🔤 R Basics & Syntax

🧠 What this note covers

This note walks through the smallest building blocks of R code: how to write comments, how to create variables, what operators are available, and a few conventions you will see everywhere once you start reading real R scripts. Think of this as the alphabet before you start forming sentences with R Functions and R Control Flow.

💬 Comments

A comment is a line of text in your code that R completely ignores when it runs. Comments exist purely so that humans reading the code later, including a future version of yourself, can understand what is going on. In R, anything after a hash symbol on a line is treated as a comment.

# This entire line is a comment and does nothing when run
x <- 5  # You can also add a comment after real code, on the same line

Comment the why, not the what

A comment like # add 1 to x next to x <- x + 1 does not tell you much that the code did not already say. A more useful comment explains why you are doing something, for example # adjusting for zero indexed months next to the same line.

📝 Assignment: putting values into names

In most languages you assign a value using a single equals sign, but R traditionally uses an arrow made of a less than sign and a hyphen, written as <-. This arrow visually points in the direction the value is flowing, from the value on the right into the name on the left.

age <- 25          # the standard and most idiomatic way to assign in R
age = 25            # also works, but is less traditional in R style
30 -> age            # a right pointing arrow also works, though it is rare

Why does R even allow three ways to assign

R inherited <- from its predecessor language S. The single equals sign = was added later for people coming from other languages and is fully functional for assignment, but the R community still strongly favors <- in scripts, largely because = is also used for a different purpose, matching arguments by name inside a function call.

🔢 The core data types

Every value in R has a type. Understanding these early makes everything else in the language click into place much faster.

Type	Example	Explanation
numeric (double)	3.14	Any real number, including whole numbers by default
integer	5L	A whole number, forced by adding an uppercase L after the digits
character	"hello"	Text, wrapped in either double or single quotes
logical	TRUE, FALSE	A boolean value, can also be shortened to T and F
complex	2+3i	A complex number, rarely used outside specialized math

class(3.14)      # returns "numeric"
class(5L)        # returns "integer"
class("hello")   # returns "character"
class(TRUE)      # returns "logical"

Numbers are doubles by default

If you type x <- 5 without the L, R stores it as a numeric double, not an integer, even though it looks like a whole number. This rarely causes problems in everyday use, but it matters if you are working with functions that specifically expect an integer type.

➕ Operators

Arithmetic operators

5 + 3    # addition, gives 8
5 - 3    # subtraction, gives 2
5 * 3    # multiplication, gives 15
5 / 3    # division, gives 1.666667
5 %% 3   # modulo, the remainder after division, gives 2
5 %/% 3  # integer division, gives 1
5 ^ 2    # exponentiation, gives 25

Comparison operators

5 > 3    # TRUE
5 < 3    # FALSE
5 == 3   # FALSE, note the double equals for comparison
5 != 3   # TRUE, meaning "not equal to"
5 >= 5   # TRUE

Do not confuse = and ==

A single equals sign assigns a value, while a double equals sign checks for equality. Writing if (x = 5) instead of if (x == 5) is one of the most common beginner mistakes, and R will usually throw an error to protect you from it inside a condition.

Logical operators

TRUE & FALSE   # element wise AND, useful when comparing vectors
TRUE | FALSE   # element wise OR
!TRUE          # NOT, flips TRUE to FALSE
TRUE && FALSE  # AND but only checks the first element, used in if statements
TRUE || FALSE  # OR but only checks the first element, used in if statements

Single symbol versus double symbol logical operators

Use the single symbol versions (& and |) when you are comparing entire vectors element by element, and use the double symbol versions (&& and ||) when you are writing a condition inside something like an if statement, where you only need one TRUE or FALSE answer at the end.

🏷️ Naming rules for variables

A variable name in R can contain letters, numbers, dots, and underscores, but it cannot start with a number, and it cannot start with an underscore. Names are case sensitive, meaning age and Age are treated as two completely different variables.

my_score <- 90     # valid, uses an underscore
my.score <- 90      # valid, R allows dots in names, unlike most languages
2nd_score <- 90      # invalid, cannot start with a number

Dots in variable names

Seeing a dot inside a variable or function name, such as data.frame, often confuses people coming from other languages, since in most languages a dot means “access a property of an object.” In R it is simply a legal character in a name, though modern style guides such as the tidyverse style guide recommend using underscores instead of dots for new code.

🧮 Special values

R has a handful of special values that represent missing, undefined, or impossible results.

NA      # represents a missing value, "Not Available"
NULL    # represents the absence of a value entirely, an empty object
NaN     # "Not a Number", the result of an undefined mathematical operation like 0/0
Inf     # represents infinity, such as the result of 1/0
-Inf    # negative infinity

Checking for these special values

Never compare directly to NA using ==, since NA == NA actually returns NA, not TRUE. Instead use the dedicated functions built for this purpose: is.na(x), is.null(x), and is.nan(x).

🖨️ Printing output

print("Hello, world")   # explicitly prints a value
"Hello, world"           # at the top level of a script, R auto prints this too
cat("Hello,", "world")   # concatenates and prints without quotes, useful for clean output

print versus cat

Use print() when you want to see a value exactly as R represents it internally, including quotation marks around text. Use cat() when you want a cleaner, more human readable line of output, especially when combining multiple pieces of text and numbers together.

🔗 Where to go next

Once these basics feel comfortable, move on to R Vectors & Data Types to see how single values combine into R’s fundamental data structure, or jump to R Control Flow to start writing logic that makes decisions.