carD = read.csv("C:/Users/User/Downloads/MAHE FILES/MAHE SEMESTER 1/R and Python/MAHE R WK3&4/mtcars3.csv")
head(carD,5)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
#To check for the data structure
str(carD)
## 'data.frame': 32 obs. of 12 variables:
## $ X : chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : int 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : int 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 : int 0 0 1 1 0 1 0 1 1 1 ...
## $ am : int 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: int 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: int 4 4 1 1 2 1 4 2 2 4 ...
nrow(carD)
## [1] 32
ncol(carD)
## [1] 12
dim(carD)
## [1] 32 12
colnames(carD)
## [1] "X" "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am"
## [11] "gear" "carb"
#To convert into columns
categorical_cols = c("vs", "am")
carD[categorical_cols] = lapply(carD[categorical_cols], as.factor)
str(carD)
## 'data.frame': 32 obs. of 12 variables:
## $ X : chr "Mazda RX4" "Mazda RX4 Wag" "Datsun 710" "Hornet 4 Drive" ...
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : int 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : int 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 : Factor w/ 2 levels "0","1": 1 1 2 2 1 2 1 2 2 2 ...
## $ am : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 1 1 1 1 ...
## $ gear: int 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: int 4 4 1 1 2 1 4 2 2 4 ...
#Selecting data
#THe SELECT() is used to select a particular column
library("tidyverse")
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.2 ✔ readr 2.1.4
## ✔ forcats 1.0.0 ✔ stringr 1.5.0
## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1
## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0
## ✔ purrr 1.0.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
select(carD, wt)
## wt
## 1 2.620
## 2 2.875
## 3 2.320
## 4 3.215
## 5 3.440
## 6 3.460
## 7 3.570
## 8 3.190
## 9 3.150
## 10 3.440
## 11 3.440
## 12 4.070
## 13 3.730
## 14 3.780
## 15 5.250
## 16 5.424
## 17 5.345
## 18 2.200
## 19 1.615
## 20 1.835
## 21 2.465
## 22 3.520
## 23 3.435
## 24 3.840
## 25 3.845
## 26 1.935
## 27 2.140
## 28 1.513
## 29 3.170
## 30 2.770
## 31 3.570
## 32 2.780
#To select with a column excluded
select(carD, -wt)
## X mpg cyl disp hp drat qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 18.90 1 0 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 17.40 0 0 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 17.60 0 0 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 18.00 0 0 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 17.98 0 0 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 17.82 0 0 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 17.42 0 0 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 19.47 1 1 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 18.52 1 1 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 19.90 1 1 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 20.01 1 0 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 16.87 0 0 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 17.30 0 0 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 15.41 0 0 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 17.05 0 0 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 18.90 1 1 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 16.70 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 16.90 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 14.50 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 15.50 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 14.60 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 18.60 1 1 4 2
#To select multiple columns
select(carD, c(am, vs))
## am vs
## 1 1 0
## 2 1 0
## 3 1 1
## 4 0 1
## 5 0 0
## 6 0 1
## 7 0 0
## 8 0 1
## 9 0 1
## 10 0 1
## 11 0 1
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 1 1
## 19 1 1
## 20 1 1
## 21 0 1
## 22 0 0
## 23 0 0
## 24 0 0
## 25 0 0
## 26 1 1
## 27 1 0
## 28 1 1
## 29 1 0
## 30 1 0
## 31 1 0
## 32 1 1
#To exclude multiple columns
select(carD, -c(am, vs))
## X mpg cyl disp hp drat wt qsec gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 4 2
#The filter() is used the select the rows
filter(carD, vs==0)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 4 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 5 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 6 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 7 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 8 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 9 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 10 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 11 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 12 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 13 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 14 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 15 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 16 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 17 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 18 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
#To select with multiple criteria
filter(carD, vs==1 & am==1)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 2 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 3 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 4 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 5 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 6 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 7 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
#To select rows with more detailed criteria
filter(carD, vs==0 & am==1 & (hp < 100 | hp > 150))
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Porsche 914-2 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
## 2 Ford Pantera L 15.8 8 351.0 264 4.22 3.17 14.5 0 1 5 4
## 3 Ferrari Dino 19.7 6 145.0 175 3.62 2.77 15.5 0 1 5 6
## 4 Maserati Bora 15.0 8 301.0 335 3.54 3.57 14.6 0 1 5 8
#To select the columns cyl and wt based on the row vs
carD %>% filter(vs==0) %>% select(c(cyl, wt))
## cyl wt
## 1 6 2.620
## 2 6 2.875
## 3 8 3.440
## 4 8 3.570
## 5 8 4.070
## 6 8 3.730
## 7 8 3.780
## 8 8 5.250
## 9 8 5.424
## 10 8 5.345
## 11 8 3.520
## 12 8 3.435
## 13 8 3.840
## 14 8 3.845
## 15 4 2.140
## 16 8 3.170
## 17 6 2.770
## 18 8 3.570
#To select the columns cyl and wt based on the rows vs=0 and hp>150
carD %>% filter(vs==0 & hp > 150) %>% select(c(cyl, wt))
## cyl wt
## 1 8 3.440
## 2 8 3.570
## 3 8 4.070
## 4 8 3.730
## 5 8 3.780
## 6 8 5.250
## 7 8 5.424
## 8 8 5.345
## 9 8 3.840
## 10 8 3.845
## 11 8 3.170
## 12 6 2.770
## 13 8 3.570
#The MUTATE() is used to create new columns
mutate(carD, wtton = 0.45*wt)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## wtton
## 1 1.17900
## 2 1.29375
## 3 1.04400
## 4 1.44675
## 5 1.54800
## 6 1.55700
## 7 1.60650
## 8 1.43550
## 9 1.41750
## 10 1.54800
## 11 1.54800
## 12 1.83150
## 13 1.67850
## 14 1.70100
## 15 2.36250
## 16 2.44080
## 17 2.40525
## 18 0.99000
## 19 0.72675
## 20 0.82575
## 21 1.10925
## 22 1.58400
## 23 1.54575
## 24 1.72800
## 25 1.73025
## 26 0.87075
## 27 0.96300
## 28 0.68085
## 29 1.42650
## 30 1.24650
## 31 1.60650
## 32 1.25100
#To calculate wtton for the wt whose row is not NA
carD %>% filter(!is.na(wt)) %>% mutate(wtton = 0.45*wt)
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## wtton
## 1 1.17900
## 2 1.29375
## 3 1.04400
## 4 1.44675
## 5 1.54800
## 6 1.55700
## 7 1.60650
## 8 1.43550
## 9 1.41750
## 10 1.54800
## 11 1.54800
## 12 1.83150
## 13 1.67850
## 14 1.70100
## 15 2.36250
## 16 2.44080
## 17 2.40525
## 18 0.99000
## 19 0.72675
## 20 0.82575
## 21 1.10925
## 22 1.58400
## 23 1.54575
## 24 1.72800
## 25 1.73025
## 26 0.87075
## 27 0.96300
## 28 0.68085
## 29 1.42650
## 30 1.24650
## 31 1.60650
## 32 1.25100
#To check if the new variable/column wtton is in the data frame
colnames(carD)
## [1] "X" "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am"
## [11] "gear" "carb"
#To create another column
carD.New = carD %>% mutate(cyltype = (ifelse(cyl > 4, 'High', 'Low')))
carD.New
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## cyltype
## 1 High
## 2 High
## 3 Low
## 4 High
## 5 High
## 6 High
## 7 High
## 8 Low
## 9 Low
## 10 High
## 11 High
## 12 High
## 13 High
## 14 High
## 15 High
## 16 High
## 17 High
## 18 Low
## 19 Low
## 20 Low
## 21 Low
## 22 High
## 23 High
## 24 High
## 25 High
## 26 Low
## 27 Low
## 28 Low
## 29 High
## 30 High
## 31 High
## 32 Low
#Chaining and creating multiple columns with mutate
carD.New = carD %>% mutate(cyltype = (ifelse(cyl > 4, 'High', 'Low')),wtton = 0.45*wt)
carD.New
## X mpg cyl disp hp drat wt qsec vs am gear carb
## 1 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## 2 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## 3 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## 4 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## 5 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## 6 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## 7 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## 8 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## 9 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## 10 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## 11 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## 12 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## 13 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## 14 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## 15 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## 16 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## 17 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## 18 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## 19 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## 20 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 21 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## 22 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## 23 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## 24 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## 25 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## 26 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## 27 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## 28 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## 29 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## 30 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 31 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## 32 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## cyltype wtton
## 1 High 1.17900
## 2 High 1.29375
## 3 Low 1.04400
## 4 High 1.44675
## 5 High 1.54800
## 6 High 1.55700
## 7 High 1.60650
## 8 Low 1.43550
## 9 Low 1.41750
## 10 High 1.54800
## 11 High 1.54800
## 12 High 1.83150
## 13 High 1.67850
## 14 High 1.70100
## 15 High 2.36250
## 16 High 2.44080
## 17 High 2.40525
## 18 Low 0.99000
## 19 Low 0.72675
## 20 Low 0.82575
## 21 Low 1.10925
## 22 High 1.58400
## 23 High 1.54575
## 24 High 1.72800
## 25 High 1.73025
## 26 Low 0.87075
## 27 Low 0.96300
## 28 Low 0.68085
## 29 High 1.42650
## 30 High 1.24650
## 31 High 1.60650
## 32 Low 1.25100
#To check if the new columns cyltype and wtton are in the data frame
colnames(carD.New)
## [1] "X" "mpg" "cyl" "disp" "hp" "drat" "wt"
## [8] "qsec" "vs" "am" "gear" "carb" "cyltype" "wtton"
#To use chaining with summarize
carD.New %>% summarise(mean(wtton))
## mean(wtton)
## 1 1.447763
#To return the mean weight of displacement and wton
carD.New %>% summarise(mean(wtton), mean(disp))
## mean(wtton) mean(disp)
## 1 1.447763 230.7219
#To chain group_by() and summarise
carD.New %>% group_by(cyltype) %>% summarise(mean(disp), mean(wtton))
## # A tibble: 2 × 3
## cyltype `mean(disp)` `mean(wtton)`
## <chr> <dbl> <dbl>
## 1 High 297. 1.67
## 2 Low 105. 1.03
carD.New %>% group_by(vs) %>% summarise(mean(wtton), mean(disp))
## # A tibble: 2 × 3
## vs `mean(wtton)` `mean(disp)`
## <fct> <dbl> <dbl>
## 1 0 1.66 307.
## 2 1 1.18 132.
#To group, summarise and arrange in descending order
carD.New %>% group_by(cyltype) %>% summarise(mwt = mean(wtton), mdisp = mean(disp))
## # A tibble: 2 × 3
## cyltype mwt mdisp
## <chr> <dbl> <dbl>
## 1 High 1.67 297.
## 2 Low 1.03 105.
carD.New %>% group_by(vs) %>% summarise(mwt = mean(wtton), mdisp = mean(disp))
## # A tibble: 2 × 3
## vs mwt mdisp
## <fct> <dbl> <dbl>
## 1 0 1.66 307.
## 2 1 1.18 132.
#To group and count
carD.New %>% count(cyltype, vs)
## cyltype vs n
## 1 High 0 17
## 2 High 1 4
## 3 Low 0 1
## 4 Low 1 10
carD.New %>% group_by(cyltype) %>% count()
## # A tibble: 2 × 2
## # Groups: cyltype [2]
## cyltype n
## <chr> <int>
## 1 High 21
## 2 Low 11
#counting and sorting
carD.New %>% count(cyltype) %>% arrange(n)
## cyltype n
## 1 Low 11
## 2 High 21