Tutorial 2: Working with Data Structures in R

Author

Rony Rodriguez-Ramirez

Working with Data Structures in R

In this tutorial, we will explore three fundamental data structures in R: matrices, lists, and data frames. Understanding these structures is essential for effective data manipulation and analysis. This tutorial includes detailed explanations, code examples, and exercises with solutions to reinforce learning.

2.1 Matrices

A matrix is a two-dimensional data structure that stores elements of the same data type arranged in rows and columns. Matrices are useful for mathematical computations and organizing data in a tabular format.

2.1.1 Creating Matrices

Use the matrix() function to create a matrix by specifying the data, number of rows, and number of columns.

# Creating a matrix of student scores across 3 subjects
scores <- c(85, 78, 92, 90, 82, 79, 78, 91, 86)
student_scores <- matrix(scores, nrow = 3, ncol = 3, byrow = TRUE)

# Assigning row and column names
rownames(student_scores) <- c("Student1", "Student2", "Student3")
colnames(student_scores) <- c("Math", "History", "Biology")

student_scores
         Math History Biology
Student1   85      78      92
Student2   90      82      79
Student3   78      91      86

Explanation: - A numeric vector scores is created containing nine score values. - The matrix() function organizes these scores into a 3x3 matrix filled by rows. - rownames() and colnames() assign descriptive labels to rows and columns for clarity.

2.1.2 Accessing and Modifying Matrix Elements

Access specific elements, rows, or columns using square brackets [].

# Accessing the score of Student2 in History
student_scores["Student2", "History"]
[1] 82
# Accessing all scores of Student3
student_scores["Student3", ]
   Math History Biology 
     78      91      86 
# Modifying a specific score
student_scores["Student1", "Biology"] <- 95

Explanation: - Specify row and column names or indices within brackets to access elements. - Assign new values to modify existing data in the matrix.

2.1.3 Matrix Operations

Perform various operations such as calculating row and column sums or means.

# Calculating total scores for each student
total_scores <- rowSums(student_scores)

# Calculating average scores for each subject
average_subject_scores <- colMeans(student_scores)

Explanation: - rowSums() computes the sum across rows, giving total scores per student. - colMeans() computes the average across columns, providing average scores per subject.

2.2 Lists

A list is a versatile data structure that can contain elements of different types, including numbers, strings, vectors, and even other lists.

2.2.1 Creating Lists

Use the list() function to create a list containing heterogeneous elements.

# Creating a list with student information
student_info <- list(
  name = "Alice",
  age = 20,
  major = "Economics",
  scores = c(88, 92, 85)
)

student_info
$name
[1] "Alice"

$age
[1] 20

$major
[1] "Economics"

$scores
[1] 88 92 85

Explanation: - The list student_info contains character, numeric, and vector elements, encapsulating diverse data related to a student.

2.2.2 Accessing and Modifying List Elements

Access list elements using the $ operator or double square brackets [[]].

# Accessing the student's major
student_info$major
[1] "Economics"
# Accessing the student's scores
student_info[["scores"]]
[1] 88 92 85
# Modifying the student's age
student_info$age <- 21

Explanation: - $ and [[]] operators retrieve specific elements from the list. - Assign new values to update existing elements within the list.

2.2.3 Nested Lists

Lists can contain other lists, allowing for complex data structures.

# Creating a nested list with course details
course_details <- list(
  course_name = "Introduction to Economics",
  credits = 3,
  instructor = list(
    name = "Dr. Smith",
    office = "Room 101",
    email = "dr.smith@university.edu"
  )
)

course_details
$course_name
[1] "Introduction to Economics"

$credits
[1] 3

$instructor
$instructor$name
[1] "Dr. Smith"

$instructor$office
[1] "Room 101"

$instructor$email
[1] "dr.smith@university.edu"

Explanation: - The instructor element is itself a list containing detailed information, demonstrating how lists can be nested for hierarchical data representation.

2.3 Data Frames

A data frame is a table-like structure where each column can contain different data types. Data frames are widely used for storing and manipulating datasets.

2.3.1 Creating Data Frames

Use the data.frame() function to create a data frame by combining vectors of equal length.

# Creating a data frame with multiple students' information
students_df <- data.frame(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(20, 22, 19),
  Major = c("Economics", "History", "Biology"),
  GPA = c(3.8, 3.6, 3.9)
)

students_df
     Name Age     Major GPA
1   Alice  20 Economics 3.8
2     Bob  22   History 3.6
3 Charlie  19   Biology 3.9

Explanation: - Each vector represents a column in the data frame, and each row represents an observation (a student in this case).

2.3.2 Accessing and Modifying Data Frame Elements

Access data frame elements using $, [], or subset().

# Accessing the 'Major' column
students_df$Major
[1] "Economics" "History"   "Biology"  
# Accessing the second row
students_df[2, ]
  Name Age   Major GPA
2  Bob  22 History 3.6
# Modifying Bob's GPA
students_df[students_df$Name == "Bob", "GPA"] <- 3.7

Explanation: - $ retrieves entire columns. - [] with row and column indices retrieves specific elements or subsets. - Logical conditions identify and modify specific rows.

2.3.3 Adding and Removing Columns

Add new columns or remove existing ones as needed.

# Adding a new column for graduation year
students_df$GraduationYear <- c(2022, 2021, 2023)

# Removing the 'Age' column
students_df$Age <- NULL

Explanation: - Assigning a vector to a new column name adds it to the data frame. - Setting a column to NULL removes it from the data frame.

2.3.4 Filtering and Subsetting Data

Use conditions to filter rows and select subsets of data.

# Filtering students with GPA above 3.7
high_gpa_students <- subset(students_df, GPA > 3.7)

# Selecting specific columns
name_major_df <- students_df[, c("Name", "Major")]

Explanation: - subset() filters rows based on specified conditions. - Column indices within [] select specific columns for a new data frame.

Exercises and Solutions

Exercise 1: Working with Matrices

  1. Create a matrix named enrollment representing the number of students enrolled in three courses over four semesters. Use the following data:
    • Semester 1: 120, 85, 90
    • Semester 2: 130, 80, 95
    • Semester 3: 125, 90, 100
    • Semester 4: 140, 88, 110
  2. Assign row names as “Semester1” to “Semester4” and column names as “Economics”, “History”, “Biology”.
  3. Calculate the average enrollment for each course across all semesters.

Solution:

# Step 1: Creating the enrollment matrix
enrollment_numbers <- c(120, 85, 90, 130, 80, 95, 125, 90, 100, 140, 88, 110)
enrollment <- matrix(enrollment_numbers, nrow = 4, byrow = TRUE)

# Step 2: Assigning row and column names
rownames(enrollment) <- c("Semester1", "Semester2", "Semester3", "Semester4")
colnames(enrollment) <- c("Economics", "History", "Biology")

# Step 3: Calculating average enrollment for each course
average_enrollment <- colMeans(enrollment)
average_enrollment
Economics   History   Biology 
   128.75     85.75     98.75

Exercise 2: Creating and Manipulating Lists

  1. Create a list named university containing:
    • name: “ABC University”
    • established: 1965
    • departments: a vector of “Economics”, “History”, “Biology”
    • student_count: a vector of enrollment numbers 5000, 3000, 4000 corresponding to the departments.
  2. Access the departments element from the list.
  3. Add a new element location with the value “Cityville”.

Solution:

# Step 1: Creating the university list
university <- list(
  name = "ABC University",
  established = 1965,
  departments = c("Economics", "History", "Biology"),
  student_count = c(5000, 3000, 4000)
)

# Step 2: Accessing the departments element
university_departments <- university$departments

# Step 3: Adding the location element
university$location <- "Cityville"

Exercise 3: Building and Modifying Data Frames

  1. Create a data frame named faculty_df with the following data:
    • Name: “Dr. Adams”, “Dr. Baker”, “Dr. Clark”
    • Department: “Economics”, “History”, “Biology”
    • ExperienceYears: 10, 8, 12
  2. Add a new row for “Dr. Davis” from the “Economics” department with 5 years of experience.
  3. Change “Dr. Baker”’s experience years to 9.

Solution:

# Step 1: Creating the faculty_df data frame
faculty_df <- data.frame(
  Name = c("Dr. Adams", "Dr. Baker", "Dr. Clark"),
  Department = c("Economics", "History", "Biology"),
  ExperienceYears = c(10, 8, 12),
  stringsAsFactors = FALSE
)

# Step 2: Adding a new row for Dr. Davis
new_faculty <- data.frame(
  Name = "Dr. Davis",
  Department = "Economics",
  ExperienceYears = 5,
  stringsAsFactors = FALSE
)
faculty_df <- rbind(faculty_df, new_faculty)

# Step 3: Updating Dr. Baker's experience years
faculty_df[faculty_df$Name == "Dr. Baker", "ExperienceYears"] <- 9

Exercise 4: Filtering and Selecting Data

  1. Using the faculty_df from Exercise 3, filter the data frame to include only faculty members from the “Economics” department.
  2. Select only the Name and ExperienceYears columns from the filtered data.

Solution:

# Step 1: Filtering faculty members from Economics department
economics_faculty <- subset(faculty_df, Department == "Economics")

# Step 2: Selecting Name and ExperienceYears columns
economics_faculty_details <- economics_faculty[, c("Name", "ExperienceYears")]

Exercise 5: Combining Data Frames

  1. Create a data frame named courses_df with:
    • CourseID: 101, 102, 103
    • CourseName: “Microeconomics”, “World History”, “Genetics”
    • Department: “Economics”, “History”, “Biology”
  2. Merge courses_df with faculty_df based on the Department column.
  3. Remove any rows where there is no matching department in both data frames.

Solution:

# Step 1: Creating the courses_df data frame
courses_df <- data.frame(
  CourseID = c(101, 102, 103),
  CourseName = c("Microeconomics", "World History", "Genetics"),
  Department = c("Economics", "History", "Biology"),
  stringsAsFactors = FALSE
)

# Step 2: Merging courses_df with faculty_df
merged_df <- merge(courses_df, faculty_df, by = "Department")

# Step 3: Ensuring all rows have matching departments (already ensured by merge function)
merged_df
  Department CourseID     CourseName      Name ExperienceYears
1    Biology      103       Genetics Dr. Clark              12
2  Economics      101 Microeconomics Dr. Adams              10
3  Economics      101 Microeconomics Dr. Davis               5
4    History      102  World History Dr. Baker               9