Why reproducible reports matter in R Programming - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the knitr package for reproducible reports.

R Programming

library([1])

Drag options to blanks, or click blank then click option'

Aknitr

Bdplyr

Cggplot2

Dshiny

Attempts:

3 left

💡 Hint

Common Mistakes

Loading unrelated packages like ggplot2 or shiny instead of knitr.

The knitr package is used to create reproducible reports in R.

2fill in blank

medium

Complete the code to create a reproducible report using R Markdown.

R Programming

rmarkdown::render('[1].Rmd')

Drag options to blanks, or click blank then click option'

Aanalysis

Bscript

Cdata

Dreport

Attempts:

3 left

💡 Hint

Common Mistakes

Using file names without the '.Rmd' extension or unrelated names.

The file name for the R Markdown report is usually descriptive, here 'report.Rmd'.

3fill in blank

hard

Fix the error in the code to include code chunks in R Markdown.

R Programming

```{r [1]
summary(cars)
```

Drag options to blanks, or click blank then click option'

Aecho=FALSE

Beval=TRUE

Csetup

Dinclude=TRUE

Attempts:

3 left

💡 Hint

Common Mistakes

Using chunk options instead of a chunk label in the curly braces.

The chunk label 'setup' is used to name the code chunk in R Markdown.

4fill in blank

hard

Fill both blanks to create a reproducible report that includes a plot and hides code.

R Programming

```{r [1], [2]=FALSE}
plot(cars)
```

Drag options to blanks, or click blank then click option'

Aplot_chunk

Becho

Ceval

Dmessage

Attempts:

3 left

💡 Hint

Common Mistakes

Using options as chunk names or forgetting to hide code properly.

The chunk is named 'plot_chunk' and the code is hidden by setting echo=FALSE.

5fill in blank

hard

Fill all three blanks to create a reproducible report that filters data, summarizes it, and prints the result.

R Programming

library(dplyr)
result <- mtcars %>% filter([1] > 20) %>% summarize([2] = mean([3]))
print(result)

Drag options to blanks, or click blank then click option'

Ahp

Bavg_mpg

Cmpg

Dcyl

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up column names or summary names.

The code filters cars with more than 20 horsepower, then calculates the average mpg and stores it as avg_mpg.

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