Base R Data Structures: Lists

Published

2025-10-24

Common Data Structures

A data scientist needs to deal with data! We need to have a firm foundation in the ways that we can store our data in R. This section goes through the most commonly used ‘built-in’ R objects that we’ll use.

  • There are five major data structures used in R

    1. Atomic Vector (1d)
    2. Matrix (2d)
    3. Array (nd)
    4. Data Frame (2d)
    5. List (1d)
Dimension Homogeneous (elements all the same) Heterogeneous (elements may differ)
1d Atomic Vector List
2d Matrix Data Frame
nd Array

List

  • A vector that can have differing elements! (still 1D)

Three larger boxes are shown. Each represents a list element. The elements have an ordering, 1st, 2nd, and 3rd value. Within each element any type of R object can be store.

  • An ordered set of objects (ordering starts at 1)

  • Useful for more complex types of data

Creating a List

  • Create with list()

  • The help gives:

list(...)

where ... is

objects, possibly named.

  • We can essentially take any objects and store them as elements of our list!
my_df <- data.frame(number = 1:5, letter = c("a", "b", "c", "d", "e"))
my_list <- list(my_df, rnorm(4), c("!", "?"))
my_list
[[1]]
  number letter
1      1      a
2      2      b
3      3      c
4      4      d
5      5      e

[[2]]
[1]  1.9118667  1.9508529  0.3379946 -0.5242446

[[3]]
[1] "!" "?"
  • Similar to creating a data frame, we can add names to the list elements upon creation
my_list <- list(my_data_frame = my_df, normVals = rnorm(4), punctuation = c("!", "?"))
my_list
$my_data_frame
  number letter
1      1      a
2      2      b
3      3      c
4      4      d
5      5      e

$normVals
[1]  0.6643841  0.2016515 -1.3898236 -1.5294647

$punctuation
[1] "!" "?"

Common Attributes of Lists

The most common attribute for a list is similar to a data frame, the names.

str(my_list)
List of 3
 $ my_data_frame:'data.frame':  5 obs. of  2 variables:
  ..$ number: int [1:5] 1 2 3 4 5
  ..$ letter: chr [1:5] "a" "b" "c" "d" ...
 $ normVals     : num [1:4] 0.664 0.202 -1.39 -1.529
 $ punctuation  : chr [1:2] "!" "?"
attributes(my_list)
$names
[1] "my_data_frame" "normVals"      "punctuation"
  • The names function gives us quick access to the names.
names(my_list)
[1] "my_data_frame" "normVals"      "punctuation"

Accessing List Elements

There are many ways to access list elements!

  • Use single square brackets [ ] for multiple list elements to be returned
my_list
$my_data_frame
  number letter
1      1      a
2      2      b
3      3      c
4      4      d
5      5      e

$normVals
[1]  0.6643841  0.2016515 -1.3898236 -1.5294647

$punctuation
[1] "!" "?"
my_list[2:3]
$normVals
[1]  0.6643841  0.2016515 -1.3898236 -1.5294647

$punctuation
[1] "!" "?"
  • Use double square brackets [[ ]] (or [ ]) for a single list element
my_list[1]
$my_data_frame
  number letter
1      1      a
2      2      b
3      3      c
4      4      d
5      5      e
my_list[[1]]
  number letter
1      1      a
2      2      b
3      3      c
4      4      d
5      5      e

  • Notice the difference in how these are returned!

    • [] returns a list with a named element (my_data_frame)
    • [[]] returns just the element itself (the data frame)
str(my_list[1])
List of 1
 $ my_data_frame:'data.frame':  5 obs. of  2 variables:
  ..$ number: int [1:5] 1 2 3 4 5
  ..$ letter: chr [1:5] "a" "b" "c" "d" ...
str(my_list[[1]])
'data.frame':   5 obs. of  2 variables:
 $ number: int  1 2 3 4 5
 $ letter: chr  "a" "b" "c" "d" ...
  • We can do multiple subsets on a single line!
my_list[[2]]
[1]  0.6643841  0.2016515 -1.3898236 -1.5294647
my_list[[2]][3:4]
[1] -1.389824 -1.529465
  • If we have named list elements, we can use $ just like with data frames!
str(my_list)
List of 3
 $ my_data_frame:'data.frame':  5 obs. of  2 variables:
  ..$ number: int [1:5] 1 2 3 4 5
  ..$ letter: chr [1:5] "a" "b" "c" "d" ...
 $ normVals     : num [1:4] 0.664 0.202 -1.39 -1.529
 $ punctuation  : chr [1:2] "!" "?"
my_list$normVals
[1]  0.6643841  0.2016515 -1.3898236 -1.5294647
  • Note that the attributes() function actually returns a list!
attributes(my_list)
$names
[1] "my_data_frame" "normVals"      "punctuation"  
str(attributes(my_list))
List of 1
 $ names: chr [1:3] "my_data_frame" "normVals" "punctuation"
  • That means we can access the named list element names via the $ operator.
attributes(my_list)$names
[1] "my_data_frame" "normVals"      "punctuation"

Lists & Data Frames

  • Big Connection: A Data Frame is a list of equal length vectors!

  • This can be seen in the similar nature of the structure of these two objects.

str(my_list)
List of 3
 $ my_data_frame:'data.frame':  5 obs. of  2 variables:
  ..$ number: int [1:5] 1 2 3 4 5
  ..$ letter: chr [1:5] "a" "b" "c" "d" ...
 $ normVals     : num [1:4] 0.664 0.202 -1.39 -1.529
 $ punctuation  : chr [1:2] "!" "?"
is.list(my_list)
[1] TRUE
str(iris)
'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
is.list(iris)
[1] TRUE
  • That means we can access parts of a data frame in the same way we did with a list. To get the 2nd column (list element) of iris we can do:
head(iris[2])
  Sepal.Width
1         3.5
2         3.0
3         3.2
4         3.1
5         3.6
6         3.9
head(iris[[2]])
[1] 3.5 3.0 3.2 3.1 3.6 3.9

  • Notice again the change in simplification between the two methods for accessing list elements. Think of [] as preserving and [[]] as simplifying!

  • We can also look at the typeof() each of these objects

typeof(my_list)
[1] "list"
typeof(iris)
[1] "list"

Quick R example

Please pop this video out and watch it in the full panopto player!

Recap!

List (1D group of objects with ordering)

  • A vector that can have differing elements

  • Create with list()

  • More flexible than a Data Frame!

  • Useful for more complex types of data

  • Access with [ ], [[ ]], or $

Big Recap!

We now know how we’ll handle data using R. We will end up using vectors, lists, and data frames a lot (although we’ll use a special form of a data frame called a tibble).

Dimension Homogeneous Heterogeneous
1d Atomic Vector List
2d Matrix Data Frame
nd Array

Common Attributes exist

  • dimnames for matrices
  • names for vectors, data frames, and lists
  • Note: colnames() is a function that generically tries to get at the names, whether you have a matrix or data frame (rownames() exists as well!)

Basic access via

  • Atomic vectors - x[ ]
  • Matrices - x[ , ]
  • Data Frames - x[ , ] or x$name
  • Lists - x[ ], x[[ ]], or x$name

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