3 R Exercises
4 R Exercises
This chapter demonstrates R exercises with cross-origin isolation enabled (coi: true in the YAML header). webR requires SharedArrayBuffer, which needs COOP/COEP headers. The blendtutor filter injects the coi-serviceworker.js shim when COI is activated.
4.1 Exercise 1: Add function
Write a function add(a, b) that returns the sum of two numbers.
Exercise 1
Write a function add(a, b) that returns the sum.
add <- function(a, b) { ___ }Hints
Use the `<-` operator to assign the function body.4.2 Exercise 2: Greeting function
Write a function greet(name) that returns a greeting string.
Exercise 2
Write a function greet(name) that returns
"hello, <name>".
greet <- function(name) { ___ }4.3 Exercise 3: Clean and aggregate messy sales data
This exercise cleans a realistic, messy sales CSV and aggregates total revenue per category. Complete the six steps in the template below.
Exercise 3
A realistic sales export is often messy. The CSV below has 24 rows with inconsistent category capitalization, dollar signs and stray spaces in prices, “unknown” prices, blank quantities, and a few exact duplicate rows. Clean it and compute total revenue per category in six steps.
Step 1 inspects the raw data. Step 2 cleans the price column by removing dollar signs and spaces, then converting it to numeric, so that “unknown” prices become missing values. Step 3 normalizes the category column by trimming whitespace, lowercasing, and mapping the truncated alias “Elec” to “electronics”. Step 4 drops every row with a missing value — drop the rows, do NOT impute missing values. Step 5 drops exact duplicate rows, keeping only the first occurrence of each identical row. Step 6 computes each row’s revenue as price times quantity and aggregates total revenue per category.
Print the result after every step with a labeled print such as print(“Step 2: cleaned price”). The final aggregate must contain exactly three normalized categories: books, clothing, and electronics.
sales <- read.csv(text = "order_id,product,category,price,quantity
1,Widget,Electronics,$12.50,3
2,Gadget,electronics,$8.00,2
3,Book A,Books,$15.00,1
4,Shirt,Clothing,$20.00,2
1,Widget,Electronics,$12.50,3
5,Gadget,ELECTRONICS ,unknown,2
6,Book B,books,$15.00,
7,Pants,Clothing ,$25.00,1
8,Widget,Elec,$12.50,3
2,Gadget,electronics,$8.00,2
9,Book C,Books,15.00,2
10,Shirt,clothing,$20.00,2
11,Hat,Clothing,$10.00,
12,Gadget,electronics,$8.00,1
13,Book D,books,$18.00,2
14,Widget,Electronics,$ 5.00,4
15,Pants,clothing ,$25.00,1
16,Book E,Books,$15.00,1
17,Gadget,ELECTRONICS,$8.00,2
18,Widget,electronics,$12.50,3
19,Shirt,Clothing,$20.00,2
3,Book A,Books,$15.00,1
20,Book F,books,unknown,1
21,Hat,clothing,$10.00,2", stringsAsFactors = FALSE)
print("Step 1: raw data")
print(sales)
# Step 2: clean the price column
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# Step 3: normalize the category column
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# Step 4: drop rows with missing values
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# Step 5: drop exact duplicate rows
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# Step 6: compute revenue and aggregate per category
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