Merging data frames in R Programming

Introduction

Merging data frames helps you combine information from two tables into one, based on matching data.

You have two lists of people with different details and want one complete list.

You want to add sales data to a list of products by matching product IDs.

You need to combine survey answers from two groups based on participant IDs.

You want to join customer info with their purchase history.

Syntax

R Programming

merge(x, y, by = NULL, by.x = NULL, by.y = NULL, all = FALSE, all.x = FALSE, all.y = FALSE)

x and y are the two data frames to merge.

by specifies the column(s) to match on. If NULL, merges on common column names.

Examples

Merges on all common columns between df1 and df2.

R Programming

merge(df1, df2)

Merges using the column named 'id' in both data frames.

R Programming

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

Merges where df1's 'id1' matches df2's 'id2'.

R Programming

merge(df1, df2, by.x = "id1", by.y = "id2")

Performs a full outer join, keeping all rows from both data frames.

R Programming

merge(df1, df2, all = TRUE)

Sample Program

This program merges two data frames by the 'id' column. It keeps all rows from both tables, filling missing values with NA.

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", all = TRUE)
print(merged_df)

Important Notes

Use all = TRUE for a full join, all.x = TRUE for left join, and all.y = TRUE for right join.

If columns have different names, use by.x and by.y to specify them.

Missing matches will show as NA in the result.

Summary

Merging combines two data frames by matching columns.

You can control which columns to match and which rows to keep.

Missing data appears as NA after merging.

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