Parameterized reports in R Programming - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a parameterized report in R?

A parameterized report is a report that takes input values (parameters) to customize the output dynamically based on user input or other variables.

beginner

How do you pass parameters to an R Markdown report?

You define parameters in the YAML header using the 'params' field, then access them in the report using 'params$parameter_name'.

beginner

Example YAML header snippet to define a parameter named 'region' with default value 'North'.

params: region: "North"

intermediate

How can you render an R Markdown report with a specific parameter value from R code?

Use the rmarkdown::render() function with the 'params' argument, e.g., rmarkdown::render('report.Rmd', params = list(region = 'South')).

beginner

Why are parameterized reports useful?

They allow creating flexible reports that adapt to different inputs without rewriting code, saving time and reducing errors.

Where do you define parameters in an R Markdown report?

A Inside the R code chunks

B In the global environment only

C In the output format section

D In the YAML header under 'params'

Parameters are defined in the YAML header under the 'params' field.

How do you access a parameter named 'year' inside an R Markdown document?

A params$year

B year

C input$year

D param.year

Parameters are accessed using 'params$parameter_name', so 'params$year' is correct.

Which function is used to render an R Markdown report with parameters from R code?

A knitr::knit()

B rmarkdown::render()

C shiny::runApp()

D library()

rmarkdown::render() is used to generate reports and can accept parameters.

What is the benefit of using parameterized reports?

A They allow dynamic report customization without changing code

B They make reports run faster

C They automatically fix errors

D They remove the need for R code

Parameterized reports let you customize output dynamically without rewriting code.

If you want to change the default value of a parameter, where do you do it?

A In the output format section

B Inside the R code chunk

C In the YAML header under 'params'

D In the console only

Default parameter values are set in the YAML header under 'params'.

Explain how to create and use a parameter in an R Markdown report.

Think about where parameters live and how you call them inside the report.

Describe the steps to render an R Markdown report with a custom parameter value from R code.

Focus on the function and how parameters are passed.

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