Chapter 28: R Data Structures

Data Structures. Think of data structures as different types of containers, each designed to hold and organize data in specific ways. Just as you wouldn’t store soup in a colander or carry groceries in a thimble, choosing the right data structure for your task is crucial for efficient and effective programming.

Part 1: Overview of R Data Structures

R has several built-in data structures, each with its own characteristics:

Let’s explore each one in detail with plenty of examples.

Part 2: Vectors – The Building Blocks

Vectors are the simplest and most fundamental data structure in R. They are 1-dimensional sequences of elements that must all be the same type.

Creating Vectors

Vector Operations

Accessing Vector Elements

Vector Coercion

Part 3: Matrices – 2D Homogeneous Data

Matrices are 2-dimensional extensions of vectors – they have rows and columns, but all elements must be the same type.

Creating Matrices

Matrix Operations

Accessing Matrix Elements

Part 4: Arrays – Multi-dimensional Homogeneous Data

Arrays extend matrices to more than 2 dimensions.

Creating Arrays

Practical Array Example

Part 5: Lists – Heterogeneous Containers

Lists are the most flexible data structure in R – they can contain elements of different types and sizes.

Creating Lists

Accessing List Elements

Manipulating Lists

Practical List Example – Model Results

Part 6: Data Frames – The Workhorse

Data frames are 2-dimensional structures where each column can be a different type. They’re the most common data structure for data analysis.

Creating Data Frames

Accessing Data Frame Elements

Manipulating Data Frames

Common Data Frame Operations

Part 7: Factors – For Categorical Data

Factors are designed to store categorical data efficiently. They store both the values and their possible levels.

Creating Factors

Working with Factors

Part 8: Tibbles – Modern Data Frames

Tibbles are a modern reimagining of data frames from the tidyverse package. They have nicer printing and stricter subsetting.

Part 9: Choosing the Right Data Structure

Decision Guide

Part 10: Converting Between Structures

Part 11: Practical Example – Complete Data Analysis Pipeline

Let’s combine everything we’ve learned in a realistic example:

Summary: The Data Structure Philosophy

Choosing the right data structure is like choosing the right tool for a job:

• Vectors: Simple lists of the same thing (like a shopping list)

• Matrices: Tables where everything is the same type (like a spreadsheet of numbers)

• Arrays: Multi-dimensional data (like a stack of matrices)

• Lists: Collections of different things (like a toolbox)

• Data Frames: Mixed data in table form (like a database table)

• Factors: Categories with levels (like survey responses)

• Tibbles: Modern, enhanced data frames

Key principles:

1. Homogeneous vs Heterogeneous: Same type or different types?

2. Dimensions: 1D, 2D, or nD?

3. Structure: Do you need row/column orientation?

4. Operations: What will you do with the data?

5. Memory efficiency: Factors for categories, matrices for numbers

Data structure selection guide:

• Simple list of numbers → Vector

• 2D table of numbers → Matrix

• 2D table with mixed types → Data Frame

• Need tidyverse features → Tibble

• Complex, nested data → List

• Multi-dimensional data → Array

• Categorical data → Factor

Mastering R’s data structures is like learning the grammar of a language – once you know them, you can express any data analysis task clearly and efficiently. Each structure has its strengths, and knowing when to use which is the key to writing elegant, efficient R code.

Would you like me to elaborate on any specific data structure or explore more advanced operations with them?