Chapter 29: R Vectors

Part 1: What is a Vector?

A vector is a basic data structure in R that contains elements of the same type. The most important characteristic of a vector is that all elements must be the same data type (all numbers, all characters, all logical values, etc.).

The Six Types of Vectors

R has six fundamental types of vectors:

1. Numeric (double) – decimal numbers

2. Integer – whole numbers

3. Character – text strings

4. Logical – TRUE/FALSE values

5. Complex – complex numbers

6. Raw – raw bytes

But for everyday use, you’ll primarily work with numeric, character, and logical vectors.

Part 2: Creating Vectors

The c() Function – Combine

The most common way to create vectors is with the c() function (combine):

Sequence Creation

Repetition

Part 3: Vector Properties

Length and Type

Attributes

Part 4: Accessing Vector Elements

Indexing (1-based!)

R uses 1-based indexing, meaning the first element is at position 1:

The Difference Between [ and [[

Part 5: Vector Operations

Arithmetic Operations

Recycling

When vectors have different lengths, R recycles the shorter vector:

Comparison Operations

Mathematical Functions

Vectorized Functions

One of R’s greatest strengths is that most functions are vectorized – they operate on entire vectors at once:

Part 6: Modifying Vectors

Changing Elements

Removing Elements

Part 7: Vector Functions and Utilities

Set Operations

Sorting and Ordering

Unique and Duplicated

Sampling and Randomization

Part 8: Special Vector Types

Integer Vectors

Logical Vectors

Character Vectors

Factor Vectors (special)

Part 9: Missing Values (NA)

Part 10: Practical Examples

Example 1: Grade Calculator

Example 2: Stock Price Analysis

Example 3: Text Analysis

Part 11: Common Mistakes and How to Avoid Them

Mistake 1: 0-based Indexing

Mistake 2: Forgetting that Vectors are Homogeneous

Mistake 3: Using = instead of == in conditions

Mistake 4: Forgetting about Recycling

Mistake 5: Confusing & and &&

Part 12: Performance Tips

Pre-allocation

Vectorization is Faster

Summary: The Vector Philosophy

Vectors are the foundation of R programming. Master these concepts:

Creating vectors:

• c() for combining

• : for sequences

• seq() for flexible sequences

• rep() for repetitions

Accessing elements:

• Single bracket [] with position

• Logical vectors for conditional access

• Names for named access

Vector operations:

• Arithmetic is element-wise

• Recycling for different lengths

• Vectorized functions for efficiency

Vector functions:

• length(), typeof(), class()

• sum(), mean(), sd(), etc.

• sort(), order(), rev()

• unique(), table()

• %in% for membership testing

Key principles:

1. All elements must be the same type

2. Indexing starts at 1

3. Most functions are vectorized

4. Missing values (NA) propagate

5. Pre-allocation for speed

Vectors are like the atoms of R – small, fundamental, and when combined, they create everything else in the language. Master vectors, and you’ve mastered the foundation of R!

Would you like me to elaborate on any specific aspect of vectors or explore more advanced vector operations?