Meeting R

From the console to your first plot with the tree leaf data

Bill Perry

2026-07-05

Meeting R

Recap from Activity 1

Collected leaf samples from sunny and shady sides of trees
Identified the independent variable (side of tree) and dependent variables (leaf mass, dimensions)
Measured and recorded leaf weight, width, and height in a spreadsheet
Agreed on column names, units, and metadata conventions
Finding: shady leaves appeared larger — today we start exploring this in R

Today’s Objectives

Get R and Positron installed and oriented
Organize a project folder the right way — data/, scripts/, figures/
Use R as a calculator and store values with <-
Write and run a script with # comments
Call functions, build vectors, understand data types
Load our leaf data from Excel with read_excel() and from CSV with read_csv()
Inspect a data frame with head(), glimpse(), and dim()
Use the pipe %>% to chain steps
Make and save a first ggplot figure

How to use this worksheet

Work through each section at your own pace. Type the code into your script file in Positron and run it line by line. Code blocks marked ▶ Run this are the ones you should execute. Blocks marked ✏️ Your turn ask you to write or modify something. The Going further section is optional — work through it if you finish early.

Part 1 · Set up R and Positron

Download and install — in this order

R (the engine) — https://cran.r-project.org/
- Choose your operating system, run the installer, accept all defaults
Positron (the editor) — https://positron.posit.co/download.html
- Install after R — Positron needs to find R already on your machine
- Open Positron and verify it finds R (check the bottom status bar)

⚠️ Watch out! Install R first, then Positron. If you installed them in the wrong order, reinstall R and then restart Positron.

Orient yourself in Positron

Four panes you will use constantly:

Pane	Where	What it does
Editor	Top-left	Your scripts live here
Console / Terminal	Bottom	Where code runs and results appear
Variables / Session	Right	Every object you have stored
Plots	Right (tab)	Your figures appear here

✏️ Your turn: Click the Console tab. Type 1 + 1 and press Enter. What did R return?

Your answer:

Part 2 · Build your project folder

Why a project folder?

In R we keep everything for one project together in one folder. Every file path is then relative to that folder — no more C:/Users/myname/Desktop/random_stuff/final_FINAL2.xlsx nightmares.

Create the folder structure

On your computer, make a new folder called tree_project. Inside it, create these three sub-folders:

tree_project/
├── data/        ← your Excel and CSV files go here
├── scripts/     ← your .R code goes here
└── figures/     ← plots you save go here

💡 Coming from Excel? In Excel you tend to keep one giant workbook. In R we keep raw data untouched and write new processed files — so you can always trace your steps and never overwrite your original numbers.

Open the folder as your workspace in Positron

In Positron: File → Open Folder… and pick your tree_project folder.

You should see tree_project appear in the Explorer panel on the left.

✏️ Your turn: What is the full path to your tree_project folder on your computer?

Your answer:

Copy the data file

Copy 2026_06_25_tree_experiment_raw_data.xlsx into tree_project/data/.

⚠️ Important: Leave this copy alone — it is your raw data. We will read it but never overwrite it.

Create your first script

In Positron:
- File → New File → R File.
- Save it immediately with Ctrl/Cmd + S, name it 02_tree_analysis.R,
- and save it inside tree_project/scripts/.

💡 Key idea: Console = scratch paper. Script = the lab notebook you keep.

✏️ Your turn: What is your script named?

Your answer:

Part 3 · How R works

R as a calculator

Type each line below into your Console (not the script yet) and press Enter after each one.

▶ Run this in the Console:

3 + 5
12 / 7
2 ^ 10
sqrt(144)

⚠️ Watch out! If you see a + at the start of a console line instead of the > prompt, R is waiting for you to finish a command. Press Esc to cancel and start over.

✏️ Your turn: What does 2 ^ 10 return? What does the ^ operator do?

Your answer:

Storing values with `<-`

To keep a value, give it a name using the assignment operator <-.

Shortcut: Alt + − (Windows) or Option + − (Mac) types <- for you.

▶ Run this in the Console:

x <- 7          # store 7 under the name x
x               # type the name to see it
x * 2           # use it in math

Look at the Variables pane on the right — x should appear there.

✏️ Your turn: Store the number 42 under the name my_number, then multiply my_number by x. What is the result?

# Write your code here:

Your answer:

Naming things well

Good names make code readable months later:

Be explicit but not too long: leaf_mass not x2
Cannot start with a number — x2 ✅, 2x ❌
R is case-sensitive — weight_g ≠ Weight_g
Use lower_snake_case — words separated by underscores, all lowercase

✏️ Your turn: Which of these names are valid in R? Write Y or N next to each.

leaf_mass       — 
3rd_leaf        — 
Width_cm        — 
my.leaf.data    — 
side            —

Comments with `#`

Anything to the right of # is ignored by R — it is a note for humans. Shortcut to toggle: Ctrl/Cmd + Shift + C.

▶ Run this in your Script:

# weights of four leaves, in grams
leaf_mass <- c(4, 3, 5, 7)

mean(leaf_mass)   # average leaf mass

💡 Key idea: If a line confused you while writing it, write a comment explaining why you did it.

Functions and arguments

A function is a mini-program you call by name. You feed it arguments (inputs) inside ( ).

▶ Run this in the Console:

sqrt(10)                         # one argument
round(3.14159)                   # default: 0 decimal places → 3
round(3.14159, 2)                # two decimal places → 3.14
round(x = 3.14159, digits = 2)  # same, with named arguments

To get help on any function:

?round          # open the help page
args(round)     # list what arguments it takes

✏️ Your turn: Use round() to round pi (R knows pi by name — just type it) to 4 decimal places.

# Write your code here:

Your answer:

Vectors — a row of values

A vector is a series of values built with c() — the combine function. A spreadsheet column is really just a vector.

▶ Run this in your Script:

weight_g <- c(4, 3, 5, 7)          # numeric vector
side     <- c("sunny", "shady")    # character vector — needs quotes

length(weight_g)    # how many values?
class(weight_g)     # what type is it?
str(weight_g)       # structure overview

⚠️ Watch out! Quotes matter: "sunny" is text. Without quotes, R searches for an object named sunny and throws an error when it cannot find one.

✏️ Your turn: Make a character vector called my_colors with three color names of your choice. Check its length() and class().

# Write your code here:

Data types inside vectors

Every vector holds one type:

Type	Example	Notes
`numeric`	`4`, `3.5`	All numbers by default
`character`	`"sunny"`	Any text, always quoted
`logical`	`TRUE` / `FALSE`	Must be all-caps

Mix types and R silently converts everything to the most flexible type — numbers become text, TRUE becomes 1. This can cause surprising bugs.

Part 4 · Packages and libraries

What is a package?

Base R is powerful but lean. Packages add new functions — like apps for your phone.

Install once — run this in the Console (not in your script):

install.packages("tidyverse")
install.packages("readxl")

Load every session — put this at the top of every script you write:

library(tidyverse)   # data wrangling + ggplot2 for plotting
library(readxl)      # reading Excel files

⚠️ Watch out! If R says “could not find function read_excel”, you forgot library(readxl). You must reload libraries every time you restart R.

▶ Run this at the very top of your Script:

# ── Packages ────────────────────────────────────────────────
library(tidyverse)
library(readxl)

✏️ Your turn: What message does R print after library(tidyverse)? Write the first line here:

Your answer:

Part 5 · Load the tree data

Two functions — two file types

▶ Run this in your Script:

# ── Load data ────────────────────────────────────────────────

# Excel files (.xlsx) — needs library(readxl)
tree_df <- read_excel("data/2026_06_25_tree_experiment_raw_data.xlsx")

tree_df    # print to the console

💡 Coming from Excel? read_excel() is the bridge from your spreadsheet into R. Your sheet’s first row becomes the column names in R.

The path "data/..." is relative to your project folder. It works on anyone’s computer — not just yours.

Also: read CSV files with `read_csv()`

Many public datasets (NOAA climate data, iNaturalist, eBird) come as .csv files. The tidyverse provides read_csv() for these:

# CSV files (.csv) — comes with tidyverse, no extra install
# noaa_df <- read_csv("data/noaa_global_yearly_temps.csv")

💡 Key difference: read_excel() needs library(readxl). read_csv() comes with library(tidyverse) — no extra install needed.

Meet the data frame

A data frame is R’s word for a spreadsheet-style table. Each column is a vector of one type; the rows line up.

Our tree_df has five columns:

Column	Type	What it holds
`index`	numeric	leaf number (1–20)
`side`	character	`"sunny"` or `"shady"`
`weight_g`	numeric	leaf weight in grams
`width_cm`	numeric	leaf width in centimetres
`height_cm`	numeric	leaf height in centimetres

Pull a single column with $:

tree_df$weight_g     # just the weight column, as a vector

Part 6 · Inspect a new dataset

Always eyeball a new dataset before doing any analysis.

▶ Run each of these in your Script:

head(tree_df)       # first 6 rows

# glimpse() is the tidyverse way — one line per column
glimpse(tree_df)    # column names, types, first values

# Base R equivalents (give similar info)
dim(tree_df)        # (rows, columns)
names(tree_df)      # column names
str(tree_df)        # type of every column
summary(tree_df)    # min / mean / max per column

💡 Prefer glimpse() over str() — it is the tidyverse-friendly version and is easier to read.

✏️ Your turn: Answer these questions from the output above.

How many rows does tree_df have?          
How many columns?                         
What type does R report for `side`?       
What is the mean weight_g?                
What is the maximum height_cm?

⚠️ Watch out! If a number column shows as <chr> in glimpse(), a stray letter or comma snuck into your spreadsheet. Check your raw data file.

Part 7 · The pipe `%>%` — read it as “then”

The pipe sends a result straight into the next function. Read %>% as the word “then”.

▶ Run this in your Script:

# Simple examples — read each %>% as "then"
tree_df %>% nrow()          # take tree_df, THEN count rows
tree_df %>% names()         # take tree_df, THEN list column names
tree_df %>% summary()       # take tree_df, THEN summarize everything

# Chain multiple steps
tree_df %>%
  head(3)                   # take tree_df, THEN show first 3 rows

💡 Two pipes — same idea: %>% — from tidyverse (we use this one throughout the course) The native pipe (a vertical bar then greater-than sign) — built into R 4.1+; you will see it in code online They behave identically for everything in this course.

✏️ Your turn: Using the pipe, write code to show only the last 3 rows of tree_df. Hint: look at tail().

# Write your code here:

In Lecture 03 we will use %>% with group_by() and summarize() to compute statistics by group — this is where the pipe really pays off.

Part 8 · Your first plot

ggplot builds pictures in layers

Every ggplot is built in three pieces, joined with +:

ggplot(data, aes(...)) — which data frame, which columns map to x and y
geom_*() — the shape to draw (points, boxes, bars, …)
labs() — axis labels and title

▶ Run this in your Script — the simplest possible plot:

ggplot(tree_df, aes(x = side, y = weight_g)) +
  geom_point()

⚠️ Watch out! The + must sit at the end of a line, never the start. A + at the start causes an error.

Improve it one layer at a time

▶ Run this in your Script:

ggplot(tree_df, aes(x = side, y = weight_g)) +
  geom_boxplot() +
  geom_jitter(width = 0.15, alpha = 0.6, color = "tomato") +
  labs(x     = "Side of tree",
       y     = "Leaf weight (g)",
       title = "Sunny vs. shady leaves")

✏️ Your turn: Make the same plot but for height_cm instead of weight_g. Copy the code above and change the relevant parts.

# Write your modified code here:

What pattern do you see — do sunny or shady leaves tend to be taller?

Your answer:

Save your plot as a PNG

Store the plot in an object, then save it to your figures/ folder.

▶ Run this in your Script:

# Save the weight plot — always use PNG, always use dpi = 300
weight_plot <- ggplot(tree_df, aes(x = side, y = weight_g)) +
  geom_boxplot() +
  geom_jitter(width = 0.15, alpha = 0.6, color = "tomato") +
  labs(x     = "Side of tree",
       y     = "Leaf weight (g)",
       title = "Sunny vs. shady leaves")

ggsave("figures/leaf_weight.png",
       plot   = weight_plot,
       width  = 6,
       height = 6,
       units  = "in",
       dpi    = 300)

Check your figures/ folder — the PNG should be there.

⚠️ Watch out! ggsave() wants the filename first, then plot =. Mixing that order is the classic first-save mistake.

💡 Always use dpi = 300 — that is publication quality (300 dots per inch). The default (72 dpi) looks blurry in reports and on posters.

Part 9 · Review and checkpoint

At this point you can:

✏️ Your turn — before you move on: Run the full script from top to bottom by pressing Ctrl/Cmd + Shift + Enter (runs the whole file). Does it complete without errors?

Did it run cleanly? Y / N
If not, what error did you see?

Part 10 · Going further — more plotting

This section is optional — work through it if you finish early or want to explore. There are no wrong answers here; the goal is to see what ggplot can do.

Scatter plots — two numeric variables

▶ Try this:

ggplot(tree_df, aes(x = width_cm, y = height_cm, color = side)) +
  geom_point(size = 3, alpha = 0.7) +
  labs(x     = "Leaf width (cm)",
       y     = "Leaf height (cm)",
       color = "Side of tree",
       title = "Leaf dimensions by side of tree")

✏️ Your turn: Does width predict height? Do the two groups cluster separately or overlap?

Your observation:

Add a trend line

▶ Try this:

ggplot(tree_df, aes(x = width_cm, y = height_cm, color = side)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(x     = "Leaf width (cm)",
       y     = "Leaf height (cm)",
       color = "Side of tree",
       title = "Leaf dimensions with regression lines")

✏️ Your turn: Do the slopes look similar or different for sunny vs. shady leaves? What might that mean biologically?

Your observation:

Violin plots

▶ Try this:

ggplot(tree_df, aes(x = side, y = weight_g, fill = side)) +
  geom_violin(alpha = 0.5) +
  geom_jitter(width = 0.1, size = 2) +
  labs(x     = "Side of tree",
       y     = "Leaf weight (g)",
       title = "Violin plot of leaf weight") +
  theme(legend.position = "none")

✏️ Your turn: What does the width of the violin show you that a boxplot does not?

Your observation:

Facets split one plot into panels, one per group.

▶ Try this:

ggplot(tree_df, aes(x = weight_g)) +
  geom_histogram(binwidth = 1, fill = "steelblue", color = "white") +
  facet_wrap(~side, ncol = 1) +
  labs(x     = "Leaf weight (g)",
       y     = "Count",
       title = "Distribution of leaf weight by side")

✏️ Your turn: Do the histograms look roughly symmetric, or skewed? Are the shapes similar between sunny and shady?

Your observation:

Themes — change the look

ggplot comes with built-in themes. Try swapping by adding one to any plot above:

+ theme_bw()          # clean, white background
+ theme_minimal()     # very minimal, no border
+ theme_classic()     # classic axis lines, no grid
+ theme_dark()        # dark background

✏️ Your turn: Which theme do you prefer for this kind of biological comparison data? Why?

Your answer:

Save a scatter plot as PNG

▶ Try this:

scatter_plot <- ggplot(tree_df, aes(x = width_cm, y = height_cm, color = side)) +
  geom_point(size = 3, alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(x = "Leaf width (cm)", y = "Leaf height (cm)", color = "Side") +
  theme_bw()

ggsave("figures/leaf_dimensions_scatter.png",
       plot   = scatter_plot,
       width  = 7,
       height = 5,
       units  = "in",
       dpi    = 300)

✏️ Your turn: Why do we save as .png rather than .pdf for most class submissions?

Your answer:

What your finished project folder looks like

After working through this worksheet your tree_project/ folder should contain:

tree_project/
├── data/
│   └── 2026_06_25_tree_experiment_raw_data.xlsx   <- never touch this
├── scripts/
│   └── 02_tree_analysis.R                          <- your complete script
└── figures/
    ├── leaf_weight.png                             <- Part 8
    └── leaf_dimensions_scatter.png                 <- Going further

This is the project structure we will use for every analysis in this course:

Folder	Contents	Rule
`data/`	All data files	Read only — never overwrite
`scripts/`	`.R` code files	One script per topic
`figures/`	Saved plots	Always PNG, always `dpi = 300`

Getting unstuck

When code breaks — and it will, that is normal — try these in order:

Read the error message out loud. R usually names the line and the problem.
Check the usual suspects: Did you load library(tidyverse) and library(readxl)? Spelling? Missing ) or + at the start of a line?
?function_name opens the built-in help page.
Tidyverse cheat sheets — https://posit.co/resources/cheatsheets/
Bring the exact error (copy-paste it) to class or office hours.

💡 Key idea: Every working data scientist googles error messages daily. Getting stuck is not failing — it is the job.

Meeting R

Meeting R

Recap from Activity 1

Today’s Objectives

How to use this worksheet

Part 1 · Set up R and Positron

Download and install — in this order

Orient yourself in Positron

Part 2 · Build your project folder

Why a project folder?

Create the folder structure

Open the folder as your workspace in Positron

Copy the data file

Create your first script

Part 3 · How R works

R as a calculator

Storing values with <-

Naming things well

Comments with #

Functions and arguments

Vectors — a row of values

Data types inside vectors

Part 4 · Packages and libraries

What is a package?

Part 5 · Load the tree data

Two functions — two file types

Also: read CSV files with read_csv()

Meet the data frame

Part 6 · Inspect a new dataset

Part 7 · The pipe %>% — read it as “then”

Part 8 · Your first plot

ggplot builds pictures in layers

Improve it one layer at a time

Save your plot as a PNG

Part 9 · Review and checkpoint

Part 10 · Going further — more plotting

Scatter plots — two numeric variables

Add a trend line

Violin plots

Facets — small multiples

Themes — change the look

Save a scatter plot as PNG

What your finished project folder looks like

Getting unstuck

Storing values with `<-`

Comments with `#`

Also: read CSV files with `read_csv()`

Part 7 · The pipe `%>%` — read it as “then”