% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-firms.R
\docType{data}
\name{firms}
\alias{firms}
\title{Spanish Food Industry Firms Dataset}
\format{
A \code{data.frame} with 917 rows and 6 columns:
\describe{
\item{total_assets}{Total assets (millions of euros).}
\item{employees}{Number of employees.}
\item{fixed_assets}{Tangible fixed assets (millions of euros).}
\item{personnel_expenses}{Personnel expenses (millions of euros).}
\item{operating_income}{Operating income (millions of euros).}
\item{autonomous_community}{Autonomous community where the firm operates.}
}
}
\source{
SABI (Sistema de Análisis de Balances Ibéricos) database, 2023.
Firms with more than 50 employees in the Spanish food industry.
}
\usage{
data(firms)
}
\description{
Dataset containing information on food industry companies located in Spain,
used to illustrate efficiency analysis within the \pkg{PEAXAI} package.
The dataset reflects the institutional and market heterogeneity that shapes firm-level efficiency
across Spain’s 17 autonomous communities.
}
\details{
The dataset includes 917 food industry firms with more than 50 employees, collected from the
\emph{SABI} database for the year 2023. Each observation corresponds to a single company.
Variables reflect both operational and financial dimensions relevant for productivity and efficiency assessment.

The output variable is:
\itemize{
\item \code{operating_income} — Operating income (in millions of euros), measuring revenues generated
from core business activities.
}

The input variables are:
\itemize{
\item \code{total_assets} — Total assets (millions of euros), representing resources employed.
\item \code{employees} — Number of employees, indicating workforce size (only firms with more than 50 workers are included).
\item \code{fixed_assets} — Tangible fixed assets (millions of euros), such as buildings and machinery.
\item \code{personnel_expenses} — Personnel expenses (millions of euros), including salaries, benefits, and training.
}

The variable \code{autonomous_community} identifies the territorial location of each firm within Spain.

The sample displays substantial dispersion across variables, encompassing both small and large firms.
This heterogeneity affects measures of central tendency—mean and median values differ considerably—thus providing a realistic challenge for efficiency and explainability analyses.
}
\examples{
data(firms)
str(firms)
summary(firms)

if (requireNamespace("ggplot2", quietly = TRUE)) {
  ggplot2::ggplot(firms, ggplot2::aes(x = employees, y = operating_income)) +
    ggplot2::geom_point(alpha = 0.6) +
    ggplot2::labs(
      x = "Number of employees",
      y = "Operating income (millions of euros)",
      title = "Spanish Food Industry Firms (2023)"
    ) +
    ggplot2::theme_minimal() +
    ggplot2::theme(
      plot.title = ggplot2::element_text(face = "bold"),
      axis.line = ggplot2::element_line(color = "black"),
      axis.ticks = ggplot2::element_line(color = "black"),
      panel.grid.minor = ggplot2::element_blank()
    )
}

}
\keyword{datasets}
