Negative indexing for exclusion in R Programming - Mini Project: Build & Apply

Negative indexing for exclusion

📖 Scenario: You have a list of fruits, but you want to create a new list that excludes some specific fruits by their positions.

🎯 Goal: Learn how to use negative indexing in R to exclude elements from a vector.

📋 What You'll Learn

Create a vector with exact fruit names

Create a vector with exact positions to exclude

Use negative indexing to exclude those positions

Print the resulting vector

💡 Why This Matters

🌍 Real World

Negative indexing is useful when you want to remove unwanted items from a list, like filtering out certain products or data points.

💼 Career

Data analysts and programmers often need to exclude specific data entries quickly and efficiently using indexing techniques.

1

Create the fruits vector

Create a vector called fruits with these exact values in order: "apple", "banana", "cherry", "date", "elderberry".

R Programming

# Create the fruits vector with the exact fruits
# Your code here

Hint

Use the c() function to combine the fruit names into a vector.

2

Create the exclude positions vector

Create a vector called exclude_positions with the exact values 2 and 4 to represent the positions of fruits to exclude.

R Programming

fruits <- c("apple", "banana", "cherry", "date", "elderberry")
# Create the exclude_positions vector with 2 and 4
# Your code here

Hint

Use the c() function to create a vector with the numbers 2 and 4.

3

Exclude fruits using negative indexing

Create a new vector called selected_fruits by excluding the positions in exclude_positions from fruits using negative indexing.

R Programming

fruits <- c("apple", "banana", "cherry", "date", "elderberry")
exclude_positions <- c(2, 4)
# Use negative indexing to exclude positions 2 and 4 from fruits
# Your code here

Hint

Use -exclude_positions inside the square brackets to exclude those positions.

4

Print the selected fruits

Print the selected_fruits vector to show the fruits after exclusion.

R Programming

fruits <- c("apple", "banana", "cherry", "date", "elderberry")
exclude_positions <- c(2, 4)
selected_fruits <- fruits[-exclude_positions]
# Print the selected_fruits vector
# Your code here

Hint

Use the print() function to display the vector.

Practice

1. In R, what does negative indexing like x[-2] do to the vector x?

easy

A. It returns the vector x with the second element doubled.

B. It returns only the second element of x.

C. It returns the vector x without the second element.

D. It causes an error because negative indexes are not allowed.

Solution

1. Step 1: Understand negative indexing in R

   Negative indexing excludes elements at the specified positions from the result.

2. Step 2: Apply negative index to x[-2]

   This means the second element is removed from the vector x, returning all others.

3. Final Answer:

   It returns the vector x without the second element. -> Option C

4. Quick Check:

   Negative index excludes elements [OK]

Hint: Negative index removes elements by position [OK]

Common Mistakes:

• Thinking negative index selects elements
• Confusing negative index with subtraction
• Assuming negative index causes error

2. Which of the following is the correct syntax to exclude the 1st and 3rd elements from a vector v in R?

easy

A. v[-c(1, 3)]

B. v[c(-1, 3)]

C. v[c(1, -3)]

D. v[-1, -3]

Solution

1. Step 1: Recall syntax for excluding multiple elements

   Use negative indexing with a vector of positions inside c(), like -c(1, 3).

2. Step 2: Check each option

   v[-c(1, 3)] correctly uses v[-c(1, 3)] to exclude 1st and 3rd elements. Others misuse negative signs or commas.

3. Final Answer:

   v[-c(1, 3)] -> Option A

4. Quick Check:

   Negative vector inside c() excludes multiple elements [OK]

Hint: Use negative vector inside c() to exclude multiple elements [OK]

Common Mistakes:

• Placing negative signs inside c() incorrectly
• Using commas instead of c() for multiple indexes
• Mixing positive and negative indexes in one vector

3. What is the output of the following R code?

vec <- c(10, 20, 30, 40, 50)
result <- vec[-c(2, 4)]
print(result)

medium

A. [1] 20 40

B. [1] 10 30 50

C. [1] 10 20 30 40 50

D. [1] 30 50

Solution

1. Step 1: Identify elements to exclude

   Negative indexing -c(2, 4) removes elements at positions 2 and 4, which are 20 and 40.

2. Step 2: List remaining elements

   Remaining elements are at positions 1, 3, and 5: 10, 30, and 50.

3. Final Answer:

   [1] 10 30 50 -> Option B

4. Quick Check:

   Exclude 2nd and 4th elements [OK]

Hint: Remove elements at given positions with negative c() [OK]

Common Mistakes:

• Including excluded elements by mistake
• Confusing element values with positions
• Expecting only excluded elements as output

4. Consider this R code snippet:

nums <- c(5, 10, 15, 20)
new_nums <- nums[-2, 3]
print(new_nums)

What is the error in this code?

medium

A. Incorrect use of comma in negative indexing causing subscript error.

B. Negative indexing cannot be used with numeric vectors.

C. The vector nums is empty, so indexing fails.

D. The code runs without error and prints the vector.

Solution

1. Step 1: Analyze the indexing syntax

   Negative indexing uses a single vector inside brackets. Using a comma like nums[-2, 3] implies matrix indexing, which is invalid for a vector.

2. Step 2: Identify the error type

   This causes a subscript error because nums is a vector, not a matrix or data frame.

3. Final Answer:

   Incorrect use of comma in negative indexing causing subscript error. -> Option A

4. Quick Check:

   Comma in vector indexing causes error [OK]

Hint: No commas in vector negative indexing [OK]

Common Mistakes:

• Using commas like in matrices for vectors
• Assuming negative indexing works with multiple arguments
• Ignoring error messages about subscripts

5. You have a vector data <- c(3, 6, 9, 12, 15, 18). You want to create a new vector excluding all elements at even positions using negative indexing. Which code correctly achieves this?

hard

A. new_data <- data[-seq(1, length(data), by=2)]

B. new_data <- data[-c(2, 4, 6, 8)]

C. new_data <- data[-c(1, 3, 5)]

D. new_data <- data[-seq(2, length(data), by=2)]

Solution

1. Step 1: Identify even positions in the vector

   Even positions are 2, 4, 6. Using seq(2, length(data), by=2) generates these positions dynamically.

2. Step 2: Use negative indexing to exclude these positions

   Applying negative indexing with -seq(2, length(data), by=2) removes elements at even positions.

3. Final Answer:

   new_data <- data[-seq(2, length(data), by=2)] -> Option D

4. Quick Check:

   Negative seq() excludes even positions [OK]

Hint: Use seq() with negative index to exclude positions [OK]

Common Mistakes:

• Hardcoding indexes without checking vector length
• Excluding odd positions instead of even
• Using indexes outside vector length