Module 0: Before You Arrive

Complete before the first day of class. This takes about 30–60 minutes. No assignment is due — just get set up and tell us where you’re starting from.

1. Install R and RStudio

Install both — R is the language, RStudio is the editor you’ll use to write it.

R → cran.r-project.org (choose your OS)
RStudio Desktop → posit.co/downloads (free version)

Open RStudio after installing. If you see a console prompt (>) you’re ready.

2. Get the Course Files

All lecture scripts, datasets, and materials live in a GitHub repository. You have two options:

Option A — Clone with Git (recommended)

If you have Git installed (git-scm.com):

git clone https://github.com/greymonroe/PLS_206.git

This creates a PLS_206/ folder on your computer. To pull new files as the course progresses:

cd PLS_206
git pull

Option B — Download as ZIP

Go to github.com/greymonroe/PLS_206, click the green Code button → Download ZIP. Re-download at the start of each week to get new files.

Set your working directory

Open RStudio, then set the working directory to the repo root so all relative file paths work:

# Replace with your actual path
setwd("/path/to/PLS_206")

# Verify it worked — you should see course files listed
list.files()

Tip

The cleanest way: in RStudio go to File → Open Project and open the PLS_206/ folder as a project. RStudio will set the working directory automatically every time you open it.

3. Install Core Packages

Run this once in RStudio. It will take a few minutes.

install.packages(c(
  "ggplot2",    # visualization
  "dplyr",      # data manipulation
  "tidyr",      # reshaping data
  "mgcv",       # GAMs (Module 4)
  "glmnet",     # regularization (Module 6)
  "MuMIn",      # model selection (Module 5)
  "vegan",      # ordination / RDA (Module 10)
  "lavaan",     # SEM (Module 10)
  "randomForest", # random forests (Module 9)
  "lme4"        # mixed effects models (Module 5)
))

Confirm ggplot2 works:

library(ggplot2)
ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point() + theme_classic()

You should see a scatter plot. If so, you’re ready.

4. Self-Assessment

There is no grade for this — it’s for your own awareness and so the instructor can calibrate the pace of Module 1.

R skills test

Open RStudio and try running this code from scratch — no notes, no Google:

# Can you do these without looking anything up?
x <- c(3, 7, 2, 9, 4)
mean(x)
x[x > 4]

df <- data.frame(group = c("A","A","B","B"), value = c(10, 12, 8, 6))
df[df$group == "A", ]

library(ggplot2)
ggplot(df, aes(x = group, y = value)) + geom_boxplot()

How far did you get before hitting a wall? That’s your starting point — no wrong answer.

Submit a one-sentence summary in the Canvas Module 0 survey (e.g., “I got through the subsetting but couldn’t remember ggplot syntax”). This is anonymous to other students and just helps calibrate the pace of Module 1.

Note

Starting from zero is completely fine — the course is designed for that. If you want interactive R practice before class, install Swirl: install.packages("swirl"); library(swirl); swirl() — it runs entirely in your R console, no account needed.

Quick self-check

Honestly rate yourself (1 = never heard of it, 5 = could teach it):

Skill	Self-rating
Creating vectors and data frames in R
Writing a for loop
Using `ggplot2`
Fitting a linear model with `lm()`
Interpreting a p-value
Understanding what a residual is

Bring this to the first class — we’ll use it to form activity groups.

--- title: "Module 0: Before You Arrive" --- ::: {.week-header} **Complete before the first day of class.** This takes about 30–60 minutes. No assignment is due — just get set up and tell us where you're starting from. ::: --- ## 1. Install R and RStudio Install both — R is the language, RStudio is the editor you'll use to write it. 1. **R** → [cran.r-project.org](https://cran.r-project.org) (choose your OS) 2. **RStudio Desktop** → [posit.co/downloads](https://posit.co/downloads) (free version) Open RStudio after installing. If you see a console prompt (`>`) you're ready. --- ## 2. Get the Course Files All lecture scripts, datasets, and materials live in a GitHub repository. You have two options: ### Option A — Clone with Git (recommended) If you have Git installed ([git-scm.com](https://git-scm.com)): ```bash git clone https://github.com/greymonroe/PLS_206.git ``` This creates a `PLS_206/` folder on your computer. To pull new files as the course progresses: ```bash cd PLS_206 git pull ``` ### Option B — Download as ZIP Go to [github.com/greymonroe/PLS_206](https://github.com/greymonroe/PLS_206), click the green **Code** button → **Download ZIP**. Re-download at the start of each week to get new files. ### Set your working directory Open RStudio, then set the working directory to the repo root so all relative file paths work: ```r # Replace with your actual path setwd("/path/to/PLS_206") # Verify it worked — you should see course files listed list.files() ``` ::: {.callout-tip} The cleanest way: in RStudio go to **File → Open Project** and open the `PLS_206/` folder as a project. RStudio will set the working directory automatically every time you open it. ::: --- ## 3. Install Core Packages Run this once in RStudio. It will take a few minutes. ```r install.packages(c( "ggplot2", # visualization "dplyr", # data manipulation "tidyr", # reshaping data "mgcv", # GAMs (Module 4) "glmnet", # regularization (Module 6) "MuMIn", # model selection (Module 5) "vegan", # ordination / RDA (Module 10) "lavaan", # SEM (Module 10) "randomForest", # random forests (Module 9) "lme4" # mixed effects models (Module 5) )) ``` Confirm ggplot2 works: ```r library(ggplot2) ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point() + theme_classic() ``` You should see a scatter plot. If so, you're ready. --- ## 4. Self-Assessment There is no grade for this — it's for your own awareness and so the instructor can calibrate the pace of Module 1. ### R skills test Open RStudio and try running this code from scratch — no notes, no Google: ```r # Can you do these without looking anything up? x <- c(3, 7, 2, 9, 4) mean(x) x[x > 4] df <- data.frame(group = c("A","A","B","B"), value = c(10, 12, 8, 6)) df[df$group == "A", ] library(ggplot2) ggplot(df, aes(x = group, y = value)) + geom_boxplot() ``` How far did you get before hitting a wall? That's your starting point — no wrong answer. **Submit a one-sentence summary in the Canvas Module 0 survey** (e.g., "I got through the subsetting but couldn't remember ggplot syntax"). This is anonymous to other students and just helps calibrate the pace of Module 1. ::: {.callout-note} Starting from zero is completely fine — the course is designed for that. If you want interactive R practice before class, install [Swirl](https://swirlstats.com): `install.packages("swirl"); library(swirl); swirl()` — it runs entirely in your R console, no account needed. ::: ### Quick self-check Honestly rate yourself (1 = never heard of it, 5 = could teach it): | Skill | Self-rating | |---|---| | Creating vectors and data frames in R | | | Writing a for loop | | | Using `ggplot2` | | | Fitting a linear model with `lm()` | | | Interpreting a p-value | | | Understanding what a residual is | | Bring this to the first class — we'll use it to form activity groups.