R/data.R

In heuristica: Heuristics Including Take the Best and Unit-Weight Linear

#' Chicago high school dropout rates. 
#'
#' Chicago high school dropout rates from 1995 and associated variables
#' like average students per teacher and percent low income students.
#' All cues are real-valued but some have N/A values.  It includes rows
#' accidentally omitted in prior research.
#'
#' The data is based on:
#'
#'  Morton, Felicia B. (1995). Charting a School's Course. Chicago.
#'     February, pp. 86-95.
#'
#'  Rodkin, Dennis. (1995). 10 Keys for Creating Top High Schools.
#'     Chicago. February, pp. 78-85.
#'
#' This is the data set used in simulations by the ABC (Adaptive Behavior
#' and Cognition) research group.
#'
#' @format A data frame.
#' \describe{
#'   \item{Name}{Name of School}
#'   \item{Running_Number}{Running Number}
#'   \item{Included_in_Web_and_Web_Corrected}{If 1, then this row was
#'     accidentally omitted in the ABC studies from 1993}
#'   \item{Dropout_Rate}{Dropout rate in percent, from 0 to 100, counting all
#'     students in grades 9 through 12 who left school permanently during the
#'     1993-4 school year}
#'   \item{Completeness_of_Data}{Completeness of data}
#'   \item{Enrollment}{Enrollment as of September 30, 1993}
#'   \item{Attendance_Rate}{Attendance rate in percent, from 0 to 100, averaged
#'     over the school year}
#'   \item{Graduation_Rate}{Graduation rate in percent, from 0 to 100, based on
#'     freshmen who finished together 4 years later, in 1994}
#'   \item{Parental_Involvement_Rate}{Parental involvement rate in percent,
#'     from 0 to 100, counted as parents who had contact with teachers as a
#'     percent of students (with no firm state rules on how to measure this)}
#'   \item{Limited_English_Students}{Limited English Students in percent, from
#'     0 to 100, based on the number of students found eligible for bilingual
#'     education}
#'   \item{Low_Income_Students}{Low Income Students in percent, from 0 to 100,
#'     based on families eligible for free or reduced price lunches or are
#'     publicly supported}
#'   \item{Average_Class_Size_Student_per_Teacher}{Calculated as number of
#'     students divided by number of teachers on the first day of May}
#'   \item{Percent_White_Students}{Percent white students, from 0 to 100}
#'   \item{Percent_Black_Students}{Percent black students, from 0 to 100}
#'   \item{Percent_Hispanic_Students}{Percent hispanic students, from 0 to 100}
#'   \item{Percent_Asian_Students}{Percent asian students, from 0 to 100}
#'   \item{Percent_Minority_Teacher}{Percent minority teacher, from 0 to 100}
#'   \item{Average_Composite_ACT_Score}{Average composite ACT Score}
#'   \item{Reading}{Reading score on Illinois Goal Assessment Program (IGAP)}
#'   \item{Math}{Math score on IGAP}
#'   \item{Science}{Science score on IGAP}
#'   \item{Social_Science}{Social science score on IGAP}
#'   \item{Writing}{Writing score on IGAP}
#' }
"highschool_dropout"


#' Population size of the 83 largest German cities. 
#'
#' Population size of the 83 German cities that had more than 100,000
#' inhabitants when this data was collected in 1993 plus cues 
#' indicating whether a city has a soccer team, intercity trainline,
#' University, etc.  All cues are binary.
#'
#' The data is based on:
#'
#'  Fischer Welt Almanach [Fischer World Almanac]. (1993).  Frankfurt,
#'    Germany: Fischer.
#'
#'
#' This is the data set used in simulations by the ABC (Adaptive Behavior
#' and Cognition) research group.
#'
#' @format A data frame.
#' \describe{
#'   \item{Name}{Name of city}
#'   \item{Running_Number}{Running Number}
#'   \item{Population}{Population size}
#'   \item{Soccer_Team}{1 indicates that the city has a soccer team, 0
#'     indicates that it does not.}
#'   \item{State_Capital}{1 indicates that the city is a state capital, 0
#'     indicates that it is not.}
#'   \item{Former_East_Germany}{1 indicates that the city belongs to former
#'     East Germany, 0 that is does not.}
#'   \item{Industrial_Belt}{1 indicates that the city is an industrial belt,
#'     0 that it is not.}
#'   \item{Licence_Plate}{1 indicates that the city has a licence plate, 0
#'     that it does not.}
#'   \item{Intercity_Trainline}{1 indicates that an intercity trainline crosses
#'     the city, 0 that it does not.}
#'   \item{Exposition_Site}{1 indicates that the city is an exposition size, 0
#'     that it is not.}
#'   \item{National_Capital}{1 indicates that the city is the national capital,
#'     0 that it is not.}
#'   \item{University}{1 indicates that the city has a University, 0 that it
#'     does not.}
#' }
"city_population"

#' Original, uncorrected Population size of the 83 largest German cities. 
#'
#' In contrast to city_population, this has some transcription errors from
#' the almanac, but it was used in published research, so it is provided for
#' reproducibility.
#'
#' @format A data frame.
#' \describe{
#'   \item{Name}{Name of city}
#'   \item{Running_Number}{Running Number}
#'   \item{Population}{Population size}
#'   \item{Soccer_Team}{1 indicates that the city has a soccer team, 0
#'     indicates that it does not.}
#'   \item{State_Capital}{1 indicates that the city is a state capital, 0
#'     indicates that it is not.}
#'   \item{Former_East_Germany}{1 indicates that the city belongs to former
#'     East Germany, 0 that is does not.}
#'   \item{Industrial_Belt}{1 indicates that the city is an industrial belt,
#'     0 that it is not.}
#'   \item{Licence_Plate}{1 indicates that the city has a licence plate, 0
#'     that it does not.}
#'   \item{Intercity_Trainline}{1 indicates that an intercity trainline crosses
#'     the city, 0 that it does not.}
#'   \item{Exposition_Site}{1 indicates that the city is an exposition size, 0
#'     that it is not.}
#'   \item{National_Capital}{1 indicates that the city is the national capital,
#'     0 that it is not.}
#'   \item{University}{1 indicates that the city has a University, 0 that it
#'     does not.}
#' }
"city_population_original"