Appendix A — Tools

TL;DR for Day 1
Please arrive with everything in the checklist below installed and working. Bring your laptop.

Day 1 setup checklist

  • R (≥ 4.3) and R Studio Desktop — base language for data analysis in this class and IDE for R.
  • Create a GitHub account with your umn.edu email address. Additionally, download the GitHub Desktop app.
  • Zotero + Zotero Connector (browser) — reference manager & PDF annotation. IMPORTANT: create a Zotero account with your umn.edu email address. Download the Zotero Desktop app.
  • At least one LLM account (your choice) — e.g., OpenAI ChatGPT, Anthropic Claude, Google Gemini, Microsoft Co-Pilot. Free tiers are fine. UMN offers enterprise level access to Google Gemini and Microsoft Co-Pilot (which is built on GPT).
  • QGIS (3.x) — open-source GIS.
  • (Optional but useful) VS Code - Mac and PC or BBEdit - Mac only (for raw text editing), Obsidian (for a personal knowledge base).

What these tools are (and why we use them)

R

R is a programming language designed for data analysis, statistics, and graphics. We’ll use it to wrangle messy datasets, analyze experiments, and make publication-quality figures. You will write clean, reproducible code so your work can be re-run months later.

  • Core packages we’ll use: tidyverse (data wrangling, plotting), sf (vector geospatial), terra (rasters), janitor (clean tables), here (project paths).

RStudio (Posit)

An IDE that makes R friendlier: script editor, console, plots, environment pane, projects, Git integration, and Quarto authoring. Think of it as “mission control” for the class.

Zotero (+ Connector)

Your second brain for papers. We’ll use Zotero to: - collect readings and PDFs, - annotate (highlights/notes), - export citations to Quarto, - keep a reading audit trail (see the Reading appendix).

QGIS

We’ll use QGIS for quick exploratory mapping, geoprocessing, and cross-checking R outputs. If you’re new to GIS, QGIS gives you a visual entry point that complements R’s scripted workflows.

Large Language Models (LLMs)

You’ll learn to use an LLM as a reading companion, coding assistant, and writing aid. We’ll practice effective prompting and documentation of AI use (see Writing & AI and Prompt Engineering appendices). Free tiers are sufficient.

How we’ll use this stack

  • Reproducible analysis: R + Quarto notebooks for labs & projects.
  • Versioned projects: Git/GitHub for collaboration and accountability.
  • Reading workflow: Zotero to annotate; export notes; connect to wiki citations.
  • Geospatial: QGIS for inspection; R for scripted pipelines.
  • AI assistance: LLMs for scaffolding ideas, debugging code, and generating first-pass outlines—with clear attribution/logs.

Mindset: You don’t have to be a “programmer” to succeed here, but you cannot afford to avoid computational tools. We will learn them together, step by step.