Code

# Load MRIO Data and Extract Key Matrices by Region

# 1. Load the MRIO data
load("~/Desktop/MRIO.RData")

# 2. Extract Input-Output Tables (IOT) for 5 Regions (NW, NE, Toscana, Centro, Sud)
IOT <- mrio_data$IOT  # Assign the IOT array from the MRIO data
regions <- c("NW", "NE", "Toscana", "Centro", "Sud")  # Define region names
IOT_regions <- list()  # Initialize a list to store the 49x52 matrices for each region

# Loop through each region and extract the corresponding 49x52 matrix
for (i in 1:5) {
  IOT_regions[[regions[i]]] <- IOT[, , i]
}

# 3. Assign matrices for each region for easier access
IOT_NW <- IOT_regions$NW
IOT_Toscana <- IOT_regions$Toscana
IOT_NE <- IOT_regions$NE
IOT_Centro <- IOT_regions$Centro
IOT_Sud <- IOT_regions$Sud

# Save the extracted IOT data for future use
save.image("~/MRIO (IOT).RData")

# 4. Process Trade Data for Intermediate and Final Demand (5x5x43 Arrays)

# Extract intermediate trade data
trade_intermedio <- mrio_data$trade_intermedio  # Assign the intermediate trade array
# Create an empty matrix (215x5) to store stacked trade data
trade_intermedio_matrix <- matrix(0, nrow = 215, ncol = 5)

# Stack the 43 sectors from each of the 5 origin regions
for (origin_region in 1:5) {
  start_row <- (origin_region - 1) * 43 + 1
  end_row <- origin_region * 43
  trade_intermedio_matrix[start_row:end_row, ] <- trade_intermedio[origin_region, , ]
}

# Extract final demand trade data
trade_finale <- mrio_data$trade_finale  # Assign the final trade array
trade_finale_matrix <- matrix(0, nrow = 215, ncol = 5)

# Stack the 43 sectors from each of the 5 origin regions
for (origin_region in 1:5) {
  start_row <- (origin_region - 1) * 43 + 1
  end_row <- origin_region * 43
  trade_finale_matrix[start_row:end_row, ] <- trade_finale[origin_region, , ]
}

# 5. Validate the dimensions and inspect the data
dim(trade_intermedio_matrix)  # Should return 215x5
dim(trade_finale_matrix)  # Should return 215x5

# Display the first few rows of the matrices
head(trade_intermedio_matrix)
head(trade_finale_matrix)

# View the IOT matrix for the NE region
View(IOT_NE)

# Save the processed trade and IOT data for future use
save.image("~/MRIO (ALL).RData")

# 6. Export All Matrices to Excel

# Install the writexl package if needed
install.packages("writexl")
library(writexl)

# Combine all matrices into a list with appropriate sheet names
all_matrices <- list(
  Trade_Intermedio = trade_intermedio_matrix,
  Trade_Finale = trade_finale_matrix,
  IOT_NW = IOT_NW,
  IOT_NE = IOT_NE,
  IOT_Toscana = IOT_Toscana,
  IOT_Centro = IOT_Centro,
  IOT_Sud = IOT_Sud
)

# Export all matrices to a single Excel file
write_xlsx(all_matrices, path = "all_matrices.xlsx")

# Convert matrices to data frames for better readability in Excel
all_matrices <- list(
  Trade_Intermedio = as.data.frame(trade_intermedio_matrix),
  Trade_Finale = as.data.frame(trade_finale_matrix),
  IOT_NW = as.data.frame(IOT_NW),
  IOT_NE = as.data.frame(IOT_NE),
  IOT_Toscana = as.data.frame(IOT_Toscana),
  IOT_Centro = as.data.frame(IOT_Centro),
  IOT_Sud = as.data.frame(IOT_Sud)
)

# Export the data frames to Excel
write_xlsx(all_matrices, path = "all_matrices.xlsx")