    library(parsermd)
    library(stringr)

## Introduction

A common workflow in educational settings involves creating homework assignments that contain both student scaffolding and instructor solutions within the same document. This vignette demonstrates how to use `parsermd` to process such documents and automatically generate separate versions for students and instructors.

The typical workflow involves:

1. Primary Document: A single Qmd/Rmd file containing both student prompts and complete solutions
2. Student Version: Contains only student chunks with scaffolding and instructions
3. Instructor Key: Contains only solution chunks with complete answers
4. Minimalist Key: A streamlined version with solutions only (no instructional text)

## Sample Assignment Structure

Let’s start by examining a sample homework assignment that follows this pattern. The assignment includes multiple exercises, each with two code chunks:

- Student chunk: labeled with `-student` suffix, contains scaffolding code
- Solution chunk: labeled with `-key` suffix, contains complete solutions

    # Load the sample assignment
    assignment_path = system.file("examples/hw03-full.qmd", package = "parsermd")
    cat(readLines(assignment_path), sep = "\n")

    #> ---
    #> title: "Homework 3 - Data Analysis with R"
    #> author: "Your Name"
    #> date: "Due: Friday, March 15, 2024"
    #> format: html
    #> execute:
    #>   warning: false
    #>   message: false
    #> ---
    #>
    #> ## Setup
    #>
    #> Load the required packages for this assignment:
    #>
    #> ```{r setup}
    #> library(tidyverse)
    #> library(palmerpenguins)
    #> ```
    #>
    #> ## Exercise 1: Basic Data Exploration
    #>
    #> Examine the `penguins` dataset from the `palmerpenguins` package. Your task is to create a summary of the dataset that shows the number of observations and variables, and identify any missing values.
    #>
    #> ```{r ex1-student}
    #> # Write your code here to:
    #> # 1. Display the dimensions of the penguins dataset
    #> # 2. Show the structure of the dataset
    #> # 3. Count missing values in each column
    #>
    #> ```
    #>
    #> ```{r ex1-key}
    #> # Solution: Basic data exploration
    #> # 1. Display dimensions
    #> cat("Dataset dimensions:", dim(penguins), "\n")
    #> cat("Rows:", nrow(penguins), "Columns:", ncol(penguins), "\n\n")
    #>
    #> # 2. Show structure
    #> str(penguins)
    #>
    #> # 3. Count missing values
    #> cat("\nMissing values by column:\n")
    #> penguins %>%
    #>   summarise(across(everything(), ~ sum(is.na(.))))
    #> ```
    #>
    #> ## Exercise 2: Data Visualization
    #>
    #> Create a scatter plot showing the relationship between flipper length and body mass for penguins. Color the points by species and add appropriate labels and a title.
    #>
    #> ```{r ex2-student}
    #> # Create a scatter plot with:
    #> # - x-axis: flipper_length_mm
    #> # - y-axis: body_mass_g
    #> # - color by species
    #> # - add appropriate labels and title
    #>
    #> ggplot(data = penguins, aes(x = ___, y = ___)) +
    #>   geom_point(aes(color = ___)) +
    #>   labs(
    #>     title = "___",
    #>     x = "___",
    #>     y = "___"
    #>   )
    #> ```
    #>
    #> ```{r ex2-key}
    #> # Solution: Scatter plot of flipper length vs body mass
    #> ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
    #>   geom_point(aes(color = species), alpha = 0.8, size = 2) +
    #>   labs(
    #>     title = "Penguin Flipper Length vs Body Mass by Species",
    #>     x = "Flipper Length (mm)",
    #>     y = "Body Mass (g)",
    #>     color = "Species"
    #>   ) +
    #>   theme_minimal() +
    #>   scale_color_viridis_d()
    #> ```
    #>
    #> ## Exercise 3: Statistical Analysis
    #>
    #> Calculate summary statistics for bill length by species. Create a table showing the mean, median, standard deviation, and count for each species.
    #>
    #> ```{r ex3-student}
    #> # Calculate summary statistics for bill_length_mm by species
    #> # Include: mean, median, standard deviation, and count
    #> # Remove missing values before calculating
    #>
    #> penguins %>%
    #>   # Add your code here
    #>
    #> ```
    #>
    #> ```{r ex3-key}
    #> # Solution: Summary statistics for bill length by species
    #> penguins %>%
    #>   filter(!is.na(bill_length_mm)) %>%
    #>   group_by(species) %>%
    #>   summarise(
    #>     count = n(),
    #>     mean_bill_length = round(mean(bill_length_mm), 2),
    #>     median_bill_length = round(median(bill_length_mm), 2),
    #>     sd_bill_length = round(sd(bill_length_mm), 2),
    #>     .groups = "drop"
    #>   ) %>%
    #>   arrange(desc(mean_bill_length))
    #> ```
    #>
    #> ## Exercise 4: Advanced Data Manipulation
    #>
    #> Filter the dataset to include only penguins with complete data (no missing values), then create a new variable called `bill_ratio` that represents the ratio of bill length to bill depth. Finally, identify which species has the highest average bill ratio.
    #>
    #> ```{r ex4-student}
    #> # Step 1: Filter for complete cases
    #> # Step 2: Create bill_ratio variable (bill_length_mm / bill_depth_mm)
    #> # Step 3: Calculate average bill_ratio by species
    #> # Step 4: Identify species with highest average ratio
    #>
    #> ```
    #>
    #> ```{r ex4-key}
    #> # Solution: Advanced data manipulation
    #> complete_penguins = penguins %>%
    #>   # Remove rows with any missing values
    #>   filter(complete.cases(.)) %>%
    #>   # Create bill_ratio variable
    #>   mutate(bill_ratio = bill_length_mm / bill_depth_mm)
    #>
    #> # Calculate average bill ratio by species
    #> bill_ratio_summary = complete_penguins %>%
    #>   group_by(species) %>%
    #>   summarise(
    #>     avg_bill_ratio = round(mean(bill_ratio), 3),
    #>     n = n(),
    #>     .groups = "drop"
    #>   ) %>%
    #>   arrange(desc(avg_bill_ratio))
    #>
    #> print(bill_ratio_summary)
    #>
    #> # Identify species with highest average bill ratio
    #> highest_ratio_species = bill_ratio_summary %>%
    #>   slice_max(avg_bill_ratio, n = 1) %>%
    #>   pull(species)
    #>
    #> cat("\nSpecies with highest average bill ratio:", as.character(highest_ratio_species))
    #> ```
    #>
    #> ## Bonus Exercise: Conditional Logic
    #>
    #> Write a function that categorizes penguins as "small", "medium", or "large" based on their body mass. Use the following criteria:
    #> - Small: body mass < 3500g
    #> - Medium: body mass between 3500g and 4500g
    #> - Large: body mass > 4500g
    #>
    #> Apply this function to create a new column and create a summary table.
    #>
    #> ```{r bonus-student}
    #> # Create a function to categorize penguins by size
    #> categorize_size = function(mass) {
    #>   # Add your conditional logic here
    #>
    #> }
    #>
    #> # Apply the function and create summary
    #> ```
    #>
    #> ```{r bonus-key}
    #> # Solution: Conditional logic for size categorization
    #> categorize_size = function(mass) {
    #>   case_when(
    #>     is.na(mass) ~ "Unknown",
    #>     mass < 3500 ~ "Small",
    #>     mass >= 3500 & mass <= 4500 ~ "Medium",
    #>     mass > 4500 ~ "Large"
    #>   )
    #> }
    #>
    #> # Apply the function and create summary
    #> penguins_with_size = penguins %>%
    #>   mutate(size_category = categorize_size(body_mass_g))
    #>
    #> # Create summary table
    #> size_summary = penguins_with_size %>%
    #>   count(species, size_category) %>%
    #>   pivot_wider(names_from = size_category, values_from = n, values_fill = 0)
    #>
    #> print(size_summary)
    #>
    #> # Overall size distribution
    #> penguins_with_size %>%
    #>   count(size_category) %>%
    #>   mutate(percentage = round(n / sum(n) * 100, 1))
    #> ```

## Parsing the Document

First, let’s parse the assignment document to understand its structure:

    # Parse the assignment
    rmd = parse_rmd(assignment_path)

    # Display the document structure
    print(rmd)
    #> ├── YAML [5 fields]
    #> ├── Heading [h2] - Setup
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 2 lines] - setup
    #> ├── Heading [h2] - Exercise 1: Basic Data Exploration
    #> │   ├── Markdown [1 line]
    #> │   ├── Chunk [r, 5 lines] - ex1-student
    #> │   └── Chunk [r, 12 lines] - ex1-key
    #> ├── Heading [h2] - Exercise 2: Data Visualization
    #> │   ├── Markdown [1 line]
    #> │   ├── Chunk [r, 13 lines] - ex2-student
    #> │   └── Chunk [r, 11 lines] - ex2-key
    #> ├── Heading [h2] - Exercise 3: Statistical Analysis
    #> │   ├── Markdown [1 line]
    #> │   ├── Chunk [r, 7 lines] - ex3-student
    #> │   └── Chunk [r, 12 lines] - ex3-key
    #> ├── Heading [h2] - Exercise 4: Advanced Data Manipulation
    #> │   ├── Markdown [1 line]
    #> │   ├── Chunk [r, 5 lines] - ex4-student
    #> │   └── Chunk [r, 25 lines] - ex4-key
    #> └── Heading [h2] - Bonus Exercise: Conditional Logic
    #>     ├── Markdown [6 lines]
    #>     ├── Chunk [r, 7 lines] - bonus-student
    #>     └── Chunk [r, 25 lines] - bonus-key

We can also examine the document as a tibble to better understand the chunk labels and structure:

    # Convert to tibble for easier inspection
    as_tibble(rmd)
    #> # A tibble: 24 × 4
    #>    sec_h2                             type         label       ast
    #>    <chr>                              <chr>        <chr>       <list>
    #>  1 <NA>                               rmd_yaml     <NA>        <yaml>
    #>  2 Setup                              rmd_heading  <NA>        <heading [h2]>
    #>  3 Setup                              rmd_markdown <NA>        <markdown>
    #>  4 Setup                              rmd_chunk    setup       <chunk [r]>
    #>  5 Exercise 1: Basic Data Exploration rmd_heading  <NA>        <heading [h2]>
    #>  6 Exercise 1: Basic Data Exploration rmd_markdown <NA>        <markdown>
    #>  7 Exercise 1: Basic Data Exploration rmd_chunk    ex1-student <chunk [r]>
    #>  8 Exercise 1: Basic Data Exploration rmd_chunk    ex1-key     <chunk [r]>
    #>  9 Exercise 2: Data Visualization     rmd_heading  <NA>        <heading [h2]>
    #> 10 Exercise 2: Data Visualization     rmd_markdown <NA>        <markdown>
    #> # ℹ 14 more rows

## Creating the Student Version

To create the student version, we need to:

1. Keep all markdown content (instructions, problem statements)
2. Keep only the student chunks (those with `-student` suffix)
3. Remove all solution chunks

    # Select student chunks and all non-chunk content
    student_version = rmd |>
      rmd_select(
        # Easier to specify the nodes we want to remove
        !has_label("*-key")
      )

    # Display the student version structure
    student_version
    #> ├── YAML [5 fields]
    #> ├── Heading [h2] - Setup
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 2 lines] - setup
    #> ├── Heading [h2] - Exercise 1: Basic Data Exploration
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 5 lines] - ex1-student
    #> ├── Heading [h2] - Exercise 2: Data Visualization
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 13 lines] - ex2-student
    #> ├── Heading [h2] - Exercise 3: Statistical Analysis
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 7 lines] - ex3-student
    #> ├── Heading [h2] - Exercise 4: Advanced Data Manipulation
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 5 lines] - ex4-student
    #> └── Heading [h2] - Bonus Exercise: Conditional Logic
    #>     ├── Markdown [6 lines]
    #>     └── Chunk [r, 7 lines] - bonus-student

If we don’t want to let the student on to the fact that the chunks are just for them we can use `rmd_modify()` to remove the `-student` suffix:

    student_version = student_version |>
      rmd_modify(
        function(node) {
          rmd_node_label(node) = stringr::str_remove(rmd_node_label(node), "-student")
          node
        },
        has_label("*-student")
      )

    # Show the first few chunks to see the label changes
    student_version
    #> ├── YAML [5 fields]
    #> ├── Heading [h2] - Setup
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 2 lines] - setup
    #> ├── Heading [h2] - Exercise 1: Basic Data Exploration
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 5 lines] - ex1
    #> ├── Heading [h2] - Exercise 2: Data Visualization
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 13 lines] - ex2
    #> ├── Heading [h2] - Exercise 3: Statistical Analysis
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 7 lines] - ex3
    #> ├── Heading [h2] - Exercise 4: Advanced Data Manipulation
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 5 lines] - ex4
    #> └── Heading [h2] - Bonus Exercise: Conditional Logic
    #>     ├── Markdown [6 lines]
    #>     └── Chunk [r, 7 lines] - bonus

Let’s see what the student version looks like as a document:

    # Convert to document and display first few sections
    as_document(student_version) |>
      cat(sep = "\n")

    #> ---
    #> title: Homework 3 - Data Analysis with R
    #> author: Your Name
    #> date: 'Due: Friday, March 15, 2024'
    #> format: html
    #> execute:
    #>   warning: false
    #>   message: false
    #> ---
    #>
    #> ## Setup
    #>
    #> Load the required packages for this assignment:
    #>
    #>
    #> ```{r setup}
    #> library(tidyverse)
    #> library(palmerpenguins)
    #> ```
    #>
    #> ## Exercise 1: Basic Data Exploration
    #>
    #> Examine the `penguins` dataset from the `palmerpenguins` package. Your task is to create a summary of the dataset that shows the number of observations and variables, and identify any missing values.
    #>
    #>
    #> ```{r ex1}
    #> # Write your code here to:
    #> # 1. Display the dimensions of the penguins dataset
    #> # 2. Show the structure of the dataset
    #> # 3. Count missing values in each column
    #>
    #> ```
    #>
    #> ## Exercise 2: Data Visualization
    #>
    #> Create a scatter plot showing the relationship between flipper length and body mass for penguins. Color the points by species and add appropriate labels and a title.
    #>
    #>
    #> ```{r ex2}
    #> # Create a scatter plot with:
    #> # - x-axis: flipper_length_mm
    #> # - y-axis: body_mass_g
    #> # - color by species
    #> # - add appropriate labels and title
    #>
    #> ggplot(data = penguins, aes(x = ___, y = ___)) +
    #>   geom_point(aes(color = ___)) +
    #>   labs(
    #>     title = "___",
    #>     x = "___",
    #>     y = "___"
    #>   )
    #> ```
    #>
    #> ## Exercise 3: Statistical Analysis
    #>
    #> Calculate summary statistics for bill length by species. Create a table showing the mean, median, standard deviation, and count for each species.
    #>
    #>
    #> ```{r ex3}
    #> # Calculate summary statistics for bill_length_mm by species
    #> # Include: mean, median, standard deviation, and count
    #> # Remove missing values before calculating
    #>
    #> penguins %>%
    #>   # Add your code here
    #>
    #> ```
    #>
    #> ## Exercise 4: Advanced Data Manipulation
    #>
    #> Filter the dataset to include only penguins with complete data (no missing values), then create a new variable called `bill_ratio` that represents the ratio of bill length to bill depth. Finally, identify which species has the highest average bill ratio.
    #>
    #>
    #> ```{r ex4}
    #> # Step 1: Filter for complete cases
    #> # Step 2: Create bill_ratio variable (bill_length_mm / bill_depth_mm)
    #> # Step 3: Calculate average bill_ratio by species
    #> # Step 4: Identify species with highest average ratio
    #>
    #> ```
    #>
    #> ## Bonus Exercise: Conditional Logic
    #>
    #> Write a function that categorizes penguins as "small", "medium", or "large" based on their body mass. Use the following criteria:
    #> - Small: body mass < 3500g
    #> - Medium: body mass between 3500g and 4500g
    #> - Large: body mass > 4500g
    #>
    #> Apply this function to create a new column and create a summary table.
    #>
    #>
    #> ```{r bonus}
    #> # Create a function to categorize penguins by size
    #> categorize_size = function(mass) {
    #>   # Add your conditional logic here
    #>
    #> }
    #>
    #> # Apply the function and create summary
    #> ```

We can also save this to a file:

    # Save student version (not run in vignette)
    as_document(student_version) |>
      writeLines("homework-student.qmd")

## Creating the Instructor Key

For the instructor key, we want to:

1. Keep all markdown content for context
2. Keep only the solution chunks (those with `-key` suffix)
3. Remove all student chunks

    # Select solution chunks and all non-chunk content
    instructor_key = rmd |>
      rmd_select(
        # Again this is easier to specify the nodes we want to remove
        !has_label("*-student")
      )

    # Display the instructor key structure
    instructor_key
    #> ├── YAML [5 fields]
    #> ├── Heading [h2] - Setup
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 2 lines] - setup
    #> ├── Heading [h2] - Exercise 1: Basic Data Exploration
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 12 lines] - ex1-key
    #> ├── Heading [h2] - Exercise 2: Data Visualization
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 11 lines] - ex2-key
    #> ├── Heading [h2] - Exercise 3: Statistical Analysis
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 12 lines] - ex3-key
    #> ├── Heading [h2] - Exercise 4: Advanced Data Manipulation
    #> │   ├── Markdown [1 line]
    #> │   └── Chunk [r, 25 lines] - ex4-key
    #> └── Heading [h2] - Bonus Exercise: Conditional Logic
    #>     ├── Markdown [6 lines]
    #>     └── Chunk [r, 25 lines] - bonus-key

Let’s examine the instructor key document:

    # Convert to document
    instructor_doc = as_document(instructor_key)

    # Display first part of the document
    cat(head(strsplit(instructor_doc, "\n")[[1]], 50), sep = "\n")
    #> ---

## Creating a Minimalist Key

Sometimes instructors may want a very streamlined version that contains only the solution code without all the instructional text.

We can create this by:

1. Keeping only exercise headings and solution chunks
2. Removing all markdown instructions
3. Setting `#| include: false` for the `setup` chunk

    # Select only headings and solution chunks
    minimalist_key = rmd |>
      rmd_select(
        # Keep yaml and exercise headings for structure
        has_type("rmd_yaml"),
        has_heading(c("Exercise *", "Bonus*")),
        # Keep only solution chunks
        has_label(c("*-key", "setup"))
      ) |>
      rmd_modify(
        function(node) {
          rmd_node_options(node) = list(include = FALSE)
          node
        },
        has_label("setup")
      )

    # Display the minimalist key structure
    minimalist_key
    #> ├── YAML [5 fields]
    #> ├── Chunk [r, 2 lines] - setup
    #> ├── Heading [h2] - Exercise 1: Basic Data Exploration
    #> │   └── Chunk [r, 12 lines] - ex1-key
    #> ├── Heading [h2] - Exercise 2: Data Visualization
    #> │   └── Chunk [r, 11 lines] - ex2-key
    #> ├── Heading [h2] - Exercise 3: Statistical Analysis
    #> │   └── Chunk [r, 12 lines] - ex3-key
    #> ├── Heading [h2] - Exercise 4: Advanced Data Manipulation
    #> │   └── Chunk [r, 25 lines] - ex4-key
    #> └── Heading [h2] - Bonus Exercise: Conditional Logic
    #>     └── Chunk [r, 25 lines] - bonus-key

    # Convert to document
    minimalist_doc = as_document(minimalist_key)
    cat(minimalist_doc, sep = "\n")

    #> ---
    #> title: Homework 3 - Data Analysis with R
    #> author: Your Name
    #> date: 'Due: Friday, March 15, 2024'
    #> format: html
    #> execute:
    #>   warning: false
    #>   message: false
    #> ---
    #>
    #> ```{r setup}
    #> #| include: false
    #> library(tidyverse)
    #> library(palmerpenguins)
    #> ```
    #>
    #> ## Exercise 1: Basic Data Exploration
    #>
    #> ```{r ex1-key}
    #> # Solution: Basic data exploration
    #> # 1. Display dimensions
    #> cat("Dataset dimensions:", dim(penguins), "\n")
    #> cat("Rows:", nrow(penguins), "Columns:", ncol(penguins), "\n\n")
    #>
    #> # 2. Show structure
    #> str(penguins)
    #>
    #> # 3. Count missing values
    #> cat("\nMissing values by column:\n")
    #> penguins %>%
    #>   summarise(across(everything(), ~ sum(is.na(.))))
    #> ```
    #>
    #> ## Exercise 2: Data Visualization
    #>
    #> ```{r ex2-key}
    #> # Solution: Scatter plot of flipper length vs body mass
    #> ggplot(data = penguins, aes(x = flipper_length_mm, y = body_mass_g)) +
    #>   geom_point(aes(color = species), alpha = 0.8, size = 2) +
    #>   labs(
    #>     title = "Penguin Flipper Length vs Body Mass by Species",
    #>     x = "Flipper Length (mm)",
    #>     y = "Body Mass (g)",
    #>     color = "Species"
    #>   ) +
    #>   theme_minimal() +
    #>   scale_color_viridis_d()
    #> ```
    #>
    #> ## Exercise 3: Statistical Analysis
    #>
    #> ```{r ex3-key}
    #> # Solution: Summary statistics for bill length by species
    #> penguins %>%
    #>   filter(!is.na(bill_length_mm)) %>%
    #>   group_by(species) %>%
    #>   summarise(
    #>     count = n(),
    #>     mean_bill_length = round(mean(bill_length_mm), 2),
    #>     median_bill_length = round(median(bill_length_mm), 2),
    #>     sd_bill_length = round(sd(bill_length_mm), 2),
    #>     .groups = "drop"
    #>   ) %>%
    #>   arrange(desc(mean_bill_length))
    #> ```
    #>
    #> ## Exercise 4: Advanced Data Manipulation
    #>
    #> ```{r ex4-key}
    #> # Solution: Advanced data manipulation
    #> complete_penguins = penguins %>%
    #>   # Remove rows with any missing values
    #>   filter(complete.cases(.)) %>%
    #>   # Create bill_ratio variable
    #>   mutate(bill_ratio = bill_length_mm / bill_depth_mm)
    #>
    #> # Calculate average bill ratio by species
    #> bill_ratio_summary = complete_penguins %>%
    #>   group_by(species) %>%
    #>   summarise(
    #>     avg_bill_ratio = round(mean(bill_ratio), 3),
    #>     n = n(),
    #>     .groups = "drop"
    #>   ) %>%
    #>   arrange(desc(avg_bill_ratio))
    #>
    #> print(bill_ratio_summary)
    #>
    #> # Identify species with highest average bill ratio
    #> highest_ratio_species = bill_ratio_summary %>%
    #>   slice_max(avg_bill_ratio, n = 1) %>%
    #>   pull(species)
    #>
    #> cat("\nSpecies with highest average bill ratio:", as.character(highest_ratio_species))
    #> ```
    #>
    #> ## Bonus Exercise: Conditional Logic
    #>
    #> ```{r bonus-key}
    #> # Solution: Conditional logic for size categorization
    #> categorize_size = function(mass) {
    #>   case_when(
    #>     is.na(mass) ~ "Unknown",
    #>     mass < 3500 ~ "Small",
    #>     mass >= 3500 & mass <= 4500 ~ "Medium",
    #>     mass > 4500 ~ "Large"
    #>   )
    #> }
    #>
    #> # Apply the function and create summary
    #> penguins_with_size = penguins %>%
    #>   mutate(size_category = categorize_size(body_mass_g))
    #>
    #> # Create summary table
    #> size_summary = penguins_with_size %>%
    #>   count(species, size_category) %>%
    #>   pivot_wider(names_from = size_category, values_from = n, values_fill = 0)
    #>
    #> print(size_summary)
    #>
    #> # Overall size distribution
    #> penguins_with_size %>%
    #>   count(size_category) %>%
    #>   mutate(percentage = round(n / sum(n) * 100, 1))
    #> ```

## Best Practices

When creating homework assignments for processing with `parsermd`, consider these best practices:

1. Clear Structure: Use headings to organize exercises and maintain hierarchy
2. Meaningful Labels: Use descriptive chunk labels that identify the document components and their type (e.g., `ex1-student`, `ex2-key`)
3. Testing: Always test the generated versions to ensure they work correctly and you haven’t lost anything important (e.g. your YAML front matter or your setup chunk)