# Managing Lists

A list is an R structure that allows you to combine elements of different types, including lists embedded in a list, and length. Many statistical outputs are provided as a list as well; therefore, its critical to understand how to work with lists. In this section I will guide you throught the basics of managing lists to include:

## Creating Lists

To create a list we can use the list() function. Note how each of the four list items are of different classes (integer, character, logical, and numeric) and different length.

l <- list(1:3, "a", c(TRUE, FALSE, TRUE), c(2.5, 4.2))
str(l)
## List of 4
##  $: int [1:3] 1 2 3 ##$ : chr "a"
##  $: logi [1:3] TRUE FALSE TRUE ##$ : num [1:2] 2.5 4.2

# a list containing a list
l <- list(1:3, list(letters[1:5], c(TRUE, FALSE, TRUE)))
str(l)
## List of 2
##  $: int [1:3] 1 2 3 ##$ :List of 2
##   ..$: chr [1:5] "a" "b" "c" "d" ... ## ..$ : logi [1:3] TRUE FALSE TRUE


To add additional list components to a list we can leverage the list() and append() functions. We can illustrate with the following list.

l1 <- list(1:3, "a", c(TRUE, FALSE, TRUE))
str(l1)
## List of 3
##  $: int [1:3] 1 2 3 ##$ : chr "a"
##  $: logi [1:3] TRUE FALSE TRUE  If we add the new elements with list() it will create a list of two components, component 1 will be a nested list of the original list and component 2 will be the new elements added: l2 <- list(l1, c(2.5, 4.2)) str(l2) ## List of 2 ##$ :List of 3
##   ..$: int [1:3] 1 2 3 ## ..$ : chr "a"
##   ..$: logi [1:3] TRUE FALSE TRUE ##$ : num [1:2] 2.5 4.2


To simply add a 4th list component without creating nested lists we use the append() function:

l3 <- append(l1, list(c(2.5, 4.2)))
str(l3)
## List of 4
##  $: int [1:3] 1 2 3 ##$ : chr "a"
##  $: logi [1:3] TRUE FALSE TRUE ##$ : num [1:2] 2.5 4.2


Alternatively, we can also add a new list component by utilizing the ‘$’ sign and naming the new item: l3$item4 <- "new list item"
str(l3)
## List of 5
##  $: int [1:3] 1 2 3 ##$      : chr "a"
##  $: logi [1:3] TRUE FALSE TRUE ##$      : num [1:2] 2.5 4.2
##  $item4: chr "new list item"  To add individual elements to a specific list component we need to introduce some subsetting which is further discussed in the subsetting lists section. We’ll continue with our original l1 list: str(l1) ## List of 3 ##$ : int [1:3] 1 2 3
##  $: chr "a" ##$ : logi [1:3] TRUE FALSE TRUE


To add additional values to a list item you need to subset for that specific list item and then you can use the c() function to add the additional elements to that list item:

l1[[1]] <- c(l1[[1]], 4:6)
str(l1)
## List of 3
##  $: int [1:6] 1 2 3 4 5 6 ##$ : chr "a"
##  $: logi [1:3] TRUE FALSE TRUE l1[[2]] <- c(l1[[2]], c("dding", "to a", "list")) str(l1) ## List of 3 ##$ : int [1:6] 1 2 3 4 5 6
##  $: chr [1:4] "a" "dding" "to a" "list" ##$ : logi [1:3] TRUE FALSE TRUE


The attributes that you can add to lists include names, general comments, and specific list item comments. Currently, our l1 list has no attributes:

attributes(l1)
## NULL


We can add names to lists in two ways. First, we can use names() to assign names to list items in a pre-existing list. Second, we can add names to a list when we are creating a list.

# adding names to a pre-existing list
names(l1) <- c("item1", "item2", "item3")

str(l1)
## List of 3
##  $item1: int [1:6] 1 2 3 4 5 6 ##$ item2: chr [1:4] "a" "dding" "to a" "list"
##  $item3: logi [1:3] TRUE FALSE TRUE attributes(l1) ##$names
## [1] "item1" "item2" "item3"

# adding names when creating lists
l2 <- list(item1 = 1:3, item2 = letters[1:5], item3 = c(T, F, T, T))

str(l2)
## List of 3
##  $item1: int [1:3] 1 2 3 ##$ item2: chr [1:5] "a" "b" "c" "d" ...
##  $item3: logi [1:4] TRUE FALSE TRUE TRUE attributes(l2) ##$names
## [1] "item1" "item2" "item3"


We can also add comments to lists. As previously mentioned, comments act as a note to the user without changing how the object behaves. With lists, we can add a general comment to the list using comment() and we can also add comments to specific list items with attr().

# adding a general comment to list l2 with comment()
comment(l2) <- "This is a comment on a list"

str(l2)
## List of 3
##  $item1: int [1:3] 1 2 3 ##$ item2: chr [1:5] "a" "b" "c" "d" ...
##  $item3: logi [1:4] TRUE FALSE TRUE TRUE ## - attr(*, "comment")= chr "This is a comment on a list" attributes(l2) ##$names
## [1] "item1" "item2" "item3"
##
## $comment ## [1] "This is a comment on a list" # adding a comment to a specific list item with attr() attr(l2, "item2") <- "Comment for item2" str(l2) ## List of 3 ##$ item1: int [1:3] 1 2 3
##  $item2: chr [1:5] "a" "b" "c" "d" ... ##$ item3: logi [1:4] TRUE FALSE TRUE TRUE
##  - attr(*, "comment")= chr "This is a comment on a list"
##  - attr(*, "item2")= chr "Comment for item2"

attributes(l2)
## $names ## [1] "item1" "item2" "item3" ## ##$comment
## [1] "This is a comment on a list"
##
## $item2 ## [1] "Comment for item2"  ## Subsetting Lists “If list x is a train carrying objects, then x[[5]] is the object in car 5; x[4:6] is a train of cars 4-6” - @RLangTip To subset lists we can utilize the single bracket [ ], double brackets [[ ]], and dollar sign $ operators. Each approach provides a specific purpose and can be combined in different ways to achieve the following subsetting objectives:

### Subset list and preserve output as a list

To extract one or more list items while preserving1 the output in list format use the [ ] operator:

# extract first list item
l2[1]
## $item1 ## [1] 1 2 3 # same as above but using the item's name l2["item1"] ##$item1
## [1] 1 2 3

# extract multiple list items
l2[c(1,3)]
## $item1 ## [1] 1 2 3 ## ##$item3
## [1]  TRUE FALSE  TRUE  TRUE

# same as above but using the items' names
l2[c("item1", "item3")]
## $item1 ## [1] 1 2 3 ## ##$item3
## [1]  TRUE FALSE  TRUE  TRUE


### Subset list and simplify output

To extract one or more list items while simplifying1 the output use the [[ ]] or $ operator: # extract first list item and simplify to a vector l2[[1]] ## [1] 1 2 3 # same as above but using the item's name l2[["item1"]] ## [1] 1 2 3 # same as above but using the $ operator
l2$item1 ## [1] 1 2 3  One thing that differentiates the [[ operator from the $ is that the [[ operator can be used with computed indices. The $ operator can only be used with literal names. ### Subset list to get elements out of a list To extract individual elements out of a specific list item combine the [[ (or $) operator with the [ operator:

# extract third element from the second list item
l2[[2]][3]
## [1] "c"

# same as above but using the item's name
l2[["item2"]][3]
## [1] "c"

# same as above but using the $ operator l2$item2[3]
## [1] "c"


### Subset list with a nested list

If you have nested lists you can expand the ideas above to extract items and elements. We’ll use the following list l3 which has a nested list in item 2.

l3 <- list(item1 = 1:3,
item2 = list(item2a = letters[1:5],
item3b = c(T, F, T, T)))
str(l3)
## List of 2
##  $item1: int [1:3] 1 2 3 ##$ item2:List of 2
##   ..$item2a: chr [1:5] "a" "b" "c" "d" ... ## ..$ item3b: logi [1:4] TRUE FALSE TRUE TRUE


If the goal is to subset l3 to extract the nested list item item2a from item2, we can perform this multiple ways.

# preserve the output as a list
l3[[2]][1]
## $item2a ## [1] "a" "b" "c" "d" "e" # same as above but simplify the output l3[[2]][[1]] ## [1] "a" "b" "c" "d" "e" # same as above with names l3[["item2"]][["item2a"]] ## [1] "a" "b" "c" "d" "e" # same as above with $ operator
l3$item2$item2a
## [1] "a" "b" "c" "d" "e"

# extract individual element from a nested list item
l3[[2]][[1]][3]
## [1] "c"


## Exercises

Install and load the nycflights13 package. Using the flights data provided by this package create the following regression model:

library(nycflights13)

flight_lm <- lm(arr_delay ~ dep_delay + month + carrier, data = flights)


flight_lm is a list containing various regression results. Answer the following questions about this list:

1. How many items are in this list?
2. What are the names of these list items?
3. Extract the coefficients of this model.
4. Extract the departure delay (dep_delay) coefficient.
1. Its important to understand the difference between simplifying and preserving subsetting. Simplifying subsets returns the simplest possible data structure that can represent the output. Preserving subsets keeps the structure of the output the same as the input. See Hadley Wickham’s section on Simplifying vs. Preserving Subsetting to learn more.  2