R’s subsetting operators are powerful and fast. Mastery of subsetting allows you to succinctly express complex operations in a way that few other languages can match.

As an illustration in this articles we will cover these topics.

- The three subsetting operators,
- The six types of subsetting,
- Important difference in subsetting behavior for different objects.
- Using subsetting in conjunction with the assignment.

**Subsetting atomic vectors**

`> x <- c(1.4, 2.2, 3.0, 4.5, 5.2, 6.9, 7.6, 8.1, 9.5, 10.0)`

**We can subset this in 5 ways**

**We can subset this in 5 ways**

**Positive integers** return elements at the specified positions:

```
> x[c(1)]
[1] 1.4
> x[c(5,6,2)]
[1] 5.2 6.9 2.2
# Duplicated indices yield duplicated values
> x[c(1,1)]
[1] 1.4 1.4
# Real numbers are silently truncated to integers
> x[c(7.1, 7.9, 7.5)]
[1] 7.6 7.6 7.6
```

**Negative integers** omit elements at the specified positions:

```
# skip the first element
> x[-1]
[1] 2.2 3.0 4.5 5.2 6.9 7.6 8.1 9.5 10.0
# skip the 3rd, 5th, and 7th
> x[-c(3, 5, 7)]
[1] 1.4 2.2 4.5 6.9 8.1 9.5 10.0
```

*You can’t mix positive and negative integers in a single subset*.

```
> x[c(-1, 4)]
Error in x[c(-1, 4)] : only 0's may be mixed with negative subscripts
```

**Logical vectors** select elements where the corresponding logical value is `TRUE`

. Hence this is probably the most useful type of subsetting because you write the expression that creates the logical vector.

```
# Logical values will be assign from the start and will repeat until it reaches the
# last element of the list and will return all TRUE values.
> x[c(TRUE, TRUE, FALSE, FALSE)]
[1] 1.4 2.2 5.2 6.9 9.5 10.0
# Can also be based on condition
> x[ x > 5]
[1] 5.2 6.9 7.6 8.1 9.5 10.0
> x[which.min(x)]
[1] 1.4
> x[which.max(x)]
[1] 10
```

**Nothing** returns the original vector. This is not useful for vectors but is very useful for matrices, data frames, and arrays. It can also be useful in conjunction with assignment.

```
> x[]
[1] 1.4 2.2 3.0 4.5 5.2 6.9 7.6 8.1 9.5 10.0
```

**Zero** returns a zero-length vector. This is not something you usually do on purpose, but it can be helpful for generating test data.

```
> x[0]
numeric(0)
```

**Subsetting lists**

In the same way as subsetting an atomic vector. Subsetting a list with [ will always return a list: `[[`

and `$`

, as described below, let you pull out the components of the list.

```
> x <- as.list(1:10)
> x[1:4]
[[1]]
[1] 1
[[2]]
[1] 2
[[3]]
[1] 3
[[4]]
[1] 4
```

Also, to extract individual elements in a list, use `[[`

operator

```
# to get element 5
> x[[2]]
[1] 2
> class(x[[2]])
[1] "integer"
# Using name
> names(x) <- letters[1:10]
> x$a
[1] 1
> x[c('a', 'b')]
$a
[1] 1
$b
[1] 2
```

**Subsetting matrices**

A matrix is a subset with two arguments within single brackets, [], and separated by a comma. So, the first argument specifies the rows and the second the columns.

```
# Create a matrix
> mat <- matrix(1:9, nrow = 3)
> colnames(mat) <- LETTERS[1:3]
> mat
A B C
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
> mat[1:3,'A']
[1] 1 2 3
> mat[1:3,'C']
[1] 7 8 9
> mat[1:3,]
A B C
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
```

**Subsetting data frames**

Also, Data frames possess the characteristics of both lists and matrices: if you subset with a single vector, they behave like lists; if you subset with two vectors, therefore they behave like matrices.

```
> df <- data.frame(x = 1:3, y = 3:1, z = letters[1:3])
# to get the row of the column where the values is 2
> df[df$x == 2, ]
x y z
2 2 2 b
# There are two ways to select a columns from data frame
# As list
> df[c('x', 'z')]
x z
1 1 a
2 2 b
3 3 c
# As a matrix
> df[, c('x', 'z')]
x z
1 1 a
2 2 b
3 3 c
> str(df)
'data.frame': 3 obs. of 3 variables:
$ x: int 1 2 3
$ y: int 3 2 1
$ z: chr "a" "b" "c"
```

**Conclusion**

Hence, we saw how to subset an atomic vector, list, matrix, and data frame. Also saw how to access elements in each of those data structures.

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