Variables, Data Types, and Basic Operations

Before working with real datasets, you need a solid grasp of how R stores and manipulates individual pieces of data. This starts with variables and R's core atomic data types.

1. Creating Variables — The Assignment Operator

R conventionally uses <- for assignment (though = also works in most contexts):

age <- 25
name <- "Alice"
is_active <- TRUE

print(age)

2. Basic Data Types

num_val <- 42.5        # numeric
int_val <- 42L         # integer
char_val <- "R lang"   # character
log_val <- TRUE        # logical

class(num_val)   # "numeric"
class(int_val)   # "integer"
typeof(char_val) # "character"

3. Checking and Converting Types

Use class() or typeof() to inspect, and as.*() functions to convert:

x <- "10"
class(x)            # "character"

y <- as.numeric(x)  # convert to numeric
class(y)            # "numeric"

z <- as.character(25)
class(z)            # "character"

4. Naming Rules for Variables

• Must start with a letter or a dot (not followed by a number).
• Can contain letters, numbers, dots, and underscores.
• Case-sensitive — Age and age are different variables.
• Cannot use reserved words like TRUE, if, or function.

# Valid names
my_var <- 1
.hidden_var <- 2
total2 <- 3

# Invalid names
# 2total <- 1     # cannot start with a number
# my-var <- 1     # hyphens not allowed

5. Basic Arithmetic Operations

6. Logical and Comparison Operators

5 > 3        # TRUE
5 == 5       # TRUE
5 != 3       # TRUE
TRUE & FALSE # FALSE  (AND)
TRUE | FALSE # TRUE   (OR)
!TRUE        # FALSE  (NOT)

7. Production-Ready Checklist

• ✅ Use <- for assignment — the R idiomatic style.
• ✅ Know the five atomic types — numeric, integer, character, logical, complex.
• ✅ Convert types explicitly — with as.numeric(), as.character(), etc.
• ✅ Follow variable naming rules — descriptive, snake_case names are conventional.