Hands-On_Activities_for_Learning_R

Introduction

Welcome to the hands-on session for learning R! In this activity, we will:

1. Explore basic R syntax.
2. Create simple data visualizations.
3. Practice using R Markdown to create reproducible documents.
4. Systematically practice core R concepts.

---

Activity 1: Your First R Command

Let’s start with a simple command to familiarize yourself with the R console:

# Run this command in your console
greeting <- "Hello, R!"
cat(greeting)

## Hello, R!

Discussion Questions:

• What did the cat() function do?
• What happens if you add a \n (newline character) inside the string?
• How is cat() different from print()?

---

Activity 2: Basic Arithmetic and Data Structures

Step 1: Perform Basic Arithmetic

# Basic arithmetic
x <- 10
y <- 5
sum <- x + y
product <- x * y
sum

## [1] 15

product

## [1] 50

Step 2: Create a Vector

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

## [1] 1 2 3 4 5

Step 3: Subset the Vector

You can use numeric indices to extract specific elements from a vector, and negative indices allow you to exclude specific elements. Logical indexing involves using a logical vector (TRUE or FALSE values) to subset elements that satisfy a condition. The which() function returns the indices of elements that meet a condition, which can then be used for subsetting. Additionally, head() and tail() functions can be used to extract the first or last n elements from a vector.

# 1. Indexing by Position (Numeric Indexing)
numbers[1:3] 

## [1] 1 2 3

# 2. Negative Indexing
numbers[-2]  # Removes the second element

## [1] 1 3 4 5

numbers[-(1:3)]  # Removes the first three elements

## [1] 4 5

# 3. Logical Indexing
numbers[numbers %% 2 == 0]  # Extracts even numbers

## [1] 2 4

# 4. Using the which() Function
numbers[which(numbers > 3)]  # Extracts numbers greater than 3

## [1] 4 5

# 5. Using head() and tail()
head(numbers, 3)  # Extracts the first 3 elements

## [1] 1 2 3

tail(numbers, 3)  # Extracts the last 3 elements

## [1] 3 4 5

---

Activity 3: Exploring Data Frames

Step 1: Create a Data Frame

# Creating a data frame
data <- data.frame(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(25, 30, 35),
  Score = c(90, 85, 88)
)
data

##      Name Age Score
## 1   Alice  25    90
## 2     Bob  30    85
## 3 Charlie  35    88

Step 2: Access Columns and Rows

# Access specific columns
data$Name

## [1] "Alice"   "Bob"     "Charlie"

# Access specific rows
data[1, ]

##    Name Age Score
## 1 Alice  25    90

Step 3: Add a New Column

# Adding a new column
data$Passed <- data$Score > 80
data

##      Name Age Score Passed
## 1   Alice  25    90   TRUE
## 2     Bob  30    85   TRUE
## 3 Charlie  35    88   TRUE

---

Activity 4: Logical Operations and Conditional Statements

Step 1: Logical Comparisons

# Logical comparisons
x <- 10
y <- 5
x > y

## [1] TRUE

x == y

## [1] FALSE

Step 2: Conditional Statements

# Using if-else
if (x > y) {
  message("x is greater than y")
} else {
  message("x is not greater than y")
}

## x is greater than y

---

Activity 5: Loop Structures

Step 1: For Loops

# Example of a for loop
for (i in 1:10) {
  print(i)
}

## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10

Step 2: While Loops

# Example of a while loop
i <- 1
while (i <= 10) {
  print(i)
  i <- i + 1
  
  # if (i > 5) {  # Break the loop when x exceeds 5
  #   break
  # }
}

## [1] 1
## [1] 2
## [1] 3
## [1] 4
## [1] 5
## [1] 6
## [1] 7
## [1] 8
## [1] 9
## [1] 10

---

Activity 6: Writing and Using Functions

Step 1: Create a Function

# Defining a function
add_numbers <- function(a, b) {
  return(a + b)
}
result <- add_numbers(10, 5)
result

## [1] 15

Step 2: Create a Function with a Conditional

# Function with conditional logic
evaluate_score <- function(score) {
  if (score >= 50) {
    return("Pass")
  } else {
    return("Fail")
  }
}
evaluate_score(75)

## [1] "Pass"

evaluate_score(45)

## [1] "Fail"

---

Activity 7: Data Visualization Challenge

Step 1: Create a Scatter Plot

# Built-in dataset
x <- 1:10
y <- x^2
plot(x, y, main="Scatter Plot", xlab="X-axis", ylab="Y-axis", col="blue")

Step 2: Advanced Challenge with ggplot2

# Using ggplot2 for enhanced visualization
library(ggplot2)
data(mpg)
ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3) +
  labs(title = "Engine Displacement vs. Highway MPG",
       x = "Engine Displacement (L)",
       y = "Highway MPG") +
  theme_minimal()

Advanced Challenge:

• Modify the plot by adjusting the color scheme. You can adjust the color scheme by: Using scale_color_manual() for custom colors.

ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3) +
  labs(title = "Engine Displacement vs. Highway MPG",
       x = "Engine Displacement (L)",
       y = "Highway MPG") +
  scale_color_manual(values = c("compact" = "blue", "midsize" = "green", "suv" = "red", "pickup" = "orange", "minivan" = "purple")) +
  theme_minimal()

Using scale_color_brewer() for predefined color palettes.

ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3) +
  labs(title = "Engine Displacement vs. Highway MPG",
       x = "Engine Displacement (L)",
       y = "Highway MPG") +
  scale_color_brewer(palette = "Set1") +  # Choose a predefined color palette
  theme_minimal()

Helpful Chunk Options:
- out.width: Controls the output width of the plot in the rendered document (useful for resizing the plot after it is generated). out.width=“50%” will make the plot width 50% of the available width in the output document.
- fig.width: Controls the width of the plot (in inches).
- **fig.height**: Controls the height of the plot (in inches).

ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3) +
  labs(title = "Engine Displacement vs. Highway MPG",
       x = "Engine Displacement (L)",
       y = "Highway MPG") +
  scale_color_brewer(palette = "Set1") +  # Choose a predefined color palette
  theme_minimal()

To view the full list of available palettes and how they look, you can use the RColorBrewer package directly:

library(RColorBrewer)
display.brewer.all()

• Add a regression line to examine trends using: geom_smooth(method = “lm”)

ggplot(data = mpg, aes(x = displ, y = hwy, color = class)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm") +
  labs(title = "Engine Displacement vs. Highway MPG",
       x = "Engine Displacement (L)",
       y = "Highway MPG") +
  theme_minimal()

## `geom_smooth()` using formula = 'y ~ x'

---

Add Code Chunks

1) The keyboard shortcut Cmd/Ctrl + Alt + I

2) The “Insert” button icon in the editor toolbar

3) Manually type the chunk delimiters

Chunk Header

Chunck Name

r, followed by an optional chunk name, followed by comma-seprated options 1. easy navigation between chunks using the drop-down code navigator
2. setup cached chunks to avoid rerun

R for Data Science

Chunk Option

60+ customized options, arguments supplied to the chunk header.

Learn more about chunk options at

eval = FALSE prevents code from being evaluated.
include = FALSE runs the code, but doesn’t show the code or results in the final document.
echo = FALSE prevents code, but not the results from appearing in the finished file.
message = FALSE or warning = FALSE prevents messages or warnings from appearing in the finished file.
results = ‘hide’ hides printed output, fig.show = ‘hide’ hides plots.
error = TRUE causes the render to continue even if code returns an error.

1. include = FALSE

2. include = TRUE, echo = TRUE

print("hello world!")

## [1] "hello world!"

3. include = TRUE, echo = FALSE

## [1] "hello world!"

Table

By default, R Markdown prints data frames and matrices as you’d see them in the console:

mtcars[1:5, ]

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2

If you prefer that data be displayed with additional formatting you can use the knitr::kable function. The code below generates Table 27.1.

knitr::kable(
  mtcars[1:5, ], 
  caption = "A knitr kable."
)

A knitr kable.

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2

Citations

You can cite the references in your document using the @ symbol followed by the citation key.
you can add a citation [@ZANTIS2023161211].

Using colours in LaTeX