Adding and removing columns in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add a new column age with value 30 to the data frame df.

R Programming

df$age <- [1]

Drag options to blanks, or click blank then click option'

A30

B"age"

Cage

Dc(30)

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around 30 makes it a string, not a number.

Assigning the column name instead of the value.

To add a new column with a fixed value, assign the value directly. Here, 30 is assigned to df$age.

2fill in blank

medium

Complete the code to remove the column age from the data frame df.

R Programming

df <- df[ , [1] ]

Drag options to blanks, or click blank then click option'

Anames(df) == "age"

B!(names(df) == "age")

C"age"

Dnames(df) != "age"

Attempts:

3 left

💡 Hint

Common Mistakes

Using == instead of != selects only the column to remove.

Using quotes alone does not filter columns.

To remove a column, select columns where the name is not equal to "age". This keeps all columns except age.

3fill in blank

hard

Fix the error in the code to add a new column height with values from the vector c(160, 170, 180).

R Programming

df$height <- [1]

Drag options to blanks, or click blank then click option'

Ac(160, 170, 180)

B[160, 170, 180]

C(160, 170, 180)

Dlist(160, 170, 180)

Attempts:

3 left

💡 Hint

Common Mistakes

Using square brackets which are not vector constructors in R.

Using parentheses without c().

In R, vectors are created with c(). Using square brackets or parentheses alone is incorrect syntax.

4fill in blank

hard

Fill both blanks to add a new column weight with values from c(60, 70, 80) and then remove the column age.

R Programming

df$[1] <- c(60, 70, 80)
df <- df[ , [2] ]

Drag options to blanks, or click blank then click option'

Aweight

Bnames(df) != "age"

Cnames(df) == "age"

Dage

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name in the first blank.

Using == instead of != in the second blank.

First, assign the vector to the new column named weight. Then, remove the age column by selecting columns where the name is not "age".

5fill in blank

hard

Fill all three blanks to create a new column with uppercase names from df$name, add a column score with values c(90, 85, 88), and remove the column height.

R Programming

df$[1] <- toupper(df$name)
df$score <- [2]
df <- df[ , [3] ]

Drag options to blanks, or click blank then click option'

Aname_upper

Bc(90, 85, 88)

Cnames(df) != "height"

Dheight

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong new column name.

Not using c() for the vector.

Using == instead of != to remove the column.

Assign uppercase names to a new column name_upper. Then add score with the given vector. Finally, remove the height column by selecting columns not named "height".

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