Descriptive statistics in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to calculate the mean of the numeric vector data.

R Programming

mean_value <- mean([1])

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

Amean

Bdata

Csum

Dmedian

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the function name inside itself instead of the data vector.

Using sum or median instead of the data vector.

The mean() function calculates the average of the values in the vector. We pass the vector data as the argument.

2fill in blank

medium

Complete the code to calculate the median of the numeric vector values.

R Programming

median_value <- [1](values)

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

Amedian

Bmean

Csum

Dmin

Attempts:

3 left

💡 Hint

Common Mistakes

Using mean instead of median.

Using sum or min which do not calculate the median.

The median() function returns the middle value of the sorted vector, which is the median.

3fill in blank

hard

Fix the error in the code to calculate the standard deviation of scores.

R Programming

std_dev <- sd([1])

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

Ascore_vector

Bscore

Cscore_list

Dscores

Attempts:

3 left

💡 Hint

Common Mistakes

Using a variable name that does not exist like score.

Misspelling the variable name.

The variable holding the data is named scores. Using the correct variable name avoids errors.

4fill in blank

hard

Fill both blanks to create a named list with mean and median of data_vec.

R Programming

stats <- list(mean = [1](data_vec), median = [2](data_vec))

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

Amean

Bmedian

Csum

Dsd

Attempts:

3 left

💡 Hint

Common Mistakes

Using sum or sd instead of mean or median.

Mixing the order of mean and median.

The list contains the mean and median calculated by their respective functions on data_vec.

5fill in blank

hard

Fill all three blanks to create a named list with mean, median, and standard deviation of nums.

R Programming

summary_stats <- list([1] = [2](nums), median = [3](nums), sd = sd(nums))

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

Amean

Cmedian

Dsd

Attempts:

3 left

💡 Hint

Common Mistakes

Using median as the key instead of mean.

Mixing up the order of keys and functions.

The list keys and functions are: mean (key and function), median (key and function), and sd (key and function).

Practice

1. Which of the following best describes the mean in descriptive statistics?

easy

A. The average value of a numeric dataset

B. The middle value when data is sorted

C. The most frequent value in the dataset

D. The difference between the largest and smallest values

Solution

1. Step 1: Understand the definition of mean

   The mean is calculated by adding all numbers and dividing by the count.

2. Step 2: Compare with other options

   Median is the middle value, mode is most frequent, range is difference between max and min.

3. Final Answer:

   The average value of a numeric dataset -> Option A

4. Quick Check:

   Mean = Average [OK]

Hint: Mean is sum divided by count, simple average [OK]

Common Mistakes:

• Confusing mean with median
• Thinking mean is the most frequent value
• Mixing mean with range

2. Which R function gives a quick summary including min, 1st Qu., median, mean, 3rd Qu., and max for numeric data?

easy

A. mean()

B. var()

C. sd()

D. summary()

Solution

1. Step 1: Recall function purposes

   mean() calculates average, var() variance, sd() standard deviation.

2. Step 2: Identify summary function

   summary() provides min, quartiles, median, mean, and max in one output.

3. Final Answer:

   summary() -> Option D

4. Quick Check:

   summary() = quick overview [OK]

Hint: summary() shows min, quartiles, median, mean, max [OK]

Common Mistakes:

• Using mean() expecting full summary
• Confusing sd() with summary()
• Trying var() for descriptive summary

3. What is the output of this R code?

data <- c(4, 8, 6, 5, 3, 7)
mean(data)

medium

A. 5.5

B. 6

C. 4.5

D. 5

Solution

1. Step 1: Sum the data values

   4 + 8 + 6 + 5 + 3 + 7 = 33

2. Step 2: Divide sum by number of values

   There are 6 values, so 33 / 6 = 5.5

3. Final Answer:

   5.5 -> Option A

4. Quick Check:

   Mean = 33/6 = 5.5 [OK]

Hint: Add all numbers, divide by count [OK]

Common Mistakes:

• Adding incorrectly
• Dividing by wrong count
• Confusing mean with median

4. Identify the error in this R code for calculating variance:

data <- c(2, 4, 6, 8)
var_data <- var(data, na.rm=TRUE)
print(var_data)

medium

A. Missing parentheses in var() function

B. No error, code runs correctly

C. na.rm argument is invalid for var()

D. Variable data is not defined

Solution

1. Step 1: Check variable definition

   data is defined as a numeric vector correctly.

2. Step 2: Verify var() usage

   var() accepts na.rm=TRUE to remove missing values; syntax is correct.

3. Final Answer:

   No error, code runs correctly -> Option B

4. Quick Check:

   var() with na.rm=TRUE is valid [OK]

Hint: var() accepts na.rm=TRUE to ignore NAs [OK]

Common Mistakes:

• Thinking na.rm is invalid for var()
• Assuming missing parentheses error
• Not defining data before use

5. You have a numeric vector v <- c(10, 20, 30, NA, 40, 50). Which R code correctly calculates the mean ignoring the missing value?

hard

A. mean(na.exclude(v))

B. mean(v, na.rm=TRUE)

C. Both B and C

D. mean(v)

Solution

1. Step 1: Understand missing value handling

   By default, mean() returns NA if vector has NA values.

2. Step 2: Check options to ignore NA

   mean(v, na.rm=TRUE) removes NA before calculation. mean(na.exclude(v)) also removes NA values before calculation, so it returns the mean ignoring NA as well. mean(v) returns NA.

3. Final Answer:

   Both mean(v, na.rm=TRUE) and mean(na.exclude(v)) -> Option C

4. Quick Check:

   na.rm=TRUE and na.exclude() both remove NA for mean calculation [OK]

Hint: Use na.rm=TRUE or na.exclude() to ignore NA [OK]

Common Mistakes:

• Using mean() without na.rm=TRUE returns NA
• Not removing NA before mean calculation
• Confusing na.rm with na.exclude