R/DrugFormulation.R

In FormulR: Comprehensive Tools for Drug Formulation Analysis and Visualization

#' Perform ANOVA analysis
#' 
#' This function conducts analysis of variance (ANOVA) to assess the impact of formulation parameters on key response variables.
#' 
#' @import dplyr
#' @import ggplot2
#' @importFrom stats aov lm sd t.test
#' @param formulation_data A data frame containing the formulation data.
#' @return A summary of the ANOVA analysis results.
#' @export
#'
#' @examples
#' formulation_data <- data.frame(
#'   Excipient_Concentration = runif(100, min = 0, max = 1),
#'   Drug_Release = rnorm(100, mean = 50, sd = 10),
#'   Particle_Size = rnorm(100, mean = 100, sd = 20)
#' )
#' anova_analysis(formulation_data)
anova_analysis <- function(formulation_data) {
  anova_result <- aov(Drug_Release ~ Excipient_Concentration, data = formulation_data)
  return(summary(anova_result))
}

#' Perform regression analysis
#'
#' This function conducts regression analysis to model relationships between formulation parameters and response variables.
#'
#' @param formulation_data A data frame containing the formulation data.
#' @return A summary of the regression analysis results.
#' @export
#'
#' @examples
#' formulation_data <- data.frame(
#'   Excipient_Concentration = runif(100, min = 0, max = 1),
#'   Drug_Release = rnorm(100, mean = 50, sd = 10),
#'   Particle_Size = rnorm(100, mean = 100, sd = 20)
#' )
#' regression_analysis(formulation_data)
regression_analysis <- function(formulation_data) {
  lm_model <- lm(Drug_Release ~ Excipient_Concentration + Particle_Size, data = formulation_data)
  return(summary(lm_model))
}

#' Perform hypothesis testing
#'
#' This function conducts hypothesis testing to compare means between different formulation groups.
#'
#' @param formulation_data A data frame containing the formulation data.
#' @return The results of the hypothesis testing.
#' @export
#'
#' @examples
#' formulation_data <- data.frame(
#'   Formulation_Type = sample(c("Type A", "Type B"), 100, replace = TRUE),
#'   Drug_Release = rnorm(100, mean = 50, sd = 10)
#' )
#' hypothesis_testing(formulation_data)
hypothesis_testing <- function(formulation_data) {
  t_test_result <- t.test(Drug_Release ~ Formulation_Type, data = formulation_data)
  return(t_test_result)
}

#' Generate scatterplot
#'
#' This function generates a scatterplot to visualize the relationship between two variables.
#'
#' @param formulation_data A data frame containing the formulation data.
#' @param x The name of the x-variable.
#' @param y The name of the y-variable.
#' @return A scatterplot.
#' @export
#'
#' @examples
#' formulation_data <- data.frame(
#'   Excipient_Concentration = runif(100, min = 0, max = 1),
#'   Drug_Release = rnorm(100, mean = 50, sd = 10)
#' )
#' scatterplot(formulation_data, "Excipient_Concentration", "Drug_Release")
scatterplot <- function(formulation_data, x, y) {
  ggplot(formulation_data, aes_string(x = x, y = y)) +
    geom_point() +
    labs(x = x, y = y, title = "Scatterplot")
}

#' Generate histogram
#'
#' This function generates a histogram to visualize the distribution of a variable.
#'
#' @param formulation_data A data frame containing the formulation data.
#' @param x The name of the variable.
#' @param bins The number of bins for the histogram.
#' @return A histogram.
#' @export
#'
#' @examples
#' formulation_data <- data.frame(
#'   Drug_Release = rnorm(100, mean = 50, sd = 10)
#' )
#' histogram(formulation_data, "Drug_Release")
histogram <- function(formulation_data, x, bins = 20) {
  ggplot(formulation_data, aes_string(x = x)) +
    geom_histogram(bins = bins, fill = "skyblue", color = "black") +
    labs(x = x, y = "Frequency", title = "Histogram")
}

#' Generate boxplot
#'
#' This function generates a boxplot to compare the distribution of a variable across different groups.
#'
#' @param formulation_data A data frame containing the formulation data.
#' @param x The name of the grouping variable.
#' @param y The name of the variable.
#' @return A boxplot.
#' @export
#'
#' @examples
#' formulation_data <- data.frame(
#'   Formulation_Type = sample(c("Type A", "Type B"), 100, replace = TRUE),
#'   Drug_Release = rnorm(100, mean = 50, sd = 10)
#' )
#' boxplot(formulation_data, "Formulation_Type", "Drug_Release")
boxplot <- function(formulation_data, x, y) {
  ggplot(formulation_data, aes_string(x = x, y = y)) +
    geom_boxplot(fill = "lightgreen", color = "black") +
    labs(x = x, y = y, title = "Boxplot")
}

#' Summary statistics of formulation data
#' 
#' This function calculates summary statistics of the provided formulation data.
#' 
#' @param formulation_data A data frame containing formulation data.
#' @return Summary statistics of the formulation data.
#' @export
summary_statistics <- function(formulation_data) {
  summary(formulation_data)
}

#' Confidence intervals of drug release
#' 
#' This function computes confidence intervals for drug release based on the provided formulation data.
#' 
#' @param formulation_data A data frame containing formulation data.
#' @return Confidence intervals for drug release.
#' @export
confidence_intervals <- function(formulation_data) {
  t.test(formulation_data$Drug_Release)$conf.int
}

#' Compare means across groups
#' 
#' This function compares the means of a response variable across groups specified by group_var.
#' 
#' @param formulation_data A data frame containing formulation data.
#' @param group_var The variable defining the groups for comparison.
#' @param response_var The response variable to compare across groups.
#' @return Results of the t-test comparing means across groups.
#' @export
compare_means <- function(formulation_data, group_var, response_var) {
  t.test(formulation_data[[response_var]] ~ formulation_data[[group_var]])
}

#' Compare distributions across groups
#' 
#' This function compares the distributions of a response variable across groups specified by group_var.
#' 
#' @param formulation_data A data frame containing formulation data.
#' @param group_var The variable defining the groups for comparison.
#' @param response_var The response variable to compare across groups.
#' @return A boxplot comparing the distributions across groups.
#' @export
compare_distributions <- function(formulation_data, group_var, response_var) {
  ggplot(formulation_data, aes_string(x = group_var, y = response_var)) +
    geom_boxplot(fill = "lightblue", color = "black") +
    labs(x = group_var, y = response_var, title = "Boxplot Comparison")
}

#' Control chart for quality control
#' 
#' This function generates a control chart for monitoring the quality control parameter over time.
#' 
#' @param formulation_data A data frame containing formulation data.
#' @param parameter The quality control parameter to monitor.
#' @return A control chart for the specified quality control parameter.
#' @export
control_chart <- function(formulation_data, parameter) {
  ggplot(formulation_data, aes_string(x = "Time", y = parameter)) +
    geom_line() +
    labs(x = "Time", y = parameter, title = "Control Chart")
}

#' Assess batch-to-batch variability
#' 
#' This function calculates the batch-to-batch variability of a specified parameter.
#' 
#' @param formulation_data A data frame containing formulation data.
#' @param parameter The parameter for which batch-to-batch variability is calculated.
#' @return The batch-to-batch variability of the specified parameter.
#' @export
batch_variability <- function(formulation_data, parameter) {
  variability <- sd(formulation_data[[parameter]])
  paste("Batch-to-batch variability in", parameter, ":", variability)
}