Part 1: Important Operators and Keyboard Shortcuts

Keyboard Shortcuts

Essential shortcuts to speed up your workflow:

Action Windows/Linux Mac
Running Code
Run current line/selection Ctrl + Enter Cmd + Enter
Run entire script Ctrl + Shift + Enter Cmd + Shift + Enter
Run from beginning to current line Ctrl + Alt + B Cmd + Option + B
Typing Helpers
Assignment <- Alt + - Option + -
Pipe %>% Ctrl + Shift + M Cmd + Shift + M
Comment/uncomment line Ctrl + Shift + C Cmd + Shift + C
File Operations
Save script Ctrl + S Cmd + S
New script Ctrl + Shift + N Cmd + Shift + N
Open file Ctrl + O Cmd + O
Editing
Find and replace Ctrl + F Cmd + F
Find in files Ctrl + Shift + F Cmd + Shift + F
Undo Ctrl + Z Cmd + Z
Redo Ctrl + Shift + Z Cmd + Shift + Z
Indent Tab Tab
Outdent Shift + Tab Shift + Tab
RStudio
Clear console Ctrl + L Cmd + L
Restart R session Ctrl + Shift + F10 Cmd + Shift + F10
Knit R Markdown Ctrl + Shift + K Cmd + Shift + K
Insert code chunk (Rmd) Ctrl + Alt + I Cmd + Option + I
Show help for function F1 (cursor on function) F1
Autocomplete Tab Tab

Operators

Assignment Operators

Used to store values in objects:

Operator Meaning Example Result
<- Assign (preferred) x <- 5 x contains 5
= Assign (also works) x = 5 x contains 5
-> Assign right 5 -> x x contains 5

Tip: Use <- for assignment. The shortcut is Alt + - (Windows) or Option + - (Mac).

Comparison Operators

Used to compare values. Always return TRUE or FALSE:

Operator Meaning Example Result
== Equal to 5 == 5 TRUE
!= Not equal to 5 != 3 TRUE
< Less than 3 < 5 TRUE
> Greater than 5 > 3 TRUE
<= Less than or equal 5 <= 5 TRUE
>= Greater than or equal 5 >= 3 TRUE
%in% Is in set "a" %in% c("a","b","c") TRUE

⚠️ Common Mistake: Don’t confuse = (assignment) with == (comparison)!

  • x = 5 → Stores 5 in x
  • x == 5 → Asks “Is x equal to 5?”

Examples in filtering:

# Filter rows where habitat equals "forest"
filter(data, habitat == "forest")

# Filter rows where abundance is greater than 50
filter(data, abundance > 50)

# Filter rows where habitat is forest OR grassland
filter(data, habitat %in% c("forest", "grassland"))

Logical Operators

Used to combine multiple conditions:

Operator Meaning Example Result
& AND (both must be TRUE) TRUE & FALSE FALSE
| OR (at least one TRUE) TRUE | FALSE TRUE
! NOT (reverses TRUE/FALSE) !TRUE FALSE

Examples:

# AND: Both conditions must be true
filter(data, habitat == "forest" & abundance > 50)

# OR: At least one condition must be true
filter(data, habitat == "forest" | habitat == "grassland")

# NOT: Exclude missing values
filter(data, !is.na(abundance))

Arithmetic Operators

Used for mathematical calculations:

Operator Meaning Example Result
+ Addition 5 + 3 8
- Subtraction 5 - 3 2
* Multiplication 5 * 3 15
/ Division 6 / 2 3
^ Exponent (power) 2^3 8
%% Modulo (remainder) 7 %% 3 1
%/% Integer division 7 %/% 3 2

Special Operators

Operator Meaning Example Description
%>% Pipe data %>% filter() Passes left side to right side. Read as “THEN”
$ Extract column data$column Gets a column from a data frame
[ ] Subset/index data[1, 2] Extracts elements by position
[[ ]] Extract single element list[[1]] Extracts single element from list
: Sequence 1:10 Creates sequence from 1 to 10
:: Package function dplyr::filter() Uses function from specific package
~ Formula y ~ x Used in models and statistics

The Pipe Operator %>%

The pipe takes the output from the left side and passes it as the first argument to the function on the right side. We will explore more in Part 2.

Read %>% as “THEN”

Shortcut: Ctrl + Shift + M (Windows) or Cmd + Shift + M (Mac)

Common Mistakes to Avoid

Wrong ❌ Correct ✅ Explanation
habitat = "forest" habitat == "forest" Use == to compare, = assigns
habitat == forest habitat == "forest" Text needs quotes
setwd("C:/Users/...") Use R Projects Absolute paths break on other computers
NA == NA is.na(x) Use is.na() to check for missing values
x = TRUE in filter x == TRUE Use == for comparison in functions

Part 2: R Basics

Assignment & Objects

x <- 5                    # Assign value to object
my_data <- c(1, 2, 3)     # Create vector

Data Types

Type Example Check with
Numeric 42, 3.14 class(x)
Character "forest" is.character(x)
Logical TRUE, FALSE is.logical(x)
Factor factor("low", "high") is.factor(x)

Functions

function_name(argument1, argument2)
mean(c(1, 2, 3))           # Calculate mean
round(3.14159, digits = 2) # Named argument

Getting Help

?mean                     # Help for function
??diversity               # Search help

Part 3: Data Wrangling (tidyverse)

Core Verbs

Verb Purpose Example
filter() Keep rows filter(data, habitat == "forest")
select() Choose columns select(data, site, abundance)
mutate() Create columns mutate(data, log_ab = log(abundance))
arrange() Sort rows arrange(data, desc(richness))
summarise() Summarize summarise(data, mean = mean(x))
group_by() Group data group_by(data, habitat)

Filtering Patterns

# Exact match
filter(data, habitat == "forest")

# Not equal
filter(data, habitat != "agriculture")

# Multiple options
filter(data, habitat %in% c("forest", "grassland"))

# Greater/less than
filter(data, abundance > 50)
filter(data, abundance <= 100)

# Range (between two values)
filter(data, abundance >= 10 & abundance <= 100)

# Combine conditions (AND)
filter(data, habitat == "forest" & abundance > 50)

# Combine conditions (OR)
filter(data, habitat == "forest" | habitat == "grassland")

# Remove missing values
filter(data, !is.na(abundance))

Creating and Modifying

# Create new column
mutate(data, log_abundance = log(abundance + 1))

# Create column with conditions
mutate(data, 
  size_class = case_when(
    abundance < 10 ~ "low",
    abundance < 50 ~ "medium",
    TRUE ~ "high"
  )
)

Summarizing

# Basic summary
summarise(data, 
  mean_ab = mean(abundance),
  sd_ab = sd(abundance),
  n = n()
)

# Summary by group
data %>%
  group_by(habitat) %>%
  summarise(
    mean_ab = mean(abundance),
    n_species = n_distinct(species)
  )

The Pipe Operator: %>%

What is The Pipe

The pipe %>% is a specieal operator from the magrittr package (loaded automatically with tidyverse). It takes the output from the left side and passees it as the first argument to the function on the right side.

Aspect Description
Symbol %>% or |>
Read as “THEN” or “AND THEN”
What it does Passes left side as first argument to right side
Why use it Makes multi-step operations readable
Shortcut Ctrl + Shift + M (Windows/Linux) / Cmd + Shift + M (Mac)
Package Loaded with tidyverse (from magrittr)

The Basic Concept

# These two lines do EXACTLY the same thing:

# Without pipe - function wraps around data
mean(c(1, 2, 3, 4, 5))

# With pipe - data flows into function
c(1, 2, 3, 4, 5) %>%  mean ()

# Read as: "Take 1, 2, 3, 4, 5 THEN calculate mean"

Why use the PIPE?“ Problem: Nested functions are hard to read.

When you need to do multiple operations, code becomes confusing:

# HARD TO READ - You must read from inside out!
round(mean(sqrt(abs(c(-4, 9, -16, 25)))), 2)

# What's happening?
# 1. c(-4, 9, -16, 25)  - create vector
# 2. abs(...)           - absolute value
# 3. sqrt(...)          - square root
# 4. mean (...)         - average
# 5. round(..., 2)      - round to 2 decimals

# This is like reading a sentence backwards!

Solution: The pipe makes code flow naturally

# EASY TO READ - Read from top to bottom, left to right!
c(-4, 9, -16, 25) %>%       # start with these numbers, THEN
  abs() %>%                 # take absolute value, THEN
  sqrt() %>%                # take square root, THEN
  mean() %>%                # calculate mean, THEN
  round(2)                  # round to 2 decimals
  
# This reads like a recipe - step by step!

How the Pipe works technically

The pipe takes whatever is on the LEFT and inserts it as the FIRST ARGUMENT of the function on the RIGHT.

# These are equivalent:
x %>% f()
f(x)

# These are also equivalent:
x %>% f(y)
f(x, y)

# And also these:
x %>% f(y, z)
f(x, y, z)

Real examples with Data Frames

# WITHOUT pipe - nested and confusing
summary(filter(select(insect_data, site, habitat, abundance), habitat == "forest"))

# WITH pipe - clear step-by-step workflow
insect_data %>% 
  select(site, habitat, abundance) %>%    # choose columns, THEN
  filter(habitat == "forest") %>%         # keep forest rows, THEN
  summary()                               # show summary
  
# Each step:
# 1. Start with insect_data.
# 2. select() receives insect_data as first argument.
# 3. filter() receives the result of select() as first argument.
# 4. summary() receives the result of filter() as first argument.

The Cooking Recipe Analogy

Think of the pipe like following a recipe:


# Recipe WITHOUT pipe (confusing):
serve(
  plate(
    garnish(
      cook(
        season(
          chop(vegetables)
        )
      )
    )
  )
)

# Recipe WITH pipe:
vegetables %>% 
  chop() %>% 
  season() %>% 
  cook() %>% 
  garnish() %>% 
  plate() %>% 
  serve()

# Just like a real recipe:
# 1. Take vegetables
# 2. Chop them
# 3. Season them
# 4. Cook them
# 5. Garnish
# 6. Plate
# 7. Serve

Common Patterns in Ecological Data Analysis


# Pattern 1: Data Exploration
insect_data %>% 
  filter(order == "Coleoptera") %>% 
  group_by(habitat) %>% 
  summarise(
    total = sum(abundance),
    richness = n_distinct(morphospecies)
)

# Read as:
# "Take insect_data, THEN
# filter to Coleoptera, THEN
# group by habitat, THEN
# summarise total and richness"

# Pattern 2: Creating community matrix
insect_data %>% 
  filter(order == "Coleoptera") %>% 
  group_by(site, morphospecies) %>% 
  summarise(abundance = sum(abundance), .groups = "drop") %>% 
  pivot_wider(names_from = morphospecies, values_from = abundance, values_fill = 0)

# Pattern 3: Saving results along the way
beetle_summary <- insect_data %>%     # save the final result
  filter(order == "Coleoptera") %>% 
  group_by(habitat) %>% 
  summarise(mean_abundance = mean(abundance))

The New Native Pipe: |> R version 4.1+ introduced a built-in-pipe |> that works similarly:


# magrittr pipe (from tidyverse)
x %>% mean()

# Native R pipe (R 4.1+)
x |> mean ()

# Both work! The native pipe is slightly faster but has fewer features.
# For this workshop, we use %>% because it's more common in existing code.

When NOT to Use the Pipe The pipe isn’t always the best choice:


# Don't pipe into functions that don't take data as first argument
c(1, 2, 3) %>% plot(main = "My Plot")   # Works, but...

# Don't use for simple one-step operations (unnecessary)
x %>% mean()                            # Just use: mean(x)

# Don't make chains too long (it will be hard to debug)
# If your chain is >10 steps, consider breaking it up and saving intermediate resutls.

# DO use pipes when you have > 2 steps that transform data
data %>% 
  step1() %>% 
  step2() %>% 
  step3() %>%

Reshaping

pivot_longer(data, cols, names_to, values_to)  # Wide → Long
pivot_wider(data, names_from, values_from)     # Long → Wide

Part 4:Community Ecology (vegan)

Alpha Diversity

specnumber(comm_matrix)              # Richness
diversity(comm_matrix, "shannon")    # Shannon H'
diversity(comm_matrix, "simpson")    # Simpson 1-D
rarefy(comm_matrix, sample = n)      # Rarefied richness

Distance & Ordination

vegdist(comm_matrix, "bray")         # Bray-Curtis distance
metaMDS(comm_matrix, k = 2)          # NMDS
pcoa(dist_matrix)                    # PCoA (ape package)

Statistical Tests

adonis2(comm ~ habitat, data = env)  # PERMANOVA
betadisper(dist, groups)             # Dispersion test
simper(comm, groups)                 # Species contributions

Part 5: Interpretation Guidelines

NMDS Stress

Value Quality
< 0.05 Excellent
0.05-0.10 Good
0.10-0.20 Acceptable
> 0.20 Poor

PERMANOVA R²

Value Effect Size
< 0.05 Tiny
0.05-0.10 Small
0.10-0.25 Medium
> 0.25 Large

Part 6: Visualization (ggplot2)

Basic Structure

ggplot(data, aes(x = var1, y = var2)) +
  geom_point()            # Add geometry

Common Geoms

Geom Plot Type
geom_point() Scatter plot
geom_boxplot() Box plot
geom_col() Bar plot
geom_histogram() Histogram
geom_smooth() Trend line

Customization

+ labs(x = "Label", y = "Label", title = "Title")
+ theme_bw()
+ scale_color_manual(values = c("red", "blue"))
+ facet_wrap(~ variable)

Part 7: Common Errors & Fixes

Error Likely Cause Fix
object not found Typo or not created Check spelling, run creation code
could not find function Package not loaded library(package)
unexpected symbol Missing comma/parenthesis Check syntax
subscript out of bounds Wrong index Check dimensions with dim()

Keep this card handy during the workshop!