R/data-orings.R

In OpenIntroStat/openintro: Datasets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

#' 1986 Challenger disaster and O-rings
#'
#' On January 28, 1986, a routine launch was anticipated for the Challenger
#' space shuttle. Seventy-three seconds into the flight, disaster happened: the
#' shuttle broke apart, killing all seven crew members on board. An
#' investigation into the cause of the disaster focused on a critical seal
#' called an O-ring, and it is believed that damage to these O-rings during a
#' shuttle launch may be related to the ambient temperature during the launch.
#' The table below summarizes observational data on O-rings for 23 shuttle
#' missions, where the mission order is based on the temperature at the time of
#' the launch.
#'
#'
#' @name orings
#' @docType data
#' @format A data frame with 23 observations on the following 4 variables.
#' \describe{
#'   \item{mission}{Shuttle mission number.}
#'   \item{temperature}{Temperature, in Fahrenheit.}
#'   \item{damaged}{Number of damaged O-rings (out of 6).}
#'   \item{undamaged}{Number of undamaged O-rings (out of 6).}
#'   }
#' @source
#' \url{https://archive.ics.uci.edu/dataset/92/challenger+usa+space+shuttle+o+ring}
#' @keywords datasets
#' @examples
#'
#' library(dplyr)
#' library(forcats)
#' library(tidyr)
#' library(broom)
#'
#' # This is a wide data frame. You can convert it to a long
#' # data frame to predict probability of O-ring damage based
#' # on temperature using logistic regression.
#'
#' orings_long <- orings |>
#'   pivot_longer(cols = c(damaged, undamaged), names_to = "outcome", values_to = "n") |>
#'   uncount(n) |>
#'   mutate(outcome = fct_relevel(outcome, "undamaged", "damaged"))
#'
#' orings_mod <- glm(outcome ~ temperature, data = orings_long, family = "binomial")
#' tidy(orings_mod)
"orings"