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.