# Load necessary libraries
library(dplyr)
# Assuming you have two datasets: df1 and df2
# Step 1: Check the structure of both datasets
str(df1)
str(df2)
# Step 2: Check the common columns between the two datasets
common_columns <- intersect(names(df1), names(df2))
# Step 3: Merge datasets based on common columns
merged_data <- merge(df1, df2, by = common_columns, all = TRUE)
# Step 4: Alternatively, if you want to merge based on specific columns
# merged_data <- merge(df1, df2, by.x = "column_name_df1", by.y = "column_name_df2", all = TRUE)
# Step 5: Check the structure of the merged dataset
str(merged_data)
# Step 6: Optionally, handle missing values if needed
# merged_data <- merged_data %>% na.omit()
# Step 7: Optionally, arrange the data based on specific columns
# merged_data <- merged_data %>% arrange(column_name)
# Step 8: Optionally, filter rows based on certain conditions
# merged_data <- merged_data %>% filter(column_name > value)
# Step 9: Save the merged dataset if necessary
# write.csv(merged_data, "merged_data.csv", row.names = FALSE)