R/distractors_less_than_given_proportion.R

In melissavanbussel/Analyze-Scantron: Analyze your multiple choice test

#' Proportion of Distractors Less than a Given Proportion
#'
#' Finds the test's distractors which were selected by less than a given proportion of the students.
#'
#'@param exam A data.frame object containing a test that follows the formatting of a typical Scantron file.
#
#'@param check_format a logical value (default = TRUE) indicating whether the "exam" object should be tested
#'for correct formatting.
#'
#'@param numchoicesperitem A vector which has the same length as the number of questions on the test, where each element in
#'the vector corresponds to the number of choices there were for each of the questions on the test. For example, if your
#'test had 35 questions which each had 5 options, numchoicesperitem = rep(5, 35).
#'
#'@param givenproportion The proportion for which you would like to calculate the proportion of the test's distractors
#'that are less than this value. For example, one definition of a non-functional distractor is a distractor which
#'less than 5% of students chose. In this case, you would set givenproportion = 0.05.
#'
#'@note The argument "exam" must be in the format of a typical Scantron results file.
#'Column 1 should correspond to ID (e.g., student number); column  2 should correspond to
#'DEPT (e.g., MATH); column 3 should correspond to COURSE CODE (e.g., 1051); the remaining
#'columns should each correspond to one of the questions on the test. The header of the data
#'frame should contain the column names, and row 1 of the data frame should contain the answer
#'key for the test. For example, if you had an exam with 25 students and 40 questions,
#'the data.frame object should have 26 rows and 43 columns.
#'
#'@note The "check_format" argument defaults to null. If this is left as null, the function will call
#'the \code{\link{num_choices_per_item}} function, which will do its best to guess the number of options
#'for each question. This is done by looking at the student answers and finding the
#'"largest" answer for each question. For example, if at least one student answered "E", but no students
#'answered "F", the function would guess that there were 5 options for that question.
#'
#'@return Returns a list object which is the same length as the number of questions on the test. Each element in
#'the list is a vector corresponding to one of the questions on the test, where this vector contains any distractors
#'for that question for which less than the "givenproportion" of students chose, along with the proportion of
#'students who chose that distractor.
#'
#'
#'@export
#'

distractors_less_than_given_proportion <- function(exam, numchoicesperitem = NULL, givenproportion = 0.05, check_format = TRUE){
  if(check_format == TRUE){
    stopifnot(correct_format(exam) == TRUE)
  }
  # Check if the user's numchoicesperitem makes sense, if they chose to include it
  if (!is.null(numchoicesperitem) == TRUE) {
    stopifnot(is.vector(numchoicesperitem), is.numeric(numchoicesperitem), length(numchoicesperitem) == num_items(exam, check_format = FALSE))
  }
  # Check if the user's givenproportion makes sense, if they chose to include it
  if (!is.null(givenproportion) == TRUE) {
    stopifnot(is.numeric(givenproportion), length(givenproportion) == 1, givenproportion >= 0 & givenproportion <= 1)
  }
  num_items <- num_items(exam, check_format = TRUE)
  answer_key <- answer_key(exam, check_format = TRUE)
  proportion_of_students_picking_choices <- proportion_of_students_picking_choices(exam, numchoicesperitem, check_format = FALSE)
  distractors <- list(length = num_items)
  distractors_less_than_given_proportion <- list(length = num_items)
  for (i in 1:num_items){
    distractors[[i]] <- proportion_of_students_picking_choices[[i]][-(letter2num(as.character(answer_key[i])))]
    distractors_less_than_given_proportion[[i]] <- proportion_of_students_picking_choices[[i]][proportion_of_students_picking_choices[[i]] <= givenproportion]
  }
  return(distractors_less_than_given_proportion)
}