Why R is essential for statistics in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a vector of numbers in R.

R Programming

numbers <- c([1])

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

A1, 2, 3, 4, 5

B1 2 3 4 5

C[1, 2, 3, 4, 5]

D(1, 2, 3, 4, 5)

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting commas between numbers

Using brackets or parentheses instead of commas

In R, the c() function combines values into a vector. Values must be separated by commas.

2fill in blank

medium

Complete the code to calculate the mean of a numeric vector in R.

R Programming

mean_value <- mean([1])

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

Adata

Bnumbers

Cvector

Dmean

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the function name instead of the vector

Using an undefined variable

The mean() function calculates the average of the values in the vector named numbers.

3fill in blank

hard

Fix the error in the code to create a data frame with columns 'age' and 'score'.

R Programming

df <- data.frame(age = c(25, 30, 22), score = [1](80, 90, 85))

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

Ac

Bvector

Cdata

Dlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using list() instead of c()

Using undefined functions

The c() function combines values into a vector, which is needed for the 'score' column.

4fill in blank

hard

Fill both blanks to create a subset of 'df' where age is greater than 23.

R Programming

subset_df <- df[df$[1] > [2], ]

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

Aage

B23

Cscore

D25

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong column name

Using a wrong comparison value

We select rows where the 'age' column is greater than 23 using df$age > 23.

5fill in blank

hard

Fill all three blanks to calculate the correlation between 'age' and 'score' in 'df'.

R Programming

correlation <- cor(df$[1], df$[2], method = [3])

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

Aage

Bscore

C"pearson"

D"spearman"

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping column names

Using method names without quotes

Using the wrong method

The cor() function calculates correlation between two vectors. We use 'age' and 'score' columns and the 'pearson' method.

Practice

1. Why is R considered essential for statistics?

easy

A. Because it is mainly used for web development

B. Because it has built-in functions for statistical analysis

C. Because it is a hardware device

D. Because it only works with text data

Solution

1. Step 1: Understand R's purpose

   R is designed to handle data and perform statistical calculations easily.

2. Step 2: Compare options

   Only Because it has built-in functions for statistical analysis correctly states R's strength in statistics; others are unrelated.

3. Final Answer:

   Because it has built-in functions for statistical analysis -> Option B

4. Quick Check:

   R is for statistics = A [OK]

Hint: R is made for stats and data analysis [OK]

Common Mistakes:

• Thinking R is for web development
• Confusing R with hardware
• Assuming R only handles text

2. Which of the following is the correct way to create a numeric vector in R?

easy

A. numVector = {1, 2, 3, 4}

B. vector = [1, 2, 3, 4]

C. c(1, 2, 3, 4)

D. numericVector = (1, 2, 3, 4)

Solution

1. Step 1: Recall R vector syntax

   In R, vectors are created using the c() function to combine elements.

2. Step 2: Check each option

   Only c(1, 2, 3, 4) uses c() correctly; others use invalid syntax for R.

3. Final Answer:

   c(1, 2, 3, 4) -> Option C

4. Quick Check:

   Use c() for vectors = C [OK]

Hint: Use c() to combine elements into a vector [OK]

Common Mistakes:

• Using square brackets like Python
• Using curly braces for vectors
• Using parentheses without c()

3. What will be the output of this R code?

data <- c(5, 10, 15, 20)
mean(data)

medium

A. 10

B. Error

C. 15

D. 12.5

Solution

1. Step 1: Understand the data vector

   The vector contains values 5, 10, 15, and 20.

2. Step 2: Calculate the mean

   Mean = (5 + 10 + 15 + 20) / 4 = 50 / 4 = 12.5

3. Final Answer:

   12.5 -> Option D

4. Quick Check:

   Mean of data = 12.5 [OK]

Hint: Add numbers and divide by count for mean [OK]

Common Mistakes:

• Adding incorrectly
• Dividing by wrong number
• Expecting median instead of mean

4. Identify the error in this R code snippet:

numbers <- c(2, 4, 6, 8)
average <- mean(numbers
print(average)

medium

A. Missing closing parenthesis in mean() function

B. Incorrect variable name 'numbers'

C. Using print() instead of cat()

D. Vector c() is not defined properly

Solution

1. Step 1: Check syntax of mean() call

   The mean function call is missing a closing parenthesis.

2. Step 2: Verify other parts

   Variable name and vector syntax are correct; print() is valid in R.

3. Final Answer:

   Missing closing parenthesis in mean() function -> Option A

4. Quick Check:

   Parenthesis error = B [OK]

Hint: Count parentheses carefully in function calls [OK]

Common Mistakes:

• Ignoring missing parenthesis errors
• Confusing print() with cat()
• Assuming variable name is wrong

5. You have a dataset with missing values. Which R function helps you quickly find the number of missing values in a vector data_vec?

hard

A. sum(is.na(data_vec))

B. mean(data_vec)

C. length(data_vec)

D. na.omit(data_vec)

Solution

1. Step 1: Understand missing value detection

   The function is.na() returns TRUE for missing values.

2. Step 2: Count missing values

   Using sum() on is.na() counts how many TRUE values exist.

3. Step 3: Check other options

   mean() calculates average, length() counts all elements, na.omit() removes missing values but does not count them.

4. Final Answer:

   sum(is.na(data_vec)) -> Option A

5. Quick Check:

   Count missing with sum(is.na()) = A [OK]

Hint: Use sum(is.na()) to count missing values [OK]

Common Mistakes:

• Using mean() to find missing values
• Using length() which counts all elements
• Thinking na.omit() counts missing values