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R Programmingprogramming~5 mins

Logical indexing in R Programming - Cheat Sheet & Quick Revision

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beginner
What is logical indexing in R?
Logical indexing is a way to select elements from a vector or other data structure using a logical vector of TRUE and FALSE values. Only elements corresponding to TRUE are selected.
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beginner
How does logical indexing differ from numeric indexing in R?
Numeric indexing uses numbers to select elements by position, while logical indexing uses TRUE/FALSE values to select elements based on a condition.
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beginner
Example: What does x[c(TRUE, FALSE, TRUE)] return if x = c(10, 20, 30)?
It returns c(10, 30) because the first and third positions are TRUE, so those elements are selected.
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intermediate
How can logical indexing be used with conditions?
You can create a logical vector by testing a condition on elements, like x > 5, and use it to select only elements that meet the condition.
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intermediate
What happens if the logical vector is shorter than the vector being indexed?
The logical vector is recycled (repeated) to match the length of the vector being indexed.
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What does logical indexing select in R?
AElements at numeric positions
BElements where the logical vector is FALSE
CElements where the logical vector is TRUE
DAll elements
If x = c(5, 10, 15), what does x[x > 7] return?
Ac(10, 15)
Bc(TRUE, TRUE, TRUE)
Cc(5)
Dc(5, 10, 15)
What happens if the logical vector is shorter than the vector being indexed?
AThe logical vector is recycled to match the length
BAn error occurs
COnly the first elements are selected
DNo elements are selected
Which of these is a valid logical index for x = c(1, 2, 3, 4)?
Ac(1, 3)
Bc(TRUE, FALSE, TRUE, FALSE)
Cc('a', 'b')
Dc(0, 1, 0, 1)
How can you select all elements of x that are equal to 10 using logical indexing?
Ax[c(10)]
Bx[10]
Cx == 10
Dx[x == 10]
Explain how logical indexing works in R and give a simple example.
Think about how TRUE and FALSE values pick elements from a vector.
You got /3 concepts.
    Describe what happens when the logical vector used for indexing is shorter than the vector being indexed.
    Consider how R handles vectors of different lengths.
    You got /3 concepts.

      Practice

      (1/5)
      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 <
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