embark-lab / TeenGrowth

eBMI calculator

TeenGrowth

A package for wrangling and predicting BMI-related data in adolescent samples

by Katherine Schaumberg

Overview

The TeenGrowth package includes functions for:

• Cleaning and processing growth data.

• Calculating Body Mass Index (BMI) and BMI z-scores (BMIZ).

• Forecasting growth metrics.

• Visualizing growth data and weight restoration plans.

Installation

To install the package using the `install_github` function: use the following code:

devtools::install.packages("remotes") 
library(remotes)
install_github("embark-lab/embarktools")
install_github("embark-lab/TeenGrowth")

Usage

Here is a brief example of how to use the TeenGrowth package:

# Load the package
library(TeenGrowth)

# Example data
data <- read.csv("path/to/your/data.csv")

# Clean the data
cleaned_data <- clean_raw_data(data)

# Forecast BMI
bmi_forecast <- forecast_bmi(clean_data)

# Plot the results of participant # 1
plot_eBMI(clean_data = cleaned_data, forecast_data = bmi_forecast, px = 1)

For a more detailed outline of how to use the R package, refer to the package vignette.

Shiny Application

For clinical application, the TeenGrowth package includes a Shiny application to provide an interactive interface for data input, model selection, and weight restoration planning.

Shiny App Structure

The Shiny application is structured into several tabs:

1. Data Input: Upload your data file (CSV or Excel) or use provided demo data.

2. Data Specification: Map your data columns to the required fields.

3. Model Selection: Select a model to run on your data and view the predictions.

4. Weight Restoration Planning: Plan a weight restoration strategy based on the predictions.

5. Background and FAQ: Provides additional information and usage instructions.

Example Workflow in the Shiny App

1. Data Input: Upload your data or select demo data.

2. Data Specification: Specify the columns corresponding to required fields.

3. Model Selection: Select a model and generate predictions.

4. Weight Restoration Planning: Input relevant parameters and visualize the restoration plan.

Contributing

We welcome contributions from the community. If you have any suggestions, bug reports, or feature requests, please open an issue or submit a pull request on GitHub.