inst/doc/vignette.R

In FormulR: Comprehensive Tools for Drug Formulation Analysis and Visualization

## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(FormulR)
library(dplyr)  # for data manipulation
library(ggplot2)  # for data visualization


## -----------------------------------------------------------------------------
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
anova_results <- anova_analysis(formulation_data)
regression_results <- regression_analysis(formulation_data)
hypothesis_test_results <- hypothesis_testing(formulation_data)


## -----------------------------------------------------------------------------
# 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
summary_stats <- summary_statistics(formulation_data)
confidence_intervals <- confidence_intervals(formulation_data)

## -----------------------------------------------------------------------------
# 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
control_chart(formulation_data, parameter = "pH")
batch_variability(formulation_data, parameter = "Drug_Content")