Lab 2: Install R and RStudio

This session introduces R and RStudio.

Why learn R?

• You will need it for your final report (if you choose the report option).
• It supports your psychology coursework.
• It enhances your coding skills, which will help you in many domains of work, including utilising AI (!).

Installing R

1. Visit CRAN at https://cran.r-project.org/.
2. Select the version for your operating system (Windows, Mac, or Linux).
3. Download and install by following the on-screen instructions.

Installing RStudio

See Johannes Karl's video for a walkthrough.

Step 1: Install RStudio

1. Go to https://www.rstudio.com/products/rstudio/download/.
2. Choose the free version of RStudio Desktop and download it for your operating system.
3. Install RStudio Desktop.
4. Open RStudio to begin setting up your project environment.

Step 2: Create a new project

1. In RStudio, go to File > New Project.
2. Choose New Directory for a new project or Existing Directory if you have a folder ready.
3. For a new project, select New Project, then provide a directory name.
4. Specify the location where the project folder will be created.
5. Click Create Project.

Step 3: Give the project structure

1. Within your project, create folders to organise scripts and data. Common folder names include R/ for R scripts, data/ for datasets, and doc/ for documentation.
2. To create a new R script, go to File > New File > R Script. Save the script in your R/ folder with a meaningful file name.
3. If you set up Git in week 1, you can initialise a Git repository when creating a new project to track your R work.

Step 4: Working with R scripts

1. Write your R code in the script editor. Execute code by selecting lines and pressing Ctrl + Enter (Windows/Linux) or Cmd + Enter (Mac).
2. Use comments (preceded by #) to document your code.
3. Save your scripts regularly (Ctrl + S or Cmd + S).

Step 5: When you exit RStudio

Before concluding your work, save your workspace or clear it to start fresh (Session > Restart R).

Basic R Commands

Assignment (<-)

Assignment in R uses the <- operator:

x <- 10 # assigns the value 10 to x
y <- 5  # assigns the value 5 to y

Concatenation (c())

The c() function combines multiple elements into a vector:

numbers <- c(1, 2, 3, 4, 5)
print(numbers)

Operations (+, -)

x <- 10
y <- 5

sum <- x + y
print(sum)

difference <- x - y
difference

Multiplication (*) and Division (/)

product <- x * y
product

quotient <- x / y
quotient

# element-wise operations on vectors
vector1 <- c(1, 2, 3)
vector2 <- c(4, 5, 6)
vector_product <- vector1 * vector2
vector_product

vector_division <- vector1 / vector2
vector_division

Be mindful of division by zero: 10 / 0 returns Inf, and 0 / 0 returns NaN.

# integer division and modulo
integer_division <- 10 %/% 3  # 3
remainder <- 10 %% 3          # 1

rm() Remove Object

devil_number <- 666
devil_number
rm(devil_number)

Logic (!, !=, ==)

x_not_y <- x != y     # TRUE
x_equal_10 <- x == 10 # TRUE

OR (| and ||)

# element-wise OR
vector_or <- c(TRUE, FALSE) | c(FALSE, TRUE) # c(TRUE, TRUE)

# single OR (first element only)
single_or <- TRUE || FALSE # TRUE

AND (& and &&)

# element-wise AND
vector_and <- c(TRUE, FALSE) & c(FALSE, TRUE) # c(FALSE, FALSE)

# single AND (first element only)
single_and <- TRUE && FALSE # FALSE

Data Types in R

Integers

x <- 42L
str(x)         # int 42

y <- as.numeric(x)
str(y)         # num 42

Integers are useful for counts or indices that do not require fractional values.

Characters

name <- "Alice"

Characters represent text: names, labels, descriptions.

Factors

colors <- factor(c("red", "blue", "green"))

Factors represent categorical data with a limited set of levels.

Ordered factors

education_levels <- c("high school", "bachelor", "master", "ph.d.")

# unordered
education_factor_no_order <- factor(education_levels, ordered = FALSE)

# ordered
education_factor <- factor(education_levels, ordered = TRUE)
education_factor

Ordered factors support logical comparisons based on level order:

edu1 <- ordered("bachelor", levels = education_levels)
edu2 <- ordered("master", levels = education_levels)
edu2 > edu1  # TRUE

Strings

you <- "world!"
greeting <- paste("hello,", you)
greeting  # "hello, world!"

Vectors

Vectors are homogeneous: all elements must be of the same type.

numeric_vector <- c(1, 2, 3, 4, 5)
character_vector <- c("apple", "banana", "cherry")
logical_vector <- c(TRUE, FALSE, TRUE, FALSE)

Manipulating vectors

vector_sum <- numeric_vector + 10
vector_multiplication <- numeric_vector * 2
vector_greater_than_three <- numeric_vector > 3

Accessing elements:

first_element <- numeric_vector[1]
some_elements <- numeric_vector[c(2, 4)]

Functions with vectors

mean(numeric_vector)
sum(numeric_vector)
sort(numeric_vector)
unique(character_vector)

Data Frames

Creating data frames

df <- data.frame(
  name = c("alice", "bob", "charlie"),
  age = c(25, 30, 35),
  gender = c("female", "male", "male")
)
head(df)
str(df)

Accessing elements

# by column name
names <- df$name

# by row and column
second_person <- df[2, ]
age_column <- df[, "age"]

# by condition
very_old_people <- subset(df, age > 25)
mean(very_old_people$age)

Exploring data frames

head(df)    # first six rows
tail(df)    # last six rows
str(df)     # structure
summary(df) # summary statistics

Manipulating data frames

# adding columns
df$employed <- c(TRUE, TRUE, FALSE)

# adding rows
new_person <- data.frame(name = "diana", age = 28, gender = "female", employed = TRUE)
df <- rbind(df, new_person)

# modifying values
df[4, "age"] <- 26

# removing columns
df$employed <- NULL

# removing rows
df <- df[-4, ]

rbind() and cbind()

rbind() combines data frames by rows; cbind() combines by columns. When using these functions, column names (for rbind) or row counts (for cbind) must match. We will use dplyr for more flexible joining in later weeks.

Summary statistics

set.seed(12345)
vector <- rnorm(n = 40, mean = 0, sd = 1)
mean(vector)
sd(vector)
min(vector)
max(vector)

table() for categorical data

set.seed(12345)
gender <- sample(c("male", "female"), size = 100, replace = TRUE, prob = c(0.5, 0.5))
education_level <- sample(c("high school", "bachelor", "master"), size = 100, replace = TRUE, prob = c(0.4, 0.4, 0.2))
df_table <- data.frame(gender, education_level)
table(df_table)
table(df_table$gender, df_table$education_level)  # cross-tabulation

First Data Visualisation with ggplot2

ggplot2 is based on the Grammar of Graphics: you build plots layer by layer.

library(ggplot2)

set.seed(12345)
student_data <- data.frame(
  name = c("alice", "bob", "charlie", "diana", "ethan", "fiona", "george", "hannah"),
  score = sample(80:100, 8, replace = TRUE),
  stringsAsFactors = FALSE
)
student_data$passed <- ifelse(student_data$score >= 90, "passed", "failed")
student_data$passed <- factor(student_data$passed, levels = c("failed", "passed"))
student_data$study_hours <- sample(5:15, 8, replace = TRUE)

Bar plot

ggplot(student_data, aes(x = name, y = score, fill = passed)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("failed" = "red", "passed" = "blue")) +
  labs(title = "student scores", x = "student name", y = "score") +
  theme_minimal()

Scatter plot

ggplot(student_data, aes(x = study_hours, y = score, color = passed)) +
  geom_point(size = 4) +
  scale_color_manual(values = c("failed" = "red", "passed" = "blue")) +
  labs(title = "scores vs. study hours", x = "study hours", y = "score") +
  theme_minimal()

Box plot

ggplot(student_data, aes(x = passed, y = score, fill = passed)) +
  geom_boxplot() +
  scale_fill_manual(values = c("failed" = "red", "passed" = "blue")) +
  labs(title = "score distribution by pass/fail status", x = "status", y = "score") +
  theme_minimal()

Histogram

ggplot(student_data, aes(x = score, fill = passed)) +
  geom_histogram(binwidth = 5, color = "black", alpha = 0.7) +
  scale_fill_manual(values = c("failed" = "red", "passed" = "blue")) +
  labs(title = "histogram of scores", x = "score", y = "count") +
  theme_minimal()

Line plot (time series)

months <- factor(month.abb[1:8], levels = month.abb[1:8])
study_hours <- c(0, 3, 15, 30, 35, 120, 18, 15)
study_data <- data.frame(month = months, study_hours = study_hours)

ggplot(study_data, aes(x = month, y = study_hours, group = 1)) +
  geom_line(linewidth = 1, color = "blue") +
  geom_point(color = "red", size = 1) +
  labs(title = "monthly study hours", x = "month", y = "study hours") +
  theme_minimal()

Base R Graphs

Base R provides plotting functions without additional packages.

# scatter plot
plot(student_data$study_hours, student_data$score,
     main = "scores vs. study hours", xlab = "study hours", ylab = "score",
     pch = 19, col = ifelse(student_data$passed == "passed", "blue", "red"))

# histogram
hist(student_data$score, breaks = 5, col = "skyblue",
     main = "histogram of student scores", xlab = "scores", border = "white")

# box plot
boxplot(score ~ passed, data = student_data,
        main = "score distribution by pass/fail status",
        xlab = "status", ylab = "scores", col = c("red", "blue"))

Summary of Today's Lab

This session covered:

• Installing and setting up R and RStudio
• Basic arithmetic operations
• Data structures: vectors, factors, data frames
• Data visualisation with ggplot2 and base R

Where to get help

1. Large language models: LLMs are effective coding tutors. Help from LLMs for coding does not constitute a breach of academic integrity in this course. For your final report, cite all sources including LLMs.
2. Stack Overflow: outstanding community resource.
3. Cross Validated: best place for statistics advice.
4. Developer websites: Tidyverse.
5. Your tutors and course coordinator.

Recommended reading

• Wickham, H., & Grolemund, G. (2016). R for Data Science. O'Reilly Media. Available online
• Megan Hall's lecture: https://meghan.rbind.io/talk/neair/
• RStudio learning materials: https://education.rstudio.com/learn/beginner/
• Johannes Karl on getting started in R: https://www.youtube.com/embed/haYxa3vWA28

Appendix A: At-Home Exercises

install.packages("remotes")
remotes::install_github("go-bayes/causalworkshop@v0.2.1")

library(causalworkshop)
d <- simulate_nzavs_data(n = 100, seed = 2026)
head(d)

Appendix B: Solutions

Solution 3: Basic operations

vector_a <- c(2, 4, 6, 8)
vector_b <- c(1, 3, 5, 7)

sum_vector <- vector_a + vector_b
diff_vector <- vector_a - vector_b
double_vector_a <- vector_a * 2
half_vector_b <- vector_b / 2

mean(vector_a); sd(vector_a)
mean(vector_b); sd(vector_b)

Solution 4: Working with data frames

student_data <- data.frame(
  id = 1:4,
  name = c("alice", "bob", "charlie", "diana"),
  score = c(88, 92, 85, 95),
  stringsAsFactors = FALSE
)
student_data$passed <- student_data$score >= 90
passed_students <- student_data[student_data$passed == TRUE, ]
summary(student_data)

Solution 5: Logical operations and subsetting

mean_score <- mean(student_data$score)
students_above_mean <- student_data[student_data$score > mean_score, ]

attendance <- c("present", "absent", "present", "present")
student_data$attendance <- attendance
present_students <- student_data[student_data$attendance == "present", ]

Solution 6: Cross-tabulation

fruit <- factor(c("apple", "banana", "apple", "orange", "banana"))
colour <- factor(c("red", "yellow", "green", "orange", "green"))
fruit_data <- data.frame(fruit, colour)
table(fruit_data$fruit, fruit_data$colour)
# apple has the most colour variety (red, green)

Solution 7: Visualisation

library(ggplot2)
ggplot(student_data, aes(x = name, y = score, fill = passed)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("TRUE" = "blue", "FALSE" = "red")) +
  labs(title = "student scores", x = "name", y = "score") +
  theme_minimal()

Appendix C: Other Data Types

Arrays and matrices

matrix_1 <- matrix(1:9, nrow = 3)
array_1 <- array(1:12, dim = c(2, 3, 2))

# convert matrix to data frame
df_matrix_1 <- data.frame(matrix_1)
colnames(df_matrix_1) <- c("col_1", "col_2", "col_3")

Lists

my_list <- list(name = "John Doe", age = 30, scores = c(90, 80, 70))

# access elements
my_list$name
my_list[["scores"]]

# modify
my_list$gender <- "Male"
my_list$age <- 31
my_list$scores <- NULL

# lists as function return values
calculate_stats <- function(numbers) {
  list(mean = mean(numbers), sum = sum(numbers))
}
results <- calculate_stats(c(1, 2, 3, 4, 5))