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

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🧲 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

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🧪 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"

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🧬 Types of Vectors

Type	Example
Numeric	c(1, 2, 3.5)
Character	c("A", "B")
Logical	c(TRUE, FALSE, TRUE)
Integer	c(1L, 2L)
Complex	c(1+2i, 2+3i)

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

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🧾 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

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🔄 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

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🔁 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

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🔍 Vector Comparison & Logical Filtering

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

x[x > 8]      # 10 15

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🧠 Useful Vector Functions

Function	Purpose	Example
length()	Count number of elements	length(x)
sort()	Sort values in ascending order	sort(x)
rev()	Reverse the vector	rev(x)
unique()	Remove duplicates	unique(c(1,1,2))
sum()	Add all elements	sum(x)
which()	Return indices of TRUE conditions	which(x > 10)
any()	TRUE if any condition is TRUE	any(x > 20)
all()	TRUE if all conditions are TRUE	all(x > 0)

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⚠️ 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.

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📌 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.

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❓ 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)]

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