Logical & Conditional Operators

1) Load the inbuilt ‘mtcars’ dataset in R

## load mtcars dataset
data(mtcars)
## attach the dataframe
attach(mtcars)
View(mtcars)

PART 1 – Creating a new column in a dataframe

1) Create a new variable hpwtRatio, defined as follows:

hpwtRatio = hp/wt

# creating the variable hpwtRatio
hpwr <- mtcars$hp/mtcars$wt

hpwtRatio <- hp/wt

2) Bind the new column to the old dataframe, using cbind()

# adding the variable into mtcars dataset
mtcars <- cbind(mtcars,hpwtRatio)

# print some rows of the updated dataframe mtcars
head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb hpwtRatio
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4  41.98473
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4  38.26087
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1  40.08621
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1  34.21462
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2  50.87209
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1  30.34682

3) Save the above data as csv file

View(mtcars)
# saving as csv
write.csv(mtcars, "mtcarsNew.csv")

PART 2 – Logical Operators

1) Take a subset of mtcars, where wt > 5

# cars having weight > 5
mtcars1 = subset(mtcars, wt > 5)
mtcars1

##                      mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8  440 230 3.23 5.345 17.42  0  0    3    4
##                     hpwtRatio
## Cadillac Fleetwood   39.04762
## Lincoln Continental  39.63864
## Chrysler Imperial    43.03087

2) Take a subset of mtcars where miles per gallon is less than the average miles per gallon

# Print the mean mpg
mean(mpg)

## [1] 20.09062

summary(mtcars$mpg)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.40   15.43   19.20   20.09   22.80   33.90

# cars having mpg < mean(mpg)
mtcars2  <- subset(mtcars, mpg < mean(mpg))
mtcars2 

##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## 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 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
## 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
## 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
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## 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
##                     hpwtRatio
## Hornet Sportabout    50.87209
## Valiant              30.34682
## Duster 360           68.62745
## Merc 280             35.75581
## Merc 280C            35.75581
## Merc 450SE           44.22604
## Merc 450SL           48.25737
## Merc 450SLC          47.61905
## Cadillac Fleetwood   39.04762
## Lincoln Continental  39.63864
## Chrysler Imperial    43.03087
## Dodge Challenger     42.61364
## AMC Javelin          43.66812
## Camaro Z28           63.80208
## Pontiac Firebird     45.51365
## Ford Pantera L       83.28076
## Ferrari Dino         63.17690
## Maserati Bora        93.83754

3) Take a subset of mtcars data for cars having 4 cylinders i.e. cyl = 4

# cars having 4 cylinders 
mtcars4 <- subset(mtcars, cyl == 4)
mtcars4  

##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb hpwtRatio
## Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1  40.08621
## Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2  19.43574
## Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2  30.15873
## Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1  30.00000
## Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2  32.19814
## Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1  35.42234
## Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  39.35091
## Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1  34.10853
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2  42.52336
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  74.68605
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  39.20863

4) Take a subset of mtcars data for cars having 4 cylinders AND wt > 3

# cars having 4 cylinders AND wt > 3
mtcars5  <- subset(mtcars, cyl == 4 & wt > 3)
mtcars5   

##            mpg cyl  disp hp drat   wt qsec vs am gear carb hpwtRatio
## Merc 240D 24.4   4 146.7 62 3.69 3.19 20.0  1  0    4    2  19.43574
## Merc 230  22.8   4 140.8 95 3.92 3.15 22.9  1  0    4    2  30.15873

5) Take a subset of mtcars data for cars having either 4 or 6 cylinders

# cars having  either 4 or 6 cylinders (i.e. not having 8 cylinders)
mtcars6 <- subset(mtcars, cyl == 4 | cyl == 6)
mtcars6

##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb hpwtRatio
## Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  41.98473
## Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  38.26087
## Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1  40.08621
## Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1  34.21462
## Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1  30.34682
## Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2  19.43574
## Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2  30.15873
## Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4  35.75581
## Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4  35.75581
## Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1  30.00000
## Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2  32.19814
## Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1  35.42234
## Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  39.35091
## Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1  34.10853
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2  42.52336
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  74.68605
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  63.17690
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  39.20863

# cars having  either 4 or 6 cylinders (i.e. not having 8 cylinders)
mtcars7 <- subset(mtcars, cyl != 8)
mtcars7

##                 mpg cyl  disp  hp drat    wt  qsec vs am gear carb hpwtRatio
## Mazda RX4      21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4  41.98473
## Mazda RX4 Wag  21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4  38.26087
## Datsun 710     22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1  40.08621
## Hornet 4 Drive 21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1  34.21462
## Valiant        18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1  30.34682
## Merc 240D      24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2  19.43574
## Merc 230       22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2  30.15873
## Merc 280       19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4  35.75581
## Merc 280C      17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4  35.75581
## Fiat 128       32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1  30.00000
## Honda Civic    30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2  32.19814
## Toyota Corolla 33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1  35.42234
## Toyota Corona  21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1  39.35091
## Fiat X1-9      27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1  34.10853
## Porsche 914-2  26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2  42.52336
## Lotus Europa   30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2  74.68605
## Ferrari Dino   19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6  63.17690
## Volvo 142E     21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2  39.20863

Detour - Using c() to create a Vector of Numbers, Characters

# Numeric vector
odd = c(1,3,5,7,9)
odd

## [1] 1 3 5 7 9

class(odd)

## [1] "numeric"

odd+5

## [1]  6  8 10 12 14

odd^2

## [1]  1  9 25 49 81

# Character vector
t0 = c("mpg","wt","cyl")
t0

## [1] "mpg" "wt"  "cyl"

class(t0)

## [1] "character"

PART 3 – Conditional Operators

1) Create a dataframe containing only the mpg, wt, cyl columnns from mtcars

mtcars8 = mtcars[, c("mpg","wt","cyl")]
mtcars8

##                      mpg    wt cyl
## Mazda RX4           21.0 2.620   6
## Mazda RX4 Wag       21.0 2.875   6
## Datsun 710          22.8 2.320   4
## Hornet 4 Drive      21.4 3.215   6
## Hornet Sportabout   18.7 3.440   8
## Valiant             18.1 3.460   6
## Duster 360          14.3 3.570   8
## Merc 240D           24.4 3.190   4
## Merc 230            22.8 3.150   4
## Merc 280            19.2 3.440   6
## Merc 280C           17.8 3.440   6
## Merc 450SE          16.4 4.070   8
## Merc 450SL          17.3 3.730   8
## Merc 450SLC         15.2 3.780   8
## Cadillac Fleetwood  10.4 5.250   8
## Lincoln Continental 10.4 5.424   8
## Chrysler Imperial   14.7 5.345   8
## Fiat 128            32.4 2.200   4
## Honda Civic         30.4 1.615   4
## Toyota Corolla      33.9 1.835   4
## Toyota Corona       21.5 2.465   4
## Dodge Challenger    15.5 3.520   8
## AMC Javelin         15.2 3.435   8
## Camaro Z28          13.3 3.840   8
## Pontiac Firebird    19.2 3.845   8
## Fiat X1-9           27.3 1.935   4
## Porsche 914-2       26.0 2.140   4
## Lotus Europa        30.4 1.513   4
## Ford Pantera L      15.8 3.170   8
## Ferrari Dino        19.7 2.770   6
## Maserati Bora       15.0 3.570   8
## Volvo 142E          21.4 2.780   4

2) Create a dataframe containing only the columns {mpg, wt, cyl} for cars having mpg > 25

2) Solution using subset() function

mtcars9 <- subset(mtcars, mpg > 25, select=c(mpg, wt, cyl))
mtcars9

##                 mpg    wt cyl
## Fiat 128       32.4 2.200   4
## Honda Civic    30.4 1.615   4
## Toyota Corolla 33.9 1.835   4
## Fiat X1-9      27.3 1.935   4
## Porsche 914-2  26.0 2.140   4
## Lotus Europa   30.4 1.513   4

2) Solution using which() function

mtcars9b <- mtcars[which(mpg > 25), c("mpg","wt","cyl")]
mtcars9b

##                 mpg    wt cyl
## Fiat 128       32.4 2.200   4
## Honda Civic    30.4 1.615   4
## Toyota Corolla 33.9 1.835   4
## Fiat X1-9      27.3 1.935   4
## Porsche 914-2  26.0 2.140   4
## Lotus Europa   30.4 1.513   4

3) Create a dataframe containing the cars which have mpg > 25 & cyl = 4, using subset()

# using subset function
mtcars10 <- subset(mtcars, mpg > 25 & cyl == 4, select=c(mpg, wt, cyl))
mtcars10

##                 mpg    wt cyl
## Fiat 128       32.4 2.200   4
## Honda Civic    30.4 1.615   4
## Toyota Corolla 33.9 1.835   4
## Fiat X1-9      27.3 1.935   4
## Porsche 914-2  26.0 2.140   4
## Lotus Europa   30.4 1.513   4