# Vector Functions in R – Master R seq, sapply, rep functions

**Are you finding difficulty working with R vectors? Don’t worry, look at some vector functions in R that will make your life easier with R programming.**

Functions are pieces of code that are created to perform specific tasks. We make functions to avoid repetition of code and errors. They also help us in making the code organized and readable.

In this R tutorial, we will take a look at a few commonly used vector functions in R. We will see how they are used and what they do. We will also look at some examples of their usage. So, let’s get going!

## What are R vector functions?

There are functions in R that either help with creating a vector or take vectors as input arguments. We call such functions as **vector functions** in R programming.

Today, we are going to study a few vector functions in R, that you will find very useful in R programming. These functions are:

- rep()
- seq()
- is.vector()
- as.vector()
- any()
- all()
- lapply()
- sapply()

These functions either take a vector as input or return a vector as output.

### 1. The rep() Function

The ** rep()** function repeats a vector, or value, a given number of times. It then returns a vector with the repeated values. For example:

Wait! Do you know what is R vector? – **vectors in R**

**Code:**

repeated_vector <- rep(c(1,2,3),4) repeated_vector

**Output:**

We can also specify the **length** of the vector instead of the number of times using the ** length.out** argument. The function keeps the repetition going until the mentioned length is reached.

**Code:**

repeated_vector2 <- rep(c(4,5,6),length.out=15) repeated_vector2

**Output:**

We can also use the ** each** argument to repeat each element of the repeating vector. For example:

**Code:**

repeated_vector3 <- rep(c(4,5,6),each=3) repeated_vector3

**Output:**

### 2. The seq() Function

The ** seq()** function creates a sequence of numbers starting from the given

**argument and ending at the**

`from`

**argument. We can also pass the**

`to`

**argument to specify the increase/decrease step. By default, the increase step is 1.**

`by`

**Example 1:**

sequence1 <- seq(from=2,to=15,by=0.5) sequence1

**Output:**

We can also use the ** length.out** argument to specify the length of the output vector. This way R keeps the sequence going until the desired length is reached.

**Example 2:**

sequence2 <- seq(from=5,length.out=12) sequence2

**Example 3:**

sequence3 <- seq(from=5,to=15,length.out=20) sequence3

**Output:**

### 3. The is.vector() Function

The ** is.vector()** function takes an object as an input and returns

**if the input object is a vector. It returns**

`TRUE`

**if the object is not a vector.**

`FALSE`

**Code:**

is.vector(sequence3)

**Output:**

### 4. The as.vector() Function

The ** as.vector()** function takes an object as an argument and converts it into a vector. Let’s take a look at the workings of this function with an example.

First, let’s create a matrix. We will name this matrix ** mat_to_vec**, as we will be converting it into a vector soon.

**Code:**

mat_to_vec <- matrix(c(1:9),c(3,3)) mat_to_vec

**Output:**

[1,] 1 4 7

[2,] 2 5 8

[3,] 3 6 9

By using the ** class() **function we can confirm that

**is in fact a matrix.**

`mat_to_vec`

**Code:**

class(mat_to_vec)

**Output:**

Now, let’s use the ** as.vector()** function on our matrix.

**Code:**

mat_to_vec <- as.vector(mat_to_vec) mat_to_vec

**Output:**

As you can see, ** mat_to_vec** is now arranged in a single row instead of 3×3 rows and columns. Instead of an integer matrix, it should now be an integer vector and should have a class of

**integer**.

**Code:**

class(mat_to_vec)

**Output:**

### 5. The any() Function

The ** any()** function takes a vector and a logical condition as input arguments. It checks the vector against the condition and creates a

**logical vector**. It then return

**, if**

`TRUE`

**any one**of the elements in the logical vector is

**.**

`TRUE`

**Code:**

vec <- as.integer(c(34,23,53,42,16,42,64,32,76)) any(vec,vec>50)

**Output:**

### 6. The all() Function

The ** all()** function takes a vector and a logical condition as input arguments. It checks the vector against the condition and creates a logical vector. It then returns

**if**

`TRUE`

**all**the elements in the logical vector are

**, and**

`TRUE`

**if**

`FALSE`

**all elements**are not

**.**

`TRUE`

**Code:**

all(vec,vec>50)

### 7. The lapply() Function

The ** lapply()** function takes a vector, list or a data frame and a function. The

**function applies a function to all elements of a vector,**

`lapply()`

**list**or data frames. The function then returns the result in the form of a

**list**. For example:

**Code:**

names <- c("JOHN","RICK","RAHUL","ABDUL") lapply(names,tolower)

**Output:**

### 8. The sapply() Function

The ** sapply()** is very similar to the

**function. The only difference is that the**

`lappy()`

**function returns the result in the form of a vector. For example:**

`sapply()`

**Code:**

sapply(names,tolower)

**Output:**

## Summary

Vector functions are functions that perform operations on vectors or give output as vectors. In this article, we studied some important vector functions in R. We looked at their uses and also saw examples of their usage.

The above functions like ** is.vector()**,

**,**

`as.vector()`

`lapply()`

,**and**

`sapply()`

, `any()`

, **are very important and commonly used functions in the R programming language.**

` seq()`

**Still, finding difficulty working with vector functions in R?**

Feel free to ask any queries related to R tutorial and our experts at **TechVidvan** will be happy to help you.