Merging data frames in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of merging data frames in R?

Merging data frames combines two or more data frames based on common columns or row names, allowing you to integrate related data into one table.

beginner

What function in R is commonly used to merge two data frames?

The merge() function is used to combine two data frames by matching rows based on one or more common columns.

beginner

What does the argument by specify in the merge() function?

The by argument specifies the column name(s) used to match rows between the two data frames during the merge.

intermediate

Explain the difference between all=TRUE and all=FALSE in merge().

all=TRUE performs a full outer join, keeping all rows from both data frames. all=FALSE (default) performs an inner join, keeping only rows with matching keys in both data frames.

intermediate

How can you merge data frames by different column names in R?

Use by.x and by.y arguments in merge() to specify the column names to match in the first and second data frames respectively.

Which function is used to merge two data frames in R?

A bind()

B combine()

C merge()

D join()

The merge() function is the standard way to merge two data frames in R.

What does all=TRUE do in the merge() function?

A Keeps all rows from both data frames

B Drops all rows

C Keeps only matching rows

D Merges only the first data frame

all=TRUE performs a full outer join, keeping all rows from both data frames.

How do you specify different column names to merge on in two data frames?

A keys

B by

C columns

D by.x and by.y

Use by.x and by.y to specify different column names in each data frame.

If you want to keep only rows that appear in both data frames, which argument should you use?

A all=TRUE

B all=FALSE

C all.x=TRUE

D all.y=TRUE

all=FALSE (default) keeps only rows with matching keys in both data frames (inner join).

What happens if you do not specify the by argument in merge()?

A Merges on all common column names automatically

B Merge fails with error

C Merges on row names only

D Merges randomly

If by is not specified, merge() uses all common column names to merge.

Describe how to merge two data frames in R when the key columns have different names.

Think about how to tell R which columns to match in each data frame.

Explain the difference between inner join and full outer join when merging data frames in R.

Consider which rows appear in the result for each join type.

Practice

1. What does the merge() function do in R when working with data frames?

easy

A. It combines two data frames by matching rows based on common columns.

B. It deletes rows from a data frame.

C. It sorts a data frame alphabetically.

D. It converts a data frame into a list.

Solution

1. Step 1: Understand the purpose of merge()

   The merge() function is designed to combine two data frames by matching rows based on one or more common columns.

2. Step 2: Eliminate incorrect options

   Options A, B, and D describe different operations unrelated to merging data frames.

3. Final Answer:

   It combines two data frames by matching rows based on common columns. -> Option A

4. Quick Check:

   merge() combines data frames by matching columns [OK]

Hint: Remember: merge() joins data frames on matching columns [OK]

Common Mistakes:

• Thinking merge() deletes rows
• Confusing merge() with sorting functions
• Assuming merge() converts data frames to lists

2. Which of the following is the correct syntax to merge two data frames df1 and df2 by a common column id in R?

easy

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

B. merge(df1, df2, by = "id")

C. merge(df1, df2, key = "id")

D. merge(df1, df2, match = "id")

Solution

1. Step 1: Recall merge() argument names

   The correct argument to specify the column to merge on is by, not on, key, or match.

2. Step 2: Check syntax correctness

   merge(df1, df2, by = "id") uses by = "id", which is the correct syntax for merging by the column named "id".

3. Final Answer:

   merge(df1, df2, by = "id") -> Option B

4. Quick Check:

   Use by= to specify merge column [OK]

Hint: Use by= to specify columns in merge() [OK]

Common Mistakes:

• Using on= instead of by=
• Using key= or match= which are invalid
• Forgetting to quote the column name

3. Given the following R code:

df1 <- data.frame(id = c(1, 2, 3), val1 = c("A", "B", "C"))
df2 <- data.frame(id = c(2, 3, 4), val2 = c("X", "Y", "Z"))
result <- merge(df1, df2, by = "id", all = FALSE)
print(result)

What will be the output?

medium

A. id val1 val2
1 2 B X
2 3 C Y

B. id val1 val2
1 1 A <NA>
2 2 B X
3 3 C Y
4 4 <NA> Z

C. id val1 val2
1 1 A X
2 2 B Y
3 3 C Z

D. Error: all is not a valid argument

Solution

1. Step 1: Understand merge with all = FALSE

   Setting all = FALSE performs an inner join, keeping only rows with matching id in both data frames.

2. Step 2: Identify matching ids and resulting rows

   Matching ids are 2 and 3. So rows with id 1 and 4 are excluded. The merged data frame has rows for id 2 and 3 with corresponding values.

3. Final Answer:

   id val1 val2 1 2 B X 2 3 C Y -> Option A

4. Quick Check:

   Inner join keeps only matching ids [OK]

Hint: all=FALSE means keep only matching rows [OK]

Common Mistakes:

• Expecting all rows to appear (confusing inner and outer join)
• Misreading which ids match
• Thinking all=FALSE causes error

4. Consider this R code snippet:

df1 <- data.frame(id = c(1, 2), val1 = c("A", "B"))
df2 <- data.frame(id = c(2, 3), val2 = c("X", "Y"))
result <- merge(df1, df2, by = "id", all.x = TRUE, all.y = TRUE)
print(result)

What is the error in this code?

medium

A. Using both all.x and all.y together is invalid syntax.

B. The merge function requires a parameter named all, not all.x or all.y.

C. The data frames have different column names, causing a merge error.

D. There is no error; the code performs a full outer join correctly.

Solution

1. Step 1: Understand all.x and all.y parameters

   In R, all.x = TRUE keeps all rows from the first data frame, all.y = TRUE keeps all rows from the second. Using both performs a full outer join.

2. Step 2: Check for syntax or logical errors

   The syntax is correct and the data frames can be merged on id even if some ids don't match. No error occurs.

3. Final Answer:

   There is no error; the code performs a full outer join correctly. -> Option D

4. Quick Check:

   all.x and all.y together = full outer join [OK]

Hint: all.x=TRUE and all.y=TRUE means full outer join [OK]

Common Mistakes:

• Thinking all.x and all.y cannot be used together
• Confusing all with all.x or all.y
• Assuming different column names cause error

5. You have two data frames:

df1 <- data.frame(id = c(1, 2, 2, 3), val1 = c("A", "B", "C", "D"))
df2 <- data.frame(id = c(2, 2, 4), val2 = c("X", "Y", "Z"))

You want to merge them to keep all rows from df1 and add matching val2 values. Which code correctly merges and keeps all df1 rows, including duplicates, showing NA where no match exists?

hard

A. merge(df1, df2, by = "id", all = TRUE)

B. merge(df1, df2, by = "id", all.y = TRUE)

C. merge(df1, df2, by = "id", all.x = TRUE)

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

Solution

1. Step 1: Understand all.x = TRUE behavior

   Setting all.x = TRUE keeps all rows from df1, including duplicates, and adds matching rows from df2. Non-matching rows get NA.

2. Step 2: Compare with other options

   merge(df1, df2, by = "id", all = TRUE) keeps all rows from both data frames (full outer join), which is not required. merge(df1, df2, by = "id", all.y = TRUE) keeps all rows from df2 only. merge(df1, df2, by = "id") keeps only matching rows (inner join).

3. Final Answer:

   merge(df1, df2, by = "id", all.x = TRUE) -> Option C

4. Quick Check:

   all.x=TRUE keeps all df1 rows with matches or NA [OK]

Hint: Use all.x=TRUE to keep all rows from first data frame [OK]

Common Mistakes:

• Using all=TRUE instead of all.x=TRUE
• Using all.y=TRUE which keeps second data frame rows
• Forgetting duplicates are kept automatically