Why advanced features enable complex work in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a vector of numbers from 1 to 5.

R Programming

numbers <- [1](1, 5)

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

Arep

Bseq

Cc

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using c creates a vector but does not generate a sequence automatically.

Using rep repeats values instead of creating a sequence.

The seq function generates a sequence of numbers from the first to the second argument.

2fill in blank

medium

Complete the code to calculate the mean of the vector 'values'.

R Programming

average <- [1](values)

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

Amean

Bsum

Cmedian

Dlength

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum returns the total, not the average.

Using length returns the count of elements, not the average.

The mean function calculates the average value of a numeric vector.

3fill in blank

hard

Fix the error in the code to subset the vector 'data' for values greater than 10.

R Programming

subset <- data[data [1] 10]

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

A>

B<=

C==

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using == selects only values exactly equal to 10.

Using <= or < selects smaller or equal values.

The operator > selects elements greater than 10 from the vector.

4fill in blank

hard

Fill both blanks to create a named list with elements 'a' and 'b'.

R Programming

my_list <- list([1] = 1, [2] = 2)

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

Aa

B1

Cb

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using numbers as names instead of letters.

Confusing values with names.

In R, you name list elements by using name = value. Here, 'a' and 'b' are the names.

5fill in blank

hard

Fill all three blanks to create a data frame with columns 'name' and 'age' and filter rows where age is greater than 20.

R Programming

df <- data.frame([1] = c("Alice", "Bob"), [2] = c(25, 19))
adults <- df[df$[3] > 20, ]

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

Aname

Bage

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up column names and values.

Using the wrong column name in the filter condition.

The data frame columns are named 'name' and 'age'. To filter rows where age is greater than 20, use df$age > 20.

Practice

1. Why do advanced features in R help programmers handle complex tasks more easily?

easy

A. They only work with simple data types like numbers.

B. They allow writing shorter and clearer code to solve bigger problems.

C. They remove the need to learn basic programming concepts.

D. They make the computer run slower but with more details.

Solution

1. Step 1: Understand the role of advanced features

   Advanced features help by simplifying complex tasks into manageable code blocks.

2. Step 2: Connect code clarity with problem solving

   Shorter and clearer code means programmers can solve bigger problems more easily and with less chance of errors.

3. Final Answer:

   They allow writing shorter and clearer code to solve bigger problems. -> Option B

4. Quick Check:

   Shorter clearer code solves bigger problems [OK]

Hint: Advanced features simplify big problems with less code [OK]

Common Mistakes:

• Thinking advanced features slow down the computer
• Believing basics are not needed anymore
• Assuming advanced features only work with simple data

2. Which of the following is the correct syntax to create a named list in R using advanced features?

easy

A. my_list <- c(name = "Alice", age = 30)

B. my_list = {name: "Alice", age: 30}

C. my_list <- ["name" = "Alice", "age" = 30]

D. my_list <- list(name = "Alice", age = 30)

Solution

1. Step 1: Recall R syntax for named lists

   In R, named lists are created with the list() function and named elements using =.

2. Step 2: Check each option's syntax

   my_list <- list(name = "Alice", age = 30) uses list() with correct naming; others use invalid syntax for R.

3. Final Answer:

   my_list <- list(name = "Alice", age = 30) -> Option D

4. Quick Check:

   list() with name = value creates named lists [OK]

Hint: Use list() with name = value for named lists in R [OK]

Common Mistakes:

• Using curly braces {} instead of list()
• Using c() which creates vectors, not lists
• Using square brackets [] which are invalid here

3. What is the output of this R code using an advanced feature (list comprehension with lapply)?

nums <- 1:3
squares <- lapply(nums, function(x) x^2)
unlist(squares)

medium

A. [1] 1 2 3

B. [1] 1 8 27

C. [1] 1 4 9

D. Error: object 'squares' not found

Solution

1. Step 1: Understand lapply with function(x) x^2

   lapply applies the function to each element: 1^2=1, 2^2=4, 3^2=9, returning a list.

2. Step 2: Use unlist to convert list to vector

   unlist(squares) converts the list of squared numbers to a numeric vector: 1, 4, 9.

3. Final Answer:

   [1] 1 4 9 -> Option C

4. Quick Check:

   Squares of 1,2,3 = 1,4,9 [OK]

Hint: lapply + unlist returns vector of function results [OK]

Common Mistakes:

• Confusing lapply with sapply output
• Expecting original numbers instead of squares
• Missing unlist to flatten the list

4. Identify the error in this R code using advanced features and fix it:

data <- list(a = 1, b = 2, c = 3)
result <- sapply(data, function(x) x + 1
print(result)

medium

A. Missing closing parenthesis in sapply function call

B. Using sapply instead of lapply causes error

C. data should be a vector, not a list

D. print() cannot display lists

Solution

1. Step 1: Check syntax of sapply call

   The function call to sapply is missing a closing parenthesis after the anonymous function.

2. Step 2: Fix the missing parenthesis

   Adding the missing ')' after function(x) x + 1 fixes the syntax error and allows print(result) to work.

3. Final Answer:

   Missing closing parenthesis in sapply function call -> Option A

4. Quick Check:

   Syntax errors often come from missing parentheses [OK]

Hint: Count parentheses carefully in function calls [OK]

Common Mistakes:

• Assuming sapply causes error instead of syntax
• Thinking data type is wrong
• Believing print() can't show lists

5. You want to create a named vector in R where names are letters and values are their ASCII codes, but only for vowels. Which advanced feature helps you do this efficiently?

letters <- c('a','b','c','d','e','f','g','i','o','u')
# Fill in the blank to create named vector of vowels with ASCII codes
vowels_ascii <- ???

hard

A. setNames(as.integer(charToRaw(letters)), letters)[letters %in% c('a','e','i','o','u')]

B. names(letters) <- as.integer(charToRaw(letters)); letters

C. letters[letters %in% c('a','e','i','o','u')] <- as.integer(charToRaw(letters))

D. as.integer(charToRaw(letters[letters %in% c('a','e','i','o','u')]))

Solution

1. Step 1: Convert letters to ASCII codes

   charToRaw converts characters to raw bytes; as.integer converts raw to ASCII numbers.

2. Step 2: Use setNames to assign names and filter vowels

   setNames assigns letter names to ASCII codes, then indexing with %in% selects vowels only.

3. Final Answer:

   setNames(as.integer(charToRaw(letters)), letters)[letters %in% c('a','e','i','o','u')] -> Option A

4. Quick Check:

   setNames + filtering vowels [OK]

Hint: Use setNames() with filtering for named vectors [OK]

Common Mistakes:

• Assigning names incorrectly
• Replacing letters vector values instead of creating new
• Not filtering vowels before conversion