---
title: "Your Document Title"
subtitle: "Your Subtitle"
author:
  - name: "Your Name"
    affiliation: "Your Institution"
    orcid: "Your ORCID"
    email: "your.email@institution.edu"
date: today
date-modified: last-modified
description: "A brief description of this document"
abstract: |
  A longer summary of the document's purpose and contents.
  This can span multiple lines.
categories:
  - "add"
  - "your"
  - "categories"
format:
  html:
    toc: true
    toc-depth: 3
    toc-title: "Contents"
    number-sections: true
    embed-resources: false
    css: assets/styles.css
    include-before-body: assets/header.html
params:
  input_file: "data/sample.csv"
---

```{r}
#| label: setup
#| include: false

# ── Getting started ────────────────────────────────────────────────────────
#
# Welcome! Before you start working on your document, take a moment to
# update the YAML header above with your own information.
#
# Required fields:
#   - title       : replace "Your Document Title" with your actual title
#   - author/name : replace "Your Name" with your name
#   - author/email: replace with your institutional email
#
# Optional but recommended:
#   - subtitle    : a short descriptor beneath the title
#   - affiliation : your department or institution
#   - orcid       : your ORCID identifier (https://orcid.org)
#   - description : one sentence summary, used in search and previews
#   - abstract    : longer summary of purpose and contents
#   - categories  : keywords that describe your document
#
# Fields you don't need:
#   Delete any YAML fields that are not relevant to your work.
#   Quarto will ignore unknown fields, but keeping unused ones
#   adds noise. When in doubt, less is more.
#
# ──────────────────────────────────────────────────────────────────────────

library(readr)
library(dplyr)
library(ggplot2)
library(toolero)
```

## Input Resolution

```{r}
#| label: input-resolution

# detect_execution_context() identifies which of three environments this
# code is running in: interactive RStudio, quarto render, or Rscript.
# Each context resolves the input file path differently.
#
#   interactive : you are running chunks in RStudio — path is hardcoded
#                 to the local data file for development convenience
#   quarto      : document is being rendered — path comes from params
#                 defined in the YAML header above
#   rscript     : document is running on a remote cluster — path comes
#                 from the first command line argument

context <- detect_execution_context()

input_file <- switch(context,
  interactive = "data/sample.csv",
  quarto      = params$input_file,
  rscript     = commandArgs(trailingOnly = TRUE)[1]
)
```

## Load Data

```{r}
#| label: load-data

# read_clean_csv() reads the file and standardizes column names
# using janitor::clean_names() — spaces and special characters
# are replaced with underscores and names are lowercased.

penguins <- read_clean_csv(input_file)

glimpse(penguins)
```

## Analysis

```{r}
#| label: summary

# A simple grouped summary across species.
# Replace this block with your own analysis.

penguins |>
  group_by(species) |>
  summarise(
    n            = n(),
    mean_mass    = mean(body_mass_g, na.rm = TRUE),
    mean_flipper = mean(flipper_length_mm, na.rm = TRUE)
  )
```

```{r}
#| label: plot

# Relationship between flipper length and body mass by species.
# Replace this block with your own plot.

penguins |>
  ggplot(aes(x = flipper_length_mm, y = body_mass_g, color = species)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = FALSE) +
  labs(
    title    = "Flipper Length vs Body Mass",
    subtitle = "By species — Palmer Penguins dataset",
    x        = "Flipper length (mm)",
    y        = "Body mass (g)",
    color    = "Species"
  ) +
  theme_minimal()
```
