# 5 Subsetting

## 5.1 Introduction

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. Subsetting is hard to learn because you need to master a number of interrelated concepts:

The three subsetting operators.

The six types of subsetting.

Important differences in behaviour for different objects (e.g., vectors, lists, factors, matrices, and data frames).

The use of subsetting in conjunction with assignment.

This chapter helps you master subsetting by starting with the simplest type of subsetting: subsetting an atomic vector with `[`

. It then gradually extends your knowledge, first to more complicated data types (like arrays and lists), and then to the other subsetting operators, `[[`

and `$`

. You’ll then learn how subsetting and assignment can be combined to modify parts of an object, and, finally, you’ll see a large number of useful applications.

Subsetting is a natural complement to `str()`

. `str()`

shows you the structure of any object, and subsetting allows you to pull out the pieces that you’re interested in.

### Quiz

Take this short quiz to determine if you need to read this chapter. If the answers quickly come to mind, you can comfortably skip this chapter. Check your answers in answers.

What is the result of subsetting a vector with positive integers, negative integers, a logical vector, or a character vector?

What’s the difference between

`[`

,`[[`

, and`$`

when applied to a list?When should you use

`drop = FALSE`

?If

`x`

is a matrix, what does`x[] <- 0`

do? How is it different to`x <- 0`

?How can you use a named vector to relabel categorical variables?

### Outline

Data types starts by teaching you about

`[`

. You’ll start by learning the six types of data that you can use to subset atomic vectors. You’ll then learn how those six data types act when used to subset lists, matrices, data frames, and S3 objects.Subsetting operators expands your knowledge of subsetting operators to include

`[[`

and`$`

, focussing on the important principles of simplifying vs. preserving.In Subsetting and assignment you’ll learn the art of subassignment, combining subsetting and assignment to modify parts of an object.

Applications leads you through eight important, but not obvious, applications of subsetting to solve problems that you often encounter in a data analysis.

## 5.2 Selecting multiple elements

It’s easiest to learn how subsetting works for atomic vectors, and then how it generalises to higher dimensions and other more complicated objects. We’ll start with `[`

, the most commonly used operator which allows you to extract any number of elements. Selecting a single element will cover `[[`

and `$`

, used to extra a single element from a data structure.

### 5.2.1 Atomic vectors

Let’s explore the different types of subsetting with a simple vector, `x`

.

Note that the number after the decimal point gives the original position in the vector.

There are five things that you can use to subset a vector:

**Positive integers**return elements at the specified positions:**Negative integers**omit elements at the specified positions:You can’t mix positive and negative integers in a single subset:

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

. This is probably the most useful type of subsetting because you write the expression that creates the logical vector:If the logical vector is shorter than the vector being subsetted, it will be

*recycled*to be the same length.A missing value in the index always yields a missing value in the output:

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

If the vector is named, you can also use:

**Character vectors**to return elements with matching names.`(y <- setNames(x, letters[1:4])) #> a b c d #> 2.1 4.2 3.3 5.4 y[c("d", "c", "a")] #> d c a #> 5.4 3.3 2.1 # Like integer indices, you can repeat indices y[c("a", "a", "a")] #> a a a #> 2.1 2.1 2.1 # When subsetting with [ names are always matched exactly z <- c(abc = 1, def = 2) z[c("a", "d")] #> <NA> <NA> #> NA NA`

### 5.2.2 Lists

Subsetting a list works in the same way as subsetting an atomic vector. Using `[`

will always return a list; `[[`

and `$`

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

### Matrices and arrays {#matrix-subsetting}

You can subset higher-dimensional structures in three ways:

- With multiple vectors.
- With a single vector.
- With a matrix.

The most common way of subsetting matrices (2d) and arrays (>2d) is a simple generalisation of 1d subsetting: you supply a 1d index for each dimension, separated by a comma. Blank subsetting is now useful because it lets you keep all rows or all columns.

```
a <- matrix(1:9, nrow = 3)
colnames(a) <- c("A", "B", "C")
a[1:2, ]
#> A B C
#> [1,] 1 4 7
#> [2,] 2 5 8
a[c(TRUE, FALSE, TRUE), c("B", "A")]
#> B A
#> [1,] 4 1
#> [2,] 6 3
a[0, -2]
#> A C
```

By default, `[`

will simplify the results to the lowest possible dimensionality. See simplifying vs. preserving to learn how to avoid this.

Because matrices and arrays are implemented as vectors with special attributes, you can subset them with a single vector. In that case, they will behave like a vector. Arrays in R are stored in column-major order:

```
(vals <- outer(1:5, 1:5, FUN = "paste", sep = ","))
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] "1,1" "1,2" "1,3" "1,4" "1,5"
#> [2,] "2,1" "2,2" "2,3" "2,4" "2,5"
#> [3,] "3,1" "3,2" "3,3" "3,4" "3,5"
#> [4,] "4,1" "4,2" "4,3" "4,4" "4,5"
#> [5,] "5,1" "5,2" "5,3" "5,4" "5,5"
vals[c(4, 15)]
#> [1] "4,1" "5,3"
```

You can also subset higher-dimensional data structures with an integer matrix (or, if named, a character matrix). Each row in the matrix specifies the location of one value, where each column corresponds to a dimension in the array being subsetted. This means that you use a 2 column matrix to subset a matrix, a 3 column matrix to subset a 3d array, and so on. The result is a vector of values:

### 5.2.3 Data frames

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, they behave like matrices.

```
df <- data.frame(x = 1:3, y = 3:1, z = letters[1:3])
df[df$x == 2, ]
#> x y z
#> 2 2 2 b
df[c(1, 3), ]
#> x y z
#> 1 1 3 a
#> 3 3 1 c
# There are two ways to select columns from a data frame
# Like a list:
df[c("x", "z")]
#> x z
#> 1 1 a
#> 2 2 b
#> 3 3 c
# Like a matrix
df[, c("x", "z")]
#> x z
#> 1 1 a
#> 2 2 b
#> 3 3 c
# There's an important difference if you select a single
# column: matrix subsetting simplifies by default, list
# subsetting does not.
str(df["x"])
#> 'data.frame': 3 obs. of 1 variable:
#> $ x: int 1 2 3
str(df[, "x"])
#> int [1:3] 1 2 3
```

### 5.2.4 Preserving dimensionality

By default, any subsetting 2d data structures with a single number, single name, or a logical vector containing a single `TRUE`

will simplify the returned output as described below. To preserve the original dimensionality, you must use `drop = FALSE`

For matrices and arrays, any dimensions with length 1 will be dropped:

Data frames with a single column will return just that column:

The default `drop = TRUE`

behaviour is a common source of bugs in functions: you check your code with a data frame or matrix with multiple columns, and it works. Six months later you (or someone else) uses it with a single column data frame and it fails with a mystifying error. When writing functions, get in the habit of always using `drop = FALSE`

when subsetting a 2d object.

Factor subsetting also has a `drop`

argument, but the meaning is rather different. It controls whether or not levels are preserved (not the dimensionality), and it defaults to `FALSE`

(levels are preserved, not simplified by default). If you find you are using `drop = TRUE`

a lot it’s often a sign that you should be using a character vector instead of a factor.

### 5.2.5 S3 objects

S3 objects are made up of atomic vectors, arrays, and lists, so you can always pull apart an S3 object using the techniques described above and the knowledge you gain from `str()`

.

### 5.2.6 S4 objects

There are also two additional subsetting operators that are needed for S4 objects: `@`

(equivalent to `$`

), and `slot()`

(equivalent to `[[`

). `@`

is more restrictive than `$`

in that it will return an error if the slot does not exist. These are described in more detail in S4.

### 5.2.7 Exercises

Fix each of the following common data frame subsetting errors:

Why does

`x <- 1:5; x[NA]`

yield five missing values? (Hint: why is it different from`x[NA_real_]`

?)What does

`upper.tri()`

return? How does subsetting a matrix with it work? Do we need any additional subsetting rules to describe its behaviour?Why does

`mtcars[1:20]`

return an error? How does it differ from the similar`mtcars[1:20, ]`

?Implement your own function that extracts the diagonal entries from a matrix (it should behave like

`diag(x)`

where`x`

is a matrix).What does

`df[is.na(df)] <- 0`

do? How does it work?

## 5.3 Selecting a single element

There are two other subsetting operators: `[[`

and `$`

. `[[`

is used for extracting single values, and `$`

is a useful shorthand for `[[`

combined with character subsetting. `[[`

is most important working with lists because subsetting a list with `[`

always returns a smaller list. To help make this easier to understand we can use a metaphor:

“If list

`x`

is a train carrying objects, then`x[[5]]`

is the object in car 5;`x[4:6]`

is a train of cars 4-6.”— @RLangTip, https://twitter.com/RLangTip/status/268375867468681216

Let’s make a simple list and draw it as a train:

When extracting a single element, you have two options: you can create a smaller train, or you can extract the contents of a carriage. This is the difference between `[`

and `[[`

:

When extracting multiple elements (or zero!), you have to make a smaller train:

Because it can return only a single value, you must use `[[`

with either a single positive integer or a string. Because data frames are lists of columns, you can use `[[`

to extract a column from data frames: `mtcars[[1]]`

, `mtcars[["cyl"]]`

. S3 and S4 objects can override the standard behaviour of `[`

and `[[`

so they behave differently for different types of objects.
If you use a vector with `[[`

, it will subset recursively:

```
b <- list(a = list(b = list(c = list(d = 1))))
b[[c("a", "b", "c", "d")]]
#> [1] 1
# Equivalent to
b[["a"]][["b"]][["c"]][["d"]]
#> [1] 1
```

`[[`

is crucial for working with lists, but I recommend using it whenever you want your code to clearly express that it’s working with a single value. That frequently arises in for loops, e.g., instead of writing:

It’s better to write:

### 5.3.1 `$`

`$`

is a shorthand operator: `x$y`

is roughly equivalent to `x[["y"]]`

. It’s often used to access variables in a data frame, as in `mtcars$cyl`

or `diamonds$carat`

. One common mistake with `$`

is to try and use it when you have the name of a column stored in a variable:

```
var <- "cyl"
# Doesn't work - mtcars$var translated to mtcars[["var"]]
mtcars$var
#> NULL
# Instead use [[
mtcars[[var]]
#> [1] 6 6 4 6 8 6 8 4 4 6 6 8 8 8 8 8 8 4 4 4 4 8 8 8 8 4 4 4 8 6 8 4
```

There’s one important difference between `$`

and `[[`

. `$`

does partial matching:

To help avoid this behaviour I highly recommend setting the global option `warnPartialMatchDollar`

to `TRUE`

:

```
options(warnPartialMatchDollar = TRUE)
x$a
#> Warning in x$a: partial match of 'a' to 'abc'
#> [1] 1
```

(For data frames specifically, you can avoid this problem by using tibbles instead: they never do partial matching.)

### 5.3.2 Missing/out of bounds indices

It’s useful to understand what happens with `[`

and `[[`

when you use an “invalid” index. The following tables summarise what happen when you subset a logical vector, list, and `NULL`

with an out-of-bounds value (OOB), a missing value (i.e `NA_integer_`

), and a zero-length object (like `NULL`

or `logical()`

) with `[`

and `[[`

. Each cell shows the result of subsetting the data structure named in the row by the type of index described in the column. I’ve only shown the results for logical vectors, but other atomic vectors behave similarly, returning elements of the same type.

`row[col]` |
Zero-length | OOB | Missing |
---|---|---|---|

`NULL` |
`NULL` |
`NULL` |
`NULL` |

Logical | `logical(0)` |
`NA` |
`NA` |

List | `list()` |
`list(NULL)` |
`list(NULL)` |

With `[`

, it doesn’t matter whether the OOB index is a position or a name, but it does for `[[`

:

`row[[col]]` |
Zero-length | OOB (int) | OOB (chr) | Missing |
---|---|---|---|---|

`NULL` |
`NULL` |
`NULL` |
`NULL` |
`NULL` |

Atomic | Error | Error | Error | Error |

List | Error | Error | `NULL` |
`NULL` |

If the input vector is named, then the names of OOB, missing, or `NULL`

components will be `"<NA>"`

.

### 5.3.3 `pluck()`

The inconsistency of the `[[`

table above lead to the development of `purrr::pluck()`

, which solves the inconsistency by always returning `NULL`

:

`pluck(row, col)` |
Zero-length | OOB (int) | OOB (chr) | Missing |
---|---|---|---|---|

`NULL` |
`NULL` |
`NULL` |
`NULL` |
`NULL` |

Atomic | `NULL` |
`NULL` |
`NULL` |
`NULL` |

List | `NULL` |
`NULL` |
`NULL` |
`NULL` |

(A future function will solve the inconsistency in the other direction: by consistently throwing an error whenever the component is absent.)

The behaviour of `pluck()`

makes it well suited for indexing into deeply nested data structures where the component you want does not exist always exist (this is common when working with JSON data from web APIs). `pluck()`

also allows you to mingle integer and character indexes, and to provide an alternative default value if the item does not exist:

### 5.3.4 Exercises

Come up with as many way as possible to extract the third value from the

`cyl`

variable in the`mtcars`

dataset.Given a linear model, e.g.,

`mod <- lm(mpg ~ wt, data = mtcars)`

, extract the residual degrees of freedom. Extract the R squared from the model summary (`summary(mod)`

)

## 5.4 Subsetting and assignment

All subsetting operators can be combined with assignment to modify selected values of the input vector.

```
x <- 1:5
x[c(1, 2)] <- 2:3
x
#> [1] 2 3 3 4 5
# The length of the LHS needs to match the RHS
x[-1] <- 4:1
x
#> [1] 2 4 3 2 1
# Duplicated indices go unchecked and may be problematic
x[c(1, 1)] <- 2:3
x
#> [1] 3 4 3 2 1
# You can't combine integer indices with NA
x[c(1, NA)] <- c(1, 2)
#> Error in x[c(1, NA)] <- c(1, 2): NAs are not allowed in subscripted assignments
# But you can combine logical indices with NA
# (where they're treated as false).
x[c(T, F, NA)] <- 1
x
#> [1] 1 4 3 1 1
# This is mostly useful when conditionally modifying vectors
df <- data.frame(a = c(1, 10, NA))
df$a[df$a < 5] <- 0
df$a
#> [1] 0 10 NA
```

Subsetting with nothing can be useful in conjunction with assignment because it will preserve the original object class and structure. Compare the following two expressions. In the first, `mtcars`

will remain as a data frame. In the second, `mtcars`

will become a list.

With lists, you can use `[[`

+ assignment + `NULL`

to remove components from a list. To add a literal `NULL`

to a list, use `[`

and `list(NULL)`

:

## 5.5 Applications

The basic principles described above give rise to a wide variety of useful applications. Some of the most important are described below. Many of these basic techniques are wrapped up into more concise functions (e.g., `subset()`

, `merge()`

, `dplyr::arrange()`

), but it is useful to understand how they are implemented with basic subsetting. This will allow you to adapt to new situations that are not dealt with by existing functions.

### 5.5.1 Lookup tables (character subsetting)

Character matching provides a powerful way to make lookup tables. Say you want to convert abbreviations:

```
x <- c("m", "f", "u", "f", "f", "m", "m")
lookup <- c(m = "Male", f = "Female", u = NA)
lookup[x]
#> m f u f f m m
#> "Male" "Female" NA "Female" "Female" "Male" "Male"
unname(lookup[x])
#> [1] "Male" "Female" NA "Female" "Female" "Male" "Male"
```

If you don’t want names in the result, use `unname()`

to remove them.

### 5.5.2 Matching and merging by hand (integer subsetting)

You may have a more complicated lookup table which has multiple columns of information. Suppose we have a vector of integer grades, and a table that describes their properties:

```
grades <- c(1, 2, 2, 3, 1)
info <- data.frame(
grade = 3:1,
desc = c("Excellent", "Good", "Poor"),
fail = c(F, F, T)
)
```

We want to duplicate the info table so that we have a row for each value in `grades`

. An elegant way to do this is by combining `match()`

and integer subsetting:

```
id <- match(grades, info$grade)
info[id, ]
#> grade desc fail
#> 3 1 Poor TRUE
#> 2 2 Good FALSE
#> 2.1 2 Good FALSE
#> 1 3 Excellent FALSE
#> 3.1 1 Poor TRUE
```

If you have multiple columns to match on, you’ll need to first collapse them to a single column (with e.g. `interaction()`

), but typically you are better off switching to a function design specifically for joining multiple tables like `merge()`

, or `dplyr::left_join()`

.

### 5.5.3 Random samples/bootstrap (integer subsetting)

You can use integer indices to perform random sampling or bootstrapping of a vector or data frame. `sample()`

generates a vector of indices, then subsetting accesses the values:

```
df <- data.frame(x = rep(1:3, each = 2), y = 6:1, z = letters[1:6])
# Randomly reorder
df[sample(nrow(df)), ]
#> x y z
#> 1 1 6 a
#> 5 3 2 e
#> 3 2 4 c
#> 6 3 1 f
#> 4 2 3 d
#> 2 1 5 b
# Select 3 random rows
df[sample(nrow(df), 3), ]
#> x y z
#> 3 2 4 c
#> 2 1 5 b
#> 6 3 1 f
# Select 6 bootstrap replicates
df[sample(nrow(df), 6, rep = TRUE), ]
#> x y z
#> 5 3 2 e
#> 6 3 1 f
#> 2 1 5 b
#> 1 1 6 a
#> 2.1 1 5 b
#> 3 2 4 c
```

The arguments of `sample()`

control the number of samples to extract, and whether sampling is performed with or without replacement.

### 5.5.4 Ordering (integer subsetting)

`order()`

takes a vector as input and returns an integer vector describing how the subsetted vector should be ordered:

To break ties, you can supply additional variables to `order()`

, and you can change from ascending to descending order using `decreasing = TRUE`

. By default, any missing values will be put at the end of the vector; however, you can remove them with `na.last = NA`

or put at the front with `na.last = FALSE`

.

For two or more dimensions, `order()`

and integer subsetting makes it easy to order either the rows or columns of an object:

```
# Randomly reorder df
df2 <- df[sample(nrow(df)), 3:1]
df2
#> z y x
#> 2 b 5 1
#> 3 c 4 2
#> 1 a 6 1
#> 6 f 1 3
#> 5 e 2 3
#> 4 d 3 2
df2[order(df2$x), ]
#> z y x
#> 2 b 5 1
#> 1 a 6 1
#> 3 c 4 2
#> 4 d 3 2
#> 6 f 1 3
#> 5 e 2 3
df2[, order(names(df2))]
#> x y z
#> 2 1 5 b
#> 3 2 4 c
#> 1 1 6 a
#> 6 3 1 f
#> 5 3 2 e
#> 4 2 3 d
```

You can sort vectors directly with `sort()`

, or use `dplyr::arrange()`

or similar to sort a data frame.

### 5.5.5 Expanding aggregated counts (integer subsetting)

Sometimes you get a data frame where identical rows have been collapsed into one and a count column has been added. `rep()`

and integer subsetting make it easy to uncollapse the data by subsetting with a repeated row index:

### 5.5.6 Removing columns from data frames (character subsetting)

There are two ways to remove columns from a data frame. You can set individual columns to `NULL`

:

Or you can subset to return only the columns you want:

```
df <- data.frame(x = 1:3, y = 3:1, z = letters[1:3])
df[c("x", "y")]
#> x y
#> 1 1 3
#> 2 2 2
#> 3 3 1
```

If you know the columns you don’t want, use set operations to work out which colums to keep:

### 5.5.7 Selecting rows based on a condition (logical subsetting)

Because it allows you to easily combine conditions from multiple columns, logical subsetting is probably the most commonly used technique for extracting rows out of a data frame.

```
mtcars[mtcars$gear == 5, ]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 27 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 29 15.8 8 351.0 264 4.22 3.17 14.5 0 1 5 4
#> 30 19.7 6 145.0 175 3.62 2.77 15.5 0 1 5 6
#> 31 15.0 8 301.0 335 3.54 3.57 14.6 0 1 5 8
mtcars[mtcars$gear == 5 & mtcars$cyl == 4, ]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 27 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
```

Remember to use the vector boolean operators `&`

and `|`

, not the short-circuiting scalar operators `&&`

and `||`

which are more useful inside if statements. Don’t forget De Morgan’s laws, which can be useful to simplify negations:

`!(X & Y)`

is the same as`!X | !Y`

`!(X | Y)`

is the same as`!X & !Y`

For example, `!(X & !(Y | Z))`

simplifies to `!X | !!(Y|Z)`

, and then to `!X | Y | Z`

.

`subset()`

is a specialised shorthand function for subsetting data frames, and saves some typing because you don’t need to repeat the name of the data frame. You’ll learn how it works in metaprogramming.

```
subset(mtcars, gear == 5)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 27 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
#> 29 15.8 8 351.0 264 4.22 3.17 14.5 0 1 5 4
#> 30 19.7 6 145.0 175 3.62 2.77 15.5 0 1 5 6
#> 31 15.0 8 301.0 335 3.54 3.57 14.6 0 1 5 8
subset(mtcars, gear == 5 & cyl == 4)
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> 27 26.0 4 120.3 91 4.43 2.14 16.7 0 1 5 2
#> 28 30.4 4 95.1 113 3.77 1.51 16.9 1 1 5 2
```

### 5.5.8 Boolean algebra vs. sets (logical & integer subsetting)

It’s useful to be aware of the natural equivalence between set operations (integer subsetting) and boolean algebra (logical subsetting). Using set operations is more effective when:

You want to find the first (or last)

`TRUE`

.You have very few

`TRUE`

s and very many`FALSE`

s; a set representation may be faster and require less storage.

`which()`

allows you to convert a boolean representation to an integer representation. There’s no reverse operation in base R but we can easily create one:

```
x <- sample(10) < 4
which(x)
#> [1] 3 6 7
unwhich <- function(x, n) {
out <- rep_len(FALSE, n)
out[x] <- TRUE
out
}
unwhich(which(x), 10)
#> [1] FALSE FALSE TRUE FALSE FALSE TRUE TRUE FALSE FALSE FALSE
```

Let’s create two logical vectors and their integer equivalents and then explore the relationship between boolean and set operations.

```
(x1 <- 1:10 %% 2 == 0)
#> [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE
(x2 <- which(x1))
#> [1] 2 4 6 8 10
(y1 <- 1:10 %% 5 == 0)
#> [1] FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
(y2 <- which(y1))
#> [1] 5 10
# X & Y <-> intersect(x, y)
x1 & y1
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE
intersect(x2, y2)
#> [1] 10
# X | Y <-> union(x, y)
x1 | y1
#> [1] FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE TRUE
union(x2, y2)
#> [1] 2 4 6 8 10 5
# X & !Y <-> setdiff(x, y)
x1 & !y1
#> [1] FALSE TRUE FALSE TRUE FALSE TRUE FALSE TRUE FALSE FALSE
setdiff(x2, y2)
#> [1] 2 4 6 8
# xor(X, Y) <-> setdiff(union(x, y), intersect(x, y))
xor(x1, y1)
#> [1] FALSE TRUE FALSE TRUE TRUE TRUE FALSE TRUE FALSE FALSE
setdiff(union(x2, y2), intersect(x2, y2))
#> [1] 2 4 6 8 5
```

When first learning subsetting, a common mistake is to use `x[which(y)]`

instead of `x[y]`

. Here the `which()`

achieves nothing: it switches from logical to integer subsetting but the result will be exactly the same. In more general cases, there are two important differences. First, when the logical vector contains NA, logical subsetting replaces these values by NA while `which()`

drops these values. Second, `x[-which(y)]`

is **not** equivalent to `x[!y]`

: if `y`

is all FALSE, `which(y)`

will be `integer(0)`

and `-integer(0)`

is still `integer(0)`

, so you’ll get no values, instead of all values. In general, avoid switching from logical to integer subsetting unless you want, for example, the first or last `TRUE`

value.

### 5.5.9 Exercises

How would you randomly permute the columns of a data frame? (This is an important technique in random forests.) Can you simultaneously permute the rows and columns in one step?

How would you select a random sample of

`m`

rows from a data frame? What if the sample had to be contiguous (i.e., with an initial row, a final row, and every row in between)?How could you put the columns in a data frame in alphabetical order?

## 5.6 Answers

Positive integers select elements at specific positions, negative integers drop elements; logical vectors keep elements at positions corresponding to

`TRUE`

; character vectors select elements with matching names.`[`

selects sub-lists. It always returns a list; if you use it with a single positive integer, it returns a list of length one.`[[`

selects an element within a list.`$`

is a convenient shorthand:`x$y`

is equivalent to`x[["y"]]`

.Use

`drop = FALSE`

if you are subsetting a matrix, array, or data frame and you want to preserve the original dimensions. You should almost always use it when subsetting inside a function.If

`x`

is a matrix,`x[] <- 0`

will replace every element with 0, keeping the same number of rows and columns.`x <- 0`

completely replaces the matrix with the value 0.A named character vector can act as a simple lookup table:

`c(x = 1, y = 2, z = 3)[c("y", "z", "x")]`