R Data Types

The Building Blocks: R's Basic Types

Every value in R belongs to one of a small set of basic types, often called atomic types. Knowing which type a value has lets you predict how R will treat it in calculations, comparisons, and function calls, and it explains a surprising number of "why isn't this working" moments.

• numeric - any decimal number, e.g. 4.5
• integer - whole numbers marked with an L, e.g. 12L
• character - text wrapped in quotes, e.g. "hello"
• logical - TRUE or FALSE
• complex - a number with an imaginary part, e.g. 2+3i

age <- 29L
price <- 19.99
name <- "Nimbus"
is_member <- TRUE
mystery <- 2 + 3i

class(age)
class(price)
class(name)
class(is_member)
class(mystery)

Checking and Converting Types

class() tells you the type R has stored a value as, while as.numeric(), as.character(), and similar as.xxx() functions convert a value from one type to another. Conversion only succeeds if the value makes sense in the new type - turning "87.5" into a number works fine, but turning "hello" into a number produces NA with a warning.

score <- "87.5"

class(score)        # "character"
is.numeric(score)   # FALSE

score <- as.numeric(score)
class(score)        # "numeric"
score + 12.5         # 100

Special Values Worth Knowing

• NA - a value that is missing or unknown
• NULL - the complete absence of a value, not even a placeholder
• NaN - the result of an undefined calculation such as 0/0
• Inf and -Inf - what you get from dividing a nonzero number by zero

Once you can spot these types on sight, reading error messages and debugging unexpected results gets much easier - most type-related bugs come down to a value being stored in a different type than you assumed.