R — Lists

What is a list?

A list is an ordered collection that can hold any type of R object — numbers, strings, vectors, matrices, other lists:

person <- list(
  name = "Ada",
  age = 36,
  scores = c(90, 85, 95),
  active = TRUE
)

Lists are R's equivalent of dictionaries in Python or objects in JavaScript.

Creating lists

# With names
person <- list(name = "Ada", age = 36, job = "Engineer")

# Without names
simple <- list(1, "hello", TRUE)

# From existing objects
x <- 42
y <- "hello"
z <- c(1, 2, 3)
my_list <- list(x, y, z)

# Empty list
empty <- list()

Accessing list elements

person <- list(name = "Ada", age = 36, scores = c(90, 85, 95))

# By name — returns a list
person["name"]        # list with name element
person[["name"]]      # "Ada" — extracts the value
person$name           # "Ada" — same as [[

# By index
person[[1]]           # "Ada"
person[[3]]           # 90 85 95

# Multiple elements
person[c("name", "age")]  # sublist
person[c(1, 2)]           # sublist

The key difference: person["name"] returns a list; person[["name"]] returns the value. Use [[ when you want the actual data.

Modifying lists

person <- list(name = "Ada", age = 36)

# Add elements
person$email <- "ada@example.com"
person[["job"]] <- "Engineer"

# Change elements
person$age <- 37

# Remove elements
person$email <- NULL

# Add at position
person <- append(person, list(city = "London"), after = 1)

List functions

x <- list(1, 2, 3, 4, 5)

length(x)     # 5
names(x)      # NULL (unnamed)

# Name the elements
names(x) <- c("a", "b", "c", "d", "e")

# str — structure
str(person)

# unlist — flatten to a vector
unlist(x)     # 1 2 3 4 5

# lapply — apply function to each element
squares <- lapply(1:5, function(x) x^2)
# returns a list

# sapply — simplified version (returns vector when possible)
squares <- sapply(1:5, function(x) x^2)
# returns a vector

Nested lists

Lists can contain other lists:

company <- list(
  name = "TechCorp",
  employees = list(
    list(name = "Ada", role = "Engineer"),
    list(name = "Grace", role = "Scientist")
  )
)

# Access nested elements
company$employees[[1]]$name  # "Ada"
company$employees[[2]]$role  # "Scientist"

Lists vs vectors

Feature	Vector	List
Element type	Same type	Any type
Access	x[1] returns vector	x[[1]] returns value
Speed	Faster	Slower
Use case	Homogeneous data	Heterogeneous data

Unlisting

x <- list(a = 1, b = 2, c = 3)
unlist(x)     # named vector: a b c \n 1 2 3

# Flatten nested lists
nested <- list(list(1, 2), list(3, 4))
unlist(nested)  # 1 2 3 4

Converting between types

# Vector to list
v <- c(1, 2, 3)
lst <- as.list(v)

# List to vector (when elements are compatible)
lst <- list(1, 2, 3)
v <- unlist(lst)

# List to data frame (when elements are same length)
lst <- list(a = 1:3, b = 4:6)
df <- as.data.frame(lst)

Common patterns

Building lists dynamically

results <- list()
for (i in 1:5) {
  results[[i]] <- i^2
}

Filtering lists

numbers <- list(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)

# Keep only even numbers
evens <- Filter(function(x) x %% 2 == 0, numbers)
# returns list(2, 4, 6, 8, 10)

# Find first match
first_even <- Position(function(x) x %% 2 == 0, numbers)
# returns 2 (index)

Combining lists

a <- list(1, 2, 3)
b <- list(4, 5, 6)
c(a, b)  # list of 6 elements

# Merge named lists
x <- list(a = 1, b = 2)
y <- list(b = 3, c = 4)
c(x, y)  # list(a=1, b=3, c=4) — y's b overwrites x's

Recursive flattening

flatten_list <- function(lst) {
  result <- list()
  for (item in lst) {
    if (is.list(item)) {
      result <- c(result, flatten_list(item))
    } else {
      result <- c(result, list(item))
    }
  }
  result
}

nested <- list(1, list(2, list(3, 4)), 5)
flatten_list(nested)  # list(1, 2, 3, 4, 5)

Lists in real R code

Lists are everywhere in R:

# Linear model output is a list
model <- lm(mpg ~ wt, data = mtcars)
str(model)  # list with coefficients, residuals, etc.

# File reading returns lists
config <- jsonlite::fromJSON("config.json")

# Functions can return lists for multiple outputs
analyze <- function(x) {
  list(
    mean = mean(x),
    sd = sd(x),
    n = length(x)
  )
}

result <- analyze(rnorm(100))
result$mean  # average of the random sample

Mini Practice

1. Create a list with your name, age, and a vector of your hobbies
2. Access each element using $, [[, and [ — explain the differences
3. Add a new element and remove an existing one
4. Use lapply to square each number in a list
5. Create a nested list representing a book with chapters and pages

Next: matrices — 2D data →