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.

Practice

1. What does the left_join function do in R's dplyr package?

easy

A. Removes all rows that have any missing values.

B. Keeps only rows that match in both tables.

C. Keeps all rows from the second table and adds matching rows from the first table.

D. Keeps all rows from the first table and adds matching rows from the second table.

Solution

1. Step 1: Understand the purpose of left_join

   left_join keeps all rows from the first (left) table regardless of matches.

2. Step 2: Understand how matching rows are added

   It adds columns from the second (right) table only where keys match, filling with NA if no match.

3. Final Answer:

   Keeps all rows from the first table and adds matching rows from the second table. -> Option D

4. Quick Check:

   left_join = all left rows + matching right rows [OK]

Hint: Remember: left_join keeps all from left, adds matches [OK]

Common Mistakes:

• Confusing left_join with inner_join
• Thinking left_join removes unmatched rows
• Mixing up which table is 'left' or 'right'

2. Which of the following is the correct syntax to perform an inner join of two data frames df1 and df2 by the column id in R?

easy

A. inner_join(df1, df2, on = "id")

B. innerjoin(df1, df2, on = "id")

C. inner_join(df1, df2, by = "id")

D. innerjoin(df1, df2, by = "id")

Solution

1. Step 1: Identify correct function name and parameters

   The function is inner_join from dplyr, and it uses by to specify join columns.

2. Step 2: Check parameter names and spelling

   Correct syntax is inner_join(df1, df2, by = "id"). Other options use wrong function names or parameter names.

3. Final Answer:

   inner_join(df1, df2, by = "id") -> Option C

4. Quick Check:

   inner_join syntax uses by = "column" [OK]

Hint: Use inner_join() with by = "column" for correct syntax [OK]

Common Mistakes:

• Using 'on' instead of 'by' for join column
• Misspelling function name as innerjoin
• Confusing parameter order

3. Given these two data frames:

df1 <- data.frame(id = c(1, 2, 3), value1 = c("A", "B", "C"))
df2 <- data.frame(id = c(2, 3, 4), value2 = c("X", "Y", "Z"))

What is the result of inner_join(df1, df2, by = "id")?

medium

A. Rows with ids 2 and 3 with columns from both data frames

B. Rows with ids 1, 2, 3, 4 with all columns

C. Rows with ids 1 and 4 only

D. Rows with ids 1, 2, 3 with only columns from df1

Solution

1. Step 1: Identify matching ids in both data frames

   df1 has ids 1, 2, 3; df2 has ids 2, 3, 4. Common ids are 2 and 3.

2. Step 2: Understand inner_join output

   inner_join keeps only rows with matching ids and combines columns from both data frames for those rows.

3. Final Answer:

   Rows with ids 2 and 3 with columns from both data frames -> Option A

4. Quick Check:

   inner_join = only matching rows [OK]

Hint: inner_join keeps only rows with matching keys [OK]

Common Mistakes:

• Including unmatched rows in inner_join result
• Confusing inner_join with left_join
• Ignoring columns from second data frame

4. You run this code:

left_join(df1, df2, by = "id")

but get an error: Error: object 'df2' not found. What is the likely cause?

medium

A. The data frame df2 was not created or loaded before the join.

B. The by argument is misspelled.

C. left_join cannot join on column named 'id'.

D. You need to use inner_join instead of left_join.

Solution

1. Step 1: Analyze the error message

   The error says df2 is not found, meaning R cannot find the object named df2.

2. Step 2: Identify cause of missing object

   This usually means df2 was not created, loaded, or is misspelled before calling left_join.

3. Final Answer:

   The data frame df2 was not created or loaded before the join. -> Option A

4. Quick Check:

   Missing data frame causes object not found error [OK]

Hint: Check if all data frames exist before joining [OK]

Common Mistakes:

• Assuming join syntax error causes object not found
• Ignoring typo in data frame names
• Thinking join functions require special packages loaded

5. You have two data frames:

students <- data.frame(student_id = c(1, 2, 3, 4), name = c("Anna", "Ben", "Cara", "Dan"))
scores <- data.frame(student_id = c(2, 3, 5), score = c(88, 92, 75))

You want a table that shows all students with their scores if available, and NA if no score exists. Which join function and code should you use?

hard

A. inner_join(students, scores, by = "student_id")

B. left_join(students, scores, by = "student_id")

C. right_join(students, scores, by = "student_id")

D. full_join(students, scores, by = "student_id")

Solution

1. Step 1: Understand the goal

   You want all students listed, even if they don't have a score, with scores added where available.

2. Step 2: Choose the correct join

   left_join keeps all rows from the first table (students) and adds matching scores, filling with NA if no match.

3. Final Answer:

   left_join(students, scores, by = "student_id") -> Option B

4. Quick Check:

   left_join keeps all left rows, adds matches, fills NA [OK]

Hint: Use left_join to keep all from first table with matches [OK]

Common Mistakes:

• Using inner_join which drops students without scores
• Using right_join which keeps all scores but drops some students
• Using full_join which adds extra rows not needed