Lab 6 – S3 for Workshop Participants Instruction

Please ensure you review the document Illustrate S3 System – U of I Women’s Basketball Team example on Canvas Module or visit the weblink https://ycjiaweijia.github.io/F24STAT385/Lab6HelpDoc/Lab6HelpDoc.html for helpful guidance on this lab.
Please complete the following questions and submit your Lab 6 to Gradescope.

Background

Suppose a university is hosting a campus-wide data science workshop with undergraduate and graduate student participants, along with many faculty members showing interest in the series of workshops. Develop S3 classes for students, faculty, etc., incorporating validators and necessary helper functions.

Question 1

[10 pts] Create the constructor function new_student() to generate objects of the “student” class. Student participants provide the following information: name, major, department, classYear (Freshman, Sophomore, Junior, Senior, or Graduate), UofI_email, and dsExperience which represents years of experience in data science.
[10 pts] Create the validator function validate_student(). Below is a list of validations to include.

Validations:

If any of the information required above is missing, print a warning message.
If dsExperience is not numeric, or the length is not 1, or the years is lower than 0 or greater than 20, print warning message that: The data science experience years information is not valid. Please use || for “or”.
If classYear doesn’t belong in one of the five categories, print the warning message that: The student’s class information is invalid.
If UofI_emailprovided is not character type, print the warning message: The student’s email information is invalid.

[10 pts] Define a helper function to fill in or complete an email address. This is necessary because some students might only provide their NetID instead of a full email address. The completeEmail() function should take UofI_email as input and add @illinois.edu if needed. The output of completeEmail() function is saved as Complete_UofI_email.

[Hint:

Consider using an if-else statement to decide whether to append the domain.
The regular expression grepl("@illinois\\.edu", UofI_email), checking if UofI_email contains @illinois.edu, might be helpful. ]

Question 2

[10 pts] Create the constructor function new_faculty() to generate objects of the “faculty” class. Faculty participants have the following information: name and department.
[10 pts] Create the constructor function new_other() to generate objects of the “Other” class. Other participants have the following information: name.

Question 3

[30 pts] Define generic and methods using the constructors from Question 1 and 2 to create classes for student, faculty, and other participants. Implement a participantInfo() function that prints specific information based on the class of the object.

For example,

Swift <- new_student(x = list(
  name = "Taylor Swift",
  major = "Fisheries and Wildlife",
  department = "Biological Sciences",
  classYear = "Junior", 
  UofI_email = "Tswift",
  dsExperience = 1.5))
participantInfo(Swift)


Ronaldo <- new_faculty(x = list(
  name = "Cristiano Ronaldo", 
  department = "Applied Health Science"
  )
)
participantInfo(Ronaldo)


Someone <- new_other(x = list(
  name = "Someone Else"
  )
)
participantInfo(Someone)

should show the following:

For student
- Example: “Taylor Swift is a Fisheries And Wildlife major. The email address is Tswift@illinois.edu. The student has 1.5 years of experience in data science.”
For faculty
- Example: “Cristiano Ronaldo is from the Applied Health Science Department.”
For other people
- Example: “Someone Else is neither a faculty member nor a student.”