Figure 1: Causal graph: we will refer to this image in the lecture and begin reviewing causal graphs in Week 2

Background readings: none today!

If you want to get started, download R and Rstudio as below
List of recommended readings at the end of this lecture.

Key concepts for the test(s):

Today we introduce the following topics relevant to the Test(s)

Confounding (introduced in week 2)
Internal Validity (introduced in week 2)
External Validity (introduced in week 3)

Today we discuss these concepts informally. We will define these concepts formally in technical detail in week 2.

Download Your Laboratory R Script Here

Once you have successfully installed R and RStudio:

Download the R script for Lab 01 by clicking the link below. This script contains the code you will work with during your laboratory session.
After downloading, open RStudio.
In RStudio, create a new R script file by going to File > New File > R Script.
Copy the contents of the downloaded .R file and paste them into the new R script file you created in RStudio.
Save the new R script file in your project directory for easy access during the lab.

Download the R script for Lab 01

Lecture: Introduction to the Course

Slides

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Lab 1: Install R and RStudio

This session introduces R and R-Studio.

Slide deck:

Slides

PREVIEW

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Why learn R?

You’ll need it for your final report.
Supports your psychology coursework.
Enhances your coding skills.

Installing R

Visit the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/.
Select the version of R suitable for your operating system (Windows, Mac, or Linux).
Download and install it by following the on-screen instructions.

Installing RStudio

After downloading R…

See Johannes Karl’s Video

Step 1: Install R-Studio

If you have already downloaded and installed R from the Comprehensive R Archive Network (CRAN):

Go to the RStudio download page at https://www.rstudio.com/products/rstudio/download/
Choose the free version of RStudio Desktop, and download it for your operating system.
Download and install RStudio Desktop.
Open RStudio to begin setting up your project environment.

Step 2: Create a new project

In RStudio, go to File > New Project.
Choose New Directory for a new project or Existing Directory if you have a folder where you want to initialise an RStudio project.
For a new project, select New Project, then provide a directory name. This name will also be the name of your project.
Specify the location where the project folder will be created.
Click Create Project.

Order your R-studio/R workflow

Clear folder structure
If you are using GitHub (or similar) create a location on your machine (i.d. not dropbox)
If you are not using GitHub choose the cloud (Dropbox or similar).
When creating new files and scripts, use clear labels that anyone could understand.
That “anyone” will be your future self, trying to make sense.

Step 3: Give project structure

Organising Files and Folders:
- Within your project, create folders to organise your scripts and data.
- Common folder names include R/ for R scripts, data/ for datasets, and doc/ for documentation.
- You can create these folders using RStudio’s Files pane or through your operating system’s file explorer.
Creating and Managing R Scripts:
- To create a new R script, go to File > New File > R Script.
- Save the script in your project directory’s R/ folder to keep your work organised. Use meaningful file names that describe the script’s purpose.
Version Control:
- If you are familiar with version control, you can initialise a Git repository within your project by selecting the Version Control option when creating a new project.
- This allows for better tracking of changes and collaboration if working with others.
- If you are not familiar with version control (or have not installed git on your machine), do not worry about initialising a Git repository.

Step 4: Working with R-scripts

Writing and executing Code:
- Write your R code in the script editor.
- Execute code by selecting lines and pressing Ctrl + Enter (Windows/Linux) or Cmd + Enter (Mac).
Commenting and documentation:
- Use comments (preceded by #) to document your code for clarity and future reference.
Saving and organising scripts:
- Regularly save your scripts (Ctrl + S or Cmd + S).
- Organise scripts into folders within your project for different analyses or data processing tasks.

Step 5: When you exit R-studio

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

Order your R-studio/R workflow

Again, use clearly defined script names
Annotate your code
Save your scripts often (Ctrl + S or Cmd + S).

Exercise 1: Install the tidyverse package

Follow these instructions to install the tidyverse package in RStudio:

Open RStudio: launch RStudio on your computer.
Access package installation:
- Navigate to the menu at the top of RStudio and click on Tools > Install Packages.... This opens the Install Packages dialogue box.
Install tidyverse:
- In the Install Packages dialogue box, you will see a field labelled “Packages (separate multiple with space or comma):”. Click in this field and type tidyverse.
- Below the packages field, ensure the checkbox for Install dependencies is checked. This ensures all packages that tidyverse depends on are also installed.
Begin installation:
- Click on the Install button to start the installation process.

The installation might take a few minutes. Monitor the progress in the “Console” pane. Once the installation is complete, you will see a message in the console indicating that the process has finished.

Load tidyverse: After successful installation, you can load the tidyverse package into your R session by typing library(tidyverse) in the console and pressing Enter.

Basic R Commands

How to copy the code on this page

R Script > New File > R
Name your new R script and save it in a folder.
Hover your cursor over the top right of the code panel, and click the copy tab.
Copy the text into your script.
Save: Ctrl + S or Cmd + S.

Assignment (`<-`)

Assignment in R is done using the ‘<-’ operator, which on my machine renders <-. This operator assigns values to variables:

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

# this does the same
x <- 10
y <- 5

# note what happens when we do this
# 10 = 5 # not run

# but we can do this
# 10 == 5 # considered below

RStudio Assignment Operator Shortcut

For macOS: Option + - (minus key) inserts <-.
For Windows and Linux: Alt + - (minus key) inserts <-.

Consult the latest RStudio documentation or access the Keyboard Shortcuts Help (Tools -> Keyboard Shortcuts Help) for up-to-date shortcuts.

Concatenation (`c()`)

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

numbers <- c(1, 2, 3, 4, 5) # a vector of numbers
print(numbers)

[1] 1 2 3 4 5

Operations (`+`, `-`)

Basic arithmetic operations include addition (+) and subtraction (-).

# this does the same
x <- 10
y <- 5

sum <- x + y # adds x and y

print(sum)

[1] 15

difference <- x - y # subtracts y from x

# note we did not need to use the `print()` function
difference

[1] 5

Executing code

Ctrl + Enter (Windows/Linux) or Cmd + Enter (Mac).

In addition to assignment, multiplication and division are fundamental arithmetic operations in R that allow you to manipulate numeric data. Here is how you can incorporate these operations into your basic R commands documentation:

Multiplication (`*`) and Division (`/`)

Multiplication and division in R are performed using the * and / operators, respectively. These operators allow for element-wise operations on vectors, as well as operations on individual numeric values.

# multiplication
product <- x * y # multiplies x by y
product

[1] 50

# division
quotient <- x / y # divides x by y
quotient

[1] 2

# element-wise multiplication on vectors
vector1 <- c(1, 2, 3)
vector2 <- c(4, 5, 6)
# multiplies each element of vector1 by the corresponding element of vector2
vector_product <- vector1 * vector2
vector_product

[1]  4 10 18

# element-wise division on vectors
# divides each element of vector1 by the corresponding element of vector2
vector_division <- vector1 / vector2
vector_division

[1] 0.25 0.40 0.50

Multiplication and division can be used for scalar (single values) and vector (multiple values) operations. When applied to vectors, these operations are performed element-wise.
Be mindful of division by zero, as this will result in Inf (infinity) or NaN (not a number) depending on the context.

# example of division by zero
result <- 10 / 0 # results in Inf
zero_division <- 0 / 0 # results in NaN

R also supports integer division using the %/% operator and modulo operation using %% to find the remainder.

# integer division
integer_division <- 10 %/% 3 # results in 3

# modulo operation
remainder <- 10 %% 3 # results in 1

`rm()` Remove Object

# `rm()` remove object ----------------------------------------------------
devil_number <- 666 # results in 1

# view
devil_number

[1] 666

# remove the devil number
rm(devil_number)

# check
# devil_number

Logic (`!`, `!=`, `==`)

Logical operations include NOT (!), NOT EQUAL (!=), and EQUAL (==).

x_not_y <- x != y # checks if x is not equal to y
x_not_y

[1] TRUE

x_equal_10 <- x == 10 # checks if x is equal to 10
x_equal_10

[1] TRUE

Logical operations are fundamental in R for controlling the flow of execution and making decisions based on conditions. In addition to NOT (!), NOT EQUAL (!=), and EQUAL (==), there are several other logical operators you should know:

OR (`|` and `||`)

The | operator performs element-wise logical OR operation. It evaluates each pair of elements in two logical vectors to see if at least one is TRUE.
The || operator performs a logical OR operation but only evaluates the first element of each vector – mainly used in if statements and not for vectorised operations.

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

[1] TRUE TRUE

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

[1] TRUE

AND (`&` and `&&`)

The & operator performs element-wise logical AND operations. It checks if both elements in the corresponding positions of two logical vectors are TRUE.
The && operator performs a logical AND operation but only evaluates the first element of each vector. Like ||, used in conditions that do not require vectorised operations.

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

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

RStudio Workflow Shortcuts

Shortcuts bring order and boost creativity

Execute Code Line: Cmd + Return (Mac) or Ctrl + Enter (Windows/Linux)
Insert Section Heading: Cmd + Shift + R (Mac) or Ctrl + Shift + R (Windows/Linux)
Align Code: Cmd + Shift + A (Mac) or Ctrl + Shift + A (Windows/Linux)
Comment/Uncomment: Cmd/Ctrl + Shift + C
Save All: Cmd/Ctrl + Shift + S
Find/Replace: Cmd/Ctrl + F, Cmd/Ctrl + Shift + F
New File: Cmd/Ctrl + Shift + N
Auto-complete: Tab

For more commands, explore the Command Palette available under Tools -> Command Palette or Shift + Cmd + P (Mac) or Shift + Ctrl + P (Windows/Linux).

Data Types in R

Understanding data types in R is essential. R supports several fundamental data types, including integers, characters, factors, and ordered factors. Each type has its specific use case and functions associated with it.

Integers

Integers are whole numbers without decimal points. In R, integers can be explicitly defined by adding an L suffix to the number.

# define an integer
x <- 42L

x

[1] 42

# check
str(x) # is integer

 int 42

# convert to numeric
y <- as.numeric(x)

str(y)

 num 42

Integers are particularly useful when dealing with counts or indices that do not require fractional values.

Characters

Character data types are used to represent text. In R, text strings are enclosed in quotes, either single (') or double (").

# define a character string
name <- "Alice"

Characters are essential for categorical data that does not fit into numerical categories, such as names, labels, and descriptions.

Factors

Factors are used to represent categorical data that can take on a limited number of values, known as levels. Factors are useful for statistical modeling as they explicitly define categories in the data.

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

Factors can improve efficiency and memory usage when dealing with categorical data, especially in large datasets. And they are useful when dealing with categorical variables (naturally).

Ordered Factors

Ordered factors are a special type of factor where the levels have an inherent order. They are defined similarly to factors but with an additional argument to denote the order.

# ordered factors ---------------------------------------------------------
# factors ordinary
education_levels <- c("high school", "bachelor", "master", "ph.d.")
education_factor_no_order <- factor(education_levels, ordered = FALSE)
str(education_factor_no_order)

 Factor w/ 4 levels "bachelor","high school",..: 2 1 3 4

# factors with inherent order
education_factor <- factor(education_levels, ordered = TRUE)
education_factor

[1] high school bachelor    master      ph.d.      
Levels: bachelor < high school < master < ph.d.

# another way to do the same
education_ordered_explicit <- factor(education_levels, levels = education_levels, ordered = TRUE)

Ordered factors allow for logical comparisons based on their order, which is particularly useful in analyses where the order of categories matters, such as ordinal regression.

Operations with Ordered Factors

Ordered factors support logical comparisons that consider the order of the levels.

# comparison of ordered factors
edu1 <- ordered("bachelor", levels = education_levels)
edu2 <- ordered("master", levels = education_levels)
edu2 > edu1 # logical comparison

[1] TRUE

# modifying ordered factors
new_levels <- c("primary school", "high school", "bachelor", "master", "ph.d.")
education_updated <- factor(education_levels, levels = new_levels, ordered = TRUE)
str(education_updated)

 Ord.factor w/ 5 levels "primary school"<..: 2 3 4 5

Checking Data with Ordered Factors

You can view the structure and summary of ordered factors just as with regular factors, but the output will indicate the order.

# view the structure
str(education_ordered_explicit)

 Ord.factor w/ 4 levels "high school"<..: 1 2 3 4

# summary to see the distribution
summary(education_ordered_explicit)

high school    bachelor      master       ph.d. 
          1           1           1           1

Modifying Ordered Factors

If you need to change the order of levels or add new levels, you can re-factor the variable using factor() or ordered() and specify the new levels.

# modifying ordered factors
new_levels <- c("primary school", "high school", "bachelor", "master", "ph.d.")
education_updated <- factor(education_levels, levels = new_levels, ordered = TRUE)
str(education_updated)

 Ord.factor w/ 5 levels "primary school"<..: 2 3 4 5

str(education_updated)

 Ord.factor w/ 5 levels "primary school"<..: 2 3 4 5

Strings

Strings are sequences of characters.

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

[1] "hello, world!"

Vectors

Vectors are one of R’s most fundamental data structures, essential for storing and manipulating a sequence of data elements.

Vectors are homogenous, meaning all elements in a vector must be of the same type (e.g., all numeric, all character, etc.).

Vectors in R can be created using the c() function, which stands for concatenate or combine:

# numeric vector
numeric_vector <- c(1, 2, 3, 4, 5)

# character vector
character_vector <- c("apple", "banana", "cherry")

# logical vector
logical_vector <- c(TRUE, FALSE, TRUE, FALSE)

Manipulating Vectors

R allows you to perform operations on vectors in a very intuitive way. Operations are vectorised, meaning they are applied element-wise:

# arithmetic operations
vector_sum <- numeric_vector + 10 # Adds 10 to each element
# show
vector_sum

[1] 11 12 13 14 15

# vector mutliplication
vector_multiplication <- numeric_vector * 2 # Multiplies each element by 2
# show
vector_multiplication

[1]  2  4  6  8 10

# logical operations
vector_greater_than_three <- numeric_vector > 3 # Returns a logical vector

# show
vector_greater_than_three

[1] FALSE FALSE FALSE  TRUE  TRUE

You can access elements of a vector by using square brackets [ ] with an index or a vector of indices:

#  the first element of numeric_vector
first_element <- numeric_vector[1]

# show
first_element

[1] 1

# multiple elements
some_elements <- numeric_vector[c(2, 4)] # Gets the 2nd and 4th elements

# show
first_element

[1] 1

Functions with vectors

R provides a rich set of functions for statistical computations and manipulations that work with vectors:

# statistical summary
vector_mean <- mean(numeric_vector)
vector_mean

[1] 3

vector_sum <- sum(numeric_vector)
vector_sum

[1] 15

# sorting
sorted_vector <- sort(numeric_vector)
sorted_vector

[1] 1 2 3 4 5

# unique values
unique_vector <- unique(character_vector)
unique_vector

[1] "apple"  "banana" "cherry"

Data Frames

Create Data Frames

Data frames can be created using the data.frame() function, specifying each column and its values. Here’s a simple example:

# clear any previous `df` object
rm(df)
df <- data.frame(
    name = c("alice", "bob", "charlie"),
    age = c(25, 30, 35),
    gender = c("female", "male", "male")
)
# check structure
head(df)

     name age gender
1   alice  25 female
2     bob  30   male
3 charlie  35   male

str(df)

'data.frame':   3 obs. of  3 variables:
 $ name  : chr  "alice" "bob" "charlie"
 $ age   : num  25 30 35
 $ gender: chr  "female" "male" "male"

table(df$gender)


female   male 
     1      2

table(df$age)


25 30 35 
 1  1  1

table(df$name)


  alice     bob charlie 
      1       1       1

In this example, df is a data frame with three columns (name, age, gender) and three rows, each representing a different individual.

Access Data Frame Elements

There are often several ways to do the same thing in R. You can access the elements of a data frame in several ways:

By column name: use the $ operator followed by the column name.

names <- df$name # extracts the `name` column
names

[1] "alice"   "bob"     "charlie"

By row and column: Use the [row, column] indexing. Rows or columns can be specified by number or name.

# access data frame elements
names <- df$name
names

[1] "alice"   "bob"     "charlie"

second_person <- df[2, ]
second_person

  name age gender
2  bob  30   male

age_column <- df[, "age"]
age_column

[1] 25 30 35

Using subset() Function: To extract subsets of the data frame based on conditions.

# use subset()
very_old_people <- subset(df, age > 25) # extracts rows where `age` is greater than 18
very_old_people

     name age gender
2     bob  30   male
3 charlie  35   male

summary(very_old_people$age)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  30.00   31.25   32.50   32.50   33.75   35.00

mean(very_old_people$age)

[1] 32.5

min(very_old_people$age)

[1] 30

Explore Your Data Frames

Functions such as head(), tail(), and str() help you explore the first few rows, last few rows, and the structure of the data frame, respectively.

head(df) # first six rows

     name age gender
1   alice  25 female
2     bob  30   male
3 charlie  35   male

tail(df) # last six rows

     name age gender
1   alice  25 female
2     bob  30   male
3 charlie  35   male

str(df) # structure of the data frame

'data.frame':   3 obs. of  3 variables:
 $ name  : chr  "alice" "bob" "charlie"
 $ age   : num  25 30 35
 $ gender: chr  "female" "male" "male"

Manipulating Data Frames

Data frames can be manipulated in various ways:

Adding Columns: You can add new columns using the $ operator.

# adds a new column "employed"
df$employed <- c(TRUE, TRUE, FALSE)

# show
head(df)

     name age gender employed
1   alice  25 female     TRUE
2     bob  30   male     TRUE
3 charlie  35   male    FALSE

Adding rows: Use the rbind() function to add new rows.

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

# show
head(df)

     name age gender employed
1   alice  25 female     TRUE
2     bob  30   male     TRUE
3 charlie  35   male    FALSE
4   diana  28 female     TRUE

Modifying values: Access the element or column and assign it a new value.

# note brackets
# changes diana's age to 26
df[4, "age"] <- 26

# view row
df[4, ]

   name age gender employed
4 diana  26 female     TRUE

Removing columns or rows: set columns to NULL to remove them, or use - with row or column indices.

head(df)

     name age gender employed
1   alice  25 female     TRUE
2     bob  30   male     TRUE
3 charlie  35   male    FALSE
4   diana  26 female     TRUE

# remove employed column
df$employed <- NULL

# check
df

     name age gender
1   alice  25 female
2     bob  30   male
3 charlie  35   male
4   diana  26 female

# remove fourth row (Diana)
df <- df[-4, ] # removes the fourth row

# show
df

     name age gender
1   alice  25 female
2     bob  30   male
3 charlie  35   male

Add Rows with `rbind()`

The rbind() function in R stands for “row bind”. It is used to combine data frames or matrices by rows. This function is particularly useful when you want to add new observations or records to an existing data frame.

#  adding a new row
new_person <- data.frame(name = "eve", age = 32, gender = "female")
df <- rbind(df, new_person)

# verify the row addition
head(df)

     name age gender
1   alice  25 female
2     bob  30   male
3 charlie  35   male
4     eve  32 female

When using rbind(), ensure that the columns in the data frames being combined match in both name and order. If they do not match, you may encounter errors or unexpected results.

Adding Columns with `cbind()`

Conversely, the cbind() function in R stands for “column bind”. It is used to combine data frames or matrices by columns. This function allows you to add new variables to an existing data frame.

# example of adding a new column
df$occupation <- c("engineer", "doctor", "artist", "doctor") # direct assignment

# or using cbind for a separate vector
occupation_vector <- c("engineer", "doctor", "artist", "doctor")
df <- cbind(df, occupation_vector)

# verify the column addition
head(df)

     name age gender occupation occupation_vector
1   alice  25 female   engineer          engineer
2     bob  30   male     doctor            doctor
3 charlie  35   male     artist            artist
4     eve  32 female     doctor            doctor

As with rbind(), when using cbind(), it is crucial that the data frames or vectors being combined have compatible dimensions. For cbind(), the number of rows must match.

Considerations for `rbind()` and `cbind()`

While rbind() and cbind() are straightforward and powerful functions for combining data, they have some limitations:

Matching Column or Row Names: for rbind(), the column names between the data frames need to match exactly. For cbind(), the row numbers must be equal.
Factor Levels: when binding factors with different levels, R will unify the levels, which can sometimes lead to unexpected results. Be mindful of factor levels when using these functions.
Performance: we will use a different approach next week (and following), draing on thedplyr package, which will connect more easily to our workflows.

By understanding and utilizing rbind() and cbind(), you can efficiently manipulate the structure of your data frames, adding flexibility to your data analysis workflows in R.

View Data Structure (`summary()`, `str()`, `head()`, `tail()`)

summary(): Provides a summary of an object’s structure.
str(): Displays the structure of an object.
head(): Shows the first few rows of a data frame or the first elements of a vector.
tail(): Shows the last few rows of a data frame or the last elements of a vector.

# iris is a preloaded dataset
str(iris) # displays structure of scores_df

'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

summary(iris) # summary statistics

  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
 Median :5.800   Median :3.000   Median :4.350   Median :1.300  
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199  
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500  
       Species  
 setosa    :50  
 versicolor:50  
 virginica :50

head(iris) # first few rows

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

tail(iris) # last few rows

    Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
145          6.7         3.3          5.7         2.5 virginica
146          6.7         3.0          5.2         2.3 virginica
147          6.3         2.5          5.0         1.9 virginica
148          6.5         3.0          5.2         2.0 virginica
149          6.2         3.4          5.4         2.3 virginica
150          5.9         3.0          5.1         1.8 virginica

mean()

Calculates the arithmetic mean of a numerical object.

set.seed(12345)

# we will cover R’s powerful simulation functions like `rnorm()`next week
vector <- rnorm(n = 40, mean = 0, sd = 1)
mean(vector) # note the sampling error here

[1] 0.2401853

sd()

Computes the standard deviation, which measures the amount of variation or dispersion of a set of values.

sd(vector) # replace 'vector' with your numerical vector

[1] 1.038425

min() and max()

These functions return a numerical object’s minimum and maximum values, respectively.

min(vector) # minimum value

[1] -1.817956

max(vector) # maximum value

[1] 2.196834

table()

Generates a frequency table of an object, useful for categorical data. It counts the number of occurrences of each unique element.

#  seed for reproducibility
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
)

# determine pass/fail
student_data$passed <- ifelse(student_data$score >= 90, "passed", "failed")
# convert 'passed' to factor
student_data$passed <- factor(student_data$passed, levels = c("failed", "passed"))

# simulate study hours
student_data$study_hours <- sample(5:15, 8, replace = TRUE)
# table for categorical data analysis
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_example <- data.frame(gender, education_level)
table(df_table_example)

        education_level
gender   bachelor high school master
  female       14          18     11
  male         23          20     14

Cross-Tabulation with table()

table() can also be used for cross-tabulation, providing a way to analyse the relationship between two or more factors.

table(df_table_example$gender, df_table_example$education_level) # crosstab

        
         bachelor high school master
  female       14          18     11
  male         23          20     14

This produces a contingency table showing the counts at each combination of factor1 and factor2 levels.

Summary Statistics

Use summary() to get a summary of each column.

# show
summary(df)

     name                age           gender           occupation       
 Length:4           Min.   :25.00   Length:4           Length:4          
 Class :character   1st Qu.:28.75   Class :character   Class :character  
 Mode  :character   Median :31.00   Mode  :character   Mode  :character  
                    Mean   :30.50                                        
                    3rd Qu.:32.75                                        
                    Max.   :35.00                                        
 occupation_vector 
 Length:4          
 Class :character  
 Mode  :character

First Data Visualisation with `ggplot2`

ggplot2 is a powerful and flexible R package for creating elegant data visualisations. It is based on the Grammar of Graphics, allowing users to build plots layer by layer, making it versatile for creating a wide range of plots.

Installing and Loading `ggplot2`

Before using ggplot2, ensure the package is installed and loaded into your R session:

# load ggplot2
if (!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)

# seed for reproducibility
set.seed(12345)

# simulate student data (more on simulation next week)
student_data <- data.frame(
    name = c("alice", "bob", "charlie", "diana", "ethan", "fiona", "george", "hannah"),
    score = sample(80:100, 8, replace = TRUE), # random scores between 80 and 100
    stringsasfactors = FALSE
)

# determine pass/fail based on score
# we will cover the ifelse() operator in detail in upcoming weeks
student_data$passed <- ifelse(student_data$score >= 90, "passed", "failed")

# convert 'passed' to factor for colour coding in ggplot2
student_data$passed <- factor(student_data$passed, levels = c("failed", "passed"))

# view the first few rows of the data frame
head(student_data)

     name score stringsasfactors passed
1   alice    93            FALSE passed
2     bob    98            FALSE passed
3 charlie    95            FALSE passed
4   diana    90            FALSE passed
5   ethan    81            FALSE failed
6   fiona    90            FALSE passed

# simulate study hours
student_data$study_hours <- sample(5:15, 8, replace = TRUE)

Basic Components of a `ggplot2` Plot

Data: the dataset you want to visualise.
Aesthetics (aes): defines how data are mapped to colour, size, shape, and other visual properties.
Geometries (geom_ functions): the type of plot or layer you want to add (e.g., points, lines, bars).

Create a Basic Plot

Start by creating a simple bar plot:

ggplot(student_data, aes(x = name, y = score)) +
    geom_bar(stat = "identity")

This code plots score for each name in the student_data dataframe. The stat = "identity" argument tells ggplot2 to use the score values directly to determine the height of the bars.

Customising the plot

To enhance your plot, you can add titles, change axis labels, and modify colours:

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 = "student name", y = "score") +
    theme_minimal()

aes(fill = passed): maps the passed variable to the colour fill of the bars, allowing for colour differentiation based on whether students passed or failed.
scale_fill_manual(): customizes the colours used for the true and FALSE values of the passed variable.
labs(): adds a main title and axis labels.
theme_minimal(): applies a minimalistic theme to the plot for a cleaner appearance.

Scatter Plot with `ggplot2`

A scatter plot is useful for examining the relationship between two continuous variables.

We next simulate a scenario where we compare student scores against study hours.

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

Box Plot with `ggplot2`

Box plots are excellent for visualising the distribution of scores by pass/fail status, showing medians, quartiles, and potential outliers.

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

Histogram with `ggplot2`

Histograms are helpful for understanding the distribution of a single continuous variable, such as scores.

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

Line Plot with `ggplot2` (Time Series)

For demonstrating a line plot, we simulate monthly study hours over a semester for a student.

# simulate monthly study hours
months <- factor(month.abb[1:8], levels = month.abb[1:8])
study_hours <- c(0, 3, 15, 30, 35, 120, 18, 15)

# make data frame
study_data <- data.frame(month = months, study_hours = study_hours)

# create a line plot
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

Although ggplot2 is renowned for its flexibility and aesthetic appeal, Base R graphics remain a staple for straightforward and quick visualisations.

Base R provides a set of plotting functions that are readily available without the need for additional packages, and it is speedy.

Basic Plotting Functions

plot(): the workhorse of Base R for creating scatter plots, line graphs, and more, with extensive customisation options.
hist(): generates histograms to explore the distribution of a single continuous variable.
boxplot(): useful for comparing distributions across groups, showing medians, quartiles, and outliers.
barplot(): Creates bar graphs for visualising categorical data.

Creating a Basic Scatter Plot with Base R

Consider simple scatter plot using the data we have just simulated

# basic scatter plot with Base R
plot(student_data$study_hours, student_data$score,
    main = "Scatter Plot of Scores vs. Study Hours",
    xlab = "Study Hours", ylab = "Score",
    pch = 19, col = ifelse(student_data$passed == "passed", "blue", "red")
)

This plot uses the plot function to create a scatter plot, with study hours on the x-axis and scores on the y-axis. The pch parameter specifies the symbol type, and col changes the colour based on whether the student passed or failed.

Generating a Histogram with Base R

To visualise the distribution of student scores:

# histogram with Base R
hist(student_data$score,
    breaks = 5,
    col = "skyblue",
    main = "Histogram of Student Scores",
    xlab = "Scores",
    border = "white"
)

This histogram provides a quick overview of the scores’ distribution, using the hist function with specified breaks, col for colour, and border for the colour of the histogram borders.

Generate Line Plot with Base R

# must be numeric
months_num <- 1:length(study_data$month) # Simple numeric sequence

# plot points with suppressed x-axis
plot(months_num, study_data$study_hours,
    type = "p", # Points
    pch = 19, # Type of point
    col = "red",
    xlab = "Month",
    ylab = "Study Hours",
    main = "Monthly Study Hours",
    xaxt = "n"
) # Suppress the x-axis

# add lines between points
lines(months_num, study_data$study_hours,
    col = "blue",
    lwd = 1
) # Line width

# add custom month labels to the x-axis at appropriate positions
axis(1, at = months_num, labels = study_data$month, las = 2) # `las=2` makes labels perpendicular to axis

# Optional: adding a box around the plot for a minimalistic look
box()

Comparing Distributions with Box Plots

Box plots in Base R can compare the score distributions across the pass/fail status:

# Box plot with Base R
boxplot(score ~ passed,
    data = student_data,
    main = "Score Distribution by Pass/Fail Status",
    xlab = "Status", ylab = "Scores",
    col = c("red", "blue")
)

This code uses the boxplot function to create box plots for scores, grouped by the pass/fail status, with custom colours for each group.

Summary of Today’s Lab

Congratulations on completing Lab 1!

This session has laid the groundwork. We have covered a lot, but we’ll have a good deal of practice throughout the course to reinforce the learning.

What We Have Learned

How to install and setup R:

You’ve successfully installed R and RStudio, setting up your workstation for statistical analysis.

How to install and use R-Studio:

You’ve familiarised yourself with the RStudio interface, including the console, source editor, environment tab, and other utilities for effective data analysis.

Basic R operations:

You’ve practided using R for basic arithmetic operations, understanding how to execute simple commands in the console.

Basic R Data Structures such as:
- Vectors and Matrices: You have learned to create and manipulate vectors and matrices, the simplest forms of data storage in R, which are crucial for handling numeric, character, and logical data types in a structured manner.
- Data Frames: You’ve been introduced to data frames, a key data structure in R for storing tabular data. Data frames accommodate columns of different data types, making them highly versatile for data analysis and manipulation.
- Factors and Ordered Factors: Understanding factors and ordered factors has provided you with the tools to handle categorical data effectively, including the ability to manage and analyse data involving categorical variables with both unordered and ordered levels.
Basics of ggplot2:

You’ve been equipped with the fundamentals of data visualisation using ggplot2, including how to create basic plots like bar charts, scatter plots, and line graphs. You’ve learned about the importance of aesthetics (aes) and geometries (geom_ functions) in creating visually appealing and informative graphics.

Customizing Plots:

Techniques for enhancing plots with titles, axis labels, and custom colour schemes have been covered. You’ve practised making your visualisations more informative and engaging by customising plot aesthetics.

How to Build Skills?

Practical Application:

Do the hands-on exercises at home. They’ll help you apply what you have learned here.

Where to Get Help

As sure as night follows day, you will need help coding. Good resources:

Large Language Models (LLMs): LLMs are trained on extensive datasets. They are extremely good coding tutors. Open AI’s GPT-4 considerably outperforms GPT-3.5. However GPT 3.5 should be good enough. Gemini has a two-month free trial. LLM’s are rapidly evolving. However, presently, to use these tools, and to spot their errors, you will need to know how to code. Which is fortunate because coding makes you smarter!

Note: you will not be assessed for R-code. Help from LLM’s for coding does not consitute a breach of academic integrity in this course. Your tests are in-class; no LLM’s allowed. For your final report, you will need to cite all sources, and how you used them, including LLMs.

Stack Overflow:: an outstanding resource for most problems. Great community.
Cross-validated the best place to go for stats advice. (LLM’s are only safe for standard statistics. They do not perform well for causal inference.)
Developer Websites and GitHub Pages: Tidyverse
Your tutors and course coordinator. We care. We’re here to help you!

Johannas Karl on Getting Started In R

Packages

report::cite_packages()

  - Chang W (2023). _extrafont: Tools for Using Fonts_. R package version 0.19, <https://CRAN.R-project.org/package=extrafont>.
  - R Core Team (2024). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
  - Wickham H (2016). _ggplot2: Elegant Graphics for Data Analysis_. Springer-Verlag New York. ISBN 978-3-319-24277-4, <https://ggplot2.tidyverse.org>.
  - Xie Y (2024). _tinytex: Helper Functions to Install and Maintain TeX Live, and Compile LaTeX Documents_. R package version 0.54, <https://github.com/rstudio/tinytex>. Xie Y (2019). "TinyTeX: A lightweight, cross-platform, and easy-to-maintain LaTeX distribution based on TeX Live." _TUGboat_, *40*(1), 30-32. <https://tug.org/TUGboat/Contents/contents40-1.html>.

Appendix A: At Home Exercises

Exercise 1: Install the tidyverse package

Follow these instructions to install the tidyverse package in RStudio:

Open RStudio: launch RStudio on your computer.
Access package installation:
- Navigate to the menu at the top of RStudio and click on Tools > Install Packages.... This opens the Install Packages dialogue box.
Install tidyverse:
- In the Install Packages dialogue box, you will see a field labelled “Packages (separate multiple with space or comma):”. Click in this field and type tidyverse.
- Below the packages field, ensure the checkbox for Install dependencies is checked. This ensures all packages that tidyverse depends on are also installed.
Begin installation:
- Click on the Install button to start the installation process.

Load tidyverse: After successful installation, you can load the tidyverse package into your R session by typing library(tidyverse) in the console and pressing Enter.

Exercise 2: Install the parameters and report packages

To install the parameters and report packages in RStudio, follow these instructions:

Open RStudio: start by launching the RStudio application on your computer.
Access Package Installation:
- Go to the RStudio menu bar at the top of the screen and click on Tools > Install Packages.... This action opens the Install Packages dialogue box.
Install parameters and report:
- In the Install Packages dialogue box, locate the field labelled “Packages (separate multiple with space or comma):”. Click in this field and type parameters, report, separating the package names with a comma.
- Make sure the checkbox for Install dependencies is selected. This ensures that any additional packages needed by parameters and report are also installed.
- Click the Install button to initiate the installation of both packages and their dependencies.

Exercise 3: Basic Operations and Data Structure Manipulation

Objective: Practice creating vectors and performing basic arithmetic operations.

Create two numeric vectors, vector_a and vector_b, with the following values:
- vector_a: 2, 4, 6, 8
- vector_b: 1, 3, 5, 7
Perform the following operations and store the results in new variables:
- Add vector_a and vector_b.
- Subtract vector_b from vector_a.
- Multiply vector_a by 2.
- Divide vector_b by 2.
Calculate the mean and standard deviation of both vector_a and vector_b.

Exercise 4: Working with Data Frames

Objective: Gain familiarity with data frame creation, manipulation, and basic data exploration functions.

Create a data frame student_data with the following columns:
- id: 1, 2, 3, 4
- name: alice, bob, charlie, diana
- score: 88, 92, 85, 95
- Ensure you set stringsAsFactors = FALSE.
Add a new column passed to student_data indicating whether the student passed. Assume a pass mark of 90.
Extract the name and score of students who passed into a new data frame.
Use summary(), head(), and str() functions to explore student_data.

Exercise 5 Logical Operations and Subsetting

Objective: Practice using logical operations to subset data frames.

Using the student_data data frame from Exercise 2, subset the data to find students who scored above the mean score of the class.
Create a vector attendance with values (present, absent, present, present) corresponding to each student’s attendance.
Add attendance as a new column to student_data and then subset the data frame to select only the rows where students were present.

Exercise 6: Cross-Tabulation and Analysis

Objective: Understand the use of table() function for cross-tabulation and analysis.

Create two-factor variables:
- fruit: apple, banana, apple, orange, banana
- colour: red, yellow, green, orange, green
Convert fruit and colour into factors and then into a data frame named fruit_data.
Use the table() function to perform a cross-tabulation of fruit by colour.
Interpret the results. Which fruit has the most colour variety?

Exercise 7: Visualization with ggplot2

Objective: (If ggplot2 was introduced) Create a simple plot to visualise the data.

Install and load the ggplot2 package if not already done.
Using student_data, create a bar plot showing the scores of students. Use name for the x-axis and score for the y-axis.
Enhance the plot by adding a title, x and y-axis labels, and use different colours for passed and failed students.

These exercises are designed to be progressively challenging, ensuring that students apply what they’ve learned about basic operations, data frame manipulation, logical operations, and simple data analysis and visualisation in R.

Appendix B: Solutions

Solutions 1 and 2

Exercises 1 and 2 have no solutions. Installation worked or it did not! If you have trouble, please see your tutor or instructor.

Solution Exercise 3: Basic Operations and Data Structure Manipulation

# e.g. create vectors
vector_a <- c(2, 4, 6, 8)
vector_b <- c(1, 3, 5, 7)

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

# view
sum_vector

[1]  3  7 11 15

diff_vector

[1] 1 1 1 1

double_vector_a

[1]  4  8 12 16

half_vector_b

[1] 0.5 1.5 2.5 3.5

# Mean and Standard Deviation
mean_a <- mean(vector_a)
sd_a <- sd(vector_a)
mean_b <- mean(vector_b)
sd_b <- sd(vector_b)

# view
mean_a

[1] 5

sd_a

[1] 2.581989

mean_b

[1] 4

sd_b

[1] 2.581989

Solution 4: Working with Data Frames

# create data frame
student_data <- data.frame(
    id = 1:4,
    name = c("alice", "bob", "charlie", "diana"),
    score = c(88, 92, 85, 95),
    stringsAsFactors = FALSE
)

# add `passed` column
student_data$passed <- student_data$score >= 90

# subset students who passed
passed_students <- student_data[student_data$passed == TRUE, ]

# explore data frame
summary(student_data)

       id           name               score         passed       
 Min.   :1.00   Length:4           Min.   :85.00   Mode :logical  
 1st Qu.:1.75   Class :character   1st Qu.:87.25   FALSE:2        
 Median :2.50   Mode  :character   Median :90.00   TRUE :2        
 Mean   :2.50                      Mean   :90.00                  
 3rd Qu.:3.25                      3rd Qu.:92.75                  
 Max.   :4.00                      Max.   :95.00

head(student_data)

  id    name score passed
1  1   alice    88  FALSE
2  2     bob    92   TRUE
3  3 charlie    85  FALSE
4  4   diana    95   TRUE

str(student_data)

'data.frame':   4 obs. of  4 variables:
 $ id    : int  1 2 3 4
 $ name  : chr  "alice" "bob" "charlie" "diana"
 $ score : num  88 92 85 95
 $ passed: logi  FALSE TRUE FALSE TRUE

Solution 5: Logical Operations and Subsetting

# subset data based on score
mean_score <- mean(student_data$score)
students_above_mean <- student_data[student_data$Score > mean_score, ]

# add attendance and subset
attendance <- c("present", "absent", "present", "present")
student_data$Attendance <- attendance
present_students <- student_data[student_data$Attendance == "present", ]

Solution 6: Cross-Tabulation and Analysis

# create factor variables
fruit <- factor(c("apple", "banana", "apple", "orange", "banana"))
colour <- factor(c("red", "yellow", "green", "orange", "green"))

# create data frame
fruit_data <- data.frame(fruit, colour)

# cross-tabulation
fruit_color_table <- table(fruit_data$fruit, fruit_data$colour)
print(fruit_color_table)

        
         green orange red yellow
  apple      1      0   1      0
  banana     1      0   0      1
  orange     0      1   0      0

# interpretation: Apple has the most colour variety with 2 colours (Red, Green).

Solution 7: Visualization with `ggplot2`

# install and load ggplot2
if (!require(ggplot2)) install.packages("ggplot2")
library(ggplot2)

# create bar plot
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 B: Other Data Types You May Encounter

Arrays and Matrices

Arrays are multi-dimensional data structures, while matrices are two-dimensional.

matrix_1 <- matrix(1:9, nrow = 3) # creates a 3x3 matrix
array_1 <- array(1:12, dim = c(2, 3, 2)) # creates a 2x3x2 array

Convert Matrix to Data Frame

A data.frame is used for storing tabular data.

# change matrix to array:
df_matrix_1 <- data.frame(matrix_1)

str(df_matrix_1)

'data.frame':   3 obs. of  3 variables:
 $ X1: int  1 2 3
 $ X2: int  4 5 6
 $ X3: int  7 8 9

head(df_matrix_1)

# change colnames
new_colnames <- c("col_1", "col_2", "col_3")

colnames(df_matrix_1) <- new_colnames

# check
str(df_matrix_1)

'data.frame':   3 obs. of  3 variables:
 $ col_1: int  1 2 3
 $ col_2: int  4 5 6
 $ col_3: int  7 8 9

head(df_matrix_1)

  col_1 col_2 col_3
1     1     4     7
2     2     5     8
3     3     6     9

Working with Lists in R

Creating lists

To create a list, you use the list() function.

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

# A list containing various types of elements, including another list
complex_list <- list(id = 1, name = "Jane Doe", preferences = list(color = "blue", hobby = "reading"))

Accessing list elements

List elements can be accessed using the [[ ]] notation for single elements, or the $ notation if you’re accessing named elements:

# Accessing elements
name <- my_list$name # or my_list[["name"]]

preference_color <- complex_list$preferences$color

Modifying lists

Lists can be modified by adding new elements, changing existing elements, or removing elements:

# Adding a new element
my_list$gender <- "Male"

# Changing an existing element
my_list$age <- 31

# Removing an element
my_list$scores <- NULL

Lists in Functions

Lists are often used as return values for functions that need to provide multiple pieces of data:

# Function returning a list
calculate_stats <- function(numbers) {
    mean_val <- mean(numbers)
    sum_val <- sum(numbers)
    return(list(mean = mean_val, sum = sum_val))
}

# Using the function
results <- calculate_stats(c(1, 2, 3, 4, 5))

Reuse

CC BY-NC-SA 4.0

--- title: "Asking questions in cross-cultural psychology" subtitle: "Lab: Getting Started with R" date: "2025-FEB-25" bibliography: /Users/joseph/GIT/templates/bib/references.bib editor_options: chunk_output_type: console format: html: html-math-method: katex warnings: FALSE error: FALSE messages: FALSE code-overflow: scroll highlight-style: kate code-tools: source: true toggle: FALSE reference-location: margin citation-location: margin --- ```{r} #| echo: FALSE #| warning: FALSE # WARNING: COMMENT THIS OUT. JB DOES THIS FOR WORKING WITHOUT WIFI # source("/Users/joseph/GIT/templates/functions/libs2.R") # WARNING: COMMENT THIS OUT. JB DOES THIS FOR WORKING WITHOUT WIFI # source("/Users/joseph/GIT/templates/functions/funs.R") # ALERT: UNCOMMENT THIS AND DOWNLOAD THE FUNCTIONS FROM JB's GITHUB # source( # "https://raw.githubusercontent.com/go-bayes/templates/main/functions/experimental_funs.R" # ) # source( # "https://raw.githubusercontent.com/go-bayes/templates/main/functions/experimental_funs.R" # ) # for making graphs library("tinytex") library("extrafont") loadfonts(device = "all") ``` ```{tikz} #| echo: FALSE #| out-width: 60% #| caption: "dag" #| label: fig-line-plot #| fig-cap: "Causal graph: we will refer to this image in the lecture and begin reviewing causal graphs in Week 2 " \usetikzlibrary{positioning} \usetikzlibrary{shapes.geometric} \usetikzlibrary{arrows} \usetikzlibrary{decorations} \tikzstyle{Arrow} = [->, thin, preaction = {decorate}] \tikzset{>=latex} \begin{tikzpicture}[{every node/.append style}=draw] \node [ellipse, draw=white] (A) at (0, 0) {$A$}; \node [rectangle, draw=white] (B) at (2, 0) {$B$}; \node [rectangle, draw=white] (C) at (4, 0) {$C$}; \draw [-latex, red] (A) to (B); \draw [-latex, bend left, red] (A) to (C); \end{tikzpicture} ``` ::: {.callout-note} ## Background readings: none today! - If you want to get started, download R and Rstudio as below - List of recommended readings at the end of this lecture. ::: ::: {.callout-important} ## Key concepts for the test(s): Today we introduce the following topics relevant to the Test(s) - **Confounding** (introduced in week 2) - **Internal Validity** (introduced in week 2) - **External Validity** (introduced in week 3) Today we discuss these concepts informally. We will define these concepts formally in technical detail in **week 2**. ::: ::: {.callout-important} ## Download Your Laboratory R Script Here Once you have successfully installed R and RStudio: 1. Download the R script for Lab 01 by clicking the link below. This script contains the code you will work with during your laboratory session. 2. After downloading, open RStudio. 3. In RStudio, create a new R script file by going to `File > New File > R Script`. 4. Copy the contents of the downloaded `.R` file and paste them into the new R script file you created in RStudio. 5. Save the new R script file in your project directory for easy access during the lab. [Download the R script for Lab 01](https://raw.githubusercontent.com/go-bayes/psych-434-2025/refs/heads/main/laboratory/01-lab.R) ::: # Lecture: Introduction to the Course ## Slides [PREVIEW](/slides/01-slides.html) <div> ```{=html} <iframe class="slide-deck" src="/slides/01-slides.html"></iframe> ``` </div> Open in browser [here](/slides/01-slides.html){target="_blank"} # Lab 1: Install R and RStudio This session introduces R and R-Studio. ## Slide deck: ## Slides [PREVIEW](/slides/01-slides-lab.html) <div> ```{=html} <iframe class="slide-deck" src="/slides/01-slides-lab.html"></iframe> ``` </div> Open in browser [here](/slides/01-slides-lab.html){target="_blank"} ## Why learn R? - You'll need it for your final report. - Supports your psychology coursework. - Enhances your coding skills. ## Installing R 1. Visit the Comprehensive R Archive Network (CRAN) at [https://cran.r-project.org/](https://cran.r-project.org/). 2. Select the version of R suitable for your operating system (Windows, Mac, or Linux). 3. Download and install it by following the on-screen instructions. ## Installing RStudio After downloading R... See [Johannes Karl's Video](#section-johannes) ### Step 1: Install R-Studio If you have already downloaded and installed R from the Comprehensive R Archive Network (CRAN): 1. Go to the RStudio download page at [https://www.rstudio.com/products/rstudio/download/](https://www.rstudio.com/products/rstudio/download/) 2. Choose the free version of RStudio Desktop, and download it for your operating system. 3. Download and 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 where you want to initialise an RStudio project. 3. For a new project, select `New Project`, then provide a directory name. This name will also be the name of your project. 4. Specify the location where the project folder will be created. 5. Click `Create Project`. ::: {.callout-tip} ## Order your R-studio/R workflow - Clear folder structure - If you are using GitHub (or similar) create a location on your machine (i.d. *not* dropbox) - If you are not using GitHub choose the cloud (Dropbox or similar). - When creating new files and scripts, use clear labels that anyone could understand. - That "anyone" will be your future self, trying to make sense. ::: ### Step 3: Give project structure 1. **Organising Files and Folders:** - Within your project, create folders to organise your scripts and data. - Common folder names include `R/` for R scripts, `data/` for datasets, and `doc/` for documentation. - You can create these folders using RStudio's `Files` pane or through your operating system's file explorer. 2. **Creating and Managing R Scripts:** - To create a new R script, go to `File > New File > R Script`. - Save the script in your project directory's `R/` folder to keep your work organised. Use meaningful file names that describe the script's purpose. 3. **Version Control:** - If you are familiar with version control, you can initialise a Git repository within your project by selecting the `Version Control` option when creating a new project. - This allows for better tracking of changes and collaboration if working with others. - If you are not familiar with version control (or have not installed `git` on your machine), do not worry about initialising a Git repository. ### Step 4: Working with R-scripts 1. **Writing and executing Code:** - Write your R code in the *script editor.* - Execute code by selecting lines and pressing `Ctrl + Enter` (Windows/Linux) or `Cmd + Enter` (Mac). 2. **Commenting and documentation:** - Use comments (preceded by `#`) to document your code for clarity and future reference. 3. **Saving and organising scripts:** - Regularly save your scripts (`Ctrl + S` or `Cmd + S`). - Organise scripts into folders within your project for different analyses or data processing tasks. ### Step 5: When you exit R-studio - Before concluding your work, save your workspace or clear it to start fresh in the next session (`Session > Restart R`). ::: {.callout-tip} ## Order your R-studio/R workflow - Again, use clearly defined script names - Annotate your code - Save your scripts often (`Ctrl + S` or `Cmd + S`). ::: ::: {.callout-note} ### Exercise 1: Install the `tidyverse` package Follow these instructions to install the `tidyverse` package in RStudio: 1. **Open RStudio:** launch RStudio on your computer. 2. **Access package installation:** - Navigate to the menu at the top of RStudio and click on `Tools > Install Packages...`. This opens the Install Packages dialogue box. 3. **Install `tidyverse`:** - In the Install Packages dialogue box, you will see a field labelled "Packages (separate multiple with space or comma):". Click in this field and type `tidyverse`. - Below the packages field, ensure the checkbox for `Install dependencies` is checked. This ensures all packages that `tidyverse` depends on are also installed. 4. **Begin installation:** - Click on the `Install` button to start the installation process. The installation might take a few minutes. Monitor the progress in the "Console" pane. Once the installation is complete, you will see a message in the console indicating that the process has finished. 6. **Load `tidyverse`:** After successful installation, you can load the `tidyverse` package into your R session by typing `library(tidyverse)` in the console and pressing `Enter`. ::: ## Basic R Commands ::: {.callout-note} ### How to copy the code on this page - `R Script > New File > R` - Name your new R script and save it in a folder. - Hover your cursor over the top right of the code panel, and click the copy tab. - Copy the text into your script. - Save: `Ctrl + S` or `Cmd + S`. ::: ### Assignment (`<-`) Assignment in R is done using the '<-' operator, which on my machine renders `<-`. This operator assigns values to variables: ```{r} x <- 10 # assigns the value 10 to x y <- 5 # assigns the value 5 to y # this does the same x <- 10 y <- 5 # note what happens when we do this # 10 = 5 # not run # but we can do this # 10 == 5 # considered below ``` ::: {.callout-note} ### RStudio Assignment Operator Shortcut - **For macOS**: `Option` + `-` (minus key) inserts `<-`. - **For Windows and Linux**: `Alt` + `-` (minus key) inserts `<-`. Consult the latest RStudio documentation or access the Keyboard Shortcuts Help (`Tools` -> `Keyboard Shortcuts Help`) for up-to-date shortcuts. ::: ### Concatenation (`c()`) The `c()` function combines multiple elements into a vector. ```{r} numbers <- c(1, 2, 3, 4, 5) # a vector of numbers print(numbers) ``` ### Operations (`+`, `-`) Basic arithmetic operations include addition (`+`) and subtraction (`-`). ```{r} # this does the same x <- 10 y <- 5 sum <- x + y # adds x and y print(sum) difference <- x - y # subtracts y from x # note we did not need to use the `print()` function difference ``` ::: {.callout-important} ### Executing code - `Ctrl + Enter` (Windows/Linux) or `Cmd + Enter` (Mac). ::: In addition to assignment, multiplication and division are fundamental arithmetic operations in R that allow you to manipulate numeric data. Here is how you can incorporate these operations into your basic R commands documentation: ### Multiplication (`*`) and Division (`/`) Multiplication and division in R are performed using the `*` and `/` operators, respectively. These operators allow for element-wise operations on vectors, as well as operations on individual numeric values. ```{r} # multiplication product <- x * y # multiplies x by y product # division quotient <- x / y # divides x by y quotient # element-wise multiplication on vectors vector1 <- c(1, 2, 3) vector2 <- c(4, 5, 6) # multiplies each element of vector1 by the corresponding element of vector2 vector_product <- vector1 * vector2 vector_product # element-wise division on vectors # divides each element of vector1 by the corresponding element of vector2 vector_division <- vector1 / vector2 vector_division ``` - Multiplication and division can be used for scalar (single values) and vector (multiple values) operations. When applied to vectors, these operations are performed element-wise. - Be mindful of division by zero, as this will result in `Inf` (infinity) or `NaN` (not a number) depending on the context. ```{r} # example of division by zero result <- 10 / 0 # results in Inf zero_division <- 0 / 0 # results in NaN ``` - R also supports integer division using the `%/%` operator and modulo operation using `%%` to find the remainder. ```{r} # integer division integer_division <- 10 %/% 3 # results in 3 # modulo operation remainder <- 10 %% 3 # results in 1 ``` ### `rm()` Remove Object ```{r} # `rm()` remove object ---------------------------------------------------- devil_number <- 666 # results in 1 # view devil_number # remove the devil number rm(devil_number) # check # devil_number ``` ### Logic (`!`, `!=`, `==`) Logical operations include NOT (`!`), NOT EQUAL (`!=`), and EQUAL (`==`). ```{r} x_not_y <- x != y # checks if x is not equal to y x_not_y x_equal_10 <- x == 10 # checks if x is equal to 10 x_equal_10 ``` Logical operations are fundamental in R for controlling the flow of execution and making decisions based on conditions. In addition to NOT (`!`), NOT EQUAL (`!=`), and EQUAL (`==`), there are several other logical operators you should know: ### OR (`|` and `||`) - The `|` operator performs element-wise logical OR operation. It evaluates each pair of elements in two logical vectors to see if at least one is `TRUE`. - The `||` operator performs a logical OR operation but only evaluates the first element of each vector -- mainly used in if statements and not for vectorised operations. ```{r} # element-wise OR vector_or <- c(TRUE, FALSE) | c(FALSE, TRUE) # returns c(TRUE, TRUE) vector_or # single OR (only looks at first element) single_or <- TRUE || FALSE # returns TRUE single_or ``` ### AND (`&` and `&&`) - The `&` operator performs element-wise logical AND operations. It checks if both elements in the corresponding positions of two logical vectors are `TRUE`. - The `&&` operator performs a logical AND operation but only evaluates the first element of each vector. Like `||`, used in conditions that do not require vectorised operations. ```{r} # element-wise AND vector_and <- c(TRUE, FALSE) & c(FALSE, TRUE) # returns c(FALSE, FALSE) # single AND (only looks at first element) single_and <- TRUE && FALSE # returns FALSE ``` ::: {.callout-tip} ## RStudio Workflow Shortcuts Shortcuts bring order and boost creativity - **Execute Code Line**: `Cmd` + `Return` (Mac) or `Ctrl` + `Enter` (Windows/Linux) - **Insert Section Heading**: `Cmd` + `Shift` + `R` (Mac) or `Ctrl` + `Shift` + `R` (Windows/Linux) - **Align Code**: `Cmd` + `Shift` + `A` (Mac) or `Ctrl` + `Shift` + `A` (Windows/Linux) - **Comment/Uncomment**: `Cmd`/`Ctrl` + `Shift` + `C` - **Save All**: `Cmd`/`Ctrl` + `Shift` + `S` - **Find/Replace**: `Cmd`/`Ctrl` + `F`, `Cmd`/`Ctrl` + `Shift` + `F` - **New File**: `Cmd`/`Ctrl` + `Shift` + `N` - **Auto-complete**: `Tab` For more commands, explore the Command Palette available under `Tools` -> `Command Palette` or `Shift` + `Cmd` + `P` (Mac) or `Shift` + `Ctrl` + `P` (Windows/Linux). ::: ## Data Types in R Understanding data types in R is essential. R supports several fundamental data types, including integers, characters, factors, and ordered factors. Each type has its specific use case and functions associated with it. ### Integers Integers are whole numbers without decimal points. In R, integers can be explicitly defined by adding an `L` suffix to the number. ```{r} # define an integer x <- 42L x # check str(x) # is integer # convert to numeric y <- as.numeric(x) str(y) ``` Integers are particularly useful when dealing with counts or indices that do not require fractional values. ### Characters Character data types are used to represent text. In R, text strings are enclosed in quotes, either single (`'`) or double (`"`). ```{r} # define a character string name <- "Alice" ``` Characters are essential for categorical data that does not fit into numerical categories, such as names, labels, and descriptions. ### Factors Factors are used to represent categorical data that can take on a limited number of values, known as levels. Factors are useful for statistical modeling as they explicitly define categories in the data. ```{r} # Define a factor colors <- factor(c("red", "blue", "green")) ``` Factors can improve efficiency and memory usage when dealing with categorical data, especially in large datasets. And they are useful when dealing with categorical variables (naturally). #### Ordered Factors Ordered factors are a special type of factor where the levels have an inherent order. They are defined similarly to factors but with an additional argument to denote the order. ```{r} # ordered factors --------------------------------------------------------- # factors ordinary education_levels <- c("high school", "bachelor", "master", "ph.d.") education_factor_no_order <- factor(education_levels, ordered = FALSE) str(education_factor_no_order) # factors with inherent order education_factor <- factor(education_levels, ordered = TRUE) education_factor # another way to do the same education_ordered_explicit <- factor(education_levels, levels = education_levels, ordered = TRUE) ``` Ordered factors allow for logical comparisons based on their order, which is particularly useful in analyses where the order of categories matters, such as ordinal regression. #### Operations with Ordered Factors Ordered factors support logical comparisons that consider the order of the levels. ```{r} # comparison of ordered factors edu1 <- ordered("bachelor", levels = education_levels) edu2 <- ordered("master", levels = education_levels) edu2 > edu1 # logical comparison # modifying ordered factors new_levels <- c("primary school", "high school", "bachelor", "master", "ph.d.") education_updated <- factor(education_levels, levels = new_levels, ordered = TRUE) str(education_updated) ``` #### Checking Data with Ordered Factors You can view the structure and summary of ordered factors just as with regular factors, but the output will indicate the order. ```{r} # view the structure str(education_ordered_explicit) # summary to see the distribution summary(education_ordered_explicit) ``` #### Modifying Ordered Factors If you need to change the order of levels or add new levels, you can re-factor the variable using `factor()` or `ordered()` and specify the new levels. ```{r} # modifying ordered factors new_levels <- c("primary school", "high school", "bachelor", "master", "ph.d.") education_updated <- factor(education_levels, levels = new_levels, ordered = TRUE) str(education_updated) str(education_updated) ``` ## Strings Strings are sequences of characters. ```{r} # sequences of characters you <- "world!" greeting <- paste("hello,", you) # hello world greeting ``` ## Vectors Vectors are one of R's most fundamental data structures, essential for storing and manipulating a sequence of data elements. Vectors are homogenous, meaning all elements in a vector must be of the same type (e.g., all numeric, all character, etc.). Vectors in R can be created using the `c()` function, which stands for concatenate or combine: ```{r} # numeric vector numeric_vector <- c(1, 2, 3, 4, 5) # character vector character_vector <- c("apple", "banana", "cherry") # logical vector logical_vector <- c(TRUE, FALSE, TRUE, FALSE) ``` ### Manipulating Vectors R allows you to perform operations on vectors in a very intuitive way. Operations are vectorised, meaning they are applied element-wise: ```{r} # arithmetic operations vector_sum <- numeric_vector + 10 # Adds 10 to each element # show vector_sum # vector mutliplication vector_multiplication <- numeric_vector * 2 # Multiplies each element by 2 # show vector_multiplication # logical operations vector_greater_than_three <- numeric_vector > 3 # Returns a logical vector # show vector_greater_than_three ``` You can access elements of a vector by using square brackets `[ ]` with an index or a vector of indices: ```{r} # the first element of numeric_vector first_element <- numeric_vector[1] # show first_element # multiple elements some_elements <- numeric_vector[c(2, 4)] # Gets the 2nd and 4th elements # show first_element ``` ### Functions with vectors R provides a rich set of functions for statistical computations and manipulations that work with vectors: ```{r} # statistical summary vector_mean <- mean(numeric_vector) vector_mean vector_sum <- sum(numeric_vector) vector_sum # sorting sorted_vector <- sort(numeric_vector) sorted_vector # unique values unique_vector <- unique(character_vector) unique_vector ``` ## Data Frames ### Create Data Frames Data frames can be created using the `data.frame()` function, specifying each column and its values. Here's a simple example: ```{r} # clear any previous `df` object rm(df) df <- data.frame( name = c("alice", "bob", "charlie"), age = c(25, 30, 35), gender = c("female", "male", "male") ) # check structure head(df) str(df) table(df$gender) table(df$age) table(df$name) ``` In this example, `df` is a data frame with three columns (`name`, `age`, `gender`) and three rows, each representing a different individual. ### Access Data Frame Elements **There are often several ways to do the same thing in R**. You can access the elements of a data frame in several ways: - **By column name:** use the `$` operator followed by the column name. ```{r} names <- df$name # extracts the `name` column names ``` - **By row and column:** Use the `[row, column]` indexing. Rows or columns can be specified by number or name. ```{r} # access data frame elements names <- df$name names second_person <- df[2, ] second_person age_column <- df[, "age"] age_column ``` - **Using `subset()` Function:** To extract subsets of the data frame based on conditions. ```{r} # use subset() very_old_people <- subset(df, age > 25) # extracts rows where `age` is greater than 18 very_old_people summary(very_old_people$age) mean(very_old_people$age) min(very_old_people$age) ``` ### Explore Your Data Frames Functions such as `head()`, `tail()`, and `str()` help you explore the first few rows, last few rows, and the structure of the data frame, respectively. ```{r} head(df) # first six rows tail(df) # last six rows str(df) # structure of the data frame ``` ### Manipulating Data Frames Data frames can be manipulated in various ways: - **Adding Columns:** You can add new columns using the `$` operator. ```{r} # adds a new column "employed" df$employed <- c(TRUE, TRUE, FALSE) # show head(df) ``` - **Adding rows:** Use the `rbind()` function to add new rows. ```{r} new_person <- data.frame(name = "diana", age = 28, gender = "female", employed = TRUE) df <- rbind(df, new_person) # show head(df) ``` - **Modifying values:** Access the element or column and assign it a new value. ```{r} # note brackets # changes diana's age to 26 df[4, "age"] <- 26 # view row df[4, ] ``` - **Removing columns or rows:** set columns to `NULL` to remove them, or use `-` with row or column indices. ```{r} head(df) # remove employed column df$employed <- NULL # check df # remove fourth row (Diana) df <- df[-4, ] # removes the fourth row # show df ``` ### Add Rows with `rbind()` The `rbind()` function in R stands for "row bind". It is used to combine data frames or matrices by rows. This function is particularly useful when you want to add new observations or records to an existing data frame. ```{r} # adding a new row new_person <- data.frame(name = "eve", age = 32, gender = "female") df <- rbind(df, new_person) # verify the row addition head(df) ``` When using `rbind()`, ensure that the columns in the data frames being combined match in both name and order. If they do not match, you may encounter errors or unexpected results. ### Adding Columns with `cbind()` Conversely, the `cbind()` function in R stands for "column bind". It is used to combine data frames or matrices by columns. This function allows you to add new variables to an existing data frame. ```{r} # example of adding a new column df$occupation <- c("engineer", "doctor", "artist", "doctor") # direct assignment # or using cbind for a separate vector occupation_vector <- c("engineer", "doctor", "artist", "doctor") df <- cbind(df, occupation_vector) # verify the column addition head(df) ``` As with `rbind()`, when using `cbind()`, it is crucial that the data frames or vectors being combined have compatible dimensions. For `cbind()`, the number of rows must match. ### Considerations for `rbind()` and `cbind()` While `rbind()` and `cbind()` are straightforward and powerful functions for combining data, they have some limitations: - **Matching Column or Row Names**: for `rbind()`, the column names between the data frames need to match exactly. For `cbind()`, the row numbers must be equal. - **Factor Levels**: when binding factors with different levels, R will unify the levels, which can sometimes lead to unexpected results. Be mindful of factor levels when using these functions. - **Performance**: we will use a different approach next week (and following), draing on the`dplyr` package, which will connect more easily to our workflows. By understanding and utilizing `rbind()` and `cbind()`, you can efficiently manipulate the structure of your data frames, adding flexibility to your data analysis workflows in R. ### View Data Structure (`summary()`, `str()`, `head()`, `tail()`) - `summary()`: Provides a summary of an object's structure. - `str()`: Displays the structure of an object. - `head()`: Shows the first few rows of a data frame or the first elements of a vector. - `tail()`: Shows the last few rows of a data frame or the last elements of a vector. ```{r} # iris is a preloaded dataset str(iris) # displays structure of scores_df summary(iris) # summary statistics head(iris) # first few rows tail(iris) # last few rows ``` ### mean() - Calculates the arithmetic mean of a numerical object. ```{r} set.seed(12345) # we will cover R’s powerful simulation functions like `rnorm()`next week vector <- rnorm(n = 40, mean = 0, sd = 1) mean(vector) # note the sampling error here ``` ### sd() - Computes the standard deviation, which measures the amount of variation or dispersion of a set of values. ```{r} sd(vector) # replace 'vector' with your numerical vector ``` ### min() and max() - These functions return a numerical object's minimum and maximum values, respectively. ```{r} min(vector) # minimum value max(vector) # maximum value ``` ### table() - Generates a frequency table of an object, useful for categorical data. It counts the number of occurrences of each unique element. ```{r} # seed for reproducibility 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 ) # determine pass/fail student_data$passed <- ifelse(student_data$score >= 90, "passed", "failed") # convert 'passed' to factor student_data$passed <- factor(student_data$passed, levels = c("failed", "passed")) # simulate study hours student_data$study_hours <- sample(5:15, 8, replace = TRUE) # table for categorical data analysis 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_example <- data.frame(gender, education_level) table(df_table_example) ``` ### Cross-Tabulation with table() - `table()` can also be used for cross-tabulation, providing a way to analyse the relationship between two or more factors. ```{r} table(df_table_example$gender, df_table_example$education_level) # crosstab ``` This produces a contingency table showing the counts at each combination of `factor1` and `factor2` levels. ### Summary Statistics Use `summary()` to get a summary of each column. ```{r} # show summary(df) ``` ## First Data Visualisation with `ggplot2` `ggplot2` is a powerful and flexible R package for creating elegant data visualisations. It is based on the Grammar of Graphics, allowing users to build plots layer by layer, making it versatile for creating a wide range of plots. #### Installing and Loading `ggplot2` Before using `ggplot2`, ensure the package is installed and loaded into your R session: ```{r} # load ggplot2 if (!require(ggplot2)) install.packages("ggplot2") library(ggplot2) # seed for reproducibility set.seed(12345) # simulate student data (more on simulation next week) student_data <- data.frame( name = c("alice", "bob", "charlie", "diana", "ethan", "fiona", "george", "hannah"), score = sample(80:100, 8, replace = TRUE), # random scores between 80 and 100 stringsasfactors = FALSE ) # determine pass/fail based on score # we will cover the ifelse() operator in detail in upcoming weeks student_data$passed <- ifelse(student_data$score >= 90, "passed", "failed") # convert 'passed' to factor for colour coding in ggplot2 student_data$passed <- factor(student_data$passed, levels = c("failed", "passed")) # view the first few rows of the data frame head(student_data) # simulate study hours student_data$study_hours <- sample(5:15, 8, replace = TRUE) ``` ### Basic Components of a `ggplot2` Plot - **Data:** the dataset you want to visualise. - **Aesthetics (`aes`):** defines how data are mapped to colour, size, shape, and other visual properties. - **Geometries (`geom_` functions):** the type of plot or layer you want to add (e.g., points, lines, bars). #### Create a Basic Plot Start by creating a simple bar plot: ```{r} ggplot(student_data, aes(x = name, y = score)) + geom_bar(stat = "identity") ``` This code plots `score` for each `name` in the `student_data` dataframe. The `stat = "identity"` argument tells `ggplot2` to use the `score` values directly to determine the height of the bars. #### Customising the plot To enhance your plot, you can add titles, change axis labels, and modify colours: ```{r} 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 = "student name", y = "score") + theme_minimal() ``` - `aes(fill = passed)`: maps the `passed` variable to the colour fill of the bars, allowing for colour differentiation based on whether students passed or failed. - `scale_fill_manual()`: customizes the colours used for the `true` and `FALSE` values of the `passed` variable. - `labs()`: adds a main title and axis labels. - `theme_minimal()`: applies a minimalistic theme to the plot for a cleaner appearance. ### Scatter Plot with `ggplot2` A scatter plot is useful for examining the relationship between two continuous variables. We next simulate a scenario where we compare student scores against study hours. ```{r} # create scatter plot ggplot(student_data, aes(x = study_hours, y = score, color = passed)) + geom_point(size = 4) + labs(title = "student scores vs. study hours", x = "study hours", y = "score") + theme_minimal() + scale_color_manual(values = c("failed" = "red", "passed" = "blue")) ``` ### Box Plot with `ggplot2` Box plots are excellent for visualising the distribution of scores by pass/fail status, showing medians, quartiles, and potential outliers. ```{r} # create box plot ggplot(student_data, aes(x = passed, y = score, fill = passed)) + geom_boxplot() + labs(title = "score distribution by pass/fail status", x = "status", y = "score") + theme_minimal() + scale_fill_manual(values = c("failed" = "red", "passed" = "blue")) ``` ### Histogram with `ggplot2` Histograms are helpful for understanding the distribution of a single continuous variable, such as scores. ```{r} # create a histogram ggplot(student_data, aes(x = score, fill = passed)) + geom_histogram(binwidth = 5, color = "black", alpha = 0.7) + labs(title = "histogram of scores", x = "score", y = "count") + theme_minimal() + scale_fill_manual(values = c("failed" = "red", "passed" = "blue")) ``` ### Line Plot with `ggplot2` (Time Series) For demonstrating a line plot, we simulate monthly study hours over a semester for a student. ```{r} # simulate monthly study hours months <- factor(month.abb[1:8], levels = month.abb[1:8]) study_hours <- c(0, 3, 15, 30, 35, 120, 18, 15) # make data frame study_data <- data.frame(month = months, study_hours = study_hours) # create a line plot 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 Although `ggplot2` is renowned for its flexibility and aesthetic appeal, Base R graphics remain a staple for straightforward and quick visualisations. Base R provides a set of plotting functions that are readily available without the need for additional packages, and it is speedy. ### Basic Plotting Functions - `plot()`: the workhorse of Base R for creating scatter plots, line graphs, and more, with extensive customisation options. - `hist()`: generates histograms to explore the distribution of a single continuous variable. - `boxplot()`: useful for comparing distributions across groups, showing medians, quartiles, and outliers. - `barplot()`: Creates bar graphs for visualising categorical data. ### Creating a Basic Scatter Plot with Base R Consider simple scatter plot using the data we have just simulated ```{r} # basic scatter plot with Base R plot(student_data$study_hours, student_data$score, main = "Scatter Plot of Scores vs. Study Hours", xlab = "Study Hours", ylab = "Score", pch = 19, col = ifelse(student_data$passed == "passed", "blue", "red") ) ``` This plot uses the `plot` function to create a scatter plot, with study hours on the x-axis and scores on the y-axis. The `pch` parameter specifies the symbol type, and `col` changes the colour based on whether the student passed or failed. ### Generating a Histogram with Base R To visualise the distribution of student scores: ```{r} # histogram with Base R hist(student_data$score, breaks = 5, col = "skyblue", main = "Histogram of Student Scores", xlab = "Scores", border = "white" ) ``` This histogram provides a quick overview of the scores' distribution, using the `hist` function with specified `breaks`, `col` for colour, and `border` for the colour of the histogram borders. ### Generate Line Plot with Base R ```{r} # must be numeric months_num <- 1:length(study_data$month) # Simple numeric sequence # plot points with suppressed x-axis plot(months_num, study_data$study_hours, type = "p", # Points pch = 19, # Type of point col = "red", xlab = "Month", ylab = "Study Hours", main = "Monthly Study Hours", xaxt = "n" ) # Suppress the x-axis # add lines between points lines(months_num, study_data$study_hours, col = "blue", lwd = 1 ) # Line width # add custom month labels to the x-axis at appropriate positions axis(1, at = months_num, labels = study_data$month, las = 2) # `las=2` makes labels perpendicular to axis # Optional: adding a box around the plot for a minimalistic look box() ``` ### Comparing Distributions with Box Plots Box plots in Base R can compare the score distributions across the pass/fail status: ```{r} # Box plot with Base R boxplot(score ~ passed, data = student_data, main = "Score Distribution by Pass/Fail Status", xlab = "Status", ylab = "Scores", col = c("red", "blue") ) ``` This code uses the `boxplot` function to create box plots for scores, grouped by the pass/fail status, with custom colours for each group. ## Summary of Today's Lab Congratulations on completing Lab 1! This session has laid the groundwork. We have covered a lot, but we'll have a good deal of practice throughout the course to reinforce the learning. ### What We Have Learned - **How to install and setup R:** You've successfully installed R and RStudio, setting up your workstation for statistical analysis. - **How to install and use R-Studio:** You've familiarised yourself with the RStudio interface, including the console, source editor, environment tab, and other utilities for effective data analysis. - **Basic R operations:** You've practided using R for basic arithmetic operations, understanding how to execute simple commands in the console. - **Basic R Data Structures such as:** - **Vectors and Matrices:** You have learned to create and manipulate vectors and matrices, the simplest forms of data storage in R, which are crucial for handling numeric, character, and logical data types in a structured manner. - **Data Frames:** You've been introduced to data frames, a key data structure in R for storing tabular data. Data frames accommodate columns of different data types, making them highly versatile for data analysis and manipulation. - **Factors and Ordered Factors:** Understanding factors and ordered factors has provided you with the tools to handle categorical data effectively, including the ability to manage and analyse data involving categorical variables with both unordered and ordered levels. - **Basics of `ggplot2`:** You've been equipped with the fundamentals of data visualisation using `ggplot2`, including how to create basic plots like bar charts, scatter plots, and line graphs. You've learned about the importance of aesthetics (`aes`) and geometries (`geom_` functions) in creating visually appealing and informative graphics. - **Customizing Plots:** Techniques for enhancing plots with titles, axis labels, and custom colour schemes have been covered. You've practised making your visualisations more informative and engaging by customising plot aesthetics. ### How to Build Skills? - **Practical Application:** Do the hands-on exercises at home. They'll help you apply what you have learned here. ### Where to Get Help As sure as night follows day, you will need help coding. Good resources: 1. **Large Language Models (LLMs):** LLMs are trained on extensive datasets. They are extremely good coding tutors. Open AI's GPT-4 considerably outperforms GPT-3.5. However GPT 3.5 should be good enough. Gemini has a two-month free trial. LLM's are rapidly evolving. However, presently, to use these tools, and to spot their errors, you will need to know how to code. Which is fortunate because coding makes you smarter! **Note:** you will not be assessed for R-code. Help from LLM's for *coding* does not consitute a breach of academic integrity in this course. Your tests are in-class; no LLM's allowed. For your final report, you will need to cite all sources, and how you used them, including LLMs. 2. [**Stack Overflow:**](https://stackoverflow.com/questions/tagged/r): an outstanding resource for most problems. Great community. 3. [Cross-validated](https://stats.stackexchange.com/) the best place to go for stats advice. (LLM's are only safe for standard statistics. They do not perform well for causal inference.) 4. **Developer Websites and GitHub Pages:** [Tidyverse](https://github.com/tidyverse) 4. Your tutors and course coordinator. We care. We’re here to help you! ### Recommended Reading - Wickham, H., & Grolemund, G. (2016). *R for Data Science*. O'Reilly Media. [Available online](https://r4ds.had.co.nz - A helpful resource for learning R is Megan Hall's lecture available at: [https://meghan.rbind.io/talk/neair/](https://meghan.rbind.io/talk/neair/). - RStudio has compiled numerous accessible materials for learning R, which can be found here: [https://education.rstudio.com/learn/beginner/](https://education.rstudio.com/learn/beginner/). - Materials from a previous course on learning R can be accessed here. [https://go-bayes.github.io/psych-447/](https://education.rstudio.com/learn/beginner/) - Johannes Karl's Video ### Johannas Karl on Getting Started In R {#section-johannes} {{< video https://www.youtube.com/embed/haYxa3vWA28 >}} ### Packages ```{r} report::cite_packages() ``` ## Appendix A: At Home Exercises ::: {.callout-note} ### Exercise 1: Install the `tidyverse` package Follow these instructions to install the `tidyverse` package in RStudio: 1. **Open RStudio:** launch RStudio on your computer. 2. **Access package installation:** - Navigate to the menu at the top of RStudio and click on `Tools > Install Packages...`. This opens the Install Packages dialogue box. 3. **Install `tidyverse`:** - In the Install Packages dialogue box, you will see a field labelled "Packages (separate multiple with space or comma):". Click in this field and type `tidyverse`. - Below the packages field, ensure the checkbox for `Install dependencies` is checked. This ensures all packages that `tidyverse` depends on are also installed. 4. **Begin installation:** - Click on the `Install` button to start the installation process. The installation might take a few minutes. Monitor the progress in the "Console" pane. Once the installation is complete, you will see a message in the console indicating that the process has finished. 6. **Load `tidyverse`:** After successful installation, you can load the `tidyverse` package into your R session by typing `library(tidyverse)` in the console and pressing `Enter`. ::: :::{.callout-note} ### Exercise 2: Install the `parameters` and `report` packages To install the `parameters` and `report` packages in RStudio, follow these instructions: 1. **Open RStudio:** start by launching the RStudio application on your computer. 2. **Access Package Installation:** - Go to the RStudio menu bar at the top of the screen and click on `Tools > Install Packages...`. This action opens the Install Packages dialogue box. 3. **Install `parameters` and `report`:** - In the Install Packages dialogue box, locate the field labelled "Packages (separate multiple with space or comma):". Click in this field and type `parameters, report`, separating the package names with a comma. - Make sure the checkbox for `Install dependencies` is selected. This ensures that any additional packages needed by `parameters` and `report` are also installed. - Click the `Install` button to initiate the installation of both packages and their dependencies. ::: ::: {.callout-note} ### Exercise 3: Basic Operations and Data Structure Manipulation **Objective:** Practice creating vectors and performing basic arithmetic operations. 1. Create two numeric vectors, `vector_a` and `vector_b`, with the following values: - `vector_a`: 2, 4, 6, 8 - `vector_b`: 1, 3, 5, 7 2. Perform the following operations and store the results in new variables: - Add `vector_a` and `vector_b`. - Subtract `vector_b` from `vector_a`. - Multiply `vector_a` by 2. - Divide `vector_b` by 2. 3. Calculate the mean and standard deviation of both `vector_a` and `vector_b`. ::: ::: {.callout-note} ### Exercise 4: Working with Data Frames **Objective:** Gain familiarity with data frame creation, manipulation, and basic data exploration functions. 1. Create a data frame `student_data` with the following columns: - `id`: 1, 2, 3, 4 - `name`: alice, bob, charlie, diana - `score`: 88, 92, 85, 95 - Ensure you set `stringsAsFactors = FALSE`. 2. Add a new column `passed` to `student_data` indicating whether the student passed. Assume a pass mark of 90. 3. Extract the `name` and `score` of students who passed into a new data frame. 4. Use `summary()`, `head()`, and `str()` functions to explore `student_data`. ::: ::: {.callout-note} ### Exercise 5 Logical Operations and Subsetting **Objective:** Practice using logical operations to subset data frames. 1. Using the `student_data` data frame from Exercise 2, subset the data to find students who scored above the mean score of the class. 2. Create a vector `attendance` with values (`present`, `absent`, `present`, `present`) corresponding to each student's attendance. 3. Add `attendance` as a new column to `student_data` and then subset the data frame to select only the rows where students were present. ::: ::: {.callout-note} ### Exercise 6: Cross-Tabulation and Analysis **Objective:** Understand the use of `table()` function for cross-tabulation and analysis. 1. Create two-factor variables: - `fruit`: apple, banana, apple, orange, banana - `colour`: red, yellow, green, orange, green 2. Convert `fruit` and `colour` into factors and then into a data frame named `fruit_data`. 3. Use the `table()` function to perform a cross-tabulation of `fruit` by `colour`. 4. Interpret the results. Which fruit has the most colour variety? ::: ::: {.callout-note} ### Exercise 7: Visualization with `ggplot2` **Objective:** (If `ggplot2` was introduced) Create a simple plot to visualise the data. 1. Install and load the `ggplot2` package if not already done. 2. Using `student_data`, create a bar plot showing the scores of students. Use `name` for the x-axis and `score` for the y-axis. 3. Enhance the plot by adding a title, x and y-axis labels, and use different colours for passed and failed students. These exercises are designed to be progressively challenging, ensuring that students apply what they've learned about basic operations, data frame manipulation, logical operations, and simple data analysis and visualisation in R. ::: ## Appendix B: Solutions ### Solutions 1 and 2 - Exercises 1 and 2 have no solutions. Installation worked or it did not! If you have trouble, please see your tutor or instructor. ### Solution Exercise 3: Basic Operations and Data Structure Manipulation ```{r} # e.g. create vectors vector_a <- c(2, 4, 6, 8) vector_b <- c(1, 3, 5, 7) # operations sum_vector <- vector_a + vector_b diff_vector <- vector_a - vector_b double_vector_a <- vector_a * 2 half_vector_b <- vector_b / 2 # view sum_vector diff_vector double_vector_a half_vector_b # Mean and Standard Deviation mean_a <- mean(vector_a) sd_a <- sd(vector_a) mean_b <- mean(vector_b) sd_b <- sd(vector_b) # view mean_a sd_a mean_b sd_b ``` ### Solution 4: Working with Data Frames ```{r} # create data frame student_data <- data.frame( id = 1:4, name = c("alice", "bob", "charlie", "diana"), score = c(88, 92, 85, 95), stringsAsFactors = FALSE ) # add `passed` column student_data$passed <- student_data$score >= 90 # subset students who passed passed_students <- student_data[student_data$passed == TRUE, ] # explore data frame summary(student_data) head(student_data) str(student_data) ``` ### Solution 5: Logical Operations and Subsetting ```{r} # subset data based on score mean_score <- mean(student_data$score) students_above_mean <- student_data[student_data$Score > mean_score, ] # add attendance and subset attendance <- c("present", "absent", "present", "present") student_data$Attendance <- attendance present_students <- student_data[student_data$Attendance == "present", ] ``` ### Solution 6: Cross-Tabulation and Analysis ```{r} # create factor variables fruit <- factor(c("apple", "banana", "apple", "orange", "banana")) colour <- factor(c("red", "yellow", "green", "orange", "green")) # create data frame fruit_data <- data.frame(fruit, colour) # cross-tabulation fruit_color_table <- table(fruit_data$fruit, fruit_data$colour) print(fruit_color_table) # interpretation: Apple has the most colour variety with 2 colours (Red, Green). ``` ### Solution 7: Visualization with `ggplot2` ```{r} # install and load ggplot2 if (!require(ggplot2)) install.packages("ggplot2") library(ggplot2) # create bar plot 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 B: Other Data Types You May Encounter #### Arrays and Matrices Arrays are multi-dimensional data structures, while matrices are two-dimensional. ```{r} matrix_1 <- matrix(1:9, nrow = 3) # creates a 3x3 matrix array_1 <- array(1:12, dim = c(2, 3, 2)) # creates a 2x3x2 array ``` #### Convert Matrix to Data Frame A `data.frame` is used for storing tabular data. ```{r} # change matrix to array: df_matrix_1 <- data.frame(matrix_1) str(df_matrix_1) head(df_matrix_1) # change colnames new_colnames <- c("col_1", "col_2", "col_3") colnames(df_matrix_1) <- new_colnames # check str(df_matrix_1) head(df_matrix_1) ``` ### Working with Lists in R #### Creating lists To create a list, you use the `list()` function. ```{r} # Creating a simple list my_list <- list(name = "John Doe", age = 30, scores = c(90, 80, 70)) # A list containing various types of elements, including another list complex_list <- list(id = 1, name = "Jane Doe", preferences = list(color = "blue", hobby = "reading")) ``` #### Accessing list elements List elements can be accessed using the `[[ ]]` notation for single elements, or the `$` notation if you're accessing named elements: ```{r} # Accessing elements name <- my_list$name # or my_list[["name"]] preference_color <- complex_list$preferences$color ``` #### Modifying lists Lists can be modified by adding new elements, changing existing elements, or removing elements: ```{r} # Adding a new element my_list$gender <- "Male" # Changing an existing element my_list$age <- 31 # Removing an element my_list$scores <- NULL ``` #### Lists in Functions Lists are often used as return values for functions that need to provide multiple pieces of data: ```{r} # Function returning a list calculate_stats <- function(numbers) { mean_val <- mean(numbers) sum_val <- sum(numbers) return(list(mean = mean_val, sum = sum_val)) } # Using the function results <- calculate_stats(c(1, 2, 3, 4, 5)) ```

Lecture: Introduction to the Course

Slides

Lab 1: Install R and RStudio

Slide deck:

Slides

Why learn R?

Installing R

Installing RStudio

Step 1: Install R-Studio

Step 2: Create a new project

Step 3: Give project structure

Step 4: Working with R-scripts

Step 5: When you exit R-studio

Basic R Commands

Assignment (<-)

Concatenation (c())

Operations (+, -)

Multiplication (*) and Division (/)

rm() Remove Object

Logic (!, !=, ==)

OR (| and ||)

AND (& and &&)

Data Types in R

Integers

Characters

Factors

Ordered Factors

Operations with Ordered Factors

Checking Data with Ordered Factors

Modifying Ordered Factors

Strings

Vectors

Manipulating Vectors

Functions with vectors

Data Frames

Create Data Frames

Access Data Frame Elements

Explore Your Data Frames

Manipulating Data Frames

Add Rows with rbind()

Adding Columns with cbind()

Considerations for rbind() and cbind()

View Data Structure (summary(), str(), head(), tail())

mean()

sd()

min() and max()

table()

Cross-Tabulation with table()

Summary Statistics

First Data Visualisation with ggplot2

Installing and Loading ggplot2

Basic Components of a ggplot2 Plot

Create a Basic Plot

Customising the plot

Scatter Plot with ggplot2

Box Plot with ggplot2

Histogram with ggplot2

Line Plot with ggplot2 (Time Series)

Base R Graphs

Basic Plotting Functions

Creating a Basic Scatter Plot with Base R

Generating a Histogram with Base R

Generate Line Plot with Base R

Comparing Distributions with Box Plots

Summary of Today’s Lab

What We Have Learned

How to Build Skills?

Where to Get Help

Recommended Reading

Johannas Karl on Getting Started In R

Packages

Appendix A: At Home Exercises

Appendix B: Solutions

Solutions 1 and 2

Solution Exercise 3: Basic Operations and Data Structure Manipulation

Solution 4: Working with Data Frames

Solution 5: Logical Operations and Subsetting

Solution 6: Cross-Tabulation and Analysis

Solution 7: Visualization with ggplot2

Appendix B: Other Data Types You May Encounter

Assignment (`<-`)

Concatenation (`c()`)

Operations (`+`, `-`)

Multiplication (`*`) and Division (`/`)

`rm()` Remove Object

Logic (`!`, `!=`, `==`)

OR (`|` and `||`)

AND (`&` and `&&`)

Add Rows with `rbind()`

Adding Columns with `cbind()`

Considerations for `rbind()` and `cbind()`

View Data Structure (`summary()`, `str()`, `head()`, `tail()`)

First Data Visualisation with `ggplot2`

Installing and Loading `ggplot2`

Basic Components of a `ggplot2` Plot

Scatter Plot with `ggplot2`

Box Plot with `ggplot2`

Histogram with `ggplot2`

Line Plot with `ggplot2` (Time Series)

Solution 7: Visualization with `ggplot2`