% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{county_income_gender.hist}
\alias{county_income_gender.hist}
\title{County Income by Gender Histogram-Valued Dataset}
\format{
A data frame with 12 observations (counties) and 4 variables:
\itemize{
    \item \code{male_income}: Histogram of male household income
          (4 bins from $0 to $100k).
    \item \code{female_income}: Histogram of female household income
          (4 bins from $0 to $100k).
    \item \code{n_males}: Number of male respondents (numeric).
    \item \code{n_females}: Number of female respondents (numeric).
}
Row names are County_1 through County_12.
}
\source{
Billard, L. and Diday, E. (2020), Table 6-16.
}
\usage{
data(county_income_gender.hist)
}
\description{
Histogram-valued dataset of 12 counties with gender-stratified income
histograms and sample sizes. Each county has a male income histogram,
a female income histogram, and the number of respondents in each group.
}
\section{Metadata}{

\tabular{ll}{
  \strong{Sample size (n)} \tab 12 \cr
  \strong{Variables (p)} \tab 4 \cr
  \strong{Subject area} \tab Economics \cr
  \strong{Symbolic format} \tab Histogram \cr
  \strong{Analytical tasks} \tab Clustering, Descriptive statistics
}
}

\examples{
data(county_income_gender.hist)
}
\references{
Billard, L. and Diday, E. (2020). \emph{Clustering Methodology for
Symbolic Data}. Wiley, Chichester. Table 6-16.
}
\keyword{datasets}
\keyword{gender}
\keyword{histogram}
\keyword{income}
