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   "language": "R"
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   "name": "R"
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  "colab": {
   "provenance": [],
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 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Session 2 · Soil data preparation with the KSSL dataset (part 1)\n",
    "**Module 1 · Introduction to R, spatial data and soil data preparation** · SoilFER Training\n",
    "\n",
    "[Course page](https://training.yigini.net/modules/01-r-soil-data/) · [Manual chapter](https://training.yigini.net/manual/introduction-to-soil-data-preparation-spatial-data.html) · [Original script](https://github.com/SoilFER/SoilFER-Training-Resources/blob/main/02_scripts/module1/Session2_Soil_Data_Preparation_Part1.R)\n",
    "\n",
    "**Before you start**\n",
    "1. Check that the runtime is **R**: *Runtime → Change runtime type → R* (this notebook should open in R automatically).\n",
    "2. Run the **Setup** cell below once per session (≈1–3 minutes). It downloads the training project, installs the R packages and downloads the course rasters and MIR data (≈1.2 GB) and sets the working folder.\n",
    "3. Then run the cells in order with **Shift + Enter**.\n",
    "\n",
    "> Colab resets when you close it or after ~90 minutes without activity. Save results you want to keep with *Files → Download* (left sidebar), or re-run the Setup cell after a reset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# ==== SoilFER · Colab setup (run first, once per session) ==================\n",
    "options(repos = c(CRAN = \"https://cloud.r-project.org\"), timeout = 3600)\n",
    "# Works in Google Colab and in any other Jupyter (JupyterHub, JupyterLab on your computer)\n",
    "on_colab <- nzchar(Sys.getenv(\"COLAB_RELEASE_TAG\")) || dir.exists(\"/content/sample_data\")\n",
    "root <- if (on_colab) \"/content/SoilFER-Training-Resources\" else path.expand(\"~/SoilFER-Training-Resources\")\n",
    "Sys.setenv(SOILFER_ROOT = root)\n",
    "\n",
    "# 1. Training project (scripts, small data, outputs, assignments)\n",
    "if (!dir.exists(root))\n",
    "  system(paste(\"git clone --depth 1 https://github.com/SoilFER/SoilFER-Training-Resources\", root))\n",
    "\n",
    "# 2. R packages (in Colab the R runtime installs ready-made binaries, so this is fast)\n",
    "pkgs <- c(\"dplyr\", \"knitr\", \"readxl\", \"sf\", \"terra\", \"tidyverse\", \"writexl\")\n",
    "need <- setdiff(pkgs, rownames(installed.packages()))\n",
    "if (length(need)) install.packages(need)\n",
    "\n",
    "# 3. Course rasters + MIR spectra (Google Drive folder of the SoilFER training, ≈1.2 GB)\n",
    "td <- file.path(root, \"01_data/module1/training_data\")\n",
    "if (!file.exists(file.path(td, \"MIR_KANSAS_data.xlsx\"))) {\n",
    "  system(\"python3 -m pip -q install gdown\")\n",
    "  drv <- file.path(dirname(root), \"soilfer_drive\")\n",
    "  system(paste(\"python3 -m gdown --folder --quiet https://drive.google.com/drive/folders/1K7tq9zX5HsqbqWcNoT27WtfPtehcKBCu -O\", shQuote(drv)))\n",
    "  f <- list.files(drv, recursive = TRUE, full.names = TRUE)\n",
    "  file.copy(f, td, overwrite = FALSE)\n",
    "}\n",
    "\n",
    "setwd(root)\n",
    "cat(\"Ready. Working folder:\", getwd(), \"\\n\")\n",
    "missing <- setdiff(pkgs, rownames(installed.packages()))\n",
    "if (length(missing)) message(\"Not installed: \", paste(missing, collapse = \", \"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SoilFER Online Training Programme — Module 1"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "###############################################################################\n",
    "# SoilFER Online Training Programme — Module 1\n",
    "# SESSION 2: Data Preparation with the KSSL Dataset — Part 1 (1.5 hours)\n",
    "# Section: Working with Soil Data, Loading, Site data, Coordinates, Depths\n",
    "###############################################################################\n",
    "#\n",
    "# LEARNING OBJECTIVES\n",
    "# -------------------\n",
    "# By the end of this session, participants will be able to:\n",
    "#   1. Set the system and organize project files\n",
    "#   2. Load R packages for soil data analysis\n",
    "#   3. Import soil data from CSV and Excel files\n",
    "#   4. Explore and understand the structure of a real soil database\n",
    "#   5. Format sampling dates for data standardization\n",
    "#   6. Add unique row identifiers for data traceability\n",
    "#   7. Create unique profile identifiers from coordinates and sampling date\n",
    "#   8. Extract, standardize, and rename site columns\n",
    "#   9. Validate and filter geographic coordinates\n",
    "#  10. Validate and correct soil depth boundaries\n",
    "#\n",
    "# DATASET\n",
    "# -------\n",
    "# KSSL (Kellogg Soil Survey Laboratory) Kansas dataset\n",
    "#   - File: 01_data/module1/kssl/KSSL_data.xlsx  (or KSSL_data.csv)\n",
    "#   - Soil horizon measurements from multiple soil profiles\n",
    "#   - Columns: site info (location, depth), soil properties, spectral IDs\n",
    "#\n",
    "# TIMING GUIDE (approximate)\n",
    "# ---------------------------\n",
    "#  0:00 – 0:20  Working directory, packages, importing data\n",
    "#  0:20 – 0:45  Exploring the dataset; date; rowID and ProfID\n",
    "#  0:45 – 1:05  Site extraction and coordinate validation\n",
    "#  1:05 – 1:30  Depth validation (missing, negative, zero-thickness,\n",
    "#               invalid logic, profiles without surface horizon)\n",
    "###############################################################################\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART 1 — SETTING UP THE ENVIRONMENT"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# PART 1 — SETTING UP THE ENVIRONMENT\n",
    "# =============================================================================\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 1.1  Setting the Working Directory\n",
    "# -----------------------------------------------------------------------------\n",
    "# The working directory is where R looks for data files and saves outputs.\n",
    "# Use relative paths (e.g., \"01_data/module1/kssl/KSSL_data.csv\") whenever possible.\n",
    "\n",
    "# Check current working directory\n",
    "# getwd()\n",
    "\n",
    "# Set new working directory\n",
    "# setwd(\"C:/Users/YourName/Documents/YourSoilProject\")\n",
    "\n",
    "# On Mac/Linux\n",
    "# setwd(\"/Users/YourName/Documents/YourSoilProject\")\n",
    "\n",
    "# Set working directory to script location\n",
    "# setwd(file.path(Sys.getenv(\"SOILFER_ROOT\"), \"02_scripts/module1\"))  # folder of this script (was rstudioapi::getActiveDocumentContext, RStudio only)\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 1.2  Load Packages\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# library(readxl)           # Read Excel files\n",
    "# library(tidyverse)        # Data manipulation and visualization\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 1.3  Importing Data from Files\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Read a CSV file (generic syntax)\n",
    "# soil_data <- read.csv(\"path/to/soil_data.csv\")\n",
    "\n",
    "# If CSV uses different separator (semicolon, tab) (generic syntax)\n",
    "# soil_data <- read.csv(\"path/to/file.csv\", sep = \";\")\n",
    "# soil_data <- read.delim(\"path/to/file.txt\", sep = \"\\t\")\n",
    "\n",
    "# Read a CSV file using the `readr::read_csv` in tidyverse  (generic syntax)\n",
    "# soil_data <- read_csv(\"path/to/file.txt\")\n",
    "\n",
    "# Read the KSSL data from a CSV file\n",
    "# soil_data <- read_csv(\"01_data/module1/kssl/KSSL_data.csv\")\n",
    "\n",
    "# View structure\n",
    "# str(soil_data)\n",
    "\n",
    "# View first rows\n",
    "# head(soil_data)\n",
    "\n",
    "# Read Excel file\n",
    "# soil_data <- read_excel(\"01_data/module1/kssl/KSSL_data.xlsx\", sheet = 1)\n",
    "\n",
    "# Or read a specific sheet by name\n",
    "# soil_data <- read_excel(\"01_data/module1/kssl/KSSL_data.xlsx\", sheet = \"SoilData\")\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 1.4  Exploring the Imported Data\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# str(soil_data)        # Structure + column types\n",
    "# summary(soil_data)    # Quick summaries for each column\n",
    "# names(soil_data)      # Column names\n",
    "#\n",
    "# head(soil_data)       # First rows\n",
    "# tail(soil_data)       # Last rows\n",
    "#\n",
    "# View(soil_data)       # Opens a spreadsheet-style viewer in RStudio\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART 2 — LOADING THE KSSL DATASET"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# PART 2 — LOADING THE KSSL DATASET\n",
    "# The KSSL dataset is the primary dataset used throughout the sessions.\n",
    "# All code below assumes the working directory is set to the project root.\n",
    "# Before cleaning, always understand what you're working with.\n",
    "#\n",
    "# WHAT TO DO:\n",
    "# - Define the file path to the actual data location\n",
    "# - Check column names and data types\n",
    "# - Inspect the first few rows\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1  Basic Data Loading and Structure"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 2.1  Basic Data Loading and Structure\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Load libraries\n",
    "library(tidyverse)        # Data manipulation and visualization\n",
    "library(readxl)           # Read Excel files\n",
    "library(writexl)          # Write Excel files\n",
    "library(knitr)            # For formatted tables\n",
    "library(sf)               # Manipulation of Vector Data\n",
    "library(terra)            # Manipulation of Raster Data\n",
    "\n",
    "# Define the folder to store the results of the exercise\n",
    "output_dir <- \"03_outputs/module1/\"\n",
    "\n",
    "# Create the output directory if not existing\n",
    "if (!file.exists(output_dir)) {\n",
    "  dir.create(output_dir, recursive = TRUE)\n",
    "}\n",
    "\n",
    "# Read Excel file containing raw soil data\n",
    "raw_data <- read_excel(\"01_data/module1/kssl/KSSL_data.xlsx\", sheet = 1)\n",
    "\n",
    "str(raw_data)       # Examine the structure of the data\n",
    "head(raw_data, 10)  # Show the first 10 rows\n",
    "summary(raw_data)   # Summarize the data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART 3 — PREPARING SITE DATA"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# PART 3 — PREPARING SITE DATA\n",
    "# - Start understanding how many valid points you have in the dataset\n",
    "# - Standardized column names make cleaning easier and less error-prone\n",
    "#\n",
    "# The raw data includes information of:\n",
    "# - Position and depth (SITE)\n",
    "# - Analytical values of soil parameters (LAB)\n",
    "# - The Dry Chemistry Data (SPECTRAL) has been stored in a separate file due to its size\n",
    "#\n",
    "# WHAT TO DO:\n",
    "# - Format the sampling date\n",
    "# - Add identifiers once, before filtering or restructuring the data\n",
    "# - Extract only the columns needed for site identification\n",
    "# - Rename to standard names: lon, lat, top, bottom, etc.\n",
    "# - Preserve repeated horizon records until laboratory data are validated\n",
    "#\n",
    "# We define a soil profile from the combination of geographic location and\n",
    "# sampling date. Horizons sharing the same longitude, latitude, and date are\n",
    "# initially assigned the same ProfID.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1  Format Sampling Date to ISO 8601 (YYYY-MM-DD)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 3.1  Format Sampling Date to ISO 8601 (YYYY-MM-DD)\n",
    "# -----------------------------------------------------------------------------\n",
    "# Sampling dates are required for soil-data standardization.\n",
    "# In this dataset, only the fiscal year is available. January 1 is therefore\n",
    "# used as an approximate reference date for the corresponding year.\n",
    "#\n",
    "# IMPORTANT:\n",
    "# - Use the actual sampling date whenever it is available.\n",
    "# - If a valid `date` column already exists, this step should be skipped.\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "raw_data <- raw_data %>%\n",
    "  mutate(date = as.Date(paste0(fiscal_year, \"-01-01\")), .before = 4)\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 3.2  Adding Unique Row and Profile Identifiers\n",
    "# -----------------------------------------------------------------------------\n",
    "# Add rowID BEFORE filtering or changing the number/order of records.\n",
    "# WHY: rowID preserves the link between every processed record and its source.\n",
    "#\n",
    "# Add ProfID once at this stage and preserve it through the workflow.\n",
    "# WHY: The same coordinates can represent observations from different dates.\n",
    "# HOW: Group by longitude + latitude + sampling date, assign a sequential ID,\n",
    "#      and format it as \"PROF0001\".\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "raw_data <- raw_data %>%\n",
    "  # Assign a unique sequential identifier to each original record\n",
    "  mutate(rowID = row_number(), .before = 1) %>%\n",
    "  # Group all horizons belonging to the same initial profile\n",
    "  group_by(Long_Site, Lat_Site, date) %>%\n",
    "  # Assign a sequential identifier to each unique profile\n",
    "  mutate(ProfID = cur_group_id(), .before = 2) %>%\n",
    "  ungroup() %>%\n",
    "  # Format as standardized IDs: PROF0001, PROF0002, etc.\n",
    "  mutate(ProfID = sprintf(\"PROF%04d\", ProfID))\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 3.3  Extracting and Standardizing Column Names for Sites\n",
    "# -----------------------------------------------------------------------------\n",
    "# The `site` object stores the location, date, depth, and identifiers of the\n",
    "# soil observations. It becomes the authoritative object for structural\n",
    "# cleaning of profiles and horizons.\n",
    "#\n",
    "# Select relevant columns and rename to a consistent naming convention.\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "site <- raw_data %>%\n",
    "  select(\n",
    "    rowID,\n",
    "    ProfID,\n",
    "    date,\n",
    "    Long_Site,             # Raw column name for longitude\n",
    "    Lat_Site,              # Raw column name for latitude\n",
    "    smp_id,                # Sample/horizon identifier\n",
    "    Top_depth_cm,          # Top depth in centimeters\n",
    "    Bottom_depth_cm        # Bottom depth in centimeters\n",
    "  )\n",
    "\n",
    "# Rename columns to standard, consistent names\n",
    "site <- site %>%\n",
    "  rename(\n",
    "    lon = Long_Site,\n",
    "    lat = Lat_Site,\n",
    "    HorID = smp_id,\n",
    "    top = Top_depth_cm,\n",
    "    bottom = Bottom_depth_cm\n",
    "  )\n",
    "\n",
    "# Reorder columns for clarity\n",
    "site <- site %>%\n",
    "  select(rowID, ProfID, HorID, date, lon, lat, top, bottom)\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 3.4  Retain Repeated Horizon Records Until Laboratory Validation\n",
    "# -----------------------------------------------------------------------------\n",
    "# Multiple records can have the same location and depth because they may\n",
    "# represent repeated analytical or spectroscopic measurements.\n",
    "#\n",
    "# OLD APPROACH:\n",
    "# Exact site duplicates were removed here.\n",
    "#\n",
    "# NEW APPROACH:\n",
    "# Keep them at this stage. Some repeated rows contain complementary analytical\n",
    "# information. Duplicate horizons are assessed and resolved only after the\n",
    "# laboratory variables have been attached and validated in Session 3.\n",
    "# -----------------------------------------------------------------------------\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART 4 — COORDINATE VALIDATION AND CORRECTION"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# PART 4 — COORDINATE VALIDATION AND CORRECTION\n",
    "# WHY: Geographic coordinates define location. Bad coordinates = bad maps\n",
    "#\n",
    "# VALIDATION CHECKS:\n",
    "# 1. Check for missing coordinates (NA values)\n",
    "# 2. Check for out-of-bounds values\n",
    "#    - Valid longitude: -180 to +180 degrees\n",
    "#    - Valid latitude: -90 to +90 degrees\n",
    "#\n",
    "# COMMON ERRORS:\n",
    "# - Missing coordinates (NA)\n",
    "# - Swapped lon/lat\n",
    "# - lon/lat too large (e.g., 45000º instead of 45º)\n",
    "# - Projected coordinates instead of geographic\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1  Check 1: Missing Coordinates"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 4.1  Check 1: Missing Coordinates\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Remove records with missing coordinates\n",
    "site <- site %>%\n",
    "  dplyr::filter(!is.na(lon) & !is.na(lat))\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 4.2  Check 2: Valid Coordinate Ranges\n",
    "# -----------------------------------------------------------------------------\n",
    "# For geographic coordinates (decimal degrees):\n",
    "#   Longitude: -180° to +180°\n",
    "#   Latitude:   -90° to +90°\n",
    "\n",
    "# Keep only rows with valid lon/lat geographic coordinates inside valid ranges\n",
    "site <- site %>%\n",
    "  dplyr::filter(\n",
    "    lon >= -180, lon <= 180,\n",
    "    lat >= -90,  lat <= 90\n",
    "  )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART 5 — SOIL DEPTH VALIDATION AND CORRECTION"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# PART 5 — SOIL DEPTH VALIDATION AND CORRECTION\n",
    "# =============================================================================\n",
    "# Depth intervals define the soil layer each observation represents.\n",
    "#\n",
    "# VALIDATION CHECKS:\n",
    "# Five sequential quality-control checks are applied:\n",
    "#   Check 1: Missing depth boundaries\n",
    "#   Check 2: Negative depth values\n",
    "#   Check 3: Zero-thickness intervals\n",
    "#   Check 4: Invalid depth logic (bottom <= top)\n",
    "#   Check 5: Profiles without a surface horizon (top > 0)\n",
    "#\n",
    "# NOTE:\n",
    "# Do NOT resolve repeated horizons or competing depth sequences at this stage.\n",
    "# These are resolved after laboratory data have been attached and validated\n",
    "# in Session 3.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1  Check 1: Missing Depth Boundaries"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 5.1  Check 1: Missing Depth Boundaries\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Keep records where both `top` and `bottom` are available\n",
    "site <- site %>%\n",
    "  dplyr::filter(!is.na(top) & !is.na(bottom))\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 5.2  Check 2: Negative Depth Values\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Keep records with non-negative top and bottom depths\n",
    "site <- site %>%\n",
    "  filter(!(top < 0 | bottom < 0))\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 5.3  Check 3: Zero-Thickness Intervals\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Remove zero-thickness horizons\n",
    "site <- site %>%\n",
    "  filter(!(bottom - top == 0))\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 5.4  Check 4: Invalid Depth Logic\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Keep only horizons where bottom depth is greater than top depth\n",
    "site <- site %>%\n",
    "  filter(bottom > top)\n",
    "\n",
    "\n",
    "# -----------------------------------------------------------------------------\n",
    "# 5.5  Check 5: Profiles Without a Surface Horizon (top > 0)\n",
    "# WHY: Each profile should represent the soil column starting at the surface.\n",
    "# DSM applications require profiles that start at top = 0 cm.\n",
    "# -----------------------------------------------------------------------------\n",
    "\n",
    "# Keep only initial profiles that contain a surface horizon\n",
    "site <- site %>%\n",
    "  dplyr::group_by(ProfID) %>%\n",
    "  dplyr::filter(!is.na(top) & min(top, na.rm = TRUE) == 0) %>%\n",
    "  dplyr::ungroup()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PART 6 — SAVE SITE TIBBLE TO DISK"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "# PART 6 — SAVE SITE TIBBLE TO DISK\n",
    "# =============================================================================\n",
    "# Save the structurally cleaned site results to 'site_KSSL.csv'.\n",
    "#\n",
    "# IMPORTANT:\n",
    "# Repeated horizon records are intentionally retained here. The `site` object\n",
    "# will be combined with laboratory measurements using rowID in Session 3.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### output <- paste0(output_dir, \"site_KSSL.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "# =============================================================================\n",
    "\n",
    "output <- paste0(output_dir, \"site_KSSL.csv\")\n",
    "write.csv(site, output, row.names = FALSE)\n",
    "\n",
    "\n",
    "###############################################################################\n",
    "# END OF SESSION 2\n",
    "# Summary of what was accomplished:\n",
    "#   - KSSL data loaded and explored\n",
    "#   - Row IDs added for traceability\n",
    "#   - Site columns extracted, renamed, and standardized\n",
    "#   - Profile IDs created from geographic coordinates\n",
    "#   - Exact duplicate records removed\n",
    "#   - Coordinates validated: missing and out-of-range records removed\n",
    "#   - Depth boundaries validated: 5 checks applied sequentially\n",
    "#\n",
    "# The 'site' object is now ready for integration with lab data in Session 3.\n",
    "#\n",
    "# Next session: Session 3 — Soil Data Preparation with KSSL Dataset (Part 2)\n",
    "###############################################################################\n"
   ]
  }
 ]
}