Merging data frames in R Programming - Step-by-Step Execution

Concept Flow - Merging data frames

Start with two data frames

↓

Choose merge key(s)

↓

Apply merge function

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Match rows by key(s)

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Combine matching rows

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Result: merged data frame

Merging data frames means combining rows from two tables based on matching values in one or more columns.

Execution Sample

R Programming

df1 <- data.frame(ID = c(1, 2, 3), Name = c("Anna", "Ben", "Cara"))
df2 <- data.frame(ID = c(2, 3, 4), Score = c(88, 92, 75))
merged_df <- merge(df1, df2, by = "ID")
print(merged_df)

This code merges two data frames on the 'ID' column, keeping only rows with matching IDs.

Execution Table

Step	Action	df1 Rows	df2 Rows	Matching IDs	Resulting Rows
1	Start with df1 and df2	IDs: 1,2,3	IDs: 2,3,4	-	-
2	Select merge key 'ID'	-	-	-	-
3	Find matching IDs	-	-	2, 3	-
4	Combine rows with matching IDs	-	-	-	Rows with ID 2 and 3
5	Create merged_df with columns ID, Name, Score	-	-	-	2 rows: (2, Ben, 88), (3, Cara, 92)
6	Print merged_df	-	-	-	ID Name Score 2 Ben 88 3 Cara 92

💡 Merge completes after combining rows with matching IDs 2 and 3.

Variable Tracker

Variable	Start	After merge	Final
df1	ID:1,2,3; Name:Anna,Ben,Cara	Unchanged	Unchanged
df2	ID:2,3,4; Score:88,92,75	Unchanged	Unchanged
merged_df	Not defined	Rows with ID 2 and 3 combined	2 rows: ID=2,3 with Name and Score columns

Key Moments - 3 Insights

Why does the merged data frame only have rows with IDs 2 and 3?

Because merge by default keeps only rows where the key 'ID' exists in both data frames, as shown in execution_table step 3 and 4.

What happens to rows with IDs 1 and 4?

They are dropped because they don't have matching IDs in the other data frame, as seen in execution_table step 3.

Can we keep all rows from both data frames?

Yes, by using parameters like all=TRUE in merge(), but this example uses default inner join behavior.

Visual Quiz - 3 Questions

Look at the execution_table, what are the matching IDs found at step 3?

A 1 and 4

B 1, 2, 3, 4

C 2 and 3

D No matching IDs

💡 Hint

Check the 'Matching IDs' column in execution_table row with Step 3.

At which step does the merged data frame get created?

A Step 5

B Step 4

C Step 2

D Step 6

💡 Hint

Look for the step mentioning 'Create merged_df' in execution_table.

If we want to keep all rows from df1 even if no match in df2, what parameter should we add to merge()?

A all=TRUE

B all.x=TRUE

C all.y=TRUE

D by.x=TRUE

💡 Hint

Recall merge() parameters for left join behavior.

Concept Snapshot

merge(x, y, by) combines two data frames x and y by matching values in 'by' columns.
Default keeps only rows with keys in both frames (inner join).
Use all=TRUE for full join, all.x=TRUE for left join.
Result has columns from both frames merged by key.

Full Transcript

Merging data frames in R means joining two tables by matching values in one or more columns. We start with two data frames, choose the column(s) to match on, then use the merge() function. The function finds rows with matching keys and combines their columns into a new data frame. By default, only rows with keys in both data frames are kept. Rows without matches are dropped. You can change this behavior with parameters like all=TRUE or all.x=TRUE. The example merges two data frames on the 'ID' column, resulting in a data frame with only IDs 2 and 3, combining their information.

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