---
title: "Internal Design"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Internal Design}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

```{r setup}
library(kvr2)
```

# Internal Design of `kvr2`

## Overview

The `kvr2` package is designed not only to calculate statistics but also to serve as an educational tool for understanding the behavior of $R^2$ definitions under various model specifications (e.g., with vs. without intercept).

To balance compatibility with external table-formatting packages (like `insight`) and high-performance visualization (using `ggplot2` and `grid`), the package adopts a **"Metadata-Rich Data Frame"** architecture.

---

## Data Flow Architecture

The internal process follows a three-stage pipeline:

### 1. Extraction Phase (`values_lm`)

The core extraction function `values_lm()` deconstructs an `lm` object into its mathematical essentials:

* $SS_{res}$ (Residual Sum of Squares)
* $SS_{tot}$ (Total Sum of Squares)
* $n$ (Sample size) and $k$ (Number of parameters)
* Identity of the response and predictor variables.

This decoupling ensures that the downstream calculation functions remain lightweight and do not depend directly on the complex `lm` object structure.

### 2. Computation and Metadata Injection (`r2`, `comp_model`)

In this phase, the package calculates the nine $R^2$ definitions. Crucially, it embeds the model's context as **S3 Attributes**.

* **`r2_kvr2` objects**: Store metadata such as `type` ("linear" or "power"), `n`, `k`, and `has_intercept`.
* **`comp_model` objects**: Inherit `data.frame` properties for easy printing, while storing the original `lm` objects as hidden attributes (`with_int` and `without_int`).

### 3. Dispatch and Visualization Phase

The S3 methods (`print`, `plot`, `model_info`) leverage these hidden attributes:

* **`model_info()`**: Acts as a "metadata extractor," providing transparency into how degrees of freedom were handled.
* **`plot.comp_model()`**: Recovers the original `lm` objects from attributes to generate diagnostic plots on the fly, without requiring the user to pass the models again.

---

## Handling Non-Standard Evaluation (NSE)

To ensure the package passes `R CMD check` without "no visible binding for global variable" notes, all `ggplot2` calls use the `.data` pronoun from the `rlang` package.

```r
# Example of internal safety
ggplot2::aes(x = .data$Definition, y = .data$Value)

```

## Dependency Strategy

The package follows a "Low-Maintenance Dependency" philosophy:

* **`ggplot2` & `tidyr**`: Core dependencies for modern visualization and data reshaping.
* **`grid`**: A base R package used for multi-panel layouts (2x2 dashboards) to avoid the overhead of additional layout packages like `patchwork` or `cowplot`.
* **`insight`**: Used for professional console output formatting.

---

## Troubleshooting and Maintenance

When debugging, developers should inspect the object's "hidden life" using the following commands:

| Command | Purpose |
| --- | --- |
| `attributes(obj)` | Check if metadata (n, k, model objects) is preserved. |
| `model_info(obj)` | Verify the mathematical assumptions used for calculation. |
| `unclass(obj)` | View the underlying list structure without S3 method interference. |
