Introduction to R Data Structures

R provides several essential data structures that are used to store and manipulate data efficiently. Each data structure has its own characteristics and use cases, making it crucial to understand their differences and applications.

Why Data Structures Matter

• Efficiency: Proper data structures allow for efficient data storage and retrieval.
• Functionality: Different data structures offer various functionalities and methods for data manipulation.
• Versatility: Choosing the right data structure can simplify coding and make data analysis more intuitive.

Working with Vectors and Lists

Vectors

Vectors are the most basic and commonly used data structure in R. They are one-dimensional arrays that hold elements of the same data type.

Creating Vectors

You can create vectors using the c() function:

Example:

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

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

# Logical vector
logical_vector <- c(TRUE, FALSE, TRUE)

Accessing and Modifying Vectors

You can access and modify elements of a vector using indexing:

Example:

# Accessing elements
first_element <- numeric_vector[1]  # Access first element
subset_vector <- numeric_vector[2:4]  # Access elements from index 2 to 4

# Modifying elements
numeric_vector[3] <- 10  # Change third element to 10

Lists

Lists are more flexible than vectors because they can hold elements of different types and structures, including other lists.

Creating Lists

Lists are created using the list() function:

Example:

# Creating a list
my_list <- list(
  name = "John",
  age = 30,
  scores = c(95, 88, 92),
  married = TRUE
)

Accessing and Modifying Lists

You can access list elements using the $ operator or double square brackets [[ ]]:

Example:

# Accessing elements
name <- my_list$name        # Using '$'
scores <- my_list[[3]]      # Using '[[ ]]'

# Modifying elements
my_list$age <- 31           # Change age to 31

Data Frames: Structure, Manipulation, and Subsetting

What is a Data Frame?

A data frame is a two-dimensional, table-like structure where each column can contain different types of data (numeric, character, etc.). It is the most commonly used data structure for handling tabular data in R.

Creating Data Frames

Data frames are created using the data.frame() function:

Example:

# Creating a data frame
my_df <- data.frame(
  ID = 1:3,
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(25, 30, 35),
  stringsAsFactors = FALSE
)

Accessing and Manipulating Data Frames

You can access data frame elements using indexing or column names:

Example:

# Accessing columns
names_column <- my_df$Name            # Access by column name
age_column <- my_df[, "Age"]           # Access by column name

# Subsetting rows
subset_df <- my_df[my_df$Age > 30, ]   # Rows where Age > 30

# Adding a new column
my_df$Country <- c("USA", "UK", "Canada")

# Modifying values
my_df[1, "Age"] <- 26                  # Change age of the first row

Data Frame Functions

1. head(): View the first few rows.

   head(my_df)
   

2. tail(): View the last few rows.

   tail(my_df)
   

3. summary(): Get a summary of each column.

   summary(my_df)
   

4. str(): Get the structure of the data frame.

   str(my_df)
   

Creating and Working with Matrices

What is a Matrix?

A matrix is a two-dimensional array where all elements must be of the same type. It is useful for mathematical and statistical operations that require matrix algebra.

Creating Matrices

Matrices are created using the matrix() function:

Example:

# Creating a matrix
my_matrix <- matrix(
  c(1, 2, 3, 4, 5, 6),
  nrow = 2,  # Number of rows
  ncol = 3,  # Number of columns
  byrow = TRUE  # Fill matrix by rows
)

Accessing and Modifying Matrices

You can access and modify elements of a matrix using row and column indices:

Example:

# Accessing elements
element <- my_matrix[1, 2]    # Element at row 1, column 2
row_vector <- my_matrix[1, ]  # Entire first row
col_vector <- my_matrix[, 2]  # Entire second column

# Modifying elements
my_matrix[2, 3] <- 10         # Change element at row 2, column 3

Matrix Functions

1. nrow() and ncol(): Get the number of rows and columns.

   nrow(my_matrix)
   ncol(my_matrix)
   

2. t(): Transpose the matrix.

   t(my_matrix)
   

3. apply(): Apply a function over rows or columns.

   apply(my_matrix, 1, sum)  # Sum of each row
   apply(my_matrix, 2, mean) # Mean of each column
   

Hands-On Examples and Use Cases

Example 1: Analyzing a Survey Dataset

Suppose you have survey data on customer satisfaction:

Data:

survey_df <- data.frame(
  CustomerID = 1:5,
  Satisfaction = c("High", "Medium", "Low", "High", "Medium"),
  Score = c(9, 7, 5, 8, 6),
  stringsAsFactors = FALSE
)

Analysis:

1. View the data:

   head(survey_df)
   

2. Subset data for high satisfaction:

   high_satisfaction <- survey_df[survey_df$Satisfaction == "High", ]
   

3. Calculate average score:

   average_score <- mean(survey_df$Score)
   

Example 2: Working with a Matrix in Linear Algebra

Suppose you have two matrices representing coefficients in a system of linear equations:

Matrices:

A <- matrix(c(2, 1, -1, -3, -1, 2, -2, 1, 2), nrow = 3, byrow = TRUE)
B <- matrix(c(8, -11, -3), nrow = 3)

Solving the System:

1. Find the solution to the linear system:

   solution <- solve(A, B)
   

2. View the solution:

   solution
   

Conclusion

Mastering R's data structures—vectors, lists, data frames, and matrices—is fundamental for efficient data analysis and manipulation. Vectors and lists provide basic storage and manipulation capabilities, while data frames and matrices are essential for handling and analyzing tabular and numerical data. By understanding how to create, access, and manipulate these structures, you can handle a wide range of data analysis tasks in R, making your work more effective and efficient.