R Data Structures – Vectors, Lists, Matrices, and More

Introduction – What Are Data Structures in R?

In R, data structures are essential tools for organizing, storing, and manipulating data. Whether you’re working with a list of names, a grid of values, or tabular data, R provides a variety of built-in data structures tailored to different types of analysis.

Each structure has specific characteristics for dimensionality, homogeneity, and indexing, making them useful for various statistical and data science tasks.

In this guide, you’ll learn:

• The key data structures in R
• How to create and access elements in each structure
• Differences between structures like vectors, lists, matrices, and data frames
• Real-world examples of when to use each

Overview of Core Data Structures in R

1. Vector – Most Basic Data Type

num_vec <- c(1, 2, 3, 4)
char_vec <- c("apple", "banana")

All elements must be of the same type.

Access:

num_vec[2]  # 2

2. List – A Collection of Mixed Objects

my_list <- list(name = "Alice", age = 25, passed = TRUE)

Elements can be of different types and lengths.

Access:

my_list$name       # "Alice"
my_list[[2]]       # 25

3. Matrix – 2D Homogeneous Structure

m <- matrix(1:6, nrow = 2, ncol = 3)

All elements must be of the same type.

Access:

m[1, 2]  # Row 1, Col 2

4. Array – Multidimensional Extension of Matrix

arr <- array(1:12, dim = c(2, 3, 2))

Useful for storing 3D or 4D data in statistical models.

5. Data Frame – Table of Heterogeneous Columns

df <- data.frame(Name = c("Tom", "Lily"), Score = c(90, 85))

Each column can be a different type, like a spreadsheet.

Access:

df$Score      # [1] 90 85
df[1, "Name"] # "Tom"

6. Factor – Encoded Categorical Data

grades <- factor(c("A", "B", "A", "C", "B"))

Internally stores as integers with labels. Useful for modeling.

Access:

levels(grades)    # "A" "B" "C"
table(grades)     # Count occurrences

Convert Between Data Structures

Summary – Recap & Next Steps

R offers flexible data structures for storing everything from raw numbers to labeled observations. Each has its role—from simple vectors to complex arrays and data frames.

Key Takeaways:

• Use vectors for 1D homogeneous data
• Use lists for storing mixed types and nested data
• Use matrices for 2D numeric or logical data
• Use arrays for higher-dimensional numeric data
• Use data frames for row-column datasets like spreadsheets
• Use factors for modeling categorical variables

Real-World Relevance:
Understanding data structures is vital for tasks like building models, wrangling datasets, and creating visualizations in R-based data science workflows.

FAQs – Data Structures in R

What is the difference between a vector and a list?
Vectors are homogeneous (same type), lists are heterogeneous (mixed types).

When should I use a matrix vs a data frame?
Use a matrix for numeric-only data; use a data frame for labeled tabular data with mixed column types.

Can a list hold another list in R?
Yes! Lists can be nested:

nested_list <- list(a = list(x = 1, y = 2))

How do I check the structure of an object?
Use str():

str(df)

What are factors used for?
Factors are used for representing categorical data in modeling and visualizations.