Descriptive statistics in R Programming - Mini Project: Build & Apply

Descriptive statistics

📖 Scenario: You work as a data analyst for a small company. You have collected sales data for a week and want to understand the basic statistics like average sales, minimum, maximum, and total sales.

🎯 Goal: Build a simple R script that calculates descriptive statistics (mean, min, max, sum) for daily sales data.

📋 What You'll Learn

Create a numeric vector with exact daily sales values

Create a variable to hold the number of days

Calculate mean, minimum, maximum, and total sales using built-in R functions

Print the results clearly

💡 Why This Matters

🌍 Real World

Descriptive statistics help summarize and understand data quickly in many fields like business, healthcare, and research.

💼 Career

Data analysts and scientists use these basic statistics daily to report insights and make decisions.

1

Create the sales data vector

Create a numeric vector called daily_sales with these exact values: 150, 200, 170, 220, 180, 210, 190.

R Programming

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

Hint

Use the c() function to create a vector with the given numbers.

2

Create a variable for the number of days

Create a variable called num_days that stores the length of the daily_sales vector using the length() function.

R Programming

daily_sales <- c(150, 200, 170, 220, 180, 210, 190)
# Create num_days using length() on daily_sales
# Your code here

Hint

Use length(daily_sales) to find how many days of sales data you have.

3

Calculate descriptive statistics

Calculate the mean, minimum, maximum, and total sales from daily_sales and store them in variables called avg_sales, min_sales, max_sales, and total_sales respectively. Use the functions mean(), min(), max(), and sum().

R Programming

daily_sales <- c(150, 200, 170, 220, 180, 210, 190)
num_days <- length(daily_sales)
# Calculate avg_sales, min_sales, max_sales, total_sales
# Your code here

Hint

Use the built-in R functions to calculate each statistic and assign them to the correct variable names.

4

Print the descriptive statistics

Print the values of avg_sales, min_sales, max_sales, and total_sales each on a new line with descriptive text using the print() function.

R Programming

daily_sales <- c(150, 200, 170, 220, 180, 210, 190)
num_days <- length(daily_sales)
avg_sales <- mean(daily_sales)
min_sales <- min(daily_sales)
max_sales <- max(daily_sales)
total_sales <- sum(daily_sales)
# Print all four statistics with descriptive text
# Your code here

Hint

Use print(paste("Text", variable)) to show descriptive text and the value on the same line.

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