Parameterized reports in R Programming - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a parameterized report that accepts a year input.

R Programming

params <- list(year = [1])
report <- render_report('sales_report.Rmd', params = params)

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

A'2023'

Byear

C2023

Dinput$year

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the year as a number without quotes causes errors.

Using variable names instead of actual values.

The year parameter should be passed as a string, so it needs quotes.

2fill in blank

medium

Complete the code to filter data by the parameter 'region' inside the report.

R Programming

filtered_data <- subset(data, region == [1])

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

A'region'

Bparams$region

Cregion

D'params$region'

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around params$region treats it as a string literal.

Using the column name 'region' instead of the parameter value.

Use params$region to access the parameter value inside the report.

3fill in blank

hard

Fix the error in the code to correctly pass multiple parameters to the report.

R Programming

params <- list(year = [1], region = 'North')

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

Ayear

B2022

Cparams$year

D'2022'

Attempts:

3 left

💡 Hint

Common Mistakes

Passing year as a number without quotes.

Using variable names instead of actual values.

Parameter values must be strings, so the year should be in quotes.

4fill in blank

hard

Fill both blanks to create a parameterized filter for sales data.

R Programming

filtered_sales <- subset(sales, [1] == [2])

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

Aregion

Bparams$region

C'region'

D'params$region'

Attempts:

3 left

💡 Hint

Common Mistakes

Putting quotes around the parameter variable.

Using the parameter name as a string instead of the column name.

The first blank is the column name region, the second is the parameter value params$region.

5fill in blank

hard

Fill all three blanks to create a parameterized summary report filtering by year and product.

R Programming

summary_data <- subset(data, [1] == [2] & [3] == params$product)

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

Ayear

Bparams$year

Cproduct

D'year'

Attempts:

3 left

💡 Hint

Common Mistakes

Using quotes around parameter variables.

Mixing up column names and parameter names.

The first blank is the column year, the second is the parameter params$year, and the third is the column product.

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