Vector in R Language

A vector in R is a set of numbers. A vector can be considered as a single column or a single row of a spreadsheet. The following examples are numbers that are not technically “vectors”. It is because these vectors are not in a column/row structure, however, they are ordered. These vectors can be referred to by index.

In R programming, vectors are the most basic data structure and a core building block of data analysis. Whether you’re new to R or brushing up on concepts, understanding vectors is essential. They form the building blocks for more complex structures like matrices, lists, and data frames.

Key Characteristics of Vectors

• Support Vectorized Operations: Arithmetic and logical operations can be applied element-wise without loops.
• Homogeneous: All elements must be of the same data type (such as numeric, character, logical, etc.).
• Indexed: Elements can be accessed using indices (starting at 1).
• Dynamic: Vectors can grow or shrink in size.

Types of Vectors in R Language

R supports several types of vectors based on the data they store:

(a) Numeric Vectors: Store real numbers (decimals or integers). For example: > c(1.5, 2.3, 4.0)

(b) Integer Vectors: Store whole numbers (explicitly defined with L). For example, > c(1L, 2L, 3L)

(c) Logical Vectors: Store TRUE, FALSE, or NA (missing value). For example: > c(TRUE, FALSE, NA)

(d) Character Vectors” Store text (strings). For example: > c("apple", "banana", "cherry")

(e) Complex Vectors: Store complex numbers. For example: > c(1+2i, 3+4i)

Creating Vectors in R

One can create vectors in R Language using:

• c() function
• seq()
• : operator

# Creating a vector with the c() function

c(1, 4, 6, 7, 9)

c(1:5, 10)

A vector in R language can be created using seq() in R, it generates a series of numbers.

# Create a vector using seq() in R

seq(1, 10, by = 2)
seq(0, 50, length = 11)
seq(1, 50, length = 11)

The vector can be created in R using the colon (:) operator. Following are the examples

# Create vector in R using : operator

1:10

## Output
[1]  1  2  3  4  5  6  7  8  9 10

5:1

## Output
[1] 5 4 3 2 1

Creating Non-Integer Sequences in R

The non-integer sequences can also be created in the R Language.

# non-integer sequences
seq(0, 100*pi, by = pi)

Assigning Vector to Variable

One can assign a vector to a variable using the assignment operator (<-) or equal symbol (=). The examples are:

a <- 1:5
b <- seq(15, 3, length=5)
c <- a * b

Performing Computation on Vectors

There are a lot of built-in functions that can be used to perform different computations on vectors. For example,

a <- 1:5

# compute the total of elements of a vector
sum(a)

## Output
15

# product of elements of a vector
prod(a)

## Output
120

# average of the vector
mean(a)

## Output
3

# standard deviation and variance of a vector
sd(a)

## Output 
1.581139

var(a)

## Output
2.5

Indexing and Slicing Vectors

One can extract the elements of a vector by using square brackets and the index of the component of the vector.

V <- seq(0, 100, by = 10)
V[] # gives all the elements of the vector

## Output
[1]   0  10  20  30  40  50  60  70  80  90 100

V[5] # 5th elements from vector z

## Output
[1] 40

V[c(2, 4, 6, 8)] #2nd, 4th, th, and 8th element

## Output
[1] 10 30 50 70

V[-c(2, 4, 6, 8)] # elements except 2nd, 4th, 6th, and 8th element

## Output
[1]   0  20  40  60  80  90 100

Updating Vector Elements

The specific / required elements of a vector can be updated

V[c(2, 4)] <- c(500, 600) # the second and 4th element is updated to 500 and 600

https://itfeature.com

https://gmstat.com

Special Vector Values

The following are special vector values used in R Language.

Important Points About Vectors

The important points about vectors in R language are:

• Data Types: Vectors can hold logical, integer, double, character, complex, or raw data.
• Creation: Use the c() function to combine elements into a vector.
• Accessing Elements: Use indexing (square brackets) to access individual elements.
• Vector Operations: Perform arithmetic, logical, and comparison operations on vectors.
• Vectorization: R excels at vectorized operations, making calculations efficient.