R Reference

A quick reference to the R concepts used throughout this book.

Functions in R

Functions perform operations (calculate, transform, model, graph) on various objects that contain information (blood pressure measurements, monthly sales, political party affiliation, etc.)

percent_of_income <- function(expense, income) {
  (expense / income) * 100
}

percent_of_income(expense = 1500, income = 5000)
#> [1] 30

The pattern is name <- function(inputs) { body }. The last expression in the body is returned automatically; return() is optional.

Types of objects in R

Vectors

Vectors are one-dimensional containers where every value has the same type.

Atomic vectors

The four atomic types: logical, integer, double, and character.

c(TRUE, FALSE, TRUE)              # logical
#> [1]  TRUE FALSE  TRUE
c(1L, 2L, 3L)                     # integer
#> [1] 1 2 3
c(1.5, 2.0, 3.14)                 # double
#> [1] 1.50 2.00 3.14
c("apple", "banana", "cherry")    # character
#> [1] "apple"  "banana" "cherry"

S3 vectors

S3 vectors (factors, dates, date-times, durations) are atomic vectors with extra class metadata that gives them specialized behavior.

factor(c("low", "med", "high"),
       levels = c("low", "med", "high"))   # factor
#> [1] low  med  high
#> Levels: low med high
as.Date("2026-01-15")                      # date
#> [1] "2026-01-15"
as.POSIXct("2026-01-15 09:30:00")          # date-time
#> [1] "2026-01-15 09:30:00 MST"
as.difftime(2, units = "hours")            # duration
#> Time difference of 2 hours

Matrices

Two dimensional objects. All values must share the same atomic type.

matrix(1:6, nrow = 2, ncol = 3)
#>      [,1] [,2] [,3]
#> [1,]    1    3    5
#> [2,]    2    4    6

Arrays

Multidimensional objects. Same-type values; dimensions defined by dim().

array(1:12, dim = c(2, 3, 2))
#> , , 1
#> 
#>      [,1] [,2] [,3]
#> [1,]    1    3    5
#> [2,]    2    4    6
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3]
#> [1,]    7    9   11
#> [2,]    8   10   12

Data frames & tibbles

Rectangular objects: each column is a vector, and columns can be different types. Tibbles are tidyverse-style data frames with nicer printing and stricter behavior.

tibble::tibble(
  name = c("Alice", "Bob", "Carol"),
  age  = c(34L, 28L, 41L),
  paid = c(TRUE, FALSE, TRUE)
)
#> # A tibble: 3 × 3
#>   name    age paid 
#>   <chr> <int> <lgl>
#> 1 Alice    34 TRUE 
#> 2 Bob      28 FALSE
#> 3 Carol    41 TRUE

Lists

Recursive objects: each element can be any object, including another list. The most flexible container in R, and the building block underneath data frames, model objects, and JSON-like data.

list(
  numbers = c(1, 2, 3),
  name    = "Alice",
  flags   = c(TRUE, FALSE),
  nested  = list(a = 1, b = "two")
)
#> $numbers
#> [1] 1 2 3
#> 
#> $name
#> [1] "Alice"
#> 
#> $flags
#> [1]  TRUE FALSE
#> 
#> $nested
#> $nested$a
#> [1] 1
#> 
#> $nested$b
#> [1] "two"