R Examples

Welcome to The Coding College! In this guide, we’ll walk you through a collection of real-world examples in R programming. Whether you’re a beginner or an experienced developer, these examples will help you grasp the core functionality of R in data analysis, visualization, and more.

By exploring these examples, you’ll learn how to:

  • Perform common operations in R.
  • Handle data structures like vectors, data frames, and matrices.
  • Create visualizations like bar plots and scatterplots.
  • Solve statistical problems using R.

Let’s dive into the examples!

1. Basic Arithmetic in R

R can perform basic arithmetic operations just like a calculator.

Example

# Perform arithmetic
addition <- 10 + 5
subtraction <- 10 - 5
multiplication <- 10 * 5
division <- 10 / 5
exponentiation <- 2^3

# Print results
print(paste("Addition:", addition))
print(paste("Subtraction:", subtraction))
print(paste("Multiplication:", multiplication))
print(paste("Division:", division))
print(paste("Exponentiation:", exponentiation))

Output:

Addition: 15
Subtraction: 5
Multiplication: 50
Division: 2
Exponentiation: 8

2. Create and Manipulate Vectors

Vectors are one of the most commonly used data structures in R.

Example

# Create a vector
numbers <- c(10, 20, 30, 40, 50)

# Access elements
print(numbers[2])  # Access the second element

# Perform operations
doubled <- numbers * 2
print(doubled)  # Multiply each element by 2

Output:

[1] 20
[1] 20 40 60 80 100

3. Working with Data Frames

Data frames are essential for managing tabular data.

Example

# Create a data frame
data <- data.frame(
  Name = c("Alice", "Bob", "Charlie"),
  Age = c(25, 30, 35),
  Score = c(90, 85, 95)
)

# Access a column
print(data$Age)

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

# Print the updated data frame
print(data)

Output:

[1] 25 30 35
      Name Age Score Passed
1    Alice  25    90   TRUE
2      Bob  30    85   TRUE
3  Charlie  35    95   TRUE

4. Calculate Descriptive Statistics

R makes it easy to calculate mean, median, standard deviation, and more.

Example

# Create a vector of numbers
numbers <- c(10, 20, 30, 40, 50)

# Calculate statistics
mean_value <- mean(numbers)
median_value <- median(numbers)
std_dev <- sd(numbers)

# Print results
print(paste("Mean:", mean_value))
print(paste("Median:", median_value))
print(paste("Standard Deviation:", std_dev))

Output:

Mean: 30
Median: 30
Standard Deviation: 15.811

5. Create a Scatter Plot

R provides powerful plotting tools for data visualization.

Example

# Create data
x <- c(1, 2, 3, 4, 5)
y <- c(10, 20, 30, 40, 50)

# Create a scatter plot
plot(x, y, main = "Scatter Plot Example", xlab = "X-Axis", ylab = "Y-Axis", col = "blue", pch = 16)

This will generate a scatter plot with blue dots.

6. Fit a Linear Model

R is widely used for statistical modeling.

Example

# Create data
x <- c(1, 2, 3, 4, 5)
y <- c(2, 4, 6, 8, 10)

# Fit a linear model
model <- lm(y ~ x)

# Print summary of the model
print(summary(model))

Output: The output will include model coefficients and statistical details.

7. Use Conditional Statements

Conditional statements like if and else are used to make decisions in R.

Example

# Check if a number is positive or negative
number <- -10

if (number > 0) {
  print("The number is positive")
} else {
  print("The number is negative")
}

Output:

The number is negative

8. Loop Through Data

Loops in R allow you to perform repetitive tasks.

Example: For Loop

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

# Loop through each element
for (num in numbers) {
  print(num^2)  # Print the square of each number
}

Output:

1
4
9
16
25

9. Handle Missing Values

Missing values in R are represented as NA.

Example

# Create a vector with missing values
numbers <- c(10, 20, NA, 40, 50)

# Remove missing values
cleaned_numbers <- na.omit(numbers)

# Calculate mean of cleaned data
mean_cleaned <- mean(cleaned_numbers)

# Print results
print(paste("Mean (excluding NA):", mean_cleaned))

Output:

Mean (excluding NA): 30

10. Perform String Operations

Strings are important for handling text data in R.

Example

# Create strings
text1 <- "Hello"
text2 <- "World"

# Concatenate strings
greeting <- paste(text1, text2, sep = " ")

# Print the result
print(greeting)

Output:

Hello World

Conclusion

R is a versatile programming language with countless applications in data analysis, statistics, and machine learning. These practical examples are just the beginning of what you can achieve with R.

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