R Coding Basics

Variables can be created using <- or =.

name <- "Girlypop"    # String
age <- 16             # Numeric
is_student <- TRUE    # Boolean (TRUE/FALSE)

# Let's print these out!
print(name)
print(age)
print(is_student)

R has multiple data structures like vectors, data frames, and lists.

Vectors

Vectors are like arrays in other languages, holding elements of the same type.

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

# Vector of characters (strings)
fruits <- c("apple", "banana", "strawberry")
print(fruits)

Data frames are used to store tabular data. Think of them as little spreadsheets.

# Create a data frame
students <- data.frame(
  Name = c("Maya", "Rosa", "Ava"),
  Grade = c(90, 85, 95),
  Passed = c(TRUE, TRUE, TRUE)
)

# View the data frame
print(students)

If-Else Statements

Control the flow of your program with conditions.

# If-else example
grade <- 95

if (grade >= 90) {
  print("A+ girlypop!")
} else {
  print("Keep working hard!")
}

For Loops Repeat actions a specific number of times.

# For loop to print numbers
for (i in 1:5) {
  print(paste("This is loop number", i))
}

While Loops Repeat until a condition is false.

# While loop example
counter <- 1

while (counter <= 3) {
  print(paste("Counter is", counter))
  counter <- counter + 1
}

Functions in R allow you to write reusable code.

# Define a function
sparkle <- function(name, mood) {
  paste(name, "is feeling", mood, "and ready to conquer the world!")
}

# Call the function
message <- sparkle("Girlypop", "fabulous")
print(message)

You can read in data from CSV files.

# Install ggplot2 if not already
install.packages("ggplot2")
library(ggplot2)

# Create a simple plot
ggplot(students, aes(x = Name, y = Grade)) +
  geom_bar(stat = "identity", fill = "lightpink") +
  ggtitle("Student Grades") +
  theme_minimal()

If you’re into data science, R is great for machine learning! Here's a super quick intro.

# Install caret package for ML
install.packages("caret")
library(caret)

# Example: Splitting data into training and test sets
data(iris)
set.seed(123)
trainIndex <- createDataPartition(iris$Species, p = .8, list = FALSE)
trainSet <- iris[trainIndex, ]
testSet <- iris[-trainIndex, ]

# Train a decision tree model
install.packages("rpart")
library(rpart)
model <- rpart(Species ~ ., data = trainSet)

# Predict on the test set
predictions <- predict(model, testSet, type = "class")
print(predictions)