Adding and removing columns in R Programming - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of adding a new column with cbind

What is the output of this R code after adding a new column using cbind?

R Programming

df <- data.frame(a = 1:3, b = 4:6)
df <- cbind(df, c = c(7, 8, 9))
print(df)

A Error in cbind(df, c = c(7, 8, 9)) : object 'c' not found

B

  a b
1 1 4
2 2 5
3 3 6
c 7 8 9

C

  a b c
1 1 4 7
2 2 5 8
3 3 6 9

D

  a b c
1 7 8 9
2 1 4 7
3 2 5 8

💡 Hint

Remember that cbind adds columns side by side and keeps row order.

The cbind function adds the new column 'c' to the existing data frame, matching rows by position.

❓ Predict Output

intermediate

Result of removing a column using NULL assignment

What will be the result of this R code after removing column 'b' by assigning NULL?

R Programming

df <- data.frame(a = 1:3, b = 4:6, c = 7:9)
df$b <- NULL
print(df)

A

  a b
1 1 4
2 2 5
3 3 6

B

  a b c
1 1 4 7
2 2 5 8
3 3 6 9

C Error: object 'b' not found

D

  a c
1 1 7
2 2 8
3 3 9

💡 Hint

Assigning NULL to a column removes it from the data frame.

Setting df$b <- NULL deletes the column 'b' from the data frame, leaving only 'a' and 'c'.

🔧 Debug

advanced

Why does this code fail to add a column?

This code tries to add a new column 'd' to the data frame but fails. What is the error?

R Programming

df <- data.frame(a = 1:3, b = 4:6)
df$d <- c(7, 8)
print(df)

A No error, column 'd' added with NA for missing values

B Error: replacement has 2 rows, data has 3

C Error: object 'd' not found

D Column 'd' added with recycled values

💡 Hint

Check if the length of the new column matches the number of rows.

The new column vector has length 2 but the data frame has 3 rows, causing a length mismatch error.

❓ Predict Output

advanced

Output after removing multiple columns with subset

What is the output of this R code after removing columns 'b' and 'c' using subset?

R Programming

df <- data.frame(a = 1:3, b = 4:6, c = 7:9)
df2 <- subset(df, select = -c(b, c))
print(df2)

A

  a
1 1
2 2
3 3

B

  a b c
1 1 4 7
2 2 5 8
3 3 6 9

C Error in subset(df, select = -c(b, c)) : invalid subscript type

D

  b c
1 4 7
2 5 8
3 6 9

💡 Hint

The select = -c(...) syntax removes specified columns.

Using subset with negative selection removes columns 'b' and 'c', leaving only 'a'.

🧠 Conceptual

expert

Why is using transform() safer than direct assignment for adding columns?

Which reason best explains why transform() is often preferred over direct assignment (e.g., df$newcol <- ...) when adding columns to a data frame?

A Because <code>transform()</code> returns a new data frame and avoids modifying the original by reference

B Because direct assignment always causes errors if the new column length differs

C Because <code>transform()</code> automatically removes NA values from the new column

D Because direct assignment cannot add columns with names longer than 5 characters

💡 Hint

Think about how data frames are copied or modified in R.

transform() returns a new data frame with the added or modified columns, which helps avoid side effects on the original data frame.

Practice

1. In R, how do you add a new column named age with values c(25, 30, 35) to an existing dataframe df?

easy

A. df <- c(df, age = c(25, 30, 35))

B. df[age] = c(25, 30, 35)

C. df$age <- c(25, 30, 35)

D. df$age = NULL

Solution

1. Step 1: Understand how to add columns in R

   In R, you add a new column to a dataframe by assigning a vector to df$column_name.

2. Step 2: Apply the assignment for the new column

   Assigning c(25, 30, 35) to df$age adds the new column age with those values.

3. Final Answer:

   df$age <- c(25, 30, 35) -> Option C

4. Quick Check:

   Use $ and assign vector to add column [OK]

Hint: Use df$column <- values to add columns fast [OK]

Common Mistakes:

• Using NULL to add a column instead of removing
• Trying to add column with c() outside assignment
• Using incorrect dataframe syntax like c(df, ...)

2. Which of the following is the correct syntax to remove the column height from dataframe df in R?

easy

A. df$height <- NULL

B. df$height = c()

C. df$height <- NA

D. remove(df$height)

Solution

1. Step 1: Recall how to remove columns in R

   Assigning NULL to a dataframe column removes it completely.

2. Step 2: Apply removal syntax

   Setting df$height <- NULL deletes the height column from df.

3. Final Answer:

   df$height <- NULL -> Option A

4. Quick Check:

   Assign NULL to remove column [OK]

Hint: Remove columns by assigning NULL: df$col <- NULL [OK]

Common Mistakes:

• Assigning empty vector c() instead of NULL
• Assigning NA which keeps column but fills with missing
• Using remove() which is not for dataframe columns

3. What will be the output of the following R code?

df <- data.frame(name = c("Ann", "Bob"), score = c(90, 85))
df$passed <- c(TRUE, FALSE)
df$score <- NULL
df

medium

A. A dataframe with columns: name, passed

B. A dataframe with columns: name, score, passed

C. A dataframe with columns: name, score

D. Error because score column cannot be removed

Solution

1. Step 1: Add new column 'passed'

   The code adds a logical column passed with values TRUE and FALSE.

2. Step 2: Remove the 'score' column

   Assigning NULL to df$score deletes the score column.

3. Final Answer:

   A dataframe with columns: name, passed -> Option A

4. Quick Check:

   Added passed, removed score, left name [OK]

Hint: NULL removes columns; new assignment adds columns [OK]

Common Mistakes:

• Thinking score column remains after NULL assignment
• Expecting error on removing existing column
• Confusing TRUE/FALSE vector with numeric

4. Consider this R code snippet:

df <- data.frame(x = 1:3, y = 4:6)
df$z = c(7, 8)
df$y <- NULL
df

What is the problem and how to fix it?

medium

A. Error because data.frame cannot have NULL columns; fix by using list

B. No error; code runs fine and removes y column

C. Error because NULL cannot remove columns; fix by using NA

D. Warning because length of z vector does not match rows; fix by matching length

Solution

1. Step 1: Check length of new column vector

   The dataframe has 3 rows, but c(7, 8) has length 2, causing a length mismatch warning.

2. Step 2: Fix by providing vector of correct length

   Use c(7, 8, 9) or recycle values properly to match 3 rows.

3. Final Answer:

   Warning due to length mismatch; fix by matching vector length -> Option D

4. Quick Check:

   New column vector length must match dataframe rows [OK]

Hint: New column vector length must equal dataframe rows [OK]

Common Mistakes:

• Ignoring vector length mismatch causing warning
• Thinking NULL cannot remove columns
• Trying to assign NA to remove columns

5. You have a dataframe df with columns id, score1, and score2. You want to create a new column average that is the mean of score1 and score2, then remove score1 and score2. Which code correctly does this?

hard

A. df$average <- mean(df$score1, df$score2)
df$score1 <- NULL
df$score2 <- NULL

B. df$average <- rowMeans(df[c("score1", "score2")])
df$score1 <- NULL
df$score2 <- NULL

C. df$average <- mean((df$score1 + df$score2) / 2)
df$score1 <- NULL
df$score2 <- NULL

D. df$average <- (score1 + score2) / 2
df$score1 <- NULL
df$score2 <- NULL

Solution

1. Step 1: Calculate average of two columns row-wise

   Use rowMeans() on the subset df[c("score1", "score2")] to get the mean for each row.

2. Step 2: Remove the original score columns

   Assign NULL to df$score1 and df$score2 to delete them.

3. Final Answer:

   df$average <- rowMeans(df[c("score1", "score2")]) df$score1 <- NULL df$score2 <- NULL -> Option B

4. Quick Check:

   Use rowMeans() for row-wise average, then remove columns [OK]

Hint: Use rowMeans() for row averages, then assign NULL to remove [OK]

Common Mistakes:

• Using mean() which computes overall mean, not row-wise
• Referencing columns without df$ prefix
• Trying to remove columns by assigning NA