Section 4.10 💡 Reproducibility Tip:
Merging data is a crucial step in many analysis projects. Whether you are using SQL-style joins or base R functions, it is essential to check the structure of your data before and after every merge.
A simple but powerful habit is to verify the number of rows using functions like
nrow(). Joins that are not specified correctly can lead to what are often called “data explosions,” where a dataset unexpectedly grows. A incorrectly specified merge can easily take a dataset from 1,000 rows to 3,000,000, when only 1,000 were expected.
Checking row counts before and after a merge helps ensure that the join behaved as intended and can save your analysis (and your computer) from serious errors.
Your analysis cannot be reproducible if its underlying data are incorrect. Developing the habit of validating merges is especially important here, as this is the first chapter where data merging is introduced.
