Logical indexing in R Programming - Mini Project: Build & Apply

Logical indexing

📖 Scenario: You are working with a list of daily temperatures recorded in a city. You want to find which days were warm, meaning the temperature was above 20 degrees Celsius.

🎯 Goal: Build a program that uses logical indexing to select only the warm days from the temperature data.

📋 What You'll Learn

Create a numeric vector called temps with the exact values: 18, 22, 19, 25, 21, 17, 23

Create a logical vector called warm_days that is TRUE where temps is greater than 20

Use logical indexing with warm_days to create a vector called warm_temps containing only temperatures above 20

Print the warm_temps vector

💡 Why This Matters

🌍 Real World

Logical indexing is useful when you want to filter data based on conditions, like selecting only warm days from weather data.

💼 Career

Data analysts and scientists often use logical indexing to quickly extract relevant data for reports and decision making.

1

Create the temperature data vector

Create a numeric vector called temps with these exact values: 18, 22, 19, 25, 21, 17, 23

R Programming

# Create the temps vector with the given values
# Your code here

Hint

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

2

Create a logical vector for warm days

Create a logical vector called warm_days that is TRUE where temps is greater than 20

R Programming

temps  20
# Your code here

Hint

Use the comparison operator > to create a logical vector.

3

Select warm temperatures using logical indexing

Use logical indexing with warm_days to create a vector called warm_temps containing only temperatures above 20

R Programming

temps  20
# Create warm_temps vector by selecting temps where warm_days is TRUE
# Your code here

Hint

Use square brackets [] with the logical vector to select elements.

4

Print the warm temperatures

Write a print statement to display the warm_temps vector

R Programming

temps  20
warm_temps <- temps[warm_days]
# Print warm_temps vector
# Your code here

Hint

Use the print() function to show the vector.

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 <