Apply family vs loops in R Programming - Hands-On Comparison

Apply Family Functions vs Loops in R

📖 Scenario: You work in a small bakery that tracks daily sales of different bread types. You want to calculate the total sales for each bread type over a week.

🎯 Goal: Build a program that calculates total weekly sales for each bread type using both a for loop and the lapply function, then compare the results.

📋 What You'll Learn

Create a list called daily_sales with 3 named numeric vectors representing sales for 'Sourdough', 'Baguette', and 'Rye' over 7 days

Create a numeric vector called total_sales_loop to store total sales for each bread type using a for loop

Create a numeric vector called total_sales_apply to store total sales for each bread type using lapply

Print both total_sales_loop and total_sales_apply to compare results

💡 Why This Matters

🌍 Real World

Bakeries and small shops often track daily sales data and need to summarize totals quickly to understand performance.

💼 Career

Knowing how to use loops and apply functions in R is essential for data analysts and anyone working with data summaries in real-world business settings.

1

Create the daily sales data

Create a list called daily_sales with these exact entries: Sourdough as c(10, 12, 11, 9, 13, 10, 14), Baguette as c(8, 7, 9, 6, 7, 8, 7), and Rye as c(5, 6, 5, 7, 6, 5, 6).

R Programming

# Create the list daily_sales with sales for Sourdough, Baguette, and Rye
# Your code here

Hint

Use the list() function with named vectors for each bread type.

2

Set up a vector to store totals from the loop

Create a numeric vector called total_sales_loop with length 3 and names matching the names of daily_sales.

R Programming

daily_sales <- list(
  Sourdough = c(10, 12, 11, 9, 13, 10, 14),
  Baguette = c(8, 7, 9, 6, 7, 8, 7),
  Rye = c(5, 6, 5, 7, 6, 5, 6)
)
# Create total_sales_loop vector with length 3 and names from daily_sales
# Your code here

Hint

Use numeric() to create a vector of zeros and names() to assign names.

3

Calculate totals using a for loop and lapply

Use a for loop with variable bread to iterate over names(daily_sales) and sum each bread's sales into total_sales_loop[bread]. Then create total_sales_apply by applying sum to each element of daily_sales using lapply and unlist the result.

R Programming

daily_sales <- list(
  Sourdough = c(10, 12, 11, 9, 13, 10, 14),
  Baguette = c(8, 7, 9, 6, 7, 8, 7),
  Rye = c(5, 6, 5, 7, 6, 5, 6)
)
total_sales_loop <- numeric(length(daily_sales))
names(total_sales_loop) <- names(daily_sales)
# Use a for loop to sum sales into total_sales_loop
# Use lapply with sum on daily_sales and unlist to create total_sales_apply
# Your code here

Hint

Use for (bread in names(daily_sales)) and sum() inside the loop. Use lapply(daily_sales, sum) and unlist() for the apply method.

4

Print the total sales vectors

Print total_sales_loop and total_sales_apply to display the total weekly sales for each bread type.

R Programming

daily_sales <- list(
  Sourdough = c(10, 12, 11, 9, 13, 10, 14),
  Baguette = c(8, 7, 9, 6, 7, 8, 7),
  Rye = c(5, 6, 5, 7, 6, 5, 6)
)
total_sales_loop <- numeric(length(daily_sales))
names(total_sales_loop) <- names(daily_sales)
for (bread in names(daily_sales)) {
  total_sales_loop[bread] <- sum(daily_sales[[bread]])
}
total_sales_apply <- unlist(lapply(daily_sales, sum))
# Print total_sales_loop and total_sales_apply
# Your code here

Hint

Use print(total_sales_loop) and print(total_sales_apply) to show the results.

Practice

1. Which of the following is a key advantage of using the apply family of functions over traditional loops in R?

easy

A. They make code shorter and easier to read

B. They always run faster than loops

C. They require less memory than loops

D. They can only be used with numeric data

Solution

1. Step 1: Understand the purpose of apply functions

   Apply functions are designed to perform repetitive tasks on data structures more concisely than loops.

2. Step 2: Compare advantages

   While apply functions often improve readability and reduce code length, they do not always guarantee faster execution or less memory use, and they work with various data types.

3. Final Answer:

   They make code shorter and easier to read -> Option A

4. Quick Check:

   Apply functions improve code clarity [OK]

Hint: Apply functions simplify repetitive tasks in fewer lines [OK]

Common Mistakes:

• Assuming apply functions always run faster
• Thinking apply functions only work with numbers
• Believing apply functions use less memory always

2. Which of the following is the correct syntax to use lapply on a list named my_list applying the function sqrt?

easy

A. lapply(my_list, sqrt)

B. lapply(my_list; sqrt)

C. lapply(my_list sqrt)

D. lapply(sqrt, my_list)

Solution

1. Step 1: Recall lapply syntax

   The correct syntax is lapply(X, FUN) where X is the list and FUN is the function to apply.

2. Step 2: Match syntax to options

   lapply(my_list, sqrt) correctly places the list first and the function second, separated by a comma.

3. Final Answer:

   lapply(my_list, sqrt) -> Option A

4. Quick Check:

   lapply(list, function) syntax [OK]

Hint: Remember lapply(list, function) order [OK]

Common Mistakes:

• Swapping the order of arguments
• Missing commas between arguments
• Using incorrect separators like semicolons

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

mat <- matrix(1:6, nrow=2)
result <- apply(mat, 2, sum)
print(result)

medium

A. [1] 4 6 8

B. [1] 5 7 9

C. [1] 3 7 11

D. [1] 2 4 6

Solution

1. Step 1: Understand the matrix and apply parameters

   The matrix has 2 rows and 3 columns: columns are (1,3,5) and (2,4,6) if filled by column, but since nrow=2, the matrix is filled column-wise as: first column (1,2), second (3,4), third (5,6). Using apply with margin 2 applies the function to columns.

2. Step 2: Calculate column sums

   Sum of columns: 1+2=3, 3+4=7, 5+6=11.

3. Final Answer:

   [1] 3 7 11 -> Option C

4. Quick Check:

   apply with margin 2 sums columns [OK]

Hint: Margin 2 means apply function to columns [OK]

Common Mistakes:

• Confusing margin 1 (rows) with margin 2 (columns)
• Adding rows instead of columns
• Misreading matrix layout

4. Consider this R code snippet:

vec <- 1:5
result <- sapply(vec, function(x) x^2)
print(result)

What is the error in this code?

medium

A. Function syntax is incorrect

B. Missing parentheses after function

C. sapply cannot be used on vectors

D. No error; code runs correctly

Solution

1. Step 1: Check sapply usage on vector

   sapply works on vectors and applies the function to each element.

2. Step 2: Verify function syntax and parentheses

   The anonymous function syntax is correct and parentheses are properly placed.

3. Final Answer:

   No error; code runs correctly -> Option D

4. Quick Check:

   sapply with anonymous function on vector is valid [OK]

Hint: Anonymous functions need parentheses and braces [OK]

Common Mistakes:

• Thinking sapply can't handle vectors
• Misreading function syntax
• Assuming missing parentheses

5. You have a list of numeric vectors: my_list <- list(a = 1:3, b = 4:6, c = 7:9). You want to create a named vector with the mean of each element but only include those means greater than 5. Which R code correctly does this using apply family functions?

hard

A. means <- lapply(my_list, mean); means[means > 5]

B. means <- sapply(my_list, mean); means[means > 5]

C. means <- apply(my_list, 1, mean); means[means > 5]

D. means <- sapply(my_list, mean > 5)

Solution

1. Step 1: Choose correct apply function for list to vector

   sapply returns a vector with names, suitable for filtering.

2. Step 2: Calculate means and filter

   Calculate means with sapply, then subset with condition means > 5.

3. Final Answer:

   means <- sapply(my_list, mean); means[means > 5] -> Option B

4. Quick Check:

   sapply returns named vector, filter with logical index [OK]

Hint: Use sapply for named vector, then filter by condition [OK]

Common Mistakes:

• Using lapply returns list, not vector for filtering
• apply does not work on lists directly
• Passing condition inside mean function