Explain with a case study how to use logical operators to select subset of a data based on condition in R.
Let us use a mt cars datasets inside R
df <- mt cars
df
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
carb
Mazda RX4
21
6
160
110
3.9
2.62
16.46
0
1
4
4
Mazda RX4 Wag
21
6
160
110
3.9
2.875
17.02
0
1
4
4
Datsun 710
22.8
4
108
93
3.85
2.32
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
3.44
17.02
0
0
3
2
Valiant
18.1
6
225
105
2.76
3.46
20.22
1
0
3
1
Duster 360
14.3
8
360
245
3.21
3.57
15.84
0
0
3
4
Merc 240D
24.4
4
146.7
62
3.69
3.19
20
1
0
4
2
Merc 230
22.8
4
140.8
95
3.92
3.15
22.9
1
0
4
2
Merc 280
19.2
6
167.6
123
3.92
3.44
18.3
1
0
4
4
Merc 280C
17.8
6
167.6
123
3.92
3.44
18.9
1
0
4
4
Merc 450SE
16.4
8
275.8
180
3.07
4.07
17.4
0
0
3
3
Merc 450SL
17.3
8
275.8
180
3.07
3.73
17.6
0
0
3
3
Merc 450SLC
15.2
8
275.8
180
3.07
3.78
18
0
0
3
3
Cadillac Fleetwood
10.4
8
472
205
2.93
5.25
17.98
0
0
3
4
Lincoln Continental
10.4
8
460
215
3
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
Fiat 128
32.4
4
78.7
66
4.08
2.2
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
19.9
1
1
4
1
Toyota Corona
21.5
4
120.1
97
3.7
2.465
20.01
1
0
3
1
Dodge Challenger
15.5
8
318
150
2.76
3.52
16.87
0
0
3
2
AMC Javelin
15.2
8
304
150
3.15
3.435
17.3
0
0
3
2
Camaro Z28
13.3
8
350
245
3.73
3.84
15.41
0
0
3
4
Pontiac Firebird
19.2
8
400
175
3.08
3.845
17.05
0
0
3
2
Fiat X1-9
27.3
4
79
66
4.08
1.935
18.9
1
1
4
1
Porsche 914-2
26
4
120.3
91
4.43
2.14
16.7
0
1
5
2
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.9
1
1
5
2
Ford Pantera L
15.8
8
351
264
4.22
3.17
14.5
0
1
5
4
Ferrari Dino
19.7
6
145
175
3.62
2.77
15.5
0
1
5
6
Maserati Bora
15
8
301
335
3.54
3.57
14.6
0
1
5
8
Volvo 142E
21.4
4
121
109
4.11
2.78
18.6
1
1
4
2
Now let us select whose mpg is greater than 20 and hp is greater than 100
df[(df['mpg'] >= 20) & (df['hp'] > 100),]
mpg
cyl
disp
hp
drat
wt
qsec
vs
am
gear
carb
Mazda RX4
21
6
160
110
3.9
2.62
16.46
0
1
4
4
Mazda RX4 Wag
21
6
160
110
3.9
2.875
17.02
0
1
4
4
Hornet 4 Drive
21.4
6
258
110
3.08
3.215
19.44
1
0
3
1
Lotus Europa
30.4
4
95.1
113
3.77
1.513
16.9
1
1
5
2
Volvo 142E
21.4
4
121
109
4.11
2.78
18.6
1
1
4
2