noiris: Data sets

#' Heart Disease data
#'
#' From the \href{https://www.kaggle.com/ronitf/heart-disease-uci}{Kaggle
#' website}: This database contains 76 attributes, but all published experiments
#' refer to using a subset of 14 of them. In particular, the Cleveland database
#' is the only one that has been used by ML researchers to this date. The "goal"
#' field refers to the presence of heart disease in the patient. It is integer
#' valued from 0 (no presence) to 4. Experiments with the Cleveland database
#' have concentrated on simply attempting to distinguish presence (values
#' 1,2,3,4) from absence (value 0).
#'
#' This data is useful for standard classification/survival (if using age).
#'
#' @format Data frame with 14 variables
#' \describe{
#'   \item{age}{age in years}
#'   \item{sex}{Male or female}
#'   \item{chest_pain_type}{typical angina, atypical angina, non-anginal pain, asymptomatic}
#'   \item{resting_bp}{resting blood pressure (in mm Hg on admission to the hospital)}
#'   \item{cholesterol}{serum cholestoral in mg/dl}
#'   \item{fasting_blood_sugar}{blood sugar > 120 mg/dl vs. less than ('lt_120', 'gt_120')}
#'   \item{resting_ecg}{resting electrocardiographic results}
#'   \item{max_heartrate}{maximum heart rate achieved}
#'   \item{exer_angina}{exercise induced angina (yes or no)}
#'   \item{old_peak}{ST depression induced by exercise relative to rest. See \href{https://en.wikipedia.org/wiki/ST_segment}{this} for explanation of ST.}
#'   \item{slope}{the slope of the peak exercise ST segment: positive flat or negative. See \href{https://en.wikipedia.org/wiki/ST_segment}{this} for explanation of ST.}
#'   \item{n_vessels}{number of major vessels (0-3) colored by flourosopy}
#'   \item{defect}{normal, fixed defect, or reversible defect}
#'   \item{heart_disease}{1 = yes, 0 = no. Left as numeric.}
#' }
#'
#' @details This is the classic heart disease data only prepped for actual use
#'   and with more useful names/labels where possible.  For reference the
#'   original names are: age, sex, cp, trestbps, chol, fbs, restecg, thalach,
#'   exang, oldpeak, slope, ca, thal, target.
#'
#' @note The values are sometimes labeled 1:4 while the actual data values
#' are 0-3 and similar.  Assumptions have been made that these coincide as one
#' would expect. Thal describes values of 3, 6, 7 but was actually coded 0-3
#' with only two zero values.  The zeros  were converted to NA.
#'
#'
#' @source \href{https://archive.ics.uci.edu/ml/datasets/Heart+Disease}{Data link}
#' @references
#' Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu,
#' S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of
#' a new probability algorithm for the diagnosis of coronary artery disease.
#' American Journal of Cardiology, 64,304--310.
#'
#' David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease
#' presence with the Cleveland database."
#'
#' Gennari, J.H., Langley, P, & Fisher, D. (1989). Models of incremental concept
#' formation. Artificial Intelligence, 40, 11-61.
#'
#' @examples
#' library(noiris)
#' str(heart_disease)
#'
"heart_disease"