Useful vector functions (length, sum, mean) in R Programming - Mini Project: Build & Apply

Useful vector functions (length, sum, mean)

📖 Scenario: You are working with a list of daily sales numbers for a small shop. You want to learn how to use basic R functions to find out how many days you have data for, the total sales, and the average sales per day.

🎯 Goal: Build a simple R script that uses the length(), sum(), and mean() functions on a vector of sales numbers to get useful information.

📋 What You'll Learn

Create a numeric vector called sales with exact values: 150, 200, 175, 300, 225

Create a variable called total_days that stores the length of the sales vector using length()

Create a variable called total_sales that stores the sum of the sales vector using sum()

Create a variable called average_sales that stores the mean of the sales vector using mean()

Print the values of total_days, total_sales, and average_sales each on its own line

💡 Why This Matters

🌍 Real World

Small business owners often track daily sales to understand their performance. Using R functions like length, sum, and mean helps quickly summarize sales data.

💼 Career

Data analysts and business intelligence professionals use these basic vector functions to prepare and analyze data before creating reports or visualizations.

1

Create the sales vector

Create a numeric vector called sales with these exact values: 150, 200, 175, 300, 225

R Programming

# Create the sales vector with the exact values
# Your code here

Hint

Use the c() function to combine numbers into a vector.

2

Calculate total days

Create a variable called total_days that stores the length of the sales vector using the length() function

R Programming

sales <- c(150, 200, 175, 300, 225)
# Calculate the total number of days using length()
# Your code here

Hint

The length() function tells you how many elements are in a vector.

3

Calculate total and average sales

Create a variable called total_sales that stores the sum of the sales vector using sum(), and a variable called average_sales that stores the mean of the sales vector using mean()

R Programming

sales <- c(150, 200, 175, 300, 225)
total_days <- length(sales)
# Calculate total_sales using sum()
# Calculate average_sales using mean()
# Your code here

Hint

Use sum() to add all numbers and mean() to find the average.

4

Print the results

Print the values of total_days, total_sales, and average_sales each on its own line using three separate print() statements

R Programming

sales <- c(150, 200, 175, 300, 225)
total_days <- length(sales)
total_sales <- sum(sales)
average_sales <- mean(sales)
# Print total_days
# Print total_sales
# Print average_sales
# Your code here

Hint

Use print() to show each value on its own line.

Practice

1. What does the length() function do when applied to a vector in R?

easy

A. It calculates the sum of all elements in the vector.

B. It sorts the elements in the vector.

C. It finds the average value of the elements in the vector.

D. It returns the number of elements in the vector.

Solution

1. Step 1: Understand the purpose of length()

   The length() function counts how many items are inside a vector.

2. Step 2: Compare with other functions

   Unlike sum() or mean(), it does not add or average values, just counts them.

3. Final Answer:

   It returns the number of elements in the vector. -> Option D

4. Quick Check:

   length() = count elements [OK]

Hint: Remember length() counts items, sum() adds, mean() averages [OK]

Common Mistakes:

• Confusing length() with sum() or mean()
• Thinking length() returns the last element
• Assuming length() sorts the vector

2. Which of the following is the correct syntax to calculate the mean of a vector v in R?

easy

A. mean(v)

B. mean = v

C. mean[v]

D. mean v()

Solution

1. Step 1: Recall function call syntax in R

   Functions are called with parentheses and the vector inside, like mean(v).

2. Step 2: Check each option

   mean(v) uses correct syntax. Options B, C, and D are invalid or incorrect syntax.

3. Final Answer:

   mean(v) -> Option A

4. Quick Check:

   Function call uses parentheses: mean(v) [OK]

Hint: Use parentheses to call functions with arguments in R [OK]

Common Mistakes:

• Using square brackets instead of parentheses
• Assigning mean instead of calling it
• Omitting parentheses after function name

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

v <- c(2, 4, 6, 8)
sum(v) + length(v)

medium

A. 20

B. 22

C. 24

D. 26

Solution

1. Step 1: Calculate sum(v)

   The vector v is c(2,4,6,8). Sum is 2+4+6+8 = 20.

2. Step 2: Calculate length(v) and add

   Length is number of elements = 4. Adding sum + length = 20 + 4 = 24.

3. Final Answer:

   24 -> Option C

4. Quick Check:

   sum=20 + length=4 = 24 [OK]

Hint: Add sum and length values separately, then combine [OK]

Common Mistakes:

• Adding elements incorrectly
• Confusing length with sum
• Forgetting to add length to sum

4. The following R code is intended to calculate the mean of vector x. What is wrong?

x <- c(5, 10, 15)
mean = mean(x)
print(mean)

medium

A. The print() function cannot print variables.

B. The variable name 'mean' overwrites the function mean(), causing errors later.

C. The vector x is not defined correctly.

D. mean() function requires two arguments.

Solution

1. Step 1: Analyze variable assignment

   Assigning mean = mean(x) overwrites the built-in mean() function with a number.

2. Step 2: Understand consequences

   After this, calling mean() again will cause errors because mean is no longer a function.

3. Final Answer:

   The variable name 'mean' overwrites the function mean(), causing errors later. -> Option B

4. Quick Check:

   Don't name variables same as functions [OK]

Hint: Avoid naming variables same as function names [OK]

Common Mistakes:

• Thinking print() cannot print variables
• Believing mean() needs two arguments
• Ignoring variable shadowing of functions

5. You have a numeric vector nums with some missing values (NA). Which R code correctly calculates the mean ignoring the missing values?

hard

A. mean(nums, na.rm = TRUE)

B. mean(nums, remove.na = TRUE)

C. sum(nums) / length(nums)

D. mean(nums, na.ignore = TRUE)

Solution

1. Step 1: Understand NA handling in mean()

   To ignore missing values, use the argument na.rm = TRUE inside mean().

2. Step 2: Check each option

   mean(nums, na.rm = TRUE) uses correct argument na.rm. Options B and D use incorrect argument names. sum(nums) / length(nums) does not handle NA and will return NA.

3. Final Answer:

   mean(nums, na.rm = TRUE) -> Option A

4. Quick Check:

   Use na.rm=TRUE to ignore NA in mean() [OK]

Hint: Use na.rm=TRUE to skip NA values in mean() [OK]

Common Mistakes:

• Using wrong argument names like remove.na or na.ignore
• Calculating mean manually without handling NA
• Ignoring that NA causes mean() to return NA