Assignment 1 - R Programming
2025-07-05
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##Invoking necessary library
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)##Printing the structure of dataset
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...##Printing the variables in dataset
names(mtcars)## [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
## [11] "carb"##Printing 15 rows from dataset
mtcars %>% slice(1:15)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4##User defined function-This function is created to identify whether the car is Manual or Automatic. Here, boolean value is used which means if the value is 1 the car is manual and if tha value is 0, the car is automatic. To invoke the function, we are passing the data of car in 5th row.
cars_type=function(am){
if (am==1){
return("Manual")}
else{
return("Automatic")
}
}
cars_type(mtcars$am[5])## [1] "Automatic"##Applying filter for sports cars. Here, the assumption made is the criteria for sports car is high hp, low weight and it is manual. After applying the filter we are counting the total number of sports car in our dataset.
mtcars %>%
filter(`hp` > 150,
`wt` < 3.5,
`am` == 1) %>%
summarise(Count = n())## Count
## 1 2##Identifing the dependent & independent variables and using reshaping techniques(cbind) and creating a new data frame called new_mtcars_df by joining those variables from mtcars dataset.
# Creating variables from the mtcars dataset
mileage = mtcars$mpg # Dependent variable
cylinders = mtcars$cyl # Independent
horsepower = mtcars$hp # Independent
weight = mtcars$wt # Independent
# Combine into a new data frame
new_mtcars_df = cbind(mileage, cylinders, horsepower, weight)
# View result
new_mtcars_df## mileage cylinders horsepower weight
## [1,] 21.0 6 110 2.620
## [2,] 21.0 6 110 2.875
## [3,] 22.8 4 93 2.320
## [4,] 21.4 6 110 3.215
## [5,] 18.7 8 175 3.440
## [6,] 18.1 6 105 3.460
## [7,] 14.3 8 245 3.570
## [8,] 24.4 4 62 3.190
## [9,] 22.8 4 95 3.150
## [10,] 19.2 6 123 3.440
## [11,] 17.8 6 123 3.440
## [12,] 16.4 8 180 4.070
## [13,] 17.3 8 180 3.730
## [14,] 15.2 8 180 3.780
## [15,] 10.4 8 205 5.250
## [16,] 10.4 8 215 5.424
## [17,] 14.7 8 230 5.345
## [18,] 32.4 4 66 2.200
## [19,] 30.4 4 52 1.615
## [20,] 33.9 4 65 1.835
## [21,] 21.5 4 97 2.465
## [22,] 15.5 8 150 3.520
## [23,] 15.2 8 150 3.435
## [24,] 13.3 8 245 3.840
## [25,] 19.2 8 175 3.845
## [26,] 27.3 4 66 1.935
## [27,] 26.0 4 91 2.140
## [28,] 30.4 4 113 1.513
## [29,] 15.8 8 264 3.170
## [30,] 19.7 6 175 2.770
## [31,] 15.0 8 335 3.570
## [32,] 21.4 4 109 2.780##Searching for missing values
##duplicating dataset for demonstrating removal of missing value as this dataset doesn't contain missing value
new_mtcars=mtcars
##searching for missing values
any(is.na(mtcars))## [1] FALSE##adding missing value
mtcars[3, "hp"] = NA
mtcars[5, "mpg"] = NA
##again checking for missing value if it is added
any(is.na(mtcars))## [1] TRUE##extracting rows where hp and mpg is NA
new_mtcars %>% filter(is.na(hp))## [1] mpg cyl disp hp drat wt qsec vs am gear carb
## <0 rows> (or 0-length row.names)new_mtcars %>% filter(is.na(mpg))## [1] mpg cyl disp hp drat wt qsec vs am gear carb
## <0 rows> (or 0-length row.names)##removing missing values and storing it in clean_mtcars
clean_mtcars = na.omit(new_mtcars)
##searching for missing values to check if it is removed or not
any(is.na(clean_mtcars))## [1] FALSE##Searching for duplicate values
##using new_mtcars for demonstrating of identifying and removal of duplicate data
##checking for duplicate data before beginning
any(duplicated(new_mtcars))## [1] FALSE# Adding a duplicate of the first row to demonstrate
new_mtcars = rbind(new_mtcars, new_mtcars[1, ])
new_mtcars = rbind(new_mtcars, new_mtcars[3, ])
##checking for duplicate data to verify if it is added
any(duplicated(new_mtcars))## [1] TRUE##removing duplicate rows
clean_mtcars = new_mtcars[!duplicated(new_mtcars), ]
##checking for duplicate data after removing
any(duplicated(clean_mtcars))## [1] FALSE##Arranging rows in descending order
mtcars%>%arrange(desc(disp))## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 NA 3.85 2.320 18.61 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1mtcars%>%arrange(desc(hp))## mpg cyl disp hp drat wt qsec vs am gear carb
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Datsun 710 22.8 4 108.0 NA 3.85 2.320 18.61 1 1 4 1mtcars%>%arrange(desc(drat))## mpg cyl disp hp drat wt qsec vs am gear carb
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 NA 3.85 2.320 18.61 1 1 4 1
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2mtcars%>%arrange(desc(wt))## mpg cyl disp hp drat wt qsec vs am gear carb
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 NA 3.85 2.320 18.61 1 1 4 1
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2mtcars%>%arrange(desc(qsec))## mpg cyl disp hp drat wt qsec vs am gear carb
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Datsun 710 22.8 4 108.0 NA 3.85 2.320 18.61 1 1 4 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4##Renaming column names in dataset
names(mtcars)[1]="MPG"
names(mtcars)[2]="Cyl"
names(mtcars)[3]="Disp"
names(mtcars)[4]="Horsepower"
names(mtcars)[5]="DRAT"
names(mtcars)[5]="WT"
names(mtcars)[7]="Qtr_mile_T"
names(mtcars)[8]="Trans"
names(mtcars)[9]="EngineShape"
mtcars## MPG Cyl Disp Horsepower WT wt Qtr_mile_T Trans
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0
## Datsun 710 22.8 4 108.0 NA 3.85 2.320 18.61 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1
## Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1
## EngineShape gear carb
## Mazda RX4 1 4 4
## Mazda RX4 Wag 1 4 4
## Datsun 710 1 4 1
## Hornet 4 Drive 0 3 1
## Hornet Sportabout 0 3 2
## Valiant 0 3 1
## Duster 360 0 3 4
## Merc 240D 0 4 2
## Merc 230 0 4 2
## Merc 280 0 4 4
## Merc 280C 0 4 4
## Merc 450SE 0 3 3
## Merc 450SL 0 3 3
## Merc 450SLC 0 3 3
## Cadillac Fleetwood 0 3 4
## Lincoln Continental 0 3 4
## Chrysler Imperial 0 3 4
## Fiat 128 1 4 1
## Honda Civic 1 4 2
## Toyota Corolla 1 4 1
## Toyota Corona 0 3 1
## Dodge Challenger 0 3 2
## AMC Javelin 0 3 2
## Camaro Z28 0 3 4
## Pontiac Firebird 0 3 2
## Fiat X1-9 1 4 1
## Porsche 914-2 1 5 2
## Lotus Europa 1 5 2
## Ford Pantera L 1 5 4
## Ferrari Dino 1 5 6
## Maserati Bora 1 5 8
## Volvo 142E 1 4 2##Adding new variable as horse power to weight by diving horsepower with weight
mtcars$HPToWT=mtcars$Horsepower/mtcars$WT
mtcars## MPG Cyl Disp Horsepower WT wt Qtr_mile_T Trans
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0
## Datsun 710 22.8 4 108.0 NA 3.85 2.320 18.61 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1
## Hornet Sportabout NA 8 360.0 175 3.15 3.440 17.02 0
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1
## EngineShape gear carb HPToWT
## Mazda RX4 1 4 4 28.20513
## Mazda RX4 Wag 1 4 4 28.20513
## Datsun 710 1 4 1 NA
## Hornet 4 Drive 0 3 1 35.71429
## Hornet Sportabout 0 3 2 55.55556
## Valiant 0 3 1 38.04348
## Duster 360 0 3 4 76.32399
## Merc 240D 0 4 2 16.80217
## Merc 230 0 4 2 24.23469
## Merc 280 0 4 4 31.37755
## Merc 280C 0 4 4 31.37755
## Merc 450SE 0 3 3 58.63192
## Merc 450SL 0 3 3 58.63192
## Merc 450SLC 0 3 3 58.63192
## Cadillac Fleetwood 0 3 4 69.96587
## Lincoln Continental 0 3 4 71.66667
## Chrysler Imperial 0 3 4 71.20743
## Fiat 128 1 4 1 16.17647
## Honda Civic 1 4 2 10.54767
## Toyota Corolla 1 4 1 15.40284
## Toyota Corona 0 3 1 26.21622
## Dodge Challenger 0 3 2 54.34783
## AMC Javelin 0 3 2 47.61905
## Camaro Z28 0 3 4 65.68365
## Pontiac Firebird 0 3 2 56.81818
## Fiat X1-9 1 4 1 16.17647
## Porsche 914-2 1 5 2 20.54176
## Lotus Europa 1 5 2 29.97347
## Ford Pantera L 1 5 4 62.55924
## Ferrari Dino 1 5 6 48.34254
## Maserati Bora 1 5 8 94.63277
## Volvo 142E 1 4 2 26.52068##Generating random 5 data from the dataset
mtcars %>% sample_n(5, replace = FALSE)## MPG Cyl Disp Horsepower WT wt Qtr_mile_T Trans
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1
## EngineShape gear carb HPToWT
## Lincoln Continental 0 3 4 71.66667
## Dodge Challenger 0 3 2 54.34783
## Camaro Z28 0 3 4 65.68365
## Porsche 914-2 1 5 2 20.54176
## Volvo 142E 1 4 2 26.52068##Printing the summary statistics of dataset
summary(mtcars)## MPG Cyl Disp Horsepower
## Min. :10.40 Min. :4.000 Min. : 71.1 Min. : 52.0
## 1st Qu.:15.35 1st Qu.:4.000 1st Qu.:120.8 1st Qu.:101.0
## Median :19.20 Median :6.000 Median :196.3 Median :123.0
## Mean :20.14 Mean :6.188 Mean :230.7 Mean :148.4
## 3rd Qu.:22.80 3rd Qu.:8.000 3rd Qu.:326.0 3rd Qu.:180.0
## Max. :33.90 Max. :8.000 Max. :472.0 Max. :335.0
## NA's :1 NA's :1
## WT wt Qtr_mile_T Trans
## Min. :2.760 Min. :1.513 Min. :14.50 Min. :0.0000
## 1st Qu.:3.080 1st Qu.:2.581 1st Qu.:16.89 1st Qu.:0.0000
## Median :3.695 Median :3.325 Median :17.71 Median :0.0000
## Mean :3.597 Mean :3.217 Mean :17.85 Mean :0.4375
## 3rd Qu.:3.920 3rd Qu.:3.610 3rd Qu.:18.90 3rd Qu.:1.0000
## Max. :4.930 Max. :5.424 Max. :22.90 Max. :1.0000
##
## EngineShape gear carb HPToWT
## Min. :0.0000 Min. :3.000 Min. :1.000 Min. :10.55
## 1st Qu.:0.0000 1st Qu.:3.000 1st Qu.:2.000 1st Qu.:26.37
## Median :0.0000 Median :4.000 Median :2.000 Median :38.04
## Mean :0.4062 Mean :3.688 Mean :2.812 Mean :43.42
## 3rd Qu.:1.0000 3rd Qu.:4.000 3rd Qu.:4.000 3rd Qu.:58.63
## Max. :1.0000 Max. :5.000 Max. :8.000 Max. :94.63
## NA's :1##Operations on numeric variables from dataset
mean(mtcars$Cyl)## [1] 6.1875median(mtcars$Disp)## [1] 196.3mode(mtcars$Horsepower)## [1] "numeric"range(mtcars$WT)## [1] 2.76 4.93Scatter plot for displacement and horsepower
####This scatter plot shows the relationship between engine displacement (Disp) on the x-axis and horsepower on the y-axis. Each point represents one car in the dataset. Key observations:There’s a clear positive correlation between displacement and horsepower. As engine displacement increases, horsepower tends to increase as well. The relationship appears roughly linear with some scatter. Most cars cluster in the lower displacement/horsepower range (around 100-200 displacement, 50-150 horsepower).A few high-performance cars have very high displacement (400+) and horsepower (200-350).
## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_point()`).
Bar plot for fuel efficiency and cylinders
##This bar plot shows the distribution of cars by their fuel efficiency (MPG - miles per gallon) on the x-axis, with the count of cars on the y-axis. Key observations:cylinder cars (red) tend to have higher fuel efficiency, mostly clustering around 21-33 MPG,6-cylinder cars (green) have moderate fuel efficiency, around 17-21 MPG, and 8-cylinder cars (blue) generally have lower fuel efficiency, concentrated around 10-17 MPG. ##This demonstrates the expected inverse relationship between number of cylinders and fuel efficiency - more cylinders typically mean lower MPG.
## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_count()`).
Note that the echo = FALSE parameter was added to the
code chunk to prevent printing of the R code that generated the
plot.
##co-relation between horsepower and weight
require("datasets")
data("mtcars")
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...head(mtcars)## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1Y<- mtcars[,"hp"] # select Target attribute i.e. horsepower
X<- mtcars[,"wt"] # select Predictor attribute i.e. weight
head(X)## [1] 2.620 2.875 2.320 3.215 3.440 3.460head(Y)## [1] 110 110 93 110 175 105xycorr<- cor(Y,X, method="pearson") # find pearson correlation coefficient
xycorr## [1] 0.6587479## To explain the relationship between horsepower and weight, the Pearson correlation coefficient between horsepower (hp) and weight (wt) is approximately 0.66, which indicates a moderate to strong positive linear relationship. This means that as the weight of the car increases, its horsepower also tends to increase. Heavier cars often require more power to perform efficiently, which might explain this relationship.