# 2 Vectors, lists, and tibbles

``library(tidyverse)``

In R, vectors are the most common data structure. In this book, we’ll often represent vectors like this: Each orange cell represents one element of the vector. As you’ll see, different kinds of vectors can hold different kinds of elements.

There are two kinds of vectors: atomic vectors and lists. Tibbles are a specific kind of list. In this chapter, we’ll cover these three data structures, explaining how they differ and showing you how to manipulate each one.

## 2.1 Atomic vectors

Atomic vectors are the “atoms” of R—the simple building blocks upon which all else is built. There are four types of atomic vector that are important for data analysis:

• integer vectors (`<int>`) contain integers.
• double vectors (`<dbl>`) contain real numbers.
• character vectors (`<chr>`) contain strings made with `""`.
• logical vectors (`<lgl>`) contain `TRUE` or `FALSE`.

Integer atomic vectors contain only integers, double atomic vectors contain only doubles, and so on. Together, integer and double vectors are known as numeric vectors. All vectors can also contain the missing value `NA`.

In R, single numbers, logicals, and strings are just atomic vectors of length 1, so

``x <- "a"``

creates a character vector. Likewise,

``y <- 1.1``

creates a double vector.

To create atomic vectors with more than one element, use `c()` to combine values.

``````logical <- c(TRUE, FALSE, FALSE)

double <- c(1.5, 2.8, pi)

character <- c("this", "is", "a character", "vector")``````

To create an integer vector by hand, you’ll need to add `L` to the end of each number.

``integer <- c(1L, 2L, 3L)``

Without the `L`s, R will create a vector of doubles.

### 2.1.1 Properties

Vectors (both atomic vectors and lists) all have two key properties: type and length.

You can check the type of any vector with `typeof()`.

``````typeof(c(1.5, 2.8, pi))
#>  "double"

typeof(c(1L, 3L, 4L))
#>  "integer"``````

Use `length()` to find a vector’s length.

``````length(c("a", "b", "c"))
#>  3``````

Vector can also have named elements.

``````v_named <- c(guava = 2, pineapple = 4, dragonfruit = 1)
v_named
#>       guava   pineapple dragonfruit
#>           2           4           1``````

You can access a vector’s names with `names()`.

``````names(v_named)
#>  "guava"       "pineapple"   "dragonfruit"``````

### 2.1.2 Subsetting

`v` is an atomic vector of doubles.

``v <- c(1, 2.7, 4, 5)``

You can subset `v` to create a new vector with selected elements, ignoring the others.

#### The `[` operator

Here are four ways to subset `v` using the `[` operator.

##### Positive integers

Subset with a vector of positive integers to extract elements by position.

``````v[c(1, 2)]
#>  1.0 2.7``````

Note that, in R, indices start at 1, not 0, so the above code extracts the first two elements of `v`.

You can also use `:` to create a vector of adjacent integers. The following select the first three elements of `v`.

``````v[1:3]
#>  1.0 2.7 4.0``````
##### Negative integers

Subset with a vector of negative integers to exclude elements. The following code removes the first and third elements of `v`.

``````v[-c(1, 3)]
#>  2.7 5.0``````
##### Names

If a vector has names, you can subset with a character vector.

``````v_named[c("guava", "dragonfruit")]
#>       guava dragonfruit
#>           2           1``````
##### Logical vectors

If you supply a vector of `TRUE`s and `FALSE`s, `[` will select the elements that correspond to the `TRUE`s. The following extracts just the first and third elements.

``````v[c(TRUE, FALSE, TRUE, FALSE)]
#>  1 4``````

You’ll rarely subset by typing out `TRUE`s and `FALSE`s. Instead, you’ll typically create a logical vector with a function or condition.

For example, the following code selects just the elements of `v` greater than 2.

``````v[v > 2]
#>  2.7 4.0 5.0``````

`v > 2` results in a logical vector the same length as `v`.

``````v > 2
#>  FALSE  TRUE  TRUE  TRUE``````

`[` then uses this logical vector to subset `v`, resulting in just the elements of `v` greater than 2.

`v_missing` has `NA`s.

``v_missing <- c(1.1, NA, 5, 6, NA)``

We can pass `!is.na(v_missing)` into `[` to extract out just the non-`NA` elements.

``````v_missing[!is.na(v_missing)]
#>  1.1 5.0 6.0``````

#### The `[[` operator

You can extract single elements from a vector with the `[[` operator.

``````v[]
#>  2.7``````

You’ll get an error if you try to use `[[` to select more than one element.

``````v[[2:3]]
#> Error in v[[2:3]]: attempt to select more than one element in vectorIndex``````

For named vectors, the `[` operator creates a subset of the original vector with the specified elements.

``````v_named["guava"]
#> guava
#>     2``````

The `[[` operator returns only the extracted element.

``````v_named[["guava"]]
#>  2``````

As you’ll see in the Lists section that the distinction between `[` and `[[` becomes more important with lists and tibbles.

### 2.1.3 Applying functions

Vectors are central to programming in R, and many R functions are designed to work with vectors.

You already saw how to call `typeof()` to return the type of a vector.

``````typeof(v)
#>  "double"``````

`sum()` sums a vector’s elements.

``````sum(v)
#>  12.7``````

You can use `sum()` with both numeric (i.e., double and integer) vectors, as well as with logical vectors.

``````logical <- c(TRUE, TRUE, FALSE)

sum(logical)
#>  2``````

When applied to a logical vector, `sum()` returns the number of `TRUE`s.

`mean()` works similarly.

``````mean(v)
#>  3.17

mean(logical)
#>  0.667``````

The Base R Cheat Sheet has some other basic helpful functions, particularly under the Vector Functions and Math Functions sections.

### 2.1.4 Augmented vectors

Augmented vectors are atomic vectors with additional metadata. There are four important augmented vectors:

• factors `<fct>`, which are used to represent categorical variables can take one of a fixed and known set of possible values (called the levels).

• ordered factors `<ord>`, which are like factors but where the levels have an intrinsic ordering (i.e. it’s reasonable to say that one level is “less than” or “greater than” another level).

• dates `<dt>`, record a date.

• date-times `<dttm>`, which are also known as POSIXct, record a date and a time.

For now, you just need to recognize these when you encounter them. You’ll learn how to create each type of augmented vector later in the course.

## 2.2 Lists

Unlike atomic vectors, which can only contain a single type, lists can contain any collection of R objects.

### 2.2.1 Basics

The following reading will introduce you to lists:

### 2.2.2 Flattening

You can flatten a list into an atomic vector with `unlist()`.

``````y <- list(1, 2, 4)
y
#> []
#>  1
#>
#> []
#>  2
#>
#> []
#>  4``````
``````unlist(y)
#>  1 2 4``````

`unlist()` returns an atomic vector even if the original list contains other lists or vectors.

``````z <- list(c(1, 2), c(3, 4))
z
#> []
#>  1 2
#>
#> []
#>  3 4``````
``````unlist(z)
#>  1 2 3 4``````

## 2.3 Tibbles

Tibbles are actually lists.

``````typeof(mpg)
#>  "list"``````

Every tibble is a named list of vectors, each of the same length. These vectors form the tibble columns.

Take the tibble `mpg`.

``````mpg
#> # A tibble: 234 × 11
#>   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class
#>   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr>
#> 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
#> 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
#> 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
#> 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
#> 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
#> 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…
#> # … with 228 more rows``````

Each variable in `mpg` (`manufacturer`, `model`, `displ`, etc.) is a vector. `manufacturer` is a character vector, `displ` is a double vector, and so on. Each vector has length 234.

### 2.3.1 Creation

There are two ways to create tibbles by hand. First, you can use `tibble()`.

``````my_tibble <-
tibble(
x = c(1, 9, 5),
y = c(TRUE, FALSE, FALSE),
z = c("apple", "pear", "banana")
)

my_tibble
#> # A tibble: 3 × 3
#>       x y     z
#>   <dbl> <lgl> <chr>
#> 1     1 TRUE  apple
#> 2     9 FALSE pear
#> 3     5 FALSE banana``````

`tibble()` takes individual vectors and turns them into a tibble.

Second, you can use `tribble()`.

``````my_tibble <-
tribble(
~x,  ~y,    ~z,
1,  TRUE,  "apple",
9,  FALSE, "pear",
5,  FALSE, "banana"
)

my_tibble
#> # A tibble: 3 × 3
#>       x y     z
#>   <dbl> <lgl> <chr>
#> 1     1 TRUE  apple
#> 2     9 FALSE pear
#> 3     5 FALSE banana``````

For small datasets, `tribble()` is likely to be less error prone, since the values for each row are next to each other.

### 2.3.2 Subsetting

There are several ways to extract variables out of tibbles. Tibbles are lists, so `[[` and `\$` work.

``````my_tibble[["x"]]
#>  1 9 5

my_tibble\$x
#>  1 9 5``````

Use `pull()`, the dplyr equivalent, when you want to use a pipe.

``````my_tibble %>%
pull(x)
#>  1 9 5``````

Note that `pull()`, like `[[` and `\$`, returns just the vector of values for a given variable.

`[` also works with tibbles.

``````my_tibble["x"]
#> # A tibble: 3 × 1
#>       x
#>   <dbl>
#> 1     1
#> 2     9
#> 3     5``````

For a pipe, use the dplyr function `select()`.

``````my_tibble %>%
select(x)
#> # A tibble: 3 × 1
#>       x
#>   <dbl>
#> 1     1
#> 2     9
#> 3     5``````

Whereas `pull()` returns a vector, `select()` returns a tibble.

### 2.3.3 Dimensions

Printing a tibble tells you the column names and overall dimensions.

``````mpg
#> # A tibble: 234 × 11
#>   manufacturer model displ  year   cyl trans      drv     cty   hwy fl    class
#>   <chr>        <chr> <dbl> <int> <int> <chr>      <chr> <int> <int> <chr> <chr>
#> 1 audi         a4      1.8  1999     4 auto(l5)   f        18    29 p     compa…
#> 2 audi         a4      1.8  1999     4 manual(m5) f        21    29 p     compa…
#> 3 audi         a4      2    2008     4 manual(m6) f        20    31 p     compa…
#> 4 audi         a4      2    2008     4 auto(av)   f        21    30 p     compa…
#> 5 audi         a4      2.8  1999     6 auto(l5)   f        16    26 p     compa…
#> 6 audi         a4      2.8  1999     6 manual(m5) f        18    26 p     compa…
#> # … with 228 more rows``````

To access the dimensions directly, you have three options:

``````dim(diamonds)
#>  53940    10

nrow(diamonds)
#>  53940

ncol(diamonds)
#>  10``````

To get the variable names, use `names()`:

``````names(diamonds)
#>   "carat"   "cut"     "color"   "clarity" "depth"   "table"   "price"
#>   "x"       "y"       "z"``````