R Data Structures Overview

Introduction

This document provides an overview of common data structures in R, including vectors, matrices, data frames, lists, factors, and arrays.

Vectors

Vectors are one-dimensional arrays that can hold elements of the same data type.

# Numeric vector
num_vec <- c(1, 2, 3, 4, 5)
print(num_vec)

## [1] 1 2 3 4 5

# Character vector
char_vec <- c("apple", "banana", "cherry")
print(char_vec)

## [1] "apple"  "banana" "cherry"

# Logical vector
log_vec <- c(TRUE, FALSE, TRUE, TRUE)
print(log_vec)

## [1]  TRUE FALSE  TRUE  TRUE

# Using the : operator for a sequence
seq_vec <- 1:10
print(seq_vec)

##  [1]  1  2  3  4  5  6  7  8  9 10

Matrices

Matrices are 2D structures with rows and columns.

# Create a 3x3 matrix
mat <- matrix(1:9, nrow = 3, ncol = 3)
print(mat)

##      [,1] [,2] [,3]
## [1,]    1    4    7
## [2,]    2    5    8
## [3,]    3    6    9

Data Frames

Data frames are used for storing tabular data.

# Create a data frame
df <- data.frame(
  name = c("Alice", "Bob", "Charlie"),
  age = c(25, 30, 35),
  height = c(165, 180, 175)
)
print(df)

##      name age height
## 1   Alice  25    165
## 2     Bob  30    180
## 3 Charlie  35    175

Lists

Lists can contain elements of different types.

# Create a list
my_list <- list(
  numbers = 1:5,
  text = "Hello, world!",
  matrix = matrix(1:4, nrow = 2)
)
print(my_list)

## $numbers
## [1] 1 2 3 4 5
## 
## $text
## [1] "Hello, world!"
## 
## $matrix
##      [,1] [,2]
## [1,]    1    3
## [2,]    2    4

Factors

Factors are used for categorical data.

# Create a factor
colors <- factor(c("red", "blue", "green", "red", "green"))
print(colors)

## [1] red   blue  green red   green
## Levels: blue green red

print(levels(colors))

## [1] "blue"  "green" "red"

Arrays

Arrays are multi-dimensional structures.

# Create a 2x3x2 array
arr <- array(1:12, dim = c(2, 3, 2))
print(arr)

## , , 1
## 
##      [,1] [,2] [,3]
## [1,]    1    3    5
## [2,]    2    4    6
## 
## , , 2
## 
##      [,1] [,2] [,3]
## [1,]    7    9   11
## [2,]    8   10   12

Conclusion

These are the basic data structures in R. Each has its own use cases:

• Vectors for simple operations on a series of values
• Matrices for linear algebra operations
• Data frames for storing and manipulating tabular data
• Lists for storing complex, hierarchical data
• Factors for working with categorical variables
• Arrays for working with data in more than two dimensions

Understanding these structures is crucial for effective data manipulation and analysis in R.