join functions (left_join, inner_join) in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does left_join() do in R?

It combines two data frames by keeping all rows from the left data frame and matching rows from the right data frame. If there is no match, it fills with NA.

beginner

What is the main difference between inner_join() and left_join()?

inner_join() keeps only rows that have matching keys in both data frames, while left_join() keeps all rows from the left data frame regardless of matches.

beginner

In left_join(df1, df2, by = "id"), what does the by argument specify?

It tells R which column(s) to use to match rows between df1 and df2. Here, rows are matched by the id column.

beginner

If inner_join() returns fewer rows than the original data frames, what does that mean?

It means only rows with matching keys in both data frames were kept. Rows without matches were dropped.

beginner

Why might you use left_join() instead of inner_join()?

Use left_join() when you want to keep all data from the main (left) table and add matching info from another table, even if some rows don’t have matches.

What does inner_join() do?

A Combines data frames without matching keys

B Keeps all rows from the left data frame

C Keeps all rows from the right data frame

D Keeps only rows with matching keys in both data frames

inner_join() returns only rows where keys match in both data frames.

Which join keeps all rows from the left data frame, even if no match is found?

A left_join()

B right_join()

C inner_join()

D full_join()

left_join() keeps all rows from the left data frame and adds matching rows from the right.

If you want to join two data frames by a column named user_id, which argument do you use?

A on = "user_id"

B by = "user_id"

C key = "user_id"

D match = "user_id"

The by argument specifies the column(s) to join on.

What happens to rows in the right data frame that do not match any row in the left data frame when using left_join()?

A They are dropped

B They are included with <code>NA</code> in left columns

C They are duplicated

D They cause an error

Rows in the right data frame without matches are dropped in left_join().

Which join would you use to keep all rows from both data frames?

A right_join()

B left_join()

C full_join()

D inner_join()

full_join() keeps all rows from both data frames, filling with NA where no match exists.

Explain in your own words how left_join() works and when you would use it.

Think about keeping your main list complete and adding extra info.

Describe the difference between inner_join() and left_join() with an example.

Imagine two contact lists with some common and some unique contacts.