Welcome aboard our enlightening journey through merging data frames using dplyr in R! In the real world, data are rarely consolidated in one location. Often, they're spread across multiple sources, waiting to be collected, organized, and analyzed. Whether we're dealing with sales data from various regions, healthcare records from a multitude of facilities, or educational scores from several institutions, joining diverse chunks of data is a routine task in any data-driven field.

In this lesson, we will learn how to use this powerful tool to combine data frames, and discover various merge operations and their usage in different scenarios. With practical examples to guide you, get ready to master the art of merging data frames with R dplyr!

We utilize the join() functions from dplyr to combine data frames. Here's a general example:

In these examples, the abstract variables df1 and df2 are merged based on a shared or common column.

We shall look at specific examples and unpack the four types of merges: inner join, outer join, left join, and right join.

For this lesson, we will use the following dataset, stored in two separate data frames:

Two important things to note:

1. The author with Author_ID=112 is missing in the df_authors data frame.
2. The book named Catcher in the df_books data frame has missing info about its author.

An inner join includes rows where there is a match in both data frames. The following examples show how you can perform an inner join:

The resultant data frame will include only rows with common Author_ID in both data frames, so books without author information will not be included.

An outer join includes all the rows from both data frames, filling in NA for missing values:

This approach includes all data from both data frames. In the case of any missing data, it adds NA. Note how it includes all the books, even if they lack author info, and all authors, even if there is no book info for them.

A left join includes all rows from the first data frame and fills in NA for missing values in the second data frame:

After performing a left merge, the resultant data frame includes all books, even those missing the author's info.

A right join includes all rows from the second data frame, similar to a left join in reverse:

After a right merge, the resultant data frame includes all authors, even if there is no corresponding book information.

Through this lesson, you have learned how to handle data-merging scenarios. This lesson has strengthened your foundation in a critical aspect of data analysis and feature engineering: combining multiple datasets.

Prepare yourself for more experiential learning in the following exercises, where you'll apply the concepts you've learned.

Without further ado, let's embark on our hands-on learning journey with exciting coding challenges — here's to happy coding!