Exercise 04: Data Manipulation and Function Scope Exercises

1 Data Import Exercises

1.1 Exercise 0: Importing Data from the online Website

You are working on the student exam analysis for UARK. You want to download the UARK Final exam table and then import it into R for data analysis.

• Hint: use rvest package to construct a data table

final_example_tbl$`Exam Time` <- ifelse(final_example_tbl$`Exam Time` == "During Posted Math Testing Center Hours", NA, final_example_tbl$`Exam Time`)
final_example_tbl2 <- final_example_tbl |> 
  separate(`Exam Time`, into = c("Start_Time", "End_Time"), sep = " to ")

final_example_tbl2 |> 
  mutate(
    Start_Time = parse_time(trimws(Start_Time)),
    End_Time = parse_time(trimws(End_Time))
  )

1.2 Exercise 1: Importing Data from CSV

You are working with the following CSV file containing employee information:

Employee ID,Full Name,Department,Salary,Years of Experience
1,John Doe,HR,50000,5
2,Jane Smith,Engineering,N/A,8
3,Michael Johnson,Marketing,55000,6
4,Susan Lee,HR,52000,4
5,David Kim,Engineering,65000,7
6,Amy Brown,Marketing,57000,5

1. Use the read_csv() function to import this data into R.

• For illustration, you can use the literal data (hint: use I()) to import the csv text.

2. Clean the data by replacing any missing salary values with NA.

3. Rename the columns to use snake case (employee_id, full_name, department, salary, years_of_experience).

1.3 Exercise 2: Importing Data Using Custom Delimiters

You have another dataset where the values are separated by semicolons (;) instead of commas:

Employee ID;Full Name;Department;Salary;Years of Experience
1;John Doe;HR;50000;5
2;Jane Smith;Engineering;60000;8
3;Michael Johnson;Marketing;55000;6
4;Susan Lee;HR;52000;4
5;David Kim;Engineering;65000;7
6;Amy Brown;Marketing;57000;5

• Use read_delim() to import this dataset with the semicolon delimiter (;).

2 Data Tidying Exercises

2.1 Exercise 3: Pivoting Data Longer

You have a dataset that tracks the number of customers visiting different stores each week:

store_visits <- tibble(
  store = c("Store A", "Store B", "Store C"),
  week_1 = c(120, 150, 110),
  week_2 = c(130, 160, 120),
  week_3 = c(140, 170, 130),
  week_4 = c(110, 140, 100),
  week_5 = c(160, 180, 150)
)

• Use pivot_longer() to convert this dataset into a long format, where each row represents a single observation for a specific week.

2.2 Exercise 4: Pivoting Data Wider

You have a long-format dataset that tracks the average test scores of students in different subjects over several terms:

scores_long <- tibble(
  student = c("Alice", "Alice", "Bob", "Bob", "Charlie", "Charlie"),
  term = c("Term 1", "Term 2", "Term 1", "Term 2", "Term 1", "Term 2"),
  subject = c("Math", "Math", "Math", "Math", "Math", "Math"),
  score = c(85, 90, 78, 80, 92, 95)
)

• Use pivot_wider() to convert this dataset into a wide format, where each term (e.g., “Term 1”, “Term 2”) is a column, and the scores are the values.

2.3 Exercise 5: Combining pivot_longer() and pivot_wider()

You have a dataset tracking the number of calls received by a customer service center, split by department and month:

calls_wide <- tibble(
  department = c("Sales", "Support", "Billing"),
  january = c(200, 150, 180),
  february = c(210, 160, 190),
  march = c(220, 170, 200)
)

• First, use pivot_longer() to convert this dataset into a long format, where each row corresponds to a specific department and month.
• Then, use pivot_wider() to convert the long dataset back into a wide format, where the months are columns and the values represent the number of calls.