Install Packages
# install.packages("VIM")
Question 3: How many missing values do you find in Petal.Length
variable?
dirty_iris <- read.csv("https://raw.githubusercontent.com/edwindj/datacleaning/master/data/dirty_iris.csv")
sum(is.na(dirty_iris$Petal.Length))
## [1] 19
Question 4: Calculate the number and the percentage of observations
that are complete.
num_complete <- sum(complete.cases(dirty_iris))
percent_complete <- (num_complete / nrow(dirty_iris)) * 100
print(num_complete)
## [1] 96
print(percent_complete)
## [1] 64
Question 5: Is there an another type of special values containing in
the numeric columns?
summary(dirty_iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. : 0.000 Min. :-3.000 Min. : 0.00 Min. :0.1
## 1st Qu.: 5.100 1st Qu.: 2.800 1st Qu.: 1.60 1st Qu.:0.3
## Median : 5.750 Median : 3.000 Median : 4.50 Median :1.3
## Mean : 6.559 Mean : 3.391 Mean : 4.45 Mean :Inf
## 3rd Qu.: 6.400 3rd Qu.: 3.300 3rd Qu.: 5.10 3rd Qu.:1.8
## Max. :73.000 Max. :30.000 Max. :63.00 Max. :Inf
## NA's :10 NA's :17 NA's :19 NA's :12
## Species
## Length:150
## Class :character
## Mode :character
##
##
##
##
Question 6: Write R code to locate the above identified special
value and replace them with a missing value placeholder.
inf_indicator <- is.infinite(dirty_iris$Petal.Width)
dirty_iris$Petal.Width[inf_indicator] <- NA
summary(dirty_iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. : 0.000 Min. :-3.000 Min. : 0.00 Min. :0.100
## 1st Qu.: 5.100 1st Qu.: 2.800 1st Qu.: 1.60 1st Qu.:0.300
## Median : 5.750 Median : 3.000 Median : 4.50 Median :1.300
## Mean : 6.559 Mean : 3.391 Mean : 4.45 Mean :1.207
## 3rd Qu.: 6.400 3rd Qu.: 3.300 3rd Qu.: 5.10 3rd Qu.:1.800
## Max. :73.000 Max. :30.000 Max. :63.00 Max. :2.500
## NA's :10 NA's :17 NA's :19 NA's :13
## Species
## Length:150
## Class :character
## Mode :character
##
##
##
##
Question 7: Write R code to find out the observations that violate
these rules. How many observations violate the above rules?
violations <- subset(dirty_iris, Sepal.Width <= 0 | Sepal.Length > 30)
print(violations)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 16 5.0 -3 3.5 1.0 versicolor
## 28 73.0 29 63.0 NA virginica
## 125 49.0 30 14.0 2.0 setosa
## 130 5.7 0 1.7 0.3 setosa
nrow(violations)
## [1] 4
Question 8: Would you locate the observation that violates the rule
of “Sepal.Width >0” and make reasonable corrections?
neg_indicator <- which(dirty_iris$Sepal.Width<0)
dirty_iris$Sepal.Width[neg_indicator]< abs(dirty_iris$Sepal.Width[neg_indicator])
## [1] TRUE
dirty_iris[neg_indicator,]
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 16 5 -3 3.5 1 versicolor
zero_indicator <- which(dirty_iris$Sepal.Width ==0)
dirty_iris$Sepal.Width[zero_indicator] <- NA
Question 9: Write the R code to do the imputation as specified
above. Mark the ones if your attached R code could achieve the
task.
library(VIM)
## Loading required package: colorspace
## Loading required package: grid
## VIM is ready to use.
## Suggestions and bug-reports can be submitted at: https://github.com/statistikat/VIM/issues
##
## Attaching package: 'VIM'
## The following object is masked from 'package:datasets':
##
## sleep
# 1. Sepal.Width: Mean
dirty_iris$Sepal.Width[is.na(dirty_iris$Sepal.Width)] <- mean(dirty_iris$Sepal.Width, na.rm = TRUE)
# 2. Petal.Length: Median
dirty_iris$Petal.Length[is.na(dirty_iris$Petal.Length)] <- median(dirty_iris$Petal.Length, na.rm = TRUE)
# 3. Sepal.Length: Linear Regression
model <- lm(Sepal.Length~Sepal.Width+Petal.Width,data=dirty_iris)
I <- is.na(dirty_iris$Sepal.Length)
to_be_imported <- dirty_iris[I,]
dirty_iris$Sepal.Length[I] <- predict(model,newdata = to_be_imported)
# 4. Petal.Width: kNN
dirty_iris <- kNN(dirty_iris, variable = "Petal.Width", k = 5)
dirty_iris$Petal.Width_imp <- NULL
colSums(is.na(dirty_iris))
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 0 0 0 0 0