R/doc_juice_drymatter.R

In industRial: Data, Functions and Support Materials from the Book "industRial Data Science"

#' Dry matter content of different juices obtained with two different measurement devices.
#' 
#' @format An object of class tibble with 108 observations on 7 variables.
#' 
#' @description This data set contains laboratory measurements of the dry matter
#' content of different fruit juices obtained with two different measurement 
#' devices. One of the devices is considered the reference (REF) and the other
#' one is a new device (DRX) on which a linearity and bias study has to be 
#' performed.
#' 
#' \describe{
#'   \item{product}{The juice base fruit ("Apple", "Beetroot")}
#'   \item{drymatter_TGT}{Target drymatter content in [g]}
#'   \item{speed}{Production line speed}
#'   \item{particle_size}{Dry matter powder particle size [micrometers]}
#'   \item{part}{Part number}
#'   \item{drymatter_DRX}{Drymatter content measured with device DRX}
#'   \item{drymatter_REF}{Drymatter content measured with reference device}
#' }
#' 
#' @source
#' Adapted from a real gage bias and linearity study performed in 2021 on 
#' industrial beverages dry matter content measurement. The structure of the
#' data corresponds to a full factorial design of 5 factors (3 with 3 levels and
#'  2 with 2 levels).
#'  
#' @references 
#' For a complete case study application refer to \url{https://j-ramalho.github.io/industRial/}.
#' 
#' @examples
#' library(dplyr)
#' # Calculate the bias between the new device and the reference:
#' juice_drymatter <- juice_drymatter %>% dplyr::mutate(bias = drymatter_DRX - drymatter_REF)
#' # Establish the analysis of variance:
#' juice_drymatter_aov <- aov(
#'      bias ~ drymatter_TGT * speed * particle_size,
#'      data = juice_drymatter)
#' summary(juice_drymatter_aov)
"juice_drymatter"