# Clean the environment
rm(list = ls())
library(readxl)
library(bipartite)
library(igraph)
library(openxlsx)

matriz <- as.matrix(read_excel("data-METANETWORKS-QC.xlsx", sheet = "aggregated")) #change for others
# Suppose you have already imported the matrix from Excel and it's named 'matriz'
# Check the dimensions and names:
dim(matriz)         # Should show (number of fragments, number of interactions)
rownames(matriz)    # Make sure the fragments have names
colnames(matriz)    # Make sure the interactions have names
rownames(matriz) <- paste0("Frag", 1:nrow(matriz))
library(igraph)

# Create the bipartite graph from the matrix
grafo_bipartito <- graph_from_incidence_matrix(matriz, weighted = NULL)

# Check the number of nodes and their distribution:
cat("Total nodes:", vcount(grafo_bipartito), "\n")
print(table(V(grafo_bipartito)$type))
# Perform the unipartite projection
proyeccion <- bipartite_projection(grafo_bipartito)

# Extract the projection corresponding to the interactions
grafo_interacciones <- proyeccion$proj2

# Check the number of nodes in the projection
cat("Nodes in the interaction projection:", vcount(grafo_interacciones), "\n")
# Calculate betweenness centrality (not automatically normalized)
bc <- betweenness(grafo_interacciones, directed = FALSE, normalized = FALSE)

# Normalize using the classical formula:
# BC_normalized = BC / [((N - 1) * (N - 2) / 2)]
N <- vcount(grafo_interacciones)
bc_normalized <- bc / ((N - 1) * (N - 2) / 2)

# Create a data frame with the results:
bc_df <- data.frame(
  Interaccion = V(grafo_interacciones)$name,
  Betweenness = bc_normalized
)

# Sort from highest to lowest betweenness
bc_df <- bc_df[order(-bc_df$Betweenness), ]
head(bc_df)
bc_df
View(bc_df)
write.csv(bc_df, "betweenness_interacciones.csv", row.names = FALSE)

# Degree
degree_interactions <- colSums(matriz)
degree_df <- data.frame(
  Interaccion = names(degree_interactions),
  Degree = degree_interactions
)

# Sort from highest to lowest degree (optional)
degree_df <- degree_df[order(-degree_df$Degree), ]
degree_df
View(degree_df)
write.csv(degree_df, "degree_interacciones.csv", row.names = FALSE)

# Merge the degree and betweenness tables based on the "Interaccion" column
tabla_completa <- merge(degree_df, bc_df, by = "Interaccion", all = TRUE)

# Sort the table from highest to lowest by Betweenness
tabla_completa <- tabla_completa[order(-tabla_completa$Betweenness), ]

# View the table in RStudio
View(tabla_completa)

# Export the table to a CSV file
write.csv(tabla_completa, "tabla_completa_interacciones.csv", row.names = FALSE)

install.packages("openxlsx")
library(openxlsx)

# Export the table to an Excel file
write.xlsx(tabla_completa, "tabla_completa_interacciones.xlsx", rowNames = FALSE)