep(NA, length(outliers)) # index of the outlier(s)
for (i in 1:length(outliers)){
out.index[i] = which(grade == outliers[i])
}
grade[out.index] <- mean(grade, na.rm = T) # impute the outliers with the average value
grade [1] 2.85 3.47 2.78 4.33 3.75 3.60 2.32 3.93 3.85 1.74# re-plot the boxplot to check that the data remains the same
boxplot(age, xlab = "age", main = "boxplot for the age after removing the outliers")
There is no outliers existing in this example (example 2).