Adding and removing columns in R Programming - Step-by-Step Execution

Concept Flow - Adding and removing columns

Start with data frame

↓

Add column: df$new_col <- values

↓

Check data frame with new column

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Remove column: df$new_col <- NULL

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Check data frame after removal

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End

Start with a data frame, add a new column by assigning values, then remove a column by setting it to NULL.

Execution Sample

R Programming

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

Create a data frame, add a column 'c', then remove it, and print the final data frame.

Execution Table

Step	Action	Data Frame Columns	Details
1	Create df with columns a and b	a, b	df = data.frame(a=1:3, b=4:6)
2	Add column c	a, b, c	df$c <- c(7,8,9) adds new column c
3	Remove column c	a, b	df$c <- NULL removes column c
4	Print df	a, b	Final df has columns a and b only

💡 No more steps; column c was added then removed.

Variable Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
df columns	none	a, b	a, b, c	a, b	a, b

Key Moments - 2 Insights

Why does setting df$c to NULL remove the column?

In R, assigning NULL to a data frame column deletes that column, as shown in step 3 of the execution_table.

What happens if you add a column with a different length than existing columns?

R will give an error or recycle values; in this example, lengths match so no error occurs (step 2).

Visual Quiz - 3 Questions

Look at the execution_table, after which step does the data frame have three columns?

A After Step 2

B After Step 1

C After Step 3

D After Step 4

💡 Hint

Check the 'Data Frame Columns' column in execution_table rows.

At the final step, which columns remain in the data frame?

A a, b, c

B a only

C a, b

D c only

💡 Hint

Look at the 'Final' column in variable_tracker and last row in execution_table.

If you skip step 3 (removing column c), what would the final data frame columns be?

A a, b

B a, b, c

C c only

D none

💡 Hint

Refer to step 2 in execution_table where column c is added.

Concept Snapshot

Adding columns: df$new_col <- values
Removing columns: df$col <- NULL
Data frame updates immediately
Column lengths must match or recycle
Use print(df) to see changes

Full Transcript

This example shows how to add and remove columns in an R data frame. First, we create a data frame df with columns a and b. Then, we add a new column c by assigning a vector to df$c. Next, we remove column c by setting df$c to NULL. Finally, we print the data frame to see that only columns a and b remain. Assigning NULL to a column deletes it. Column lengths should match existing rows to avoid errors. This step-by-step trace helps beginners see how the data frame changes after each operation.

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