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🔤 R Core Language Concepts – Master Variables, Data Types, Operators & Control Flow

💡 Learn the essential elements of R that power every script—from variables and data types to logic, loops, and functions!

🧲 Introduction – Build a Strong Foundation in R Syntax and Logic

To write effective R code, you must understand the core language building blocks. These include how to declare and use variables, manage different data types, utilize operators, control execution flow with conditional statements and loops, and encapsulate logic using functions.

Whether you’re working on simple data summaries or advanced statistical models, these basics form the backbone of all R programming.

🎯 What You’ll Learn:

How to use variables and data types in R
How to apply arithmetic, logical, and relational operators
How to implement control structures like if, else, for, and while
How to define and reuse functions in your R scripts

📘 Topics Covered

🧠 Topic	📖 Description
🔡 R – Variables	Declaring, assigning, and accessing variables in R.
🧮 R – Data Types	Overview of numeric, character, logical, factor, and complex types.
➕ R – Numbers & Math	Using mathematical operators and functions with numeric data.
🧵 R – Strings	Working with text: concatenation, formatting, and string manipulation.
🔘 R – Booleans	Understanding `TRUE` / `FALSE` values and logical expressions.
⚙️ R – Operators	Use of arithmetic, logical, relational, and assignment operators.
🔀 R – If…Else / Decision Making	Writing conditional code using `if`, `else`, `else if`, and `switch`.
🔁 R – Loops (For, While)	Iterating over sequences using `for`, `while`, and `repeat` loops.
🧩 R – Functions	Creating and calling built-in and custom functions in R.

🔡 R – Variables

Variables in R store data that you can reuse throughout your script.

name <- "Alice"
age <- 30

📌 Use <- or = for assignment. Variable names should be meaningful and follow naming conventions.

🧮 R – Data Types

R supports the following primary data types:

Numeric: x <- 42
Character: name <- "John"
Logical: flag <- TRUE
Factor: gender <- factor(c("Male", "Female"))
Complex: z <- 1 + 2i

Use class(x) or typeof(x) to check the type of a variable.

➕ R – Numbers & Math

R has rich support for arithmetic:

a <- 10
b <- 3
result <- a + b  # Addition
sqrt(a)          # Square root

🧮 Use built-in math functions like log(), exp(), round() for numeric operations.

🧵 R – Strings

Work with textual data using functions like paste(), substr(), nchar().

greeting <- paste("Hello", "World")

Use sprintf() for formatting and tolower() / toupper() for case conversion.

🔘 R – Booleans

Boolean values (TRUE, FALSE) are used in comparisons and control flows.

x <- 5
y <- 10
x < y  # Returns TRUE

Combine conditions using &, |, and !.

⚙️ R – Operators

R supports:

Arithmetic: +, -, *, /, ^
Relational: ==, !=, <, >, <=, >=
Logical: &, |, !
Assignment: <-, =, ->

🔎 Example:

a <- 10
b <- 20
(a < b) & (a != 0)

🔀 R – If…Else / Decision Making

Conditional logic allows your program to decide based on values.

x <- 10
if (x > 5) {
  print("Greater than 5")
} else {
  print("5 or less")
}

Also use else if and switch() for complex conditions.

🔁 R – Loops (For, While)

🔄 For Loop:

for (i in 1:5) {
  print(i)
}

🔁 While Loop:

x <- 1
while (x <= 5) {
  print(x)
  x <- x + 1
}

Use repeat for infinite loops with break conditions.

🧩 R – Functions

Functions help you modularize code.

add <- function(x, y) {
  return(x + y)
}

add(5, 3)

💡 Functions can return values, take default arguments, and even be nested.

📌 Summary – Recap & Next Steps

🔍 Key Takeaways:

Variables and data types are the backbone of R scripting
Operators drive mathematical, logical, and relational tasks
Conditional statements and loops control execution flow
Functions promote reusable and organized code

⚙️ Real-World Relevance:
These foundational elements are essential for data analysis, automation, and statistical modeling in R.

🎓 Next Steps:
Practice each topic using real datasets. Try creating a script that takes user input, processes data, and returns a formatted result.

❓ Frequently Asked Questions (FAQs)

Q1: What’s the difference between <- and = in R?
✅ <- is the traditional assignment operator in R. = also works but is more common in function arguments.

Q2: Can I store different data types in one variable?
✅ No, R variables store one data type at a time. Use lists or data frames for mixed types.

Q3: What’s the difference between == and = in R?
✅ == checks equality, while = assigns values (like <-).

Q4: How do I write multi-line comments in R?
✅ Use # at the beginning of each line. R doesn’t support block comments.

Q5: Can I define a function inside another function in R?
✅ Yes, R supports nested functions and closures.

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