R/document_data.R

In idarps: Datasets and Functions for the Class "Modelling and Data Analysis for Pharmaceutical Sciences"

#'
#' Intensive care admission of COVID-19 patients in Belgium
#'
#' @description Data from Parisi, et al., (2021) which studies the applicability of predictive models for intensive care
#' admission of COVID-19 patients in a secondary care hospital in Belgium. This study is based on data of
#' patients admitted to an emergency department with a positive RT-PCR SARS-CoV-2 test.
#'
#' @format A data frame with 64 rows and 5 variables:
#' \describe{
#'   \item{icu}{admission to an Intensive Care Unit (0 for no, 1 for yes)}
#'   \item{sex}{sex (men, women)}
#'   \item{age}{age in years}
#'   \item{ldh}{lactate dehydrogenase in U/L}
#'   \item{spo2}{oxygen saturation in percentage}
#' }
#'
#' @references Parisi, Nicolas, et al. "Non applicability of validated predictive models for intensive care admission and death of COVID-19 patients in a secondary care hospital in Belgium.", Journal of Emergency and Critical Care Medicine, (2021).
#' @source \url{https://jeccm.amegroups.org/article/view/6927/html}
"covid"
#'

#'
#' Customer attendance of a pharmacy in Geneva
#'
#' @description This dataset contains the number of clients in a pharmacy for each hour over two years.
#'
#' @format A data frame with 17520 rows and 4 variables:
#' \describe{
#'   \item{date}{the date}
#'   \item{hours}{the hour of the day}
#'   \item{weekday}{the week day}
#'   \item{attendance}{the recorded number of clients}
#' }
"pharmacy"
#'



#'
#' Biomarkers in pigs fed with various diets
#'
#' @description This dataset contains measured biomarkers in pigs fed with various diets.
#'
#' @format A data frame with 61 rows and 9 variables:
#' \describe{
#'   \item{id}{the id of the pig}
#'   \item{group}{the diet fed to the pig (chipped diet or non-chipped diet)}
#'   \item{gender}{the gender of the pig}
#'   \item{cortisol}{urine costisol in pg/ml}
#'   \item{acth}{serum acth in pg/ml}
#'   \item{crh}{serum crh in pg/ml}
#'   \item{testosterone}{testosterone in ng/ml}
#'   \item{lh}{LH in ng/ml}
#'   \item{caloric}{daily caloric intake in kcal}
#'   }
"cortisol"
#'
#'


#'
#' codex
#'
#' @description This dataset is based on an observational study conducted at Geneva University Hospitals to assess the impact of weight on the pharmacokinetics of dexamethasone in normal-weight versus obese patients hospitalized for COVID-19.
#'
#' @format
#' \describe{
#'   \item{id}{ID of the patient}
#'   \item{gender}{Gender (0 for men and 1 for women)}
#'   \item{age}{Age}
#'   \item{bmi}{Body mass index}
#'   \item{weight}{Weight in kg}
#'   \item{number_doses}{Number of doses of the dexamethasone (DEX) drug}
#'   \item{tmax}{The time it takes for the drug to reach the maximum concentration (i.e. Cmax) after its administration in hours (h)}
#'   \item{cmax}{The maximum concentration that  achieves in the blood after the drug has been administered (ng/m)}
#'   \item{t1_2}{t1_2 is the time required to decrease the drug concentration within the body by one-half during elimination in hours (h)}
#'   \item{auc}{The integral (from 0 to 8 hours) of a curve that describes the variation of a drug concentration in the blood as a function of time it takes for a drug to reach the maximum concentration (Cmax) after administration of a drug (ng.h/m)}
#'   \item{length_hospital}{Number of days the patient were hospitalized}
#'   \item{length_intermed}{Number of days the patient were hospitalized at the intermediate and intensive care unit}
#'   \item{crp}{crp}
#'   \item{comor_e}{Presence of cormobidity type e}
#'   \item{comor_p}{Presence of cormobidity type p}
#'   \item{comor_v}{Presence of cormobidity type v}
#'   \item{comor_c}{Presence of cormobidity type c}
#'   \item{comor_r}{Presence of cormobidity type r}
#'   \item{obese}{Indicator variable based on whether the subject is obese (i.e. with BMI > 30), 0 for no and 1 for yes.}
#' }
"codex"
#'


#' Bronchitis
#'
#' @description Data collected in a study to assess the effects of smoking and pollution on being diagnosed with bronchitis. This dataset is based on 212 subjects.
#'
#' @format
#' \describe{
#' \item{bron}{Presence of bronchitis (0 for no and 1 for yes)}
#' \item{cigs}{Average daily number of smoked cigarettes}
#' \item{poll}{Pollution index}
#'
#' }
"bronchitis"
#'


#'
#' Diet
#'
#' @format
#' \describe{
#'   \item{id}{ID}
#'   \item{gender}{Gender (male or female)}
#'   \item{age}{Age in years}
#'   \item{height}{Height in m}
#'   \item{diet.type}{Type of diet (A, B or C)}
#'   \item{initial.weight}{Initial weight in kg}
#'   \item{final.weight}{Final weight in kg}
#' }
"diet"


#' Reading
#'
#' @description This dataset is based on the effectiveness of directed reading activities for elementary school students (6-12 years old).
#'
#' @format
#' \describe{
#'   \item{id}{Student id}
#'   \item{score}{Degree of Reading Power (DRP) test score}
#'   \item{age}{Age of the students}
#'   \item{group}{Binary variable indicating whether a student participated to the directed reading activities (Treatment if the student participated, Control otherwise)}
#' }
"reading"



#'
#' Students
#'
#'
#' @format
#' \describe{
#'   \item{day}{day}
#'   \item{case}{case}
#' }
"students"
#'


#'
#' COVID-19 Spatial
#'
#' @description Data from the COVID-19 Data Hub joined with spatial features for Switzerland.
#'
#' @format
#' \describe{
#'   \item{admin}{Country}
#'   \item{iso_alpha_3}{3-letter code of the country according to the standard ISO 3166-1 Alpha-3}
#'   \item{date}{Date}
#'   \item{confirmed}{Cumulative number of confirmed cases}
#'   \item{population}{Total population}
#'   \item{tests}{Cumulative number of tests}
#'   \item{diff_confirmed}{Daily number of confirmed cases}
#'   \item{diff_test}{Daily number of tests}
#'   \item{confirmed_per_pop}{Number of daily confirmed cases divided per the country population}
#'   \item{confirmed_per_pop_ma}{Moving Average applied to confirmed_per_pop with a window of 7 days}
#'   \item{geometry}{`sf` geometry list of country}
#' }
#'
#' @source \url{https://covid19datahub.io/}
#'
"data_covid_switzerland_spatial"
#'
#'
#'


#' HP13Cbicarbonate
#' @description Data from an experiment made on rats which compares the HP13C bicarbonate signal intensities normalized to
#' the total sum of metabolites and corresponding initial reaction rate as a function of the injected dose of HP1-13C pyruvate.
#' Two groups of rats were compared (i.e. fed and overnight-fasted). Dataset from Can et al. 2022.
#'
#'
#' @format
#' \describe{
#'   \item{signal}{HP13C bicarbonate signal intensities normalized to the total sum of metabolites}
#'   \item{dose}{initial reaction rate as a function of the injected dose of HP13C pyruvate}
#'   \item{group}{fed and overnight-fasted}
#' }
#'
#' @source \url{https://www.nature.com/articles/s42003-021-02978-2}
"HP13Cbicarbonate"
#'







#' Snoring
#' @description This dataset is based on a study on the physical and behavioral characteristics of snorers.
#'
#'
#' @format
#' \describe{
#' \item{sex}{gender of the person (0 for males and 1 for females)}
#' \item{age}{age in years}
#' \item{height}{height in cm}
#' \item{weight}{weight in kg}
#' \item{smoke}{smoking behavior (0 for non-smokers and 1 for smokers)}
#' \item{alcohol}{number of glasses drunk per day (in red wine equivalent)}
#' \item{snore}{snoring diagnosis (0 for not snoring, 1 for snoring)}
#' }
#'
"snoring"
#'
#'
#'






#' Forced Expiratory Volume
#' @description This dataset is based on a study conducted in suburban Boston in the late 1970s to investigate the relationship between forced expiratory volume and smoking behavior in 654 youths between the ages of 3 and 19.
#'
#'
#' @format
#' \describe{
#' \item{fev}{forced expiratory volume or FEV, which measures the amount of air a person can exhale during a forced breath.}
#' \item{age}{age in years}
#' \item{sex}{gender of the person (0 for males and 1 for females)}
#' \item{height}{height in cm}
#' \item{smoke}{smoking behavior (0 for non-smokers and 1 for smokers)}
#' }
#'
"fev"
#'




#' Peruvian Blood Pressure
#' @description  This dataset consists of variables possibly relating to blood pressures of 39 Peruvians who have moved from rural high-altitude areas to urban lower-altitude areas.
#'
#'
#' @format
#' \describe{
#' \item{Age}{Age in years}
#' \item{Years}{Years in urban area}
#' \item{Weight}{Weight in kg}
#' \item{Height}{Height in mm}
#' \item{Chin}{Chin skinfold}
#' \item{Forearm}{Forearm skinfold}
#' \item{Calf}{Calf skinfold}
#' \item{Pulse}{Resting pulse rate}
#' \item{Systol}{Systolic blood pressure}
#' }
#'
"PeruvianBP"
#'


#' Breast Cancer
#' @description  This dataset consists of several clinical features observed or measured for 116 participants in a study of breast cancer.
#'
#'
#' @format
#' \describe{
#' \item{Age}{Age in years}
#' \item{BMI}{Body mass index in kg/\eqn{m^2}}
#' \item{Glucose}{Glucose in mg/dL}
#' \item{Insulin}{Insulin in \eqn{\mu}U/mL}
#' \item{HOMA}{Homeostasis model assessment}
#' \item{Classification}{Presence of breast cancer (0 if no cancer, 1 if with cancer)}
#' }
#'
#' @references Patricio, Miguel, et al. "Using Resistin, glucose, age and BMI to predict the presence of breast cancer", BMC Cancer, (2018).
#' @source \url{https://bmccancer.biomedcentral.com/articles/10.1186/s12885-017-3877-1}
"BreastCancer"
#'
#'
#'
#'



#' Diabetes study in Bangladesh
#' @description This dataset contains reports of diabetes symptoms from 520 individuals, encompassing symptoms potentially associated with the condition. It was compiled through a questionnaire aimed at recently diagnosed diabetics or individuals displaying one or more symptoms. Data collection took place via direct questionnaire at Sylhet Diabetes Hospital in Bangladesh.
#'
#'
#' @format
#' \describe{
#' \item{age}{Age of the patient in years}
#' \item{gender}{Gender of the patient (Male, Female)}
#' \item{polyuria}{Presence of polyuria (excessive urination) (Yes, No)}
#' \item{polydipsia}{Presence of polydipsia (excessive thirst) (Yes, No)}
#' \item{sudden_weight_loss}{Presence of sudden weight loss (Yes, No)}
#' \item{weakness}{Presence of weakness (Yes, No)}
#' \item{polyphagia}{Presence of polyphagia (excessive hunger) (Yes, No)}
#' \item{genital_thrush}{Presence of genital thrush (Yes, No)}
#' \item{visual_blurring}{Presence of visual blurring (Yes, No)}
#' \item{itching}{Presence of itching (Yes, No)}
#' \item{irritability}{Presence of irritability (Yes, No)}
#' \item{delayed_healing}{Presence of delayed healing (Yes, No)}
#' \item{partial_paresis}{Presence of partial paresis (Yes, No)}
#' \item{muscle_stiffness}{Presence of muscle stiffness (Yes, No)}
#' \item{alopecia}{Presence of alopecia (Yes, No)}
#' \item{obesity}{Presence of obesity (Yes, No)}
#' \item{class}{Diagnosis class (1 if presence of diabetes, 0 otherwise)}
#' }
#'
#' @references Islam, M. M. F., et al. "Likelihood prediction of diabetes at early stage using data mining techniques", Computer vision and machine intelligence in medical image analysis, (2020).
#' @source \url{https://link.springer.com/chapter/10.1007/978-981-13-8798-2_12}
"diabetes"
#'



#' Kuwait Blood Pressure
#' @description This dataset contains a collection of variables believed to be potentially associated with the blood pressure measurements of 213 individuals from Kuwait. The dataset lists the following variables:
#'
#'
#' @format
#' \describe{
#' \item{age}{Age in years}
#' \item{weight}{Weight in kg}
#' \item{height}{Height in mm}
#' \item{chin}{Chin skinfold in cm}
#' \item{forearm}{Forearm skinfold in cm}
#' \item{calf}{Calf skinfold in cm}
#' \item{pulse}{Resting pulse rate}
#' \item{left_handed}{Whether or not the participant is left-handed}
#' \item{bmi}{The Body Mass Index (BMI) of the participant}
#' \item{systol}{Systolic blood pressure}
#' }
#'
"kuwait_bp"
#'






#' Centenarian Blood Pressure
#' @description This dataset consists of variables that are potentially related to blood pressure measurements and contains one group of patients aged between 52 and 89 years old who live in urban areas, and another group of 50 centenarian women aged between 101-121 who live in the island of Okinawa, which is known for its high number of centenarians.The dataset lists the following variables:
#'
#'
#' @format
#' \describe{
#' \item{Age}{Age in years}
#' \item{Chin}{Chin skinfold in cm}
#' \item{Forearm}{Forearm skinfold in cm}
#' \item{Calf}{Calf skinfold in cm}
#' \item{Pulse}{Resting pulse rate}
#' \item{BMI}{The Body Mass Index (BMI) of the participant}
#' \item{Centenarian}{A dummy variable indicating if the participant is Centenarian}
#' \item{Cystol}{Systolic blood pressure}
#' }
#'
"centenarian"
#'