Genetic Association Analysis

Description

It gathers information, meta-data and scripts in a two-part Henry-Stewart talk by Zhao (2009, doi:10.69645/DCRY5578), which showcases analysis in aspects such as testing of polymorphic variant(s) for Hardy-Weinberg equilibrium, association with trait using genetic and statistical models as well as Bayesian implementation, power calculation in study design and genetic annotation. It also covers R integration with the Linux environment, GitHub, package creation and web applications.

Details

Available data and function are listed in the following table.

We can add references such as Francois (2014).

Usage

Vignettes on package usage:

• Genetic Association Analysis with R (II), vignette("gaawr2").

• Web facilities, vignette("web").

Author(s)

Jing Hua Zhao in collaboration with other colleagues.

References

Romain Francois (2014). bibtex: bibtex parser. R package version 0.4.0.

See Also

Useful links:

• https://jinghuazhao.github.io/gaawr2/

• https://github.com/jinghuazhao/gaawr2

• Report bugs at https://github.com/jinghuazhao/gaawr2/issues

Examples

welcome(3)

DiaHealth

Description

A Bangladeshi dataset for Type 2 diabetes prediction.

Usage

DiaHealth

Format

A data frame with 5,437 patients and 14 variables on demographics, clinical parameters, and medical history.

age

Years (age of the person).

gender

Categorical variable (Female, Male).

pulse_rate

Beats per minute (bpm).

systolic_bp

SBP in millimeters of mercury (mmHg).

diastolic_bp

DBP (mmHg).

glucose

Milligrams per deciliter (mg/dL).

height

Meter (m).

weight

Kilogram (kg).

bmi

Body mass index (BMI).

family_diabetes

Family history of diabetes.

hypertensive

Hypertension.

family_hypertension

Family history of hypertension.

cardiovascular_disease

CVD.

stroke

Stroke.

diabetic

Diabetic.

Details

Key features include age, gender, pulse rate, blood pressure (systolic and diastolic), glucose level, BMI, and family history of diabetes and related conditions like hypertension and cardiovascular disease. The dataset is labeled with a binary outcome indicating whether each patient has diabetes. This rich dataset is designed to support the development and evaluation of machine learning models for diabetes detection, management, and treatment.

Source

Prama TT, Zaman M, Sarker F, Mamun KA. (2024), “DiaHealth: A Bangladeshi Dataset for Type 2 Diabetes Prediction ”, Mendeley Data, V1, doi: 10.17632/7m7555vgrn.1

See Also

diabetes

Examples

data(DiaHealth)
knitr::kable(head(DiaHealth,5),caption="Five individauls in DiaHealth")

Diabetes Dataset

Description

A diabetes dataset on 1,000 patients.

Usage

diabetes

Format

A data frame with 1,000 rows and 14 variables:

ID

Unique identifier for each patient (unitless).

No_Pation

Patient number (unitless).

Gender

Categorical variable (Female, Male).

AGE

Years (age of the person).

Urea

Chief nitrogenous end product of the metabolic breakdown of proteins in milligrams per deciliter (mg/dL).

Cr

Creatinine ratio (Cr) (mg/dL).

HbA1c

Hemoglobin A1c (HbA1c) % (percentage).

Chol

Cholesterol (Chol) (mg/dL).

TG

Triglycerides (TG) (mg/dL).

HDL

High-density lipoprotein (HDL) (mg/dL).

LDL

Low-density lipoprotein (LDL) (mg/dL).

VLDL

Very-low-density lipoprotein (VLDL) (mg/dL).

BMI

Body mass index (BMI).

CLASS

Class (the patient's diabetes disease class may be Diabetic, Non-Diabetic, or Predict-Diabetic).

Details

The data were collected from the Iraqi society, as they data were acquired from the laboratory of Medical City Hospital and (the Specializes Center for Endocrinology and Diabetes-Al-Kindy Teaching Hospital).

Source

Rashid A (2020), “Diabetes Dataset”, Mendeley Data, V1, doi: 10.17632/wj9rwkp9c2.1.

See Also

DiaHealth.

Examples

data(diabetes)
knitr::kable(head(diabetes,5),caption="Five individuals in diabetes data")

An enhanced welcome

Description

It prints a welcome message, saying number of times.

Usage

welcome(n)

Arguments

Value

Prints a welcome message to the console.

Examples

welcome(3)