--- a
+++ b/man/enrich_circo_bar.Rd
@@ -0,0 +1,155 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/EnrichCircoBar.R
+\name{enrich_circo_bar}
+\alias{enrich_circo_bar}
+\title{Combine and Visualize Data with Circular Bar Chart}
+\usage{
+enrich_circo_bar(data_list)
+}
+\arguments{
+\item{data_list}{A list of data frames to be combined.}
+}
+\value{
+A `ggplot` object representing the Circular Bar Chart.
+}
+\description{
+This function combines multiple data frames, arranges them, and visualizes the combined data
+in a Circular Bar Chart using the 'ggplot2' and 'ggalluvial' packages.
+}
+\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)
+
+}