Introduction to Vectors in R

A vector is a one-dimensional data structure that stores a sequence of elements of the same data type. All elements in a vector must belong to the same type; if different types are mixed, R automatically converts them to a common type (this process is called type coercion).

Examples of vectors:

• A list of numbers
• A set of names
• A sequence of TRUE/FALSE values

Vectors are important because:

• R is a vectorized language
• Operations on vectors are faster than loops
• Most R functions expect vector inputs
• Data frames and matrices are built from vectors

R encourages vectorized operations, which means you can operate on an entire vector at once without using loops.

Creating a Vector Using c() Function

The most common way to create a vector is using the c() (combine) function.

numbers <- c(10, 20, 30, 40)
names <- c("Alice", "Bob", "Charlie")
logical_values <- c(TRUE, FALSE, TRUE)

Creating an Empty Vector

You can create an empty vector and fill it later.

empty_vec <- c()

Or create a vector of a specific type and length:

numeric_vec <- numeric(5)
character_vec <- character(3)
logical_vec <- logical(4)

R supports atomic vectors, which include:

Numeric Vector

Stores decimal numbers.

x <- c(1.5, 2.3, 4.7)

Integer Vector

Stores integers (use L).

y <- c(1L, 2L, 3L)

Character Vector

Stores text strings.

z <- c("R", "Data", "Science")

Logical Vector

Stores TRUE/FALSE values.

flag <- c(TRUE, FALSE, TRUE)

Complex Vector

Stores complex numbers.

cplx <- c(2+3i, 4+5i)

Use class() or typeof().

class(numbers)
typeof(numbers)

When different data types are combined in a vector, R automatically converts all elements to the most flexible type.

Coercion Hierarchy

logical → integer → numeric → character

Example:

mixed <- c(1, TRUE, "R")
print(mixed)

Output:

[1] "1" "TRUE" "R"

Indexing is used to access elements in a vector.

Indexing by Position

R uses 1-based indexing.

v <- c(10, 20, 30, 40)
v[1]    # First element
v[4]    # Fourth element

Indexing with Multiple Positions

v[c(1, 3)]

Negative Indexing

Negative indices remove elements.

v[-2]

Logical Indexing

Logical vectors can be used to filter data.

v[v > 20]

You can assign names to vector elements.

scores <- c(85, 90, 78)
names(scores) <- c("Math", "Science", "English")

Access by name:

scores["Math"]

When performing operations on vectors of unequal length, R recycles the shorter vector.

Example:

v1 <- c(1, 2, 3, 4)
v2 <- c(10, 20)
v1 + v2

R repeats v2:

1+10, 2+20, 3+10, 4+20

Warning is issued if lengths are not multiples.

R allows operations on entire vectors without loops.

v <- c(1, 2, 3, 4)
v * 2
v + 10

This is faster and cleaner than using loops.

Length of Vector

length(v)

Sorting a Vector

sort(v)

Finding Minimum and Maximum

min(v)
max(v)

Sum and Mean

sum(v)
mean(v)

Using :

1:10

Using seq()

seq(from = 1, to = 10, by = 2)

Using rep()

rep(1:3, times = 2)

Updating Elements

v[2] <- 100

Replacing Based on Condition

v[v < 10] <- 0

v <- v[-3]

Use %in% to check if elements exist.

5 %in% v

marks <- c(45, 67, 89, 90, 34)
passed <- marks[marks >= 40]
average <- mean(passed)

print(passed)
print(average)

• Mixing data types unintentionally
• Forgetting R uses 1-based indexing
• Ignoring recycling warnings
• Using loops instead of vectorized operations

Vectors are the backbone of R programming. They store data in a one-dimensional format and support fast, vectorized operations. Understanding how to create, index, modify, and operate on vectors is crucial for working with data frames, matrices, and advanced statistical functions in R.