R/data.R

In OnAnalytics/OnBusiness: Data for Business Case Studies

#' @title  Automotive Industry Brand Perception
#'
#' @description A dataset containing brand names, industry type and perception levels on different measures.
#'
#' @name auto
#'
#' @docType data
#'
#' @format  A data frame with 39 rows and 63 variables:
#' \describe{
#'   \item{Brand_Name}{Brand Name}
#'   \item{Category_Name}{Category (automotive)}
#'   }
"auto"

#' @title  NYC Airbnb Data
#'
#' @description NYC Airbnb Data
#'
#' @name ny_airbnb
#'
#' @docType data
#'
#' @format  A data frame with 38306 rows and 23 variables:
#' \describe{
#'   \item{city}{City of Listing}
#'   \item{state}{State of Listing}
#'   }
"ny_airbnb"

#' @title  soup
#'
#' @description Store sales data of Progresso and Campbell's soup, combined with demographic information.
#'
#' @name soup
#'
#' @docType data
#'
#' @format  A data frame with 88409 rows and 34 variables:
#' \describe{
#'   \item{IRI_KEY}{Unique ID}
#'   \item{Winter}{Variable defining "Winter" or "nonwinter" status}
#'   }
"soup"


#' @title  soup_store
#'
#' @description Store level soup sales data
#'
#' @name soup_store
#'
#' @docType data
#'
#' @format  A data frame with 2055 rows and 22 variables:
#' \describe{
#'   \item{IRI_KEY}{Unique ID}
#'   \item{FIPS}{Location FIPS code}
#'   }
"soup_store"


#' @title  sqft
#'
#' @description Median home prices per square foot
#'
#' @name sqft
#'
#' @docType data
#'
#' @format  A data frame with 146333 rows and 6 variables:
#' \describe{
#'   \item{RegionID}{Unique region identifier}
#'   \item{RegionName}{Name of region}
#'   }
#' @source \url{https://www.zillow.com/research/data/}
"sqft"