Introduction
In this chapter, you will learn about various data structures in R. Understanding these data structures is crucial for effectively managing and manipulating data in your R programs. The main data structures in R include vectors, lists, matrices, arrays, data frames, and factors.
Vectors
Vectors are the most basic data structures in R. They are one-dimensional arrays that can hold numeric, character, or logical data.
Creating Vectors
Example:
# Numeric vector num_vec <- c(1, 2, 3, 4, 5) print(num_vec) # Character vector char_vec <- c("a", "b", "c", "d", "e") print(char_vec) # Logical vector log_vec <- c(TRUE, FALSE, TRUE, TRUE, FALSE) print(log_vec) Accessing Elements
Example:
# Accessing elements print(num_vec[1]) # Output: 1 print(char_vec[2]) # Output: "b" print(log_vec[3]) # Output: TRUE Vector Operations
Example:
# Vector operations vec1 <- c(1, 2, 3) vec2 <- c(4, 5, 6) # Addition print(vec1 + vec2) # Output: 5 7 9 # Subtraction print(vec1 - vec2) # Output: -3 -3 -3 # Multiplication print(vec1 * vec2) # Output: 4 10 18 # Division print(vec1 / vec2) # Output: 0.25 0.4 0.5 Lists
Lists are versatile data structures that can hold elements of different types, including other lists.
Creating Lists
Example:
# Creating a list my_list <- list(num_vec, char_vec, log_vec) print(my_list) Accessing Elements
Example:
# Accessing elements print(my_list[[1]]) # Access first element (num_vec) print(my_list[[2]][3]) # Access third element of char_vec Matrices
Matrices are two-dimensional arrays that can hold numeric, character, or logical data. All elements in a matrix must be of the same type.
Creating Matrices
Example:
# Creating a matrix mat <- matrix(1:9, nrow = 3, ncol = 3) print(mat) Accessing Elements
Example:
# Accessing elements print(mat[1, 2]) # Access element at first row, second column print(mat[, 3]) # Access all elements of the third column print(mat[2, ]) # Access all elements of the second row Arrays
Arrays are multi-dimensional generalizations of matrices.
Creating Arrays
Example:
# Creating an array arr <- array(1:12, dim = c(3, 2, 2)) print(arr) Accessing Elements
Example:
# Accessing elements print(arr[1, , ]) # Access all elements of the first row across all dimensions print(arr[, 2, 1]) # Access all elements of the second column in the first matrix Data Frames
Data frames are two-dimensional data structures that can hold elements of different types (numeric, character, logical) in each column.
Creating Data Frames
Example:
# Creating a data frame df <- data.frame(Name = c("Alice", "Bob", "Charlie"), Age = c(25, 30, 35), Gender = c("F", "M", "M")) print(df) Accessing Elements
Example:
# Accessing elements print(df$Name) # Access the 'Name' column print(df[1, ]) # Access the first row print(df[, 2]) # Access the second column print(df[2, 3]) # Access element at second row, third column Factors
Factors are used to represent categorical data. They can be ordered or unordered.
Creating Factors
Example:
# Creating a factor factor_data <- factor(c("low", "medium", "high", "medium", "low")) print(factor_data) print(levels(factor_data)) # Output: "high" "low" "medium" Changing Factor Levels
Example:
# Changing factor levels levels(factor_data) <- c("low", "medium", "high") print(factor_data) Example Program Using Different Data Structures
Here is an example program that demonstrates the use of various data structures in R:
# Example program using different data structures # Vectors num_vec <- c(1, 2, 3, 4, 5) char_vec <- c("a", "b", "c", "d", "e") log_vec <- c(TRUE, FALSE, TRUE, TRUE, FALSE) # Lists my_list <- list(num_vec, char_vec, log_vec) # Matrices mat <- matrix(1:9, nrow = 3, ncol = 3) # Arrays arr <- array(1:12, dim = c(3, 2, 2)) # Data Frames df <- data.frame(Name = c("Alice", "Bob", "Charlie"), Age = c(25, 30, 35), Gender = c("F", "M", "M")) # Factors factor_data <- factor(c("low", "medium", "high", "medium", "low")) # Print data structures print("Numeric Vector:") print(num_vec) print("Character Vector:") print(char_vec) print("Logical Vector:") print(log_vec) print("List:") print(my_list) print("Matrix:") print(mat) print("Array:") print(arr) print("Data Frame:") print(df) print("Factor:") print(factor_data) Conclusion
In this chapter, you learned about various data structures in R, including vectors, lists, matrices, arrays, data frames, and factors. These data structures are fundamental for managing and manipulating data in R. By mastering these data structures, you can efficiently handle different types of data and perform complex data analysis tasks in your R programs.