R/heartdisease.R

In MLDataR: Collection of Machine Learning Datasets for Supervised Machine Learning

#' Heart disease dataset
#'
#' The dataset is to be used with a supervised classification ML model to classify heart disease.
#' @docType data
#' @keywords heart disease heart disease machine learning classification
#' @format A data frame with 918 rows and 10 variables:
#' \describe{
#'   \item{Age}{age of the patient presenting with heart disease}
#'   \item{Sex}{gender of the patient}
#'   \item{RestingBP}{blood pressure for resting heart beat}
#'   \item{Cholesterol}{Cholesterol reading}
#'   \item{FastingBS}{blood sample of glucose after a patient fasts \url{https://www.diabetes.co.uk/diabetes_care/fasting-blood-sugar-levels.html}}
#'   \item{RestingECG}{Resting echocardiography is an indicator of previous myocardial infarction e.g. heart attack}
#'   \item{MaxHR}{Maximum heart rate}
#'   \item{Angina}{chest pain caused by decreased flood flow \url{https://www.nhs.uk/conditions/angina/}}
#'   \item{HeartPeakReading}{reading at the peak of the heart rate}
#'   \item{HeartDisease}{the classification label of whether patient has heart disease or not}
#'
#' }
#' @source Collected by Gary Hutson \email{hutsons-hacks@outlook.com}, Dec-2021
#' @examples
#' library(dplyr)
#' library(ConfusionTableR)
#' data(heartdisease)
#'
#' # Convert diabetes data to factor'
#' hd <- heartdisease %>%
#'  glimpse() %>%
#'  mutate(HeartDisease = as.factor(HeartDisease))
#' # Check that the label is now a factor
#'  is.factor(hd$HeartDisease)
#'  # Dummy encoding
#' # Get categorical columns
#' hd_cat <- hd  %>%
#'  dplyr::select_if(is.character)

#'  # Dummy encode the categorical variables
#'  # Specify the columns to encode
#'  cols <- c("RestingECG", "Angina", "Sex")
#'  # Dummy encode using dummy_encoder in ConfusionTableR package
#'  coded <- ConfusionTableR::dummy_encoder(hd_cat, cols, remove_original = TRUE)
#' coded <- coded %>%
#'     select(RestingECG_ST, RestingECG_LVH, Angina=Angina_Y,
#'     Sex=Sex_F)
#' # Remove column names we have encoded from original data frame
#' hd_one <- hd[,!names(hd) %in% cols]
#' # Bind the numerical data on to the categorical data
#' hd_final <- bind_cols(coded, hd_one)
#' # Output the final encoded data frame for the ML task
#' glimpse(hd_final)
"heartdisease"