Logical indexing in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select elements greater than 3 from the vector.

R Programming

vec <- c(1, 4, 3, 5, 2)
result <- vec[vec [1] 3]
print(result)

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

A<

B!=

C==

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' selects elements less than 3, which is incorrect.

Using '==' selects elements equal to 3, not greater.

Using vec > 3 creates a logical vector selecting elements greater than 3.

2fill in blank

medium

Complete the code to select elements that are even numbers.

R Programming

vec <- c(2, 5, 8, 7, 10)
even_nums <- vec[vec %% 2 [1] 0]
print(even_nums)

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

A==

B<

C>

D!=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '!=' selects odd numbers, not even.

Using '>' or '<' does not correctly check for evenness.

Using vec %% 2 == 0 selects elements divisible by 2, i.e., even numbers.

3fill in blank

hard

Fix the error in the code to select elements not equal to 4.

R Programming

vec <- c(4, 6, 4, 8, 5)
result <- vec[vec [1] 4]
print(result)

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

A!=

B>=

C==

D<=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '==' selects only elements equal to 4, not excluding them.

Using '>=' or '<=' does not exclude 4 properly.

Using vec != 4 selects elements that are not equal to 4.

4fill in blank

hard

Fill both blanks to select elements less than 5 and greater than 2.

R Programming

vec <- c(1, 3, 4, 6, 2)
result <- vec[vec [1] 5 & vec [2] 2]
print(result)

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

A<

B>

C<=

D>=

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<=' or '>=' changes the range to include boundary values.

Mixing up the operators reverses the logic.

Use < 5 and > 2 to select elements between 2 and 5.

5fill in blank

hard

Fill all three blanks to create a named vector with lengths of words longer than 3 characters.

R Programming

words <- c("cat", "house", "dog", "elephant")
lengths <- c([1] = [2] for [3] in words if nchar([3]) > 3)
print(lengths)

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

Aword

Bnchar(word)

Dlengths

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names causes errors.

Not using nchar() returns wrong values.

Use word as the name, nchar(word) as the value, and iterate over word in words.

Practice

1. What does logical indexing in R do when applied to a vector like v[c(TRUE, FALSE, TRUE)]?

easy

A. Selects elements of v where the index is TRUE

B. Selects elements of v where the index is FALSE

C. Returns the length of v

D. Returns an error because logical vectors cannot index

Solution

1. Step 1: Understand logical indexing

   Logical indexing uses a TRUE/FALSE vector to pick elements from the original vector where TRUE is present.

2. Step 2: Apply logical vector to v

   Elements at positions with TRUE in the logical vector are selected; others are skipped.

3. Final Answer:

   Selects elements of v where the index is TRUE -> Option A

4. Quick Check:

   TRUE picks elements [OK]

Hint: TRUE means pick element, FALSE means skip [OK]

Common Mistakes:

• Thinking FALSE picks elements
• Confusing logical indexing with numeric indexing
• Assuming logical vector length doesn't matter

2. Which of the following is the correct syntax to select elements of vector x where values are greater than 5 using logical indexing?

easy

A. x[> 5]

B. x > 5[x]

C. x[x > 5]

D. x[5 > x]

Solution

1. Step 1: Understand logical condition syntax

   The condition x > 5 creates a logical vector TRUE where elements are greater than 5.

2. Step 2: Use logical vector to index x

   Using x[x > 5] selects elements of x where the condition is TRUE.

3. Final Answer:

   x[x > 5] -> Option C

4. Quick Check:

   Correct logical indexing syntax [OK]

Hint: Put condition inside square brackets after vector [OK]

Common Mistakes:

• Placing condition outside brackets
• Using invalid syntax like x[> 5]
• Swapping operands in condition

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

v <- c(10, 3, 7, 2, 9)
v[v >= 7]

medium

A. [1] 10 3 7 2 9

B. [1] 3 2

C. Error: invalid logical indexing

D. [1] 10 7 9

Solution

1. Step 1: Evaluate the logical condition v >= 7

   This creates a logical vector: TRUE, FALSE, TRUE, FALSE, TRUE for elements 10, 3, 7, 2, 9 respectively.

2. Step 2: Select elements where condition is TRUE

   Elements at TRUE positions are 10, 7, and 9.

3. Final Answer:

   [1] 10 7 9 -> Option D

4. Quick Check:

   Only values >= 7 selected [OK]

Hint: Check which elements satisfy condition, pick those [OK]

Common Mistakes:

• Including elements less than 7
• Confusing >= with >
• Expecting an error due to indexing

4. Identify the error in this R code snippet:

nums <- c(4, 8, 15, 16, 23, 42)
nums[nums > 10 & TRUE]

medium

A. No error, code runs correctly

B. Logical vector length mismatch

C. Incorrect use of & with TRUE

D. Syntax error due to missing parentheses

Solution

1. Step 1: Analyze logical condition nums > 10 & TRUE

   The expression nums > 10 returns a logical vector; AND with TRUE keeps it unchanged.

2. Step 2: Check for errors

   The logical vector length matches nums, and syntax is correct, so no error occurs.

3. Final Answer:

   No error, code runs correctly -> Option A

4. Quick Check:

   Logical AND with TRUE keeps vector same [OK]

Hint: AND with TRUE keeps condition unchanged [OK]

Common Mistakes:

• Thinking & TRUE causes error
• Expecting length mismatch error
• Missing that & works element-wise

5. Given a vector temps <- c(23, NA, 19, 25, NA, 21), how can you use logical indexing to select only the non-missing values?

hard

A. temps[is.na(temps)]

B. temps[!is.na(temps)]

C. temps[temps != NA]

D. temps[temps > NA]

Solution

1. Step 1: Understand how to detect missing values

   The function is.na(temps) returns TRUE for missing values (NA) and FALSE otherwise.

2. Step 2: Use logical NOT to select non-missing

   Using !is.na(temps) creates a logical vector TRUE where values are not missing.

3. Step 3: Index temps with this logical vector

   This selects only the non-NA values from temps.

4. Final Answer:

   temps[!is.na(temps)] -> Option B

5. Quick Check:

   Use !is.na() to filter out NA [OK]

Hint: Use !is.na() to pick non-missing values [OK]

Common Mistakes:

• Using temps != NA which is invalid
• Selecting is.na(temps) to get missing values
• Trying to compare with NA using > or <