R/heartfailure.R

In dlookr: Tools for Data Diagnosis, Exploration, Transformation

#' Heart Failure Data
#'
#' @description 
#' A dataset containing the ages and other attributes of almost 300 cases. 
#' 
#' @details 
#' Heart failure is a common event caused by Cardiovascular diseasess and this dataset contains 12 features that can be used to predict mortality by heart failure.
#' 
#' @format A data frame with 299 rows and 13 variables. The variables are as follows:
#' \describe{
#'   \item{age}{patient's age.}
#'   \item{anaemia}{decrease of red blood cells or hemoglobin (boolean), Yes, No.}
#'   \item{cpk_enzyme}{level of the CPK(creatinine phosphokinase) enzyme in the blood (mcg/L).}
#'   \item{diabetes}{if the patient has diabetes (boolean), Yes, No.}
#'   \item{ejection_fraction}{percentage of blood leaving the heart at each contraction (percentage).}
#'   \item{hblood_pressure}{high_blood_pressure. if the patient has hypertension (boolean), Yes, No.}
#'   \item{platelets}{platelets in the blood (kiloplatelets/mL).}
#'   \item{creatinine}{level of serum creatinine in the blood (mg/dL).}
#'   \item{sodium}{level of serum sodium in the blood (mEq/L).}
#'   \item{sex}{patient's sex (binary), Male, Female.}
#'   \item{smoking}{if the patient smokes or not (boolean), Yes, No.}
#'   \item{time}{follow-up period (days).}
#'   \item{death_event}{if the patient deceased during the follow-up period (boolean), Yes, No.}
#' }
#' @docType data
#' @keywords datasets
#' @name heartfailure
#' @usage data(heartfailure)
#' @references {
#' Davide Chicco, Giuseppe Jurman: Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making 20, 16 (2020). 
#' <https://doi.org/10.1186/s12911-020-1023-5>
#' }
#' @source {
#' "Heart Failure Prediction" in Kaggle <https://www.kaggle.com/andrewmvd/heart-failure-clinical-data>, License : CC BY 4.0
#' }
NULL

# library(dplyr)
# heartfailure <- read.csv("pkg_data/heart_failure_clinical_records_dataset.csv")
# as_yn <- function(x) {
#   as.factor(ifelse(x == 1 , "Yes", "No"))
# }
# 
# heartfailure <- heartfailure %>% 
#   rename_all(tolower) %>% 
#   mutate(age = as.integer(age),
#          anaemia = as_yn(anaemia),
#          creatinine_phosphokinase = as.numeric(creatinine_phosphokinase),
#          diabetes = as_yn(diabetes),
#          ejection_fraction = as.numeric(ejection_fraction),
#          high_blood_pressure = as_yn(high_blood_pressure),
#          platelets = as.numeric(platelets),
#          serum_creatinine = as.numeric(serum_creatinine),
#          serum_sodium = as.numeric(serum_sodium),
#          sex = as.factor(ifelse(sex == 1 , "Male", "Female")),
#          smoking = as_yn(smoking),
#          time = as.integer(time),
#          death_event = as_yn(death_event)) %>% 
#   rename(cpk_enzyme = creatinine_phosphokinase,
#          hblood_pressure = high_blood_pressure,
#          creatinine = serum_creatinine,
#          sodium = serum_sodium) 

# save(heartfailure, file = "data/heartfailure.rda")