---
title: "FormulR"
author: "George Oche Ambrose"
date: "3/18/2024"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{vignette}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```


# Load necessary libraries
```{r setup}
library(FormulR)
library(dplyr)  # for data manipulation
library(ggplot2)  # for data visualization

```

# Introduction to Drug Formulation Analysis
# Overview
Welcome to the Drug Formulation Analysis vignette! In this tutorial, we'll explore how to analyze simulated data related to drug formulation using R. We'll cover various aspects of statistical analysis, data visualization, interpretation of results, comparative analysis, and quality control tools commonly used in pharmaceutical research.

# Simulated Data Generation
First, let's generate some simulated data to work with. Our dataset contains information on drug release, particle size, formulation type, viscosity, stability index, storage condition, pH, and drug content over time.

# Simulated data with two levels of Formulation_Type and time points
```{r}
formulation_data <- data.frame(
  Time = seq(1, 100),  # Assuming 100 time points
  Excipient_Concentration = runif(100, min = 0, max = 1),
  Drug_Release = rnorm(100, mean = 50, sd = 10),
  Particle_Size = rnorm(100, mean = 100, sd = 20),
  Formulation_Type = sample(c("Type A", "Type B"), 100, replace = TRUE),
  Viscosity = rnorm(100, mean = 10, sd = 2),
  Stability_Index = rnorm(100, mean = 95, sd = 5),
  Storage_Condition = sample(c("Room", "Cold", "Warm"), 100, replace = TRUE),
  pH = rnorm(100, mean = 7, sd = 0.5),
  Drug_Content = rnorm(100, mean = 95, sd = 2)
)

```

# Statistical Analysis
Let's start by conducting statistical analysis on our data. We'll perform ANOVA and regression analysis to explore relationships between variables.

```{r}
# Statistical Analysis
anova_results <- anova_analysis(formulation_data)
regression_results <- regression_analysis(formulation_data)
hypothesis_test_results <- hypothesis_testing(formulation_data)

```

# Data Visualization
Next, we'll visualize our data using scatterplots, histograms, and boxplots to gain insights into the distribution and relationships between variables.

```{r}
# Data Visualization
scatterplot(formulation_data, x = "Excipient_Concentration", y = "Drug_Release")
histogram(formulation_data, x = "Particle_Size", bins = 20)
boxplot(formulation_data, x = "Formulation_Type", y = "Viscosity")
```


# Interpretation of Results
We'll interpret the results obtained from our analyses, including summary statistics and confidence intervals.

```{r}
# Interpretation of Results
summary_stats <- summary_statistics(formulation_data)
confidence_intervals <- confidence_intervals(formulation_data)
```


# Comparative Analysis
We'll compare means and distributions across different formulation types and storage conditions to identify any significant differences.

```{r}
# Interpretation of Results
# Comparative Analysis
compare_means(formulation_data, group_var = "Formulation_Type", response_var = "Stability_Index")
compare_distributions(formulation_data, group_var = "Storage_Condition", response_var = "Drug_Content")
```


# Quality Control Tools
Finally, we'll use quality control tools such as control charts and batch variability analysis to monitor and assess the consistency and quality of our formulations.

```{r}
# Quality Control Tools
control_chart(formulation_data, parameter = "pH")
batch_variability(formulation_data, parameter = "Drug_Content")
```

