Package 'catool'

Filter Course Schedule by College, Department, Program, Subject, and/or Instructor

Description

Applies one or more filters to a course schedule using pattern matching on instructor, subject, college, department, and program. All matching is case-insensitive and based on partial string matching.

Usage

filter_schedule(
  schedule,
  subject_pattern = NULL,
  instructor_pattern = NULL,
  college_pattern = NULL,
  department_pattern = NULL,
  program_pattern = NULL
)

Arguments

schedule	A data frame containing the course schedule with required columns: INSTRUCTOR, SUBJ. Optional columns include: COLLEGE, DEPARTMENT, PROGRAM.
subject_pattern	Optional regex pattern to match subject codes (e.g., "CSCI", "^MATH").
instructor_pattern	Optional regex pattern to match instructor names (e.g., "Smith", "^Jones").
college_pattern	Optional regex pattern to match college names (e.g., "Science", "Engineering").
department_pattern	Optional regex pattern to match department names (e.g., "Math", "Biology").
program_pattern	Optional regex pattern to match program names (e.g., "Undergraduate", "MBA").

Value

A filtered data frame of matching courses.

Examples

schedule <- data.frame(
  INSTRUCTOR = c("Lee", "Smith", "Jones", "Dawson", "Garcia"),
  SUBJ = c("MATH", "NURS", "CSCI", "ENGL", "COMM"),
  COLLEGE = c("Science", "Nursing", "Science", "Arts and Sciences", "Arts and Communication"),
  DEPARTMENT = c("Math", "Nursing", "CS", "English", "Comm Studies"),
  PROGRAM = c("BS", "BSN", "BS", "BA", "BA"),
  stringsAsFactors = FALSE
)

filter_schedule(schedule, subject_pattern = "^MATH|^STAT")
filter_schedule(schedule, instructor_pattern = "smith")
filter_schedule(schedule, college_pattern = "Science")
filter_schedule(schedule, department_pattern = "Comm")
filter_schedule(schedule, program_pattern = "^BA$")

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Filter Course Schedule by Instructor (Regex-Friendly, Case-Insensitive)

Description

Returns a subset of the course schedule containing courses taught by the specified instructor. Matching is case-insensitive and supports regular expressions, allowing flexible partial or pattern-based matching. If no match is found, a warning is issued and an empty data frame is returned.

Usage

get_instructor_schedule(instructor_name, schedule_df = schedule)

Arguments

instructor_name	A character string (or regular expression) used to match against values in the INSTRUCTOR column.
schedule_df	A data frame containing course schedule data with an INSTRUCTOR column. Defaults to schedule if not specified.

Value

A data frame of courses assigned to instructors matching the given pattern, sorted by descending enrollment.

Examples

get_instructor_schedule("smith", schedule_df = schedule)  # partial match
get_instructor_schedule("^Smith,", schedule_df = schedule)  # regex: starts with Smith
get_instructor_schedule("Robinson|Smith", schedule_df = schedule)  # regex: matches either

---

Filter Course Schedule by Subject Code (Regex-Friendly)

Description

Returns a subset of the schedule where the SUBJ (subject code) column matches a given pattern. Matching is case-insensitive and supports regular expressions.

Usage

get_subject_schedule(subject_pattern, schedule)

Arguments

subject_pattern	A character string or regular expression to match subject codes.
schedule	A data frame containing course schedule data with a SUBJ column.

Value

A filtered data frame containing only matching subject codes.

Examples

schedule <- data.frame(SUBJ = c("CSCI", "MATH", "STAT"))
get_subject_schedule("CSCI", schedule)
get_subject_schedule("^MATH|^STAT", schedule)

---

Get Unique Instructor Names

Description

Extracts a sorted vector of unique, non-empty instructor names from a schedule data frame.

Usage

get_unique_instructors(schedule_df)

Arguments

schedule_df

A data frame containing an INSTRUCTOR column.

Value

A character vector of instructor names, sorted alphabetically.

Examples

get_unique_instructors(schedule)

---

Calculate Overload Compensation for One Instructor

Description

Computes prorated overload pay and qualified credit hours for a single instructor based on course credit hours, enrollment, and institutional overload rules.

Usage

ol_comp(
  instructor_schedule,
  L = 4,
  U = 9,
  rate_per_cr = 2500/3,
  reg_load = 12,
  favor_institution = TRUE
)

Arguments

instructor_schedule	A data frame of the instructor's courses, with columns INSTRUCTOR, ENRLD, and HRS.
L	Lower enrollment threshold for overload pay qualification (default = 4).
U	Upper limit of proration; courses with ENRLD > U get full-rate pay (default = 9).
rate_per_cr	Base overload pay per credit hour (default = 2500/3).
reg_load	Regular teaching load in credit hours (default = 12).
favor_institution	Logical: if TRUE (default), prioritizes high-enrollment courses for regular load.

Details

If favor_institution = TRUE (default), the function assigns high-enrollment qualified courses to the regular load first, resulting in lower compensation because only low-enrollment courses are left for overload pay — this favors the institution.

If favor_institution = FALSE, the function assigns low-enrollment qualified courses to the regular load first, preserving high-enrollment courses for compensation — this favors the instructor.

Note: This function assumes that instructor_schedule is already filtered for one instructor. Use get_instructor_schedule() to extract an instructor’s schedule using flexible, case-insensitive pattern matching (regex supported, e.g., "smith|jones").

Value

A tibble with course-level compensation and a human-readable summary block.

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Calculate Overload Compensation for One Instructor (by Index)

Description

Retrieves an instructor's name by index from the schedule and calculates their overload compensation using ol_comp(). Returns a clean, readable course-level compensation summary.

Usage

ol_comp_byindex(
  i,
  schedule_df,
  L = 4,
  U = 9,
  rate_per_cr = 2500/3,
  reg_load = 12,
  favor_institution = TRUE
)

Arguments

i	Integer index of the instructor (as returned by get_unique_instructors()).
schedule_df	A data frame of the full course schedule containing an INSTRUCTOR column.
L	Lower enrollment threshold for overload pay eligibility (inclusive). Default is 4.
U	Upper enrollment limit for proration (inclusive). Default is 9.
rate_per_cr	Overload pay rate per qualified credit hour. Default is 2500/3.
reg_load	Regular teaching load in credit hours. Default is 12.
favor_institution	Logical: if TRUE (default), favors the institution by prioritizing high-enrollment courses for regular load.

Details

If favor_institution = TRUE (default), the function assigns high-enrollment courses to the regular load first, minimizing compensation.

If favor_institution = FALSE, low-enrollment courses are used toward the regular load first, preserving high-enrollment courses for overload pay.

This function internally uses get_instructor_schedule(), which supports flexible, case-insensitive regex matching for instructor names (e.g., "^smith$" or "johnson|williams").

Value

Invisibly returns a tibble with the instructor’s course-level overload compensation summary.

Examples

# Example usage with a schedule dataframe:
# ol_comp_byindex(1, schedule_df = schedule)

---

Summarize Overload Compensation

Description

Calculates overload compensation for each instructor based on institutional policy. The output includes course-level payments, qualified credit hours, and a readable instructor-level summary block that follows each instructor's courses.

Usage

ol_comp_summary(
  schedule_df,
  instructor = NULL,
  L = 4,
  U = 9,
  rate_per_cr = 2500/3,
  reg_load = 12,
  favor_institution = TRUE
)

Arguments

schedule_df	A data frame containing course schedule information. Must include columns such as INSTRUCTOR, HRS, and ENRLD.
instructor	Optional string. If provided, limits the summary to a single instructor. Default is NULL (includes all instructors).
L	Minimum enrollment required for overload pay eligibility. Default is 4.
U	Upper threshold for proration. Courses with ENRLD > U receive full-rate pay. Default is 9.
rate_per_cr	Overload pay rate per credit hour. Default is 2500/3.
reg_load	Regular teaching load in credit hours. Default is 12.
favor_institution	Logical: if TRUE (default), prioritizes high-enrollment courses for regular load.

Details

If the instructor argument is specified, the function limits the summary to that instructor. Matching is exact and case-sensitive unless pre-filtered using get_instructor_schedule(), which supports regex-based, case-insensitive pattern matching (e.g., "smith|jones").

Value

A tibble combining course-level compensation and a summary section for each instructor.

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Sample Schedule Dataset

Description

This dataset represents a sample class schedule used by several institutions, with slight variations in variable names. Users must ensure that the key variables—HRS, ENRLD, and INSTRUCTOR—are properly named and formatted for the functions in this package to work correctly.

Usage

schedule

Format

An object of class data.frame with 665 rows and 15 columns.

Details

A dataset containing instructor schedules for overload analysis.

The dataset has 665 rows and 15 columns, including:

HRS

Credit hours of the course.

ENRLD

Number of students enrolled in the course.

INSTRUCTOR

Name of the instructor teaching the course (anonymized).

Source

Included in the package under data/schedule.rda

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