R/data-evals.R

In OpenIntroStat/openintro-r-package: Data Sets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

#' Professor evaluations and beauty
#'
#' The data are gathered from end of semester student evaluations for 463 courses
#' taught by a sample of 94 professors from the University of Texas at Austin. In addition,
#' six students rate the professors' physical appearance. The result is a data
#' frame where each row contains a different course and each column has
#' information on the course and the professor who taught that course.
#'
#' @name evals
#' @docType data
#' @format A data frame with 463 observations on the following 23 variables.
#' \describe{
#'   \item{course_id}{Variable identifying the course (out of 463 courses).}
#'   \item{prof_id}{Variable identifying the professor who taught the course (out of 94 professors).}
#'   \item{score}{Average professor evaluation score: (1) very unsatisfactory -
#'   (5) excellent.}
#'   \item{rank}{Rank of professor: teaching, tenure track, tenured.}
#'   \item{ethnicity}{Ethnicity of professor: not minority, minority.}
#'   \item{gender}{Gender of professor: female, male.}
#'   \item{language}{Language of school where professor received education:
#'   English or non-English.}
#'   \item{age}{Age of professor.}
#'   \item{cls_perc_eval}{Percent of students in class who completed evaluation.}
#'   \item{cls_did_eval}{Number of students in class who completed evaluation.}
#'   \item{cls_students}{Total number of students in class.}
#'   \item{cls_level}{Class level: lower, upper.}
#'   \item{cls_profs}{Number of professors teaching sections in course in
#'   sample: single, multiple.}
#'   \item{cls_credits}{Number of credits of class: one credit (lab, PE, etc.),
#'   multi credit.}
#'   \item{bty_f1lower}{Beauty rating of professor from lower level
#'   female: (1) lowest - (10) highest.}
#'   \item{bty_f1upper}{Beauty rating of professor from upper level female:
#'   (1) lowest - (10) highest.}
#'   \item{bty_f2upper}{Beauty rating of professor from second level female:
#'   (1) lowest - (10) highest.}
#'   \item{bty_m1lower}{Beauty rating of professor from lower level male:
#'   (1) lowest - (10) highest.}
#'   \item{bty_m1upper}{Beauty rating of professor from upper level male:
#'   (1) lowest - (10) highest.}
#'   \item{bty_m2upper}{Beauty rating of professor from second upper level male:
#'   (1) lowest - (10) highest.}
#'   \item{bty_avg}{Average beauty rating of professor.}
#'   \item{pic_outfit}{Outfit of professor in picture: not formal, formal.}
#'   \item{pic_color}{Color of professor's picture: color, black & white.}
#'   }
#' @source Daniel S. Hamermesh, Amy Parker, Beauty in the classroom:
#' instructors’ pulchritude and putative pedagogical productivity, Economics
#' of Education Review, Volume 24, Issue 4, 2005.
#' \doi{10.1016/j.econedurev.2004.07.013}.
#' @keywords datasets
#' @examples
#'
#' evals
"evals"