Chapter 2 Getting started: the course, your tools, and your data

Skills this lab builds. 4DEE: Ecology Practices → Quantitative reasoning and computational thinking. BioSkills: Quantitative Reasoning → “Record, organize, and annotate simple data sets”; “Apply the tools of graphing, statistics, and data science to analyze biological data.”

Welcome to ENV 226 lab! Today has three jobs, and we do them in this order:

  1. Meet the people you’ll be working with, and collect the first dataset of the semester — from each other.
  2. Get R and RStudio onto your computer.
  3. Run your first script, using the data you just collected.

R is a free, open-source program that ecologists and other scientists use to work with data — from organizing spreadsheets to making figures and running analyses. You do not need prior coding experience, and you do not need to understand everything today. The goal is to get R working and take the first steps.

If something feels confusing at first, that’s normal. Everyone learns R by using it, not by memorizing it.

2.0.1 Downloads for this lab

Get these before you start. You’ll be told where to put them in Step 6.

Software you’ll install (Steps 1 and 2):

2.1 Part 1 — Start your downloads

The installers take a while, and there is no reason to sit and watch them. Start them now and go meet your classmates while they run.

2.1.1 Step 1: Start the R download

  1. Go to https://www.r-project.org
  2. Click Download R from CRAN
  3. Choose any mirror — it truly does not matter
  4. Download the version for your operating system (Mac, Windows, or Linux)
  5. Leave it downloading and move on to Step 2

2.1.2 Step 2: Start the RStudio download

  1. Go to https://posit.co/download/rstudio-desktop/
  2. Download RStudio Desktop (Free)
  3. Leave it downloading and go to Part 2

You need two programs because they do two different jobs. R does the calculations; RStudio helps you write and organize code. Think of R as the engine and RStudio as the dashboard. You only install each one once per computer.

2.2 Part 2 — Meet your class and collect data

While those download: this is the meet-and-greet, and it is also the first data collection of the semester. Everything you measure today gets analyzed in the next two labs.

2.2.1 Step 3: Interview your group

Find your group and ask each person:

  1. How far from home are you? Use Google Maps to get the mileage.
  2. Do you prefer cats or dogs? (“Neither” is a valid answer.)
  3. How many roommates do you have?

2.2.2 Step 4: Measure some trees

If the weather is nice, follow your TA outside to measure the diameter of aspen trees. Your TA will show you how — the measurement is DBH, diameter at breast height, and you’ll use it again in several later labs.

If the weather isn’t cooperating, your TA will tell you what to do instead.

2.2.3 Step 5: Add your data to the class sheet

  1. Open the class data sheet
  2. Add one row per person, filling in all four columns
  3. Do not rename the columns, add extra columns, or type units into the cells — the script expects the sheet exactly as it is
  4. Tell your TA when your group is done

Once everyone has entered their data, your TA will check the sheet over. Wait for the green light before downloading it — if you download early you’ll get a half-empty file.

2.3 Part 3 — Set up your computer

Your downloads should be finished by now.

2.3.1 Step 6: Install R and RStudio

  1. Run the R installer, using the default options
  2. Run the RStudio installer, using the default options

RStudio will automatically find R once both are installed.

2.3.2 Step 7: Make your lab folder

Create a folder on your desktop called EcologyLab. Everything for this course goes in it. One folder, all semester.

2.3.3 Step 8: Put your files in it

  1. Download the practice R script and save it in EcologyLab
  2. Once your TA gives the green light, download the class data sheet as a .csv and save it in EcologyLab
  3. Rename it to exactly WelcomeToENV226.csv. Your browser may save it as something like WelcomeToENV226 - Sheet1.csv. Fix the name, or the script will not find the file.

If the class data isn’t ready, use the example file instead — it has the same columns, so the script runs either way.

You should now have two files in EcologyLab: the R script and the CSV.

2.3.4 Step 9: Open RStudio and look around

When you open RStudio you’ll see four panes. You do not need to customize anything.

  • Console — where R runs code
  • Source — where you write and save code
  • Environment — shows what data you have loaded
  • Plots / Files / Help — where figures and help appear

Your layout may look slightly different from your neighbor’s. That’s fine.

2.3.5 Step 10: Install your packages

R comes with built-in tools, but most ecological work uses packages — collections of tools written by other scientists.

Run this once, in the Console, and you’re set for every lab this semester:

install.packages(c(
  "tidyverse",    # used in almost every lab
  "car",          # invasive species
  "ggpubr",       # invasive species
  "gridExtra",    # natural selection
  "gplots",       # plant population biology
  "popbio",       # plant population biology - matrix models and PVA
  "dplR"          # dendrochronology - tree rings
))

If asked whether to install dependencies, choose Yes. This takes a few minutes.

The species distribution modeling lab needs a larger set of spatial packages, and its script installs those for you the first time you run it.

If a lab script later stops with there is no package called '...', you’ve found one this list missed — install it and tell your instructor so it can be added.

2.3.6 Step 11: Load the tidyverse

Installing is once per computer. Loading is every session. Every time you open R:

library(tidyverse)

Forgetting to load a package is one of the most common R mistakes, and it produces errors that look much scarier than they are.

2.4 Part 4 — Run your first script

2.4.1 Step 12: Open the script

In RStudio: File → Open File → select intro.R from your EcologyLab folder.

Look at what’s in it:

  • Lines starting with # — these are notes, not code
  • Code for installing and loading packages
  • Code for setting a working directory
  • Code for importing data
  • Code for visualizing your data

We’ll go through these together.

2.4.2 Step 13: Set your working directory

A working directory tells R where your files live. This is the single most common thing to go wrong, so do it deliberately.

The easy way, with the script already open:

Session → Set Working Directory → To Source File Location

Because your script and your data are in the same folder, R can now find the CSV without you typing any file paths.

The other way, through the Files pane:

  1. Go to the Files pane
  2. Navigate to your EcologyLab folder
  3. Click More, then Set As Working Directory

2.4.3 Step 14: Import your data

Once your working directory is set, the read.csv() line in the script will just work. Run it.

You can also import through the menus, which is useful while you’re learning or troubleshooting a file path:

  1. Go to the Environment pane
  2. Click Import Dataset
  3. Choose From Text (readr)
  4. Navigate to your CSV and select it
  5. Click Import

RStudio will load the data, show it in a spreadsheet-like view, and — usefully — write out the R code it used. For reproducible work we’ll rely on code-based imports, but the menu is a fine crutch early on.

2.4.4 Step 15: Make your figures

Run the visualization code at the bottom of the script. You should get four plots.

2.4.5 Step 16: Save your figures

For each figure:

  1. Click the Plots tab (lower-right pane)
  2. Make sure the figure you want is showing
  3. Click Export
  4. Choose a format (PNG or PDF)
  5. Save it into your EcologyLab folder

Saving this way lets you control file type and size, which matters when you’re dropping figures into reports and posters later in the semester.

2.5 Two habits worth starting today

2.5.1 Comment your code

Anything after a # is ignored by R.

# This is a note to myself

Use comments to explain what the code does, why you wrote it, and anything future-you will forget. Good code is readable code.

2.5.2 Get unstuck efficiently

Everyone — including professional scientists — gets errors in R. When you’re stuck:

  1. Read the error message. It usually names the problem.
  2. Copy it and search it, or paste it into an AI assistant and ask “What does this error mean?”
  3. Check the two usual suspects first: is your working directory set, and is your file named exactly right?

Learning to debug is part of learning R, not a sign you’re doing it wrong.

2.6 Assignment

Put your four figures into a Word document and submit it through Canvas.