Merging Data in R

Adding Columns

To merge two data frames (datasets) horizontally, use the merge function. In most cases, you join two data frames by one or more common key variables (i.e., an inner join).

# merge two data frames by ID
total <- merge(data frameA,data frameB,by="ID")

# merge two data frames by ID and Country
total <- merge(data frameA,data frameB,by=c("ID","Country"))

Adding Rows

To join two data frames (datasets) vertically, use the rbind function. The two data frames must have the same variables, but they do not have to be in the same order.

total <- rbind(data frameA, data frameB)

If data frameA has variables that data frameB does not, then either:

1. Delete the extra variables in data frameA or
2. Create the additional variables in data frameB and set them to NA (missing)

before joining them with rbind( ).

Tips on Merging Data in R

Merging data is a common task in data analysis, especially when working with large datasets. The merge function in R is a powerful tool that allows you to combine two or more datasets based on shared variables. Here are some tips to ensure a smooth and efficient merging process:

1. Understand Your Data:

Before merging, always inspect your datasets using functions like head(), str(), and summary(). This helps you understand the structure and identify key variables for merging.

2. Choose the Right Key Variables:

Ensure that the variables you're merging on are unique and don't have duplicates unless it's intentional. This prevents unintended data duplication.

3. Specify Merge Type:

R's merge function allows for different types of joins: left, right, inner, and outer. Understand the differences and choose the one that best fits your needs. left: includes all rows from the first dataset and matching rows from the second. right: includes all rows from the second dataset and matching rows from the first. inner: includes only rows with matching keys in both datasets. outer: includes all rows from both datasets.

4. Handle Missing Values:

After merging, check for NA values. These can arise if there's no match for a particular key. Decide how you want to handle these: remove, replace, or impute.

5. Check Column Names:

If the datasets have columns with the same names but different data, R will append a suffix (e.g., .x and .y) to distinguish them. Rename these columns if necessary for clarity.

6. Sort Your Data:

After merging, it's often helpful to sort your data using the order() function. This can make subsequent analyses easier and more intuitive.

7. Large Datasets Consideration:

For very large datasets, consider using the data.table package. It offers a faster merging process compared to the base R merge function.

8. Consistent Data Types:

Ensure that the key variables in both datasets have the same data type. For instance, merging on a character variable in one dataset and a factor in another can lead to unexpected results.

9. Test on a Subset:

If you're unsure about the merge, try it on a small subset of your data first. This allows you to quickly spot and rectify any issues.

10. Document Your Process:

Always keep a record of the steps and decisions you made during the merging process. This ensures reproducibility and clarity for future reference.

Remember, merging data is as much an art as it is a science. With practice and attention to detail, you'll become adept at combining datasets seamlessly in R. Happy coding!

Going Further

To practice manipulating data frames with the dplyr package, try this interactive course on data frame manipulation in R.