Author: George Oche Ambrose

# FormulR

#### George Oche Ambrose

#### 3/18/2024

# Load necessary libraries

    library(FormulR)
    #> Loading required package: dplyr
    #> Warning: package 'dplyr' was built under R version 4.1.3
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union
    #> Loading required package: ggplot2
    #>
    #> Attaching package: 'FormulR'
    #> The following object is masked from 'package:graphics':
    #>
    #>     boxplot
    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

    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.

    # 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.

    # 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.

    # 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.

    # Interpretation of Results
    # Comparative Analysis
    compare_means(formulation_data, group_var = "Formulation_Type", response_var = "Stability_Index")
    #>
    #>  Welch Two Sample t-test
    #>
    #> data:  formulation_data[[response_var]] by formulation_data[[group_var]]
    #> t = 0.72569, df = 96.915, p-value = 0.4698
    #> alternative hypothesis: true difference in means between group Type A and group Type B is not equal to 0
    #> 95 percent confidence interval:
    #>  -1.249161  2.689124
    #> sample estimates:
    #> mean in group Type A mean in group Type B
    #>             95.48371             94.76373
    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.

    # Quality Control Tools
    control_chart(formulation_data, parameter = "pH")

    batch_variability(formulation_data, parameter = "Drug_Content")
    #> [1] "Batch-to-batch variability in Drug_Content : 2.08288565837046"