Pre-Processing

##Pre-Processing in R Here , we are trying to do basic data pre processing.

setwd(dir="C:\\Users\\pahar\\OneDrive\\IIM-K\\Classes\\2022-07-30")
cars <- read.csv ("cars.csv")
summary(cars)

##      Sl..No         mpg          cylinders     cubicinches          hp       
##  Min.   :  1   Min.   :10.00   Min.   :3.00   Min.   : 68.0   Min.   : 46.0  
##  1st Qu.: 66   1st Qu.:16.90   1st Qu.:4.00   1st Qu.:101.0   1st Qu.: 75.0  
##  Median :131   Median :22.00   Median :6.00   Median :156.0   Median : 95.0  
##  Mean   :131   Mean   :23.14   Mean   :5.59   Mean   :201.1   Mean   :106.4  
##  3rd Qu.:196   3rd Qu.:28.80   3rd Qu.:8.00   3rd Qu.:302.0   3rd Qu.:138.0  
##  Max.   :261   Max.   :46.60   Max.   :8.00   Max.   :455.0   Max.   :230.0  
##    weightlbs      time.to.60         year         brand          
##  Min.   :1613   Min.   : 8.00   Min.   :1971   Length:261        
##  1st Qu.:2246   1st Qu.:14.00   1st Qu.:1974   Class :character  
##  Median :2835   Median :16.00   Median :1977   Mode  :character  
##  Mean   :3005   Mean   :15.55   Mean   :1977                     
##  3rd Qu.:3664   3rd Qu.:17.00   3rd Qu.:1980                     
##  Max.   :4997   Max.   :25.00   Max.   :1983

str(cars)

## 'data.frame':    261 obs. of  9 variables:
##  $ Sl..No     : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ mpg        : num  14 31.9 17 15 30.5 23 13 14 25.4 37.7 ...
##  $ cylinders  : int  8 4 8 8 4 8 8 8 5 4 ...
##  $ cubicinches: int  350 89 302 400 98 350 351 440 183 89 ...
##  $ hp         : int  165 71 140 150 63 125 158 215 77 62 ...
##  $ weightlbs  : int  4209 1925 3449 3761 2051 3900 4363 4312 3530 2050 ...
##  $ time.to.60 : int  12 14 11 10 17 17 13 9 20 17 ...
##  $ year       : int  1972 1980 1971 1971 1978 1980 1974 1971 1980 1982 ...
##  $ brand      : chr  " US." " Europe." " US." " US." ...

GRAPHICAL METHODS FOR IDENTIFYING OUTLIERS

Set up the plot area

Here we are trying to examine the presence of outlier using histogram, which is very informal

#par(mfrow = c(1,3))


hist(cars$weightlbs,
     breaks = 30,
     xlim = c(1500, 5000),
     col = "blue",
     border = "black",
     ylim = c(0, 20),
     xlab = "Weight",
     ylab = "Counts",
     main = "Histogram
of Car Weights")

Scatter Plot

Here we are examining scatter plot to explore the presence of outlier

plot(cars$weightlbs,
     cars$mpg,
     xlim = c(1500, 5000),
     ylim = c(0, 50),
     xlab = "Weight",
     ylab = "MPG",
     main = "Scatterplot of MPG by Weight",
     type = "p",
     pch = 20,
     col = "blue")

Results : Scatter Plot

From the scatter plot, we found that the majority of the data points hovered around the mean, there is no much evidence of outlier between MPG and weight of the cars

Box Plot

Here we create box plots to identify the outliers

boxplot(mpg ~ cylinders, data = cars, xlab = "Number of Cylinders",
        ylab = "Miles Per Gallon", main = "Mileage Data")

box <- boxplot(mpg ~ brand, data = cars, xlab = "brand",
        ylab = "Miles Per Gallon", main = "Mileage Data")

##Box plot for only mileage

box <- boxplot(cars$mpg,  xlab = "box",
               ylab = "Miles Per Gallon", main = "Mileage Data")$out

#Box plot for cars1 data

#box1 <- boxplot(cars1$mpg)

#New Data Creating a data with outlier

cars1<-edit(cars)
cars1<-cars1

#box1 <- boxplot(cars1$mpg,  xlab = "box",
               #ylab = "Miles Per Gallon", main = "Mileage Data")$out
box1 <- boxplot(cars1$mpg)$out

outliers_data <- cars1[which(cars1$mpg %in% box1),]
outliers_data

##    Sl..No mpg cylinders cubicinches  hp weightlbs time.to.60 year   brand
## 8       8  56         8         440 215      4312          9 1971     US.
## 19     19  59         4         113  95      2278         16 1973  Japan.

write.csv(outliers_data,"outlier.csv")
#data2 <- cars1[-which(cars1$mpg %in% box),]

#deleting outlier Here we are deleting outlier from original data i.e. cars1

cars2<-cars1[-which(cars1$mpg %in% box1),]