R Vectors – Create, Access, and Manipulate 1D Data in R

Introduction – What Are Vectors in R?

Vectors are the most basic and essential data structure in R. A vector is a one-dimensional array that contains elements of the same data type—such as numbers, characters, or logical values.

Everything in R is built upon vectors. Whether you’re storing a list of names, test scores, or Boolean results, you’re working with vectors. Mastering vectors will make you more fluent in data analysis and R scripting.

In this guide, you’ll learn:

• How to create vectors using different functions
• Access and modify vector elements
• Perform vectorized operations and comparisons
• Use logical indexing and built-in vector functions

Creating Vectors in R

Using c() – Combine Function

numbers <- c(1, 2, 3, 4, 5)
names <- c("Alice", "Bob", "Charlie")

Using : – Sequence Operator

seq1 <- 1:5     # 1 2 3 4 5

Using seq() – Custom Sequence

seq(1, 10, by = 2)  # 1 3 5 7 9

Using rep() – Repeat Elements

rep(3, times = 4)       # 3 3 3 3
rep(c("A", "B"), 2)     # "A" "B" "A" "B"

Types of Vectors

Use typeof() or class() to inspect vector type.

Accessing Elements in a Vector

By Index

x <- c(10, 20, 30, 40)
x[2]      # 20

By Range

x[2:4]    # 20 30 40

Exclude Elements

x[-1]     # Removes first element: 20 30 40

By Logical Index

x[x > 25] # 30 40

Modifying Vector Elements

x[2] <- 100       # Change second element
x                 # 10 100 30 40

You can also extend a vector:

x[5] <- 50        # Adds a fifth element

Vectorized Arithmetic Operations

R is vectorized—operations apply to each element automatically.

a <- c(1, 2, 3)
b <- c(10, 20, 30)

a + b        # 11 22 33
a * 2        # 2 4 6

Vector Comparison & Logical Filtering

x <- c(5, 10, 15)
x > 8         # FALSE TRUE TRUE

x[x > 8]      # 10 15

Useful Vector Functions

Type Coercion in Mixed-Type Vectors

c(1, "two", TRUE)  # All elements become character

R coerces to the most flexible type: logical < integer < numeric < character.

Summary – Recap & Next Steps

Vectors are at the core of data manipulation in R. They are flexible, fast, and power almost every structure in R programming.

Key Takeaways:

• Use c(), : and seq() to create vectors
• Index elements with numbers, ranges, logical conditions
• Perform vectorized math and logical filtering
• Understand coercion rules for mixed types
• Use built-in functions like sum(), which(), and unique()

Real-World Relevance:
Vectors are crucial for filtering datasets, summarizing values, handling user input, and automating batch processing in statistical and machine learning projects.

FAQs – Vectors in R

What is the difference between a vector and a list in R?
A vector is homogeneous (all elements same type), while a list can store mixed types (e.g., numbers and strings).

Can I store a matrix in a vector?
No. Matrices are separate 2D structures. But you can flatten a matrix into a vector using as.vector(matrix).

How do I check if a variable is a vector?
Use is.vector():

is.vector(c(1,2,3))  # TRUE

What happens if I index beyond the length of a vector?
You get NA:

x <- c(1,2)
x[5]  # NA

How do I remove NA values from a vector?
Use na.omit() or logical indexing:

x[!is.na(x)]