Subsetting in R Programming
Last Updated :
08 Nov, 2021
In R Programming Language, subsetting allows the user to access elements from an object. It takes out a portion from the object based on the condition provided. There are 4 ways of subsetting in R programming. Each of the methods depends on the usability of the user and the type of object. For example, if there is a dataframe with many columns such as states, country, and population and suppose the user wants to extract states from it, then subsetting is used to do this operation. In this article, let us discuss the implementation of different types of subsetting in R programming.
R – subsetting
Method 1: Subsetting in R Using [ ] Operator
Using the ‘[ ]’ operator, elements of vectors and observations from data frames can be accessed. To neglect some indexes, ‘-‘ is used to access all other indexes of vector or data frame.
Example 1:
In this example, let us create a vector and perform subsetting using the [ ] operator.
R
x <- 1:15
cat("Original vector: ", x, "\n")
cat("First 5 values of vector: ", x[1:5], "\n")
cat("Without values present at index 1, 2 and 3: ",
x[-c(1, 2, 3)], "\n")
|
Output:
Original vector: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
First 5 values of vector: 1 2 3 4 5
Without values present at index 1, 2 and 3: 4 5 6 7 8 9 10 11 12 13 14 15
Example 2:
In this example, let us use mtcars data frame present in R base package for subsetting.
R
cat("Original dataset: \n")
print(mtcars)
cat("HP values of all cars:\n")
print(mtcars['hp'])
cat("Without mpg and cyl column:\n")
print(mtcars[1:10, -c(1, 2)])
|
Output:
Original dataset:
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
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
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 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
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
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 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
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
HP values of all cars:
hp
Mazda RX4 110
Mazda RX4 Wag 110
Datsun 710 93
Hornet 4 Drive 110
Hornet Sportabout 175
Valiant 105
Duster 360 245
Merc 240D 62
Merc 230 95
Merc 280 123
Merc 280C 123
Merc 450SE 180
Merc 450SL 180
Merc 450SLC 180
Cadillac Fleetwood 205
Lincoln Continental 215
Chrysler Imperial 230
Fiat 128 66
Honda Civic 52
Toyota Corolla 65
Toyota Corona 97
Dodge Challenger 150
AMC Javelin 150
Camaro Z28 245
Pontiac Firebird 175
Fiat X1-9 66
Porsche 914-2 91
Lotus Europa 113
Ford Pantera L 264
Ferrari Dino 175
Maserati Bora 335
Volvo 142E 109
Without mpg and cyl column:
disp hp drat wt qsec vs am gear carb
Mazda RX4 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 167.6 123 3.92 3.440 18.30 1 0 4 4
Method 2: Subsetting in R Using [[ ]] Operator
[[ ]] operator is used for subsetting of list-objects. This operator is the same as [ ] operator but the only difference is that [[ ]] selects only one element whereas [ ] operator can select more than 1 element in a single command.
Example 1: In this example, let us create a list and select the elements using [[]] operator.
R
ls <- list(a = 1, b = 2, c = 10, d = 20)
cat("Original List: \n")
print(ls)
cat("First element of list: ", ls[[1]], "\n")
|
Output:
Original List:
$a
[1] 1
$b
[1] 2
$c
[1] 10
$d
[1] 20
First element of list: 1
Example 2: In this example, let us create a list and recursively select elements using c() function.
R
z <- list(a = list(x = 1, y = "GFG"), b = 1:10)
cat("Original list:\n")
print(z)
cat("Using c() function:\n")
print(z[[c(1, 2)]])
cat("Using [[]] operator:\n")
print(z[[1]][[2]])
|
Output:
Original list:
$a
$a$x
[1] 1
$a$y
[1] "GFG"
$b
[1] 1 2 3 4 5 6 7 8 9 10
Using c() function:
[1] "GFG"
Using [[]] operator:
[1] "GFG"
Method 3: Subsetting in R Using $ Operator
$ operator can be used for lists and data frames in R. Unlike [ ] operator, it selects only a single observation at a time. It can be used to access an element in named list or a column in data frame. $ operator is only applicable for recursive objects or list-like objects.
Example 1: In this example, let us create a named list and access the elements using $ operator
R
ls <- list(a = 1, b = 2, c = "Hello", d = "GFG")
cat("Original list:\n")
print(ls)
cat("Using $ operator:\n")
print(ls$d)
|
Output:
Original list:
$a
[1] 1
$b
[1] 2
$c
[1] "Hello"
$d
[1] "GFG"
Using $ operator:
[1] "GFG"
Example 2: In this example, let us use the mtcars dataframe and select a particular column using $ operator.
R
cat("Original data frame:\n")
print(mtcars)
cat("Using $ operator:\n")
print(mtcars$hp)
|
Output:
Original data frame:
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
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
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 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
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
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 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
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
Using $ operator:
[1] 110 110 93 110 175 105 245 62 95 123 123 180 180 180 205 215 230 66 52
[20] 65 97 150 150 245 175 66 91 113 264 175 335 109
Method 4: Subsetting in R Using subset() Function
subset() function in R programming is used to create a subset of vectors, matrices, or data frames based on the conditions provided in the parameters.
Syntax: subset(x, subset, select)
Parameters:
- x: indicates the object
- subset: indicates the logical expression on the basis of which subsetting has to be done
- select: indicates columns to select
Example 1: In this example, let us use airquality data frame present in R base package and select Month where Temp < 65.
R
airq <- subset(airquality, Temp < 65,
select = c(Month))
print(airq)
|
Output:
Month
4 5
5 5
8 5
9 5
15 5
16 5
18 5
20 5
21 5
23 5
24 5
25 5
26 5
27 5
144 9
148 9
Example 2: In this example, let us use mtcars data frame present in R base package and selects the car with 5 gears and hp > 200.
R
mtc <- subset(mtcars, gear == 5 & hp > 200,
select = c(gear, hp))
print(mtc)
|
Output:
gear hp
Ford Pantera L 5 264
Maserati Bora 5 335
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