{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "kernelspec": {
   "name": "ir",
   "display_name": "R",
   "language": "R"
  },
  "language_info": {
   "name": "R"
  },
  "colab": {
   "provenance": []
  }
 },
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 0 · Check your Colab setup\n",
    "**SoilFER Training** · [Course page](https://training.yigini.net/modules/00-welcome/)\n",
    "\n",
    "1. The runtime must be **R**: *Runtime → Change runtime type → R*.\n",
    "2. Run the **Setup** cell (≈1–3 minutes), then the check cells, with **Shift + Enter**.\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",
    "root <- \"/content/SoilFER-Training-Resources\"\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 — Colab's R runtime installs CRAN binaries through apt (r2u), so this is fast\n",
    "pkgs <- c(\"terra\", \"sf\", \"tidyverse\", \"readxl\")\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",
    "  system(\"python3 -m gdown --folder --quiet https://drive.google.com/drive/folders/1K7tq9zX5HsqbqWcNoT27WtfPtehcKBCu -O /content/soilfer_drive\")\n",
    "  f <- list.files(\"/content/soilfer_drive\", 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": [
    "## 1. R and the spatial packages"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "R.version.string\n",
    "library(terra); library(sf); library(tidyverse); library(readxl)\n",
    "cat(\"terra\", as.character(packageVersion(\"terra\")), \"| GDAL\", sf::sf_extSoftVersion()[[\"GDAL\"]], \"\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. The training project and data"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "list.files()\n",
    "list.files(\"01_data/module1/training_data\", pattern = \"\\\\.(tif|xlsx)$\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Soil data: KSSL topsoil table"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "kssl <- read_csv(\"03_outputs/module1/KSSL_DSM_0-30.csv\", show_col_types = FALSE)\n",
    "glimpse(kssl)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. A first map: elevation and soil sampling points"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "dem <- rast(\"01_data/module1/training_data/DEM_KANSAS.tif\")\n",
    "pts <- vect(kssl, geom = c(\"lon\", \"lat\"), crs = \"EPSG:4326\")\n",
    "pts <- project(pts, crs(dem))\n",
    "plot(dem, main = \"Kansas: elevation and KSSL sampling points\")\n",
    "points(pts, pch = 20, cex = .5)\n",
    "cat(\"All good, you are ready for Module 1.\\n\")\n"
   ]
  }
 ]
}