Parameterized reports in R Programming - Mini Project: Build & Apply

Parameterized Reports in R

📖 Scenario: You work in a small bookstore. You want to create a report that shows sales data for a specific month. Instead of making a new report every time, you will make a parameterized report that can show data for any month you choose.

🎯 Goal: Build a simple R script that uses a parameter (month) to filter sales data and then prints the total sales for that month.

📋 What You'll Learn

Create a data frame called sales_data with columns month and sales and exact values.

Create a variable called selected_month to hold the month to filter.

Use a filter to select rows from sales_data where month matches selected_month.

Calculate the total sales for the selected month and store it in total_sales.

Print the total sales with a clear message.

💡 Why This Matters

🌍 Real World

Stores and businesses often need reports filtered by dates or categories. Parameterized reports save time and avoid repeating work.

💼 Career

Data analysts and business intelligence professionals use parameterized reports to quickly generate insights for different time periods or segments.

1

Create the sales data frame

Create a data frame called sales_data with two columns: month and sales. Use these exact values: months are "January", "February", "March", "April", and sales are 150, 200, 170, 220 respectively.

R Programming

# Your code here

Need a hint?

Use data.frame() with two vectors named month and sales.

2

Set the selected month parameter

Create a variable called selected_month and set it to the string "March".

R Programming

sales_data <- data.frame(
  month = c("January", "February", "March", "April"),
  sales = c(150, 200, 170, 220)
)
# Your code here

Need a hint?

Assign the string "March" to selected_month.

3

Filter data and calculate total sales

Create a variable called filtered_data that contains only rows from sales_data where month equals selected_month. Then create a variable called total_sales that sums the sales column of filtered_data.

R Programming

sales_data <- data.frame(
  month = c("January", "February", "March", "April"),
  sales = c(150, 200, 170, 220)
)
selected_month <- "March"
# Your code here

Need a hint?

Use sales_data[sales_data$month == selected_month, ] to filter rows and sum() to add sales.

4

Print the total sales

Write a print() statement that outputs the text: Total sales in March: 170 using the variable total_sales and selected_month.

R Programming

sales_data <- data.frame(
  month = c("January", "February", "March", "April"),
  sales = c(150, 200, 170, 220)
)
selected_month <- "March"
filtered_data <- sales_data[sales_data$month == selected_month, ]
total_sales <- sum(filtered_data$sales)
# Your code here

Need a hint?

Use paste() inside print() to combine text and variables.