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

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

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

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

Hint

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

Practice

1. What is the main purpose of using parameterized reports in R Markdown?

easy

A. To speed up the knitting process

B. To customize report content without changing the code

C. To automatically generate plots

D. To create interactive dashboards

Solution

1. Step 1: Understand parameterized reports concept

   Parameterized reports allow passing different inputs to the same report code.

2. Step 2: Identify the main benefit

   This lets you customize the report output without editing the code itself.

3. Final Answer:

   To customize report content without changing the code -> Option B

4. Quick Check:

   Parameterized reports = customize content without code change [OK]

Hint: Think: change inputs, not code, to customize reports [OK]

Common Mistakes:

• Confusing parameterized reports with speeding up knitting
• Thinking it automatically creates plots
• Mixing with dashboard creation

2. Which YAML header syntax correctly defines a parameter named year with default value 2023?

easy

A. params:
year: "2023"

B. parameters:
year = 2023

C. parameters:
year: 2023

D. params:
year: 2023

Solution

1. Step 1: Recall YAML parameter syntax in R Markdown

   Parameters are defined under params: with key-value pairs.

2. Step 2: Check correct key and value format

   params: year: 2023 uses params: and year: 2023 correctly without quotes needed for numbers.

3. Final Answer:

   params: year: 2023 -> Option D

4. Quick Check:

   YAML params use 'params:' and colon syntax [OK]

Hint: Use 'params:' and colon, no equals sign in YAML [OK]

Common Mistakes:

• Using 'parameters:' instead of 'params:'
• Using equals sign '=' instead of colon ':'
• Putting numbers in quotes unnecessarily

3. Given this R Markdown snippet, what will be the output when params$region is set to "West"?

```{r}
if (params$region == "West") {
  print("Sales are high in the West")
} else {
  print("Sales data not available")
}
```

medium

A. [1] "Sales are high in the West"

B. [1] "Sales data not available"

C. Error: object 'params' not found

D. [1] "Sales are high in the East"

Solution

1. Step 1: Check the condition with given parameter

   The condition checks if params$region equals "West". It is set to "West".

2. Step 2: Determine which print statement runs

   Since condition is true, it prints "Sales are high in the West".

3. Final Answer:

   [1] "Sales are high in the West" -> Option A

4. Quick Check:

   params$region == "West" -> print message for West [OK]

Hint: Match param value exactly to choose output branch [OK]

Common Mistakes:

• Confusing the else branch output
• Assuming params is undefined causing error
• Mixing region names in output

4. Identify the error in this R Markdown YAML header for parameterized report:

---
title: "Sales Report"
params:
  month = "January"
output: html_document
---

medium

A. Incorrect output format specified

B. Missing quotes around 'January'

C. Using '=' instead of ':' to assign parameter value

D. Missing 'params' section

Solution

1. Step 1: Review YAML parameter syntax

   Parameters use colon ':' to assign values, not equals '='.

2. Step 2: Identify the syntax error

   Here, month = "January" uses '=' which is invalid YAML syntax.

3. Final Answer:

   Using '=' instead of ':' to assign parameter value -> Option C

4. Quick Check:

   YAML params need ':' not '=' [OK]

Hint: YAML uses colon ':' for key-value pairs, never '=' [OK]

Common Mistakes:

• Using '=' instead of ':' in YAML
• Thinking quotes are mandatory for strings
• Confusing output format with params syntax

5. You want to create a parameterized report that filters a dataset by category and year. Which approach correctly uses parameters in the R code chunk to filter df?

hard

A. filtered_df <- subset(df, category == params$category & year == params$year)

B. filtered_df <- df[df$category = params$category & df$year = params$year, ]

C. filtered_df <- filter(df, category = params$category, year = params$year)

D. filtered_df <- df[category == params$category & year == params$year]

Solution

1. Step 1: Understand filtering syntax in R

   The subset() function uses logical conditions with '==' to filter rows.

2. Step 2: Check parameter usage in filtering

   filtered_df <- subset(df, category == params$category & year == params$year) correctly uses params$category and params$year with '==' inside subset().

3. Final Answer:

   filtered_df <- subset(df, category == params$category & year == params$year) -> Option A

4. Quick Check:

   Use '==' for comparison and subset() for filtering [OK]

Hint: Use subset() with '==' and params$ for filtering [OK]

Common Mistakes:

• Using '=' instead of '==' for comparison
• Incorrect subsetting syntax without dataframe reference
• Confusing filter() syntax without library