Why reproducible reports matter in R Programming - See It in Action

Why reproducible reports matter

📖 Scenario: You work as a data analyst in a company. Your manager asks you to create a report that shows sales data summaries. Later, your manager wants to update the report with new data and check the results quickly. To do this well, you need to make your report reproducible.

🎯 Goal: Build a simple R script that loads sales data, calculates total sales, and prints the result. This script should be easy to run again with new data, showing why reproducible reports matter.

📋 What You'll Learn

Create a data frame called sales_data with exact sales values

Create a variable called threshold to filter sales

Use a for loop with variables product and amount to sum sales above the threshold

Print the total sales amount

💡 Why This Matters

🌍 Real World

Reproducible reports let analysts update data and results quickly without errors or confusion.

💼 Career

Data analysts and scientists use reproducible reports to share clear, trustworthy results with teams and managers.

1

DATA SETUP: Create the sales data frame

Create a data frame called sales_data with these exact entries: product as 'A', 'B', 'C', 'D' and amount as 150, 80, 200, 50.

R Programming

# Create the sales_data data frame with product and amount columns
# Your code here

Hint

Use data.frame() with two columns named product and amount.

2

CONFIGURATION: Set the sales threshold

Create a variable called threshold and set it to 100.

R Programming

sales_data <- data.frame(
  product = c('A', 'B', 'C', 'D'),
  amount = c(150, 80, 200, 50)
)
# Create the threshold variable and set it to 100
# Your code here

Hint

Just assign the number 100 to a variable named threshold.

3

CORE LOGIC: Sum sales above the threshold

Create a variable called total_sales and set it to 0. Then use a for loop with variables product and amount to iterate over sales_data rows. Inside the loop, add amount to total_sales only if amount is greater than threshold.

R Programming

sales_data <- data.frame(
  product = c('A', 'B', 'C', 'D'),
  amount = c(150, 80, 200, 50)
)
threshold <- 100

# Initialize total_sales and sum amounts above threshold using a for loop
# Your code here

Hint

Use a for loop from 1 to the number of rows in sales_data. Extract product and amount inside the loop. Add amount to total_sales only if it is greater than threshold.

4

OUTPUT: Print the total sales

Write a print statement to display the text Total sales above threshold: followed by the value of total_sales.

R Programming

sales_data  threshold) {
    total_sales <- total_sales + amount
  }
}

# Print the total sales with a message
# Your code here

Hint

Use print(paste("Total sales above threshold:", total_sales)) to show the message and value.

Practice

1. Why is it important to create reproducible reports in R using tools like R Markdown?

easy

A. They hide the code so others cannot see it.

B. They make the code run faster on any computer.

C. They automatically fix errors in your code.

D. They combine code and output in one document, making work clear and easy to update.

Solution

1. Step 1: Understand the purpose of reproducible reports

   Reproducible reports combine code and results in one place, so others can see exactly how results were made.

2. Step 2: Identify benefits of using R Markdown

   R Markdown allows mixing code and output, making reports clear and easy to update when data or code changes.

3. Final Answer:

   They combine code and output in one document, making work clear and easy to update. -> Option D

4. Quick Check:

   Reproducible reports = combine code and output [OK]

Hint: Remember: reproducible means code + results together [OK]

Common Mistakes:

• Thinking reproducible reports speed up code execution
• Believing they hide code from others
• Assuming they fix code errors automatically

2. Which of the following is the correct way to start an R Markdown document header?

easy

A. # My Report
This is an R script

B. <html>

My Report

C. ---
title: "My Report"
output: html_document
---

D. print("My Report")

Solution

1. Step 1: Recognize R Markdown header syntax

   R Markdown documents start with a YAML header enclosed by triple dashes (---) specifying title and output format.

2. Step 2: Compare options to R Markdown syntax

   --- title: "My Report" output: html_document --- uses the correct YAML header format; others are HTML, plain R script, or incorrect.

3. Final Answer:

   --- title: "My Report" output: html_document --- -> Option C

4. Quick Check:

   R Markdown header = YAML between --- [OK]

Hint: YAML header in R Markdown starts and ends with --- [OK]

Common Mistakes:

• Confusing R Markdown with HTML syntax
• Using plain R script instead of markdown
• Missing the triple dashes around header

3. Given this R Markdown code chunk:

```{r}
x <- 5
y <- 10
x + y
```

What will be the output shown in the report?

medium

A. x + y

B. [1] 15

C. Error: object 'x' not found

D. NULL

Solution

1. Step 1: Understand code chunk execution in R Markdown

   The code inside the chunk runs as normal R code, so variables x and y are assigned 5 and 10.

2. Step 2: Calculate the expression result

   The last line computes x + y = 5 + 10 = 15, which is printed as [1] 15 in R output.

3. Final Answer:

   [1] 15 -> Option B

4. Quick Check:

   5 + 10 = 15 output [OK]

Hint: Last expression in chunk prints result in output [OK]

Common Mistakes:

• Thinking code is shown as text, not executed
• Expecting error because variables are defined
• Confusing output with code itself

4. You wrote this R Markdown chunk:

```{r}
summary(data)
```

But when knitting the report, you get an error: object 'data' not found. What is the likely cause?

medium

A. The variable 'data' was not created or loaded before this chunk runs.

B. The summary() function is not available in R.

C. R Markdown does not support the summary() function.

D. The chunk header is missing the 'echo=TRUE' option.

Solution

1. Step 1: Understand error meaning

   The error 'object not found' means the variable 'data' does not exist in the current R session when the chunk runs.

2. Step 2: Identify cause in R Markdown workflow

   If 'data' was not created or loaded in a previous chunk or script, the summary() call fails.

3. Final Answer:

   The variable 'data' was not created or loaded before this chunk runs. -> Option A

4. Quick Check:

   Missing variable causes 'object not found' error [OK]

Hint: Load or create variables before using them in chunks [OK]

Common Mistakes:

• Thinking summary() is unavailable in R
• Believing chunk options cause variable errors
• Assuming R Markdown disables functions

5. You want to create a reproducible report that shows a plot and its summary statistics for a dataset. Which approach best ensures your report stays reproducible and easy to update?

hard

A. Include code chunks that load the data, create the plot, and calculate summaries all inside the R Markdown document.

B. Manually copy the plot image and summary numbers into the report without code.

C. Write the code in a separate R script and paste the output as text in the report.

D. Use screenshots of the plot and type summary results by hand.

Solution

1. Step 1: Understand reproducibility requirements

   Reproducible reports must include all code to generate results so anyone can rerun and update them.

2. Step 2: Identify best practice for report creation

   Including data loading, plotting, and summary code inside R Markdown ensures the report updates automatically when data changes.

3. Final Answer:

   Include code chunks that load the data, create the plot, and calculate summaries all inside the R Markdown document. -> Option A

4. Quick Check:

   Code inside report = reproducible and updateable [OK]

Hint: Put all code inside R Markdown for easy updates [OK]

Common Mistakes:

• Copying outputs manually breaks reproducibility
• Separating code and output causes confusion
• Using screenshots loses dynamic updating ability