#' Extract and Count Descriptions with Specified Color

#'

#' This function filters a data frame for specified descriptions, selects the 'Description' and 'Count' columns,

#' and adds a new column with a specified color.

#'

#' @param df A data frame containing at least 'Description' and 'Count' columns.

#' @param descriptions A vector of descriptions to filter in the data frame.

#' @param color A character string specifying the color to be added as a new column.

#' @return A data frame filtered by descriptions, containing 'Description', 'Count', and a new 'color' column.

#' @export

#'

#' @examples

#' # Generate Sample Input Data for extract_descriptions_counts Function

#'

#' # Create a sample data frame with 'Description' and 'Count' columns

#' data <- data.frame(

#'   Description = c(

#'     "immunoglobulin production",

#'     "B cell mediated immunity",

#'     "T cell activation",

#'     "antigen processing and presentation",

#'     "cytokine signaling",

#'     "natural killer cell activity",

#'     "phagocytosis",

#'     "complement activation",

#'     "antibody-dependent cellular cytotoxicity",

#'     "regulatory T cell function"

#'   ),

#'   Count = c(

#'     150,  # immunoglobulin production

#'     200,  # B cell mediated immunity

#'     175,  # T cell activation

#'     125,  # antigen processing and presentation

#'     190,  # cytokine signaling

#'     160,  # natural killer cell activity

#'     140,  # phagocytosis

#'     180,  # complement activation

#'     130,  # antibody-dependent cellular cytotoxicity

#'     170   # regulatory T cell function

#'   ),

#'   stringsAsFactors = FALSE  # Ensure that strings are not converted to factors

#' )

#'

#'

#'

#' descriptions_to_filter <- c("immunoglobulin production", "B cell mediated immunity")

#' specified_color <- "red"  # You can specify any color you desire

#' filtered_data_with_color <- extract_descriptions_counts(

#'   data, descriptions_to_filter,

#'   specified_color)

#' print(filtered_data_with_color)

#'

extract_descriptions_counts <- function(df, descriptions, color) {

  # Filter rows where the Description column values are in the descriptions vector

  result_df <- df[df$Description %in% descriptions, ]

  # Select Description and Count columns

  result_df <- result_df[, c("Description", "Count")]

  # Add a new column 'color' with the specified color value

  result_df$color <- color

  return(result_df)

}

#' Combine and Visualize Data with Circular Bar Chart

#'

#' This function combines multiple data frames, arranges them, and visualizes the combined data

#' in a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages.

#'

#' @importFrom dplyr bind_rows arrange desc row_number mutate

#' @importFrom ggplot2 ggplot geom_bar geom_text scale_fill_manual scale_y_continuous scale_x_continuous coord_polar labs theme_minimal theme element_rect element_blank

#' @importFrom rlang .data

#' @param data_list A list of data frames to be combined.

#' @return A `ggplot` object representing the Circular Bar Chart.

#' @export

#'

#' @examples

#' # Create sample data frames for each enrichment category

#'

#' # 1. Biological Process (BP)

#' filtered_data_BP <- data.frame(

#'   Description = c(

#'     "immune response",

#'     "cell proliferation",

#'     "signal transduction",

#'     "apoptotic process",

#'     "metabolic process"

#'   ),

#'   Count = c(120, 85, 150, 60, 95),

#'   color = c(

#'     "#1f77b4",  # blue

#'     "#ff7f0e",  # orange

#'     "#2ca02c",  # green

#'     "#d62728",  # red

#'     "#9467bd"   # purple

#'   ),

#'   stringsAsFactors = FALSE

#' )

#'

#' # 2. Cellular Component (CC)

#' filtered_data_CC <- data.frame(

#'   Description = c(

#'     "nucleus",

#'     "cytoplasm",

#'     "membrane",

#'     "mitochondrion",

#'     "extracellular space"

#'   ),

#'   Count = c(90, 110, 75, 65, 80),

#'   color = c(

#'     "#1f77b4",

#'     "#ff7f0e",

#'     "#2ca02c",

#'     "#d62728",

#'     "#9467bd"

#'   ),

#'   stringsAsFactors = FALSE

#' )

#'

#' # 3. Molecular Function (MF)

#' filtered_data_MF <- data.frame(

#'   Description = c(

#'     "protein binding",

#'     "DNA binding",

#'     "enzyme activity",

#'     "transporter activity",

#'     "receptor activity"

#'   ),

#'   Count = c(140, 130, 100, 70, 90),

#'   color = c(

#'     "#1f77b4",

#'     "#ff7f0e",

#'     "#2ca02c",

#'     "#d62728",

#'     "#9467bd"

#'   ),

#'   stringsAsFactors = FALSE

#' )

#'

#' # 4. Disease Ontology (DO)

#' filtered_data_DO <- data.frame(

#'   Description = c(

#'     "cancer",

#'     "cardiovascular disease",

#'     "neurological disorder",

#'     "metabolic disease",

#'     "infectious disease"

#'   ),

#'   Count = c(200, 150, 120, 90, 160),

#'   color = c(

#'     "#1f77b4",

#'     "#ff7f0e",

#'     "#2ca02c",

#'     "#d62728",

#'     "#9467bd"

#'   ),

#'   stringsAsFactors = FALSE

#' )

#'

#' # 5. Reactome Pathways

#' filtered_data_Reactome <- data.frame(

#'   Description = c(

#'     "Cell Cycle",

#'     "Apoptosis",

#'     "DNA Repair",

#'     "Signal Transduction",

#'     "Metabolism of Proteins"

#'   ),

#'   Count = c(110, 95, 80, 130, 85),

#'   color = c(

#'     "#1f77b4",

#'     "#ff7f0e",

#'     "#2ca02c",

#'     "#d62728",

#'     "#9467bd"

#'   ),

#'   stringsAsFactors = FALSE

#' )

#'

#' # 6. KEGG Pathways

#' filtered_data_kegg <- data.frame(

#'   Description = c(

#'     "PI3K-Akt signaling pathway",

#'     "MAPK signaling pathway",

#'     "NF-kappa B signaling pathway",

#'     "JAK-STAT signaling pathway",

#'     "Toll-like receptor signaling pathway"

#'   ),

#'   Count = c(175, 160, 145, 130, 155),

#'   color = c(

#'     "#1f77b4",

#'     "#ff7f0e",

#'     "#2ca02c",

#'     "#d62728",

#'     "#9467bd"

#'   ),

#'   stringsAsFactors = FALSE

#' )

#'

#' # Combine all filtered data frames into a list

#' data_list <- list(

#'   BP = filtered_data_BP,

#'   CC = filtered_data_CC,

#'   MF = filtered_data_MF,

#'   DO = filtered_data_DO,

#'   Reactome = filtered_data_Reactome,

#'   KEGG = filtered_data_kegg

#' )

#'

#' # Create the Circular Bar Chart

#' combined_and_visualized_data <- enrich_circo_bar(data_list)

#'

enrich_circo_bar <- function(data_list) {

  # Combine data frames

  combined_data <- dplyr::bind_rows(data_list)

  # Sort by 'Count' column in descending order and add an 'id' column

  combined_data <- combined_data %>%

    dplyr::arrange(.data$Count) %>%

    dplyr::mutate(id = dplyr::row_number())

  # Ensure 'Description' is a factor with correct level order

  combined_data <- combined_data %>%

    dplyr::mutate(Description = factor(.data$Description, levels = unique(.data$Description)))

  # Extract fill colors, ensuring colors match 'Description' levels

  fill_colors <- combined_data$color[match(levels(combined_data$Description), combined_data$Description)]

  # Calculate the expanded max values for Count and id

  max_count <- max(combined_data$Count) + (max(combined_data$Count) / 5)

  max_id <- max(combined_data$id) + 1.5

  # Create the plot

  p <- ggplot2::ggplot(combined_data, ggplot2::aes(x = .data$id, y = .data$Count, fill = .data$Description)) +

    ggplot2::geom_bar(stat = 'identity', width = 0.7) +

    ggplot2::geom_text(ggplot2::aes(x = .data$id, y = 0, label = .data$Description), hjust = 1.03, size = 3.5, color = fill_colors) +

    ggplot2::scale_fill_manual(values = fill_colors, guide = "none") +

    ggplot2::scale_y_continuous(expand = c(0, 0), limits = c(0, max_count), position = 'right') +

    ggplot2::scale_x_continuous(expand = c(0, 0), limits = c(-1, max_id)) +

    ggplot2::coord_polar(theta = 'y') +

    ggplot2::labs(title = 'Enrichment CircularBar Chart', subtitle = "Including: BP/MF/CC/DO/KEGG/Reactome") +

    ggplot2::theme_minimal() +

    ggplot2::theme(plot.background = ggplot2::element_rect(fill = 'white', color = 'white'),

                   axis.title = ggplot2::element_blank(),

                   axis.text = ggplot2::element_blank())

  return(p)

}