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+#' 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)
+}