bookclub-advr

03.qmd (45023B)

---

 ---
 engine: knitr
 title: Vectors
 ---
 
 ## Learning objectives:
 
 -   Learn about different types of vectors and their attributes
 -   Navigate through vector types and their value types
 -   Venture into factors and date-time objects
 -   Discuss the differences between data frames and tibbles
 -   Do not get absorbed by the `NA` and `NULL` black hole
 
 
 ## Session Info
 
 ```{r ch9_setup, message = FALSE, warning = FALSE}
 library("dplyr")
 library("gt")
 library("palmerpenguins")
 ```
 
 
 <details>
 <summary>Session Info</summary>
 ```{r}
 utils::sessionInfo()
 ```
 </details>
 
 ## Aperitif
 
 ![Palmer Penguins](images/vectors/lter_penguins.png)
 
 ## Counting Penguins
 
 Consider this code to count the number of Gentoo penguins in the `penguins` data set. We see that there are 124 Gentoo penguins.
 
 ```{r, eval = FALSE}
 sum("Gentoo" == penguins$species)
 # output: 124
 ```
 
 ## In
 
 One subtle error can arise in trying out `%in%` here instead.
 
 ```{r, results = 'hide'}
 species_vector <- penguins |> select(species)
 print("Gentoo" %in% species_vector)
 # output: FALSE
 ```
 
 ![Where did the penguins go?](images/vectors/lter_penguins_no_gentoo.png)
 
 ## Fix: base R 
 
 ```{r, results = 'hide'}
 species_unlist <- penguins |> select(species) |> unlist()
 print("Gentoo" %in% species_unlist)
 # output: TRUE
 ```
 
 ## Fix: dplyr
 
 ```{r, results = 'hide'}
 species_pull <- penguins |> select(species) |> pull()
 print("Gentoo" %in% species_pull)
 # output: TRUE
 ```
 
 ## Motivation
 
 * What are the different types of vectors?
 * How does this affect accessing vectors?
 
 <details>
 <summary>Side Quest: Looking up the `%in%` operator</summary>
 If you want to look up the manual pages for the `%in%` operator with the `?`, use backticks:
 
 ```{r, eval = FALSE}
 ?`%in%`
 ```
 
 and we find that `%in%` is a wrapper for the `match()` function.
 
 </details>
 
 
 ## Types of Vectors
 
 ![Image Credit: Advanced R](images/vectors/summary-tree.png) 
 
 Two main types:
 
 -   **Atomic**: Elements all the same type.
 -   **List**: Elements are different Types.
 
 Closely related but not technically a vector:
 
 -   **NULL**: Null elements. Often length zero.
 
 
 ## Types of Atomic Vectors (1/2)
 
 ![Image Credit: Advanced R](images/vectors/summary-tree-atomic.png){width=50%} 
 
 ## Types of Atomic Vectors (2/2)
 
 -   **Logical**: True/False
 -   **Integer**: Numeric (discrete, no decimals)
 -   **Double**: Numeric (continuous, decimals)
 -   **Character**: String
 
 ## Vectors of Length One
 
 **Scalars** are vectors that consist of a single value.
 
 ## Logicals
 
 ```{r vec_lgl}
 lgl1 <- TRUE
 lgl2 <- T #abbreviation for TRUE
 lgl3 <- FALSE
 lgl4 <- F #abbreviation for FALSE
 ```
 
 ## Doubles
 
 ```{r vec_dbl}
 # integer, decimal, scientific, or hexidecimal format
 dbl1 <- 1
 dbl2 <- 1.234 # decimal
 dbl3 <- 1.234e0 # scientific format
 dbl4 <- 0xcafe # hexidecimal format
 ```
 
 ## Integers
 
 Integers must be followed by L and cannot have fractional values
 
 ```{r vec_int}
 int1 <- 1L
 int2 <- 1234L
 int3 <- 1234e0L
 int4 <- 0xcafeL
 ```
 
 <details>
 <summary>Pop Quiz: Why "L" for integers?</summary>
 Wickham notes that the use of `L` dates back to the **C** programming language and its "long int" type for memory allocation.
 </details>
 
 ## Strings
 
 Strings can use single or double quotes and special characters are escaped with \
 
 ```{r vec_str}
 str1 <- "hello" # double quotes
 str2 <- 'hello' # single quotes
 str3 <- "مرحبًا" # Unicode
 str4 <- "\U0001f605" # sweaty_smile 😅
 ```
 
 ## Longer 1/2
 
 There are several ways to make longer vectors:
 
 **1. With single values** inside c() for combine.
 
 ```{r long_single}
 lgl_var <- c(TRUE, FALSE)
 int_var <- c(1L, 6L, 10L)
 dbl_var <- c(1, 2.5, 4.5)
 chr_var <- c("these are", "some strings")
 ```
 
 ![Image Credit: Advanced R](images/vectors/atomic.png) 
 
 ## Longer 2/2
 
 **2. With other vectors**
 
 ```{r long_vec}
 c(c(1, 2), c(3, 4)) # output is not nested
 ```
 
 ## Type and Length
 
 We can determine the type of a vector with `typeof()` and its length with `length()`
 
 ```{r type_length, echo = FALSE}
 # typeof(lgl_var)
 # typeof(int_var)
 # typeof(dbl_var)
 # typeof(chr_var)
 # 
 # length(lgl_var)
 # length(int_var)
 # length(dbl_var)
 # length(chr_var)
 
 var_names <- c("lgl_var", "int_var", "dbl_var", "chr_var")
 var_values <- c("TRUE, FALSE", "1L, 6L, 10L", "1, 2.5, 4.5", "'these are', 'some strings'")
 var_type <- c("logical", "integer", "double", "character")
 var_length <- c(2, 3, 3, 2)
 
 type_length_df <- data.frame(var_names, var_values, var_type, var_length)
 
 # make gt table
 type_length_df |>
   gt() |>
   cols_align(align = "center") |>
   cols_label(
     var_names ~ "name",
     var_values ~ "value",
     var_type ~ "typeof()",
     var_length ~ "length()"
   ) |>
   tab_header(
     title = "Types of Atomic Vectors",
     subtitle = ""
   ) |>
   tab_footnote(
     footnote = "Source: https://adv-r.hadley.nz/index.html",
     locations = cells_title(groups = "title")
   ) |>
   tab_style(
     style = list(cell_fill(color = "#F9E3D6")),
     locations = cells_body(columns = var_type)
   ) |>
   tab_style(
     style = list(cell_fill(color = "lightcyan")),
     locations = cells_body(columns = var_length)
   )
 ```
 
 ## Side Quest: Penguins
 
 <details>
 ```{r}
 typeof(penguins$species)
 class(penguins$species)
 
 typeof(species_unlist)
 class(species_unlist)
 
 typeof(species_pull)
 class(species_pull)
 ```
 
 </details>
 
 ## Missing values: Contagion
 
 For most computations, an operation over values that includes a missing value yields a missing value (unless you're careful)
 
 ```{r na_contagion}
 # contagion
 5*NA
 sum(c(1, 2, NA, 3))
 ```
 
 ## Missing values: Contagion Exceptions
 
 ```{r na_exceptions, eval = FALSE}
 NA ^ 0
 #> [1] 1
 NA | TRUE
 #> [1] TRUE
 NA & FALSE
 #> [1] FALSE
 ```
 
 
 #### Innoculation
 
 ```{r na_innoculation, eval = FALSE}
 sum(c(1, 2, NA, 3), na.rm = TRUE)
 # output: 6
 ```
 
 To search for missing values use `is.na()`
 
 ```{r na_search, eval = FALSE}
 x <- c(NA, 5, NA, 10)
 x == NA
 # output: NA NA NA NA [BATMAN!]
 ```
 
 ```{r na_search_better, eval = FALSE}
 is.na(x)
 # output: TRUE FALSE TRUE FALSE
 ```
 
 ## Missing Values: NA Types 
 
 <details>
 Each type has its own NA type
 
 -   Logical: `NA`
 -   Integer: `NA_integer`
 -   Double: `NA_double`
 -   Character: `NA_character`
 
 This may not matter in many contexts.
 
 Can matter for operations where types matter like `dplyr::if_else()`.
 </details>
 
 
 ## Testing (1/2)
 
 **What type of vector `is.*`() it?**
 
 Test data type:
 
 -   Logical: `is.logical()`
 -   Integer: `is.integer()`
 -   Double: `is.double()`
 -   Character: `is.character()`
 
 
 ## Testing (2/2)
 
 **What type of object is it?**
 
 Don't test objects with these tools:
 
 -   `is.vector()`
 -   `is.atomic()`
 -   `is.numeric()` 
 
 They don’t test if you have a vector, atomic vector, or numeric vector; you’ll need to carefully read the documentation to figure out what they actually do (preview: *attributes*)
 
 ## Side Quest: rlang `is_*()`
 
 <details>
 <summary>Maybe use `{rlang}`?</summary>
 
 -   `rlang::is_vector`
 -   `rlang::is_atomic`
 
 ```{r test_rlang}
 # vector
 rlang::is_vector(c(1, 2))
 rlang::is_vector(list(1, 2))
 
 # atomic
 rlang::is_atomic(c(1, 2))
 rlang::is_atomic(list(1, "a"))
 
 ```
 
 See more [here](https://rlang.r-lib.org/reference/type-predicates.html)
 </details>
 
 
 ## Coercion
 
 * R follows rules for coercion: character → double → integer → logical
 
 * R can coerce either automatically or explicitly
 
 #### **Automatic**
 
 Two contexts for automatic coercion:
 
 1.  Combination
 2.  Mathematical
 
 
 
 ## Coercion by Combination:
 
 ```{r coerce_c}
 str(c(TRUE, "TRUE"))
 ```
 
 ## Coercion by Mathematical operations:
 
 ```{r coerce_math}
 # imagine a logical vector about whether an attribute is present
 has_attribute <- c(TRUE, FALSE, TRUE, TRUE)
 
 # number with attribute
 sum(has_attribute)
 ```
 
 ## **Explicit**
 
 <!--
 
 Use `as.*()`
 
 -   Logical: `as.logical()`
 -   Integer: `as.integer()`
 -   Double: `as.double()`
 -   Character: `as.character()`
 
 -->
 
 ```{r explicit_coercion, echo = FALSE}
 # dbl_var
 # as.integer(dbl_var)
 # lgl_var
 # as.character(lgl_var)
 
 var_names <- c("lgl_var", "int_var", "dbl_var", "chr_var")
 var_values <- c("TRUE, FALSE", "1L, 6L, 10L", "1, 2.5, 4.5", "'these are', 'some strings'")
 as_logical <- c("TRUE FALSE", "TRUE TRUE TRUE", "TRUE TRUE TRUE", "NA NA")
 as_integer <- c("1 0", "1 6 10", "1 2 4", 'NA_integer')
 as_double <- c("1 0", "1 6 10", "1.0 2.5 4.5", 'NA_double')
 as_character <- c("'TRUE' 'FALSE'", "'1' '6' '10'", "'1' '2.5' '4.5'", "'these are', 'some strings'")
 
 coercion_df <- data.frame(var_names, var_values, as_logical, as_integer, as_double, as_character)
 
 coercion_df |>
   gt() |>
   cols_align(align = "center") |>
   cols_label(
     var_names ~ "name",
     var_values ~ "value",
     as_logical ~ "as.logical()",
     as_integer ~ "as.integer()",
     as_double ~ "as.double()",
     as_character ~ "as.character()"
   ) |>
   tab_header(
     title = "Coercion of Atomic Vectors",
     subtitle = ""
   ) |>
   tab_footnote(
     footnote = "Source: https://adv-r.hadley.nz/index.html",
     locations = cells_title(groups = "title")
   ) |>
   tab_style(
     style = list(cell_fill(color = "#F9E3D6")),
     locations = cells_body(columns = c(as_logical, as_double))
   ) |>
   tab_style(
     style = list(cell_fill(color = "lightcyan")),
     locations = cells_body(columns = c(as_integer, as_character))
   )
 ```
 
 But note that coercion may fail in one of two ways, or both:
 
 -   With warning/error
 -   NAs
 
 ```{r coerce_error}
 as.integer(c(1, 2, "three"))
 ```
 
 ## Exercises 1/5
 
 1. How do you create raw and complex scalars?
 
 <details><summary>Answer(s)</summary>
 ```{r, eval = FALSE}
 as.raw(42)
 #> [1] 2a
 charToRaw("A")
 #> [1] 41
 complex(length.out = 1, real = 1, imaginary = 1)
 #> [1] 1+1i
 ```
 </details>
 
 ## Exercises 2/5
 
 2. Test your knowledge of the vector coercion rules by predicting the output of the following uses of c():
 
 ```{r, eval = FALSE}
 c(1, FALSE)
 c("a", 1)
 c(TRUE, 1L)
 ```
 
 <details><summary>Answer(s)</summary>
 ```{r, eval = FALSE}
 c(1, FALSE)      # will be coerced to double    -> 1 0
 c("a", 1)        # will be coerced to character -> "a" "1"
 c(TRUE, 1L)      # will be coerced to integer   -> 1 1
 ```
 </details>
 
 ## Exercises 3/5
 
 3. Why is `1 == "1"` true? Why is `-1 < FALSE` true? Why is `"one" < 2` false?
 
 <details><summary>Answer(s)</summary>
 These comparisons are carried out by operator-functions (==, <), which coerce their arguments to a common type. In the examples above, these types will be character, double and character: 1 will be coerced to "1", FALSE is represented as 0 and 2 turns into "2" (and numbers precede letters in lexicographic order (may depend on locale)).
 
 </details>
 
 ## Exercises 4/5
 
 4. Why is the default missing value, NA, a logical vector? What’s special about logical vectors?
 
 <details><summary>Answer(s)</summary>
 The presence of missing values shouldn’t affect the type of an object. Recall that there is a type-hierarchy for coercion from character → double → integer → logical. When combining `NA`s with other atomic types, the `NA`s will be coerced to integer (`NA_integer_`), double (`NA_real_`) or character (`NA_character_`) and not the other way round. If `NA` were a character and added to a set of other values all of these would be coerced to character as well.
 </details>
 
 ## Exercises 5/5
 
 5. Precisely what do `is.atomic()`, `is.numeric()`, and `is.vector()` test for?
 
 <details><summary>Answer(s)</summary>
 
 * `is.atomic()` tests if an object is an atomic vector or is `NULL` (!). Atomic vectors are objects of type logical, integer, double, complex, character or raw.
 * `is.numeric()` tests if an object has type integer or double and is not of class `factor`, `Date`, `POSIXt` or `difftime`.
 * `is.vector()` tests if an object is a vector or an expression and has no attributes, apart from names. Vectors are atomic vectors or lists.
  
 </details>
 
 
 ## Attributes
 
 Attributes are name-value pairs that attach metadata to an object (vector).
 
 * **Name-value pairs**: attributes have a name and a value
 * **Metadata**: not data itself, but data about the data
  
 ## Getting and Setting
 
 Three functions:
 
 1. retrieve and modify single attributes with `attr()`
 2. retrieve en masse with `attributes()`
 3. set en masse with `structure()`
 
 ## Single attribute
 
 Use `attr()`
 
 ```{r attr_single}
 # some object
 a <- c(1, 2, 3)
 
 # set attribute
 attr(x = a, which = "attribute_name") <- "some attribute"
 
 # get attribute
 attr(a, "attribute_name")
 ```
 
 ## Multiple attributes
 
 `structure()`: set multiple attributes, `attributes()`: get multiple attributes
 
 :::: columns
 ::: column
 ```{r attr_multiple}
 a <- 1:3
 attr(a, "x") <- "abcdef"
 attr(a, "x")
 
 attr(a, "y") <- 4:6
 str(attributes(a))
 
 b <- structure(
   1:3, 
   x = "abcdef",
   y = 4:6
 )
 identical(a, b)
 ```
 :::
 
 ::: column
 ![Image Credit: Advanced R](images/vectors/attr.png) 
 :::
 ::::
 
 
 ## Why
 
 Three particularly important attributes: 
 
 1. **names** - a character vector giving each element a name
 2. **dimension** - (or dim) turns vectors into matrices and arrays 
 3. **class** - powers the S3 object system (we'll learn more about this in chapter 13)
 
 Most attributes are lost by most operations.  Only two attributes are routinely preserved: names and dimension.
 
 ## Names
 
 ~~Three~~ Four ways to name:
 
 :::: columns
 
 ::: {.column width="50%"}
 ```{r names}
 # (1) On creation: 
 x <- c(A = 1, B = 2, C = 3)
 x
 
 # (2) Assign to names():
 y <- 1:3
 names(y) <- c("a", "b", "c")
 y
 
 # (3) Inline:
 z <- setNames(1:3, c("a", "b", "c"))
 z
 ```
 :::
 
 ::: {.column width="50%"}
 ![proper diagram](images/vectors/attr-names-1.png) 
 :::
 
 ::::
 
 ## rlang Names
 
 :::: columns
 
 ::: {.column width="50%"}
 
 ```{r names_via_rlang}
 # (4) Inline with {rlang}:
 a <- 1:3
 rlang::set_names(
   a,
   c("a", "b", "c")
 )
 ```
 
 :::
 
 ::: {.column width="50%"}
 ![simplified diagram](images/vectors/attr-names-2.png) 
 :::
 
 ::::
 
 
 ## Removing names
 
 * `x <- unname(x)` or `names(x) <- NULL`
 * Thematically but not directly related: labelled class vectors with `haven::labelled()`
 
 
 ## Dimensions: `matrix()` and `array()`
 
 ```{r dimensions}
 # Two scalar arguments specify row and column sizes
 x <- matrix(1:6, nrow = 2, ncol = 3)
 x
 # One vector argument to describe all dimensions
 y <- array(1:12, c(2, 3, 2)) # rows, columns, no of arrays
 y
 ```
 
 ## Dimensions: assign to `dim()`
 
 ```{r dimensions2}
 # You can also modify an object in place by setting dim()
 z <- 1:6
 dim(z) <- c(2, 3) # rows, columns
 z
 a <- 1:12
 dim(a) <- c(2, 3, 2) # rows, columns, no of arrays
 a
 ```
 
 
 ## Functions for working with vectors, matrices and arrays (1/2):
 
 Vector | Matrix	| Array
 :----- | :---------- | :-----
 `names()` | `rownames()`, `colnames()` | `dimnames()`
 `length()` | `nrow()`, `ncol()` | `dim()`
 `c()` | `rbind()`, `cbind()` | `abind::abind()`
 — | `t()` | `aperm()`
 `is.null(dim(x))` | `is.matrix()` | `is.array()`
 
 * **Caution**: Vector without `dim` set has `NULL` dimensions, not `1`.
 * One dimension?
 
 ## Functions for working with vectors, matrices and arrays (2/2):
 
 ```{r examples_of_1D, eval = FALSE}
 str(1:3)                   # 1d vector
 #>  int [1:3] 1 2 3
 str(matrix(1:3, ncol = 1)) # column vector
 #>  int [1:3, 1] 1 2 3
 str(matrix(1:3, nrow = 1)) # row vector
 #>  int [1, 1:3] 1 2 3
 str(array(1:3, 3))         # "array" vector
 #>  int [1:3(1d)] 1 2 3
 ```
 
 
 ## Exercises 1/4
 
 1. How is `setNames()` implemented? Read the source code.
 
 <details><summary>Answer(s)</summary>
 
 ```{r, eval = FALSE}
 setNames <- function(object = nm, nm) {
   names(object) <- nm
   object
 }
 ```
 
 - Data arg 1st = works well with pipe.
 - 1st arg is optional
 
 ```{r, eval = FALSE}
 setNames( , c("a", "b", "c"))
 #>   a   b   c 
 #> "a" "b" "c"
 ```
 </details>
 
 ## Exercises 1/4 (cont)
 
 1. How is `unname()` implemented? Read the source code.
 
 <details><summary>Answer(s)</summary>
 
 ```{r, eval = FALSE}
 unname <- function(obj, force = FALSE) {
   if (!is.null(names(obj))) 
     names(obj) <- NULL
   if (!is.null(dimnames(obj)) && (force || !is.data.frame(obj))) 
     dimnames(obj) <- NULL
   obj
 }
 ```
 `unname()` sets existing `names` or `dimnames` to `NULL`.
 </details>
 
 ## Exercises 2/4
 
 2. What does `dim()` return when applied to a 1-dimensional vector? When might you use `NROW()` or `NCOL()`?
 
 <details><summary>Answer(s)</summary>
 
 > `dim()` returns `NULL` when applied to a 1d vector.
 
 `NROW()` and `NCOL()` treats `NULL` and vectors like they have dimensions:
 
 ```{r, eval = FALSE}
 x <- 1:10
 nrow(x)
 #> NULL
 ncol(x)
 #> NULL
 NROW(x)
 #> [1] 10
 NCOL(x)
 #> [1] 1
 ```
 
 </details>
 
 ## Exercises 3/4
 
 3. How would you describe the following three objects? What makes them different from `1:5`?
 
 ```{r}
 x1 <- array(1:5, c(1, 1, 5))
 x2 <- array(1:5, c(1, 5, 1))
 x3 <- array(1:5, c(5, 1, 1))
 ```
 
 <details><summary>Answer(s)</summary>
 ```{r, eval = FALSE}
 x1 <- array(1:5, c(1, 1, 5))  # 1 row,  1 column,  5 in third dim.
 x2 <- array(1:5, c(1, 5, 1))  # 1 row,  5 columns, 1 in third dim.
 x3 <- array(1:5, c(5, 1, 1))  # 5 rows, 1 column,  1 in third dim.
 ```
 </details>
 
 ## Exercises 4/4
 
 4. An early draft used this code to illustrate `structure()`:
 
 ```{r, eval = FALSE}
 structure(1:5, comment = "my attribute")
 #> [1] 1 2 3 4 5
 ```
 
 Why don't you see the comment attribute on print? Is the attribute missing, or is there something else special about it?
 
 <details><summary>Answer(s)</summary>
 The documentation states (see `?comment`):
 
 > Contrary to other attributes, the comment is not printed (by print or print.default).
 
 ## Exercises 4/4 (cont)
 
 <details><summary>Answer(s)</summary>
 Also, from `?attributes:`
 
 > Note that some attributes (namely class, comment, dim, dimnames, names, row.names and tsp) are treated specially and have restrictions on the values which can be set.
 
 Retrieve comment attributes with `attr()`:
 
 ```{r, eval = FALSE}
 foo <- structure(1:5, comment = "my attribute")
 
 attributes(foo)
 #> $comment
 #> [1] "my attribute"
 attr(foo, which = "comment")
 #> [1] "my attribute"
 ```
 
 </details>
 
 
 
 ## **Class** - S3 atomic vectors
 
 ![](images/vectors/summary-tree-s3-1.png) 
 
 Credit: [Advanced R](https://adv-r.hadley.nz/index.html) by Hadley Wickham
 
 **Having a class attribute turns an object into an S3 object.**
 
 What makes S3 atomic vectors different?
 
 1. behave differently from a regular vector when passed to a generic function 
 2. often store additional information in other attributes
 
 
 ## Four important S3 vectors used in base R:
 
 1. **Factors** (categorical data)
 2. **Dates**
 3. **Date-times** (POSIXct)
 4. **Durations** (difftime)
 
 ## Factors
 
 A factor is a vector used to store categorical data that can contain only predefined values.
 
 Factors are integer vectors with:
 
 -   Class: "factor"
 -   Attributes: "levels", or the set of allowed values
 
 ## Factors examples
 
 ```{r factor}
 colors = c('red', 'blue', 'green','red','red', 'green')
 colors_factor <- factor(
   x = colors, levels = c('red', 'blue', 'green', 'yellow')
 )
 ```
 
 :::: columns
 
 ::: column
 
 ```{r factor_table}
 table(colors)
 table(colors_factor)
 ```
 :::
 
 ::: column
 ```{r factor_type}
 typeof(colors_factor)
 class(colors_factor)
 
 attributes(colors_factor)
 ```
 :::
 ::::
 
 ## Custom Order
 
 Factors can be ordered. This can be useful for models or visualizations where order matters.
 
 ```{r factor_ordered}
 
 values <- c('high', 'med', 'low', 'med', 'high', 'low', 'med', 'high')
 ordered_factor <- ordered(
   x = values,
   levels = c('low', 'med', 'high') # in order
 )
 ordered_factor
 
 table(values)
 table(ordered_factor)
 ```
 
 ## Dates
 
 Dates are:
 
 -   Double vectors
 -   With class "Date"
 -   No other attributes
 
 ```{r dates}
 notes_date <- Sys.Date()
 
 # type
 typeof(notes_date)
 
 # class
 attributes(notes_date)
 ```
 
 ## Dates Unix epoch
 
 The double component represents the number of days since since the [Unix epoch](https://en.wikipedia.org/wiki/Unix_time) `1970-01-01`
 
 ```{r days_since_1970}
 date <- as.Date("1970-02-01")
 unclass(date)
 ```
 
 ## Date-times
 
 There are 2 Date-time representations in base R:
 
 -   POSIXct, where "ct" denotes *calendar time*
 -   POSIXlt, where "lt" designates *local time*
 
 <!--
 
 Just for fun:
 "How to pronounce 'POSIXct'?"
 https://www.howtopronounce.com/posixct
 
 -->
 
 ## Dates-times: POSIXct
 
 We'll focus on POSIXct because:
 
 -   Simplest
 -   Built on an atomic (double) vector
 -   Most appropriate for use in a data frame
 
 Let's now build and deconstruct a Date-time
 
 ```{r date_time}
 # Build
 note_date_time <- as.POSIXct(
   x = Sys.time(), # time
   tz = "America/New_York" # time zone, used only for formatting
 )
 
 # Inspect
 note_date_time
 
 # - type
 typeof(note_date_time)
 
 # - attributes
 attributes(note_date_time)
 
 structure(note_date_time, tzone = "Europe/Paris")
 ```
 
 ```{r date_time_format}
 date_time <- as.POSIXct("2024-02-22 12:34:56", tz = "EST")
 unclass(date_time)
 ```
 
 
 ## Durations
 
 Durations represent the amount of time between pairs of dates or date-times.
 
 -   Double vectors
 -   Class: "difftime"
 -   Attributes: "units", or the unit of duration (e.g., weeks, hours, minutes, seconds, etc.)
 
 ```{r durations}
 # Construct
 one_minute <- as.difftime(1, units = "mins")
 # Inspect
 one_minute
 
 # Dissect
 # - type
 typeof(one_minute)
 # - attributes
 attributes(one_minute)
 ```
 
 ```{r durations_math}
 time_since_01_01_1970 <- notes_date - date
 time_since_01_01_1970
 ```
 
 
 See also:
 
 -   [`lubridate::make_difftime()`](https://lubridate.tidyverse.org/reference/make_difftime.html)
 -   [`clock::date_time_build()`](https://clock.r-lib.org/reference/date_time_build.html)
 
 
 ## Exercises 1/3
 
 1. What sort of object does `table()` return? What is its type? What attributes does it have? How does the dimensionality change as you tabulate more variables?
 
 <details><summary>Answer(s)</summary>
 
 `table()` returns a contingency table of its input variables. It is implemented as an integer vector with class table and dimensions (which makes it act like an array). Its attributes are dim (dimensions) and dimnames (one name for each input column). The dimensions correspond to the number of unique values (factor levels) in each input variable.
 
 ```{r, eval = FALSE}
 x <- table(mtcars[c("vs", "cyl", "am")])
 
 typeof(x)
 #> [1] "integer"
 attributes(x)
 #> $dim
 #> [1] 2 3 2
 #> 
 #> $dimnames
 #> $dimnames$vs
 #> [1] "0" "1"
 #> 
 #> $dimnames$cyl
 #> [1] "4" "6" "8"
 #> 
 #> $dimnames$am
 #> [1] "0" "1"
 #> 
 #> 
 #> $class
 #> [1] "table"
 ```
 </details>
 
 ## Exercises 2/3
 
 2. What happens to a factor when you modify its levels?
 
 ```{r, eval = FALSE}
 f1 <- factor(letters)
 levels(f1) <- rev(levels(f1))
 ```
 
 <details><summary>Answer(s)</summary>
 The underlying integer values stay the same, but the levels are changed, making it look like the data has changed.
 
 ```{r, eval = FALSE}
 f1 <- factor(letters)
 f1
 #>  [1] a b c d e f g h i j k l m n o p q r s t u v w x y z
 #> Levels: a b c d e f g h i j k l m n o p q r s t u v w x y z
 as.integer(f1)
 #>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 #> [26] 26
 
 levels(f1) <- rev(levels(f1))
 f1
 #>  [1] z y x w v u t s r q p o n m l k j i h g f e d c b a
 #> Levels: z y x w v u t s r q p o n m l k j i h g f e d c b a
 as.integer(f1)
 #>  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
 #> [26] 26
 ```
 </details>
 
 ## Exercises 3/3
 
 3. What does this code do? How do `f2` and `f3` differ from `f1`?
 
 ```{r, eval = FALSE}
 f2 <- rev(factor(letters))
 f3 <- factor(letters, levels = rev(letters))
 ```
 
 <details><summary>Answer(s)</summary>
 For `f2` and `f3` either the order of the factor elements or its levels are being reversed. For `f1` both transformations are occurring.
 
 ```{r, eval = FALSE}
 # Reverse element order
 (f2 <- rev(factor(letters)))
 #>  [1] z y x w v u t s r q p o n m l k j i h g f e d c b a
 #> Levels: a b c d e f g h i j k l m n o p q r s t u v w x y z
 as.integer(f2)
 #>  [1] 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10  9  8  7  6  5  4  3  2
 #> [26]  1
 
 # Reverse factor levels (when creating factor)
 (f3 <- factor(letters, levels = rev(letters)))
 #>  [1] a b c d e f g h i j k l m n o p q r s t u v w x y z
 #> Levels: z y x w v u t s r q p o n m l k j i h g f e d c b a
 as.integer(f3)
 #>  [1] 26 25 24 23 22 21 20 19 18 17 16 15 14 13 12 11 10  9  8  7  6  5  4  3  2
 #> [26]  1
 ```
 </details>
 
 
 ## Lists
 
 * sometimes called a generic vector or recursive vector
 * Recall ([section 2.3.3](https://adv-r.hadley.nz/names-values.html#list-references)): each element is really a *reference* to another object
 * an be composed of elements of different types (as opposed to atomic vectors which must be of only one type)
 
 ## Constructing
 
 Simple lists:
 
 ```{r list_simple}
 # Construct
 simple_list <- list(
   c(TRUE, FALSE),   # logicals
   1:20,             # integers
   c(1.2, 2.3, 3.4), # doubles
   c("primo", "secundo", "tercio") # characters
 )
 
 simple_list
 
 # Inspect
 # - type
 typeof(simple_list)
 # - structure
 str(simple_list)
 
 # Accessing
 simple_list[1]
 simple_list[2]
 simple_list[3]
 simple_list[4]
 
 simple_list[[1]][2]
 simple_list[[2]][8]
 simple_list[[3]][2]
 simple_list[[4]][3]
 ```
 
 ## Even Simpler List
 
 ```{r list_simpler}
 # Construct
 simpler_list <- list(TRUE, FALSE, 
                     1, 2, 3, 4, 5, 
                     1.2, 2.3, 3.4, 
                     "primo", "secundo", "tercio")
 
 # Accessing
 simpler_list[1]
 simpler_list[5]
 simpler_list[9]
 simpler_list[11]
 ```
 
 ## Nested lists:
 
 ```{r list_nested}
 nested_list <- list(
   # first level
   list(
     # second level
     list(
       # third level
       list(1)
     )
   )
 )
 
 str(nested_list)
 ```
 
 Like JSON.
 
 ## Combined lists
 
 ```{r list_combined}
 list_comb1 <- list(list(1, 2), list(3, 4)) # with list()
 list_comb2 <- c(list(1, 2), list(3, 4)) # with c()
 
 # compare structure
 str(list_comb1)
 str(list_comb2)
 
 # does this work if they are different data types?
 list_comb3 <- c(list(1, 2), list(TRUE, FALSE))
 str(list_comb3)
 ```
 
 ## Testing
 
 Check that is a list:
 
 -   `is.list()`
 -   \`rlang::is_list()\`\`
 
 The two do the same, except that the latter can check for the number of elements
 
 ```{r list_test}
 # is list
 base::is.list(list_comb2)
 rlang::is_list(list_comb2)
 
 # is list of 4 elements
 rlang::is_list(x = list_comb2, n = 4)
 
 # is a vector (of a special type)
 # remember the family tree?
 rlang::is_vector(list_comb2)
 ```
 
 ## Coercion
 
 Use `as.list()`
 
 ```{r list_coercion}
 list(1:3)
 as.list(1:3)
 ```
 
 ## Matrices and arrays
 
 Although not often used, the dimension attribute can be added to create **list-matrices** or **list-arrays**.
 
 ```{r list_matrices_arrays}
 l <- list(1:3, "a", TRUE, 1.0)
 dim(l) <- c(2, 2); l
 
 l[[1, 1]]
 ```
 
 
 ## Exercises 1/3
 
 1. List all the ways that a list differs from an atomic vector.
 
 <details><summary>Answer(s)</summary>
 
 * Atomic vectors are always homogeneous (all elements must be of the same type). Lists may be heterogeneous (the elements can be of different types) as described in the introduction of the vectors chapter.
 * Atomic vectors point to one address in memory, while lists contain a separate reference for each element. (This was described in the list sections of the vectors and the names and values chapters.)
 
 ```{r, eval = FALSE}
 lobstr::ref(1:2)
 #> [1:0x7fcd936f6e80] <int>
 lobstr::ref(list(1:2, 2))
 #> █ [1:0x7fcd93d53048] <list> 
 #> ├─[2:0x7fcd91377e40] <int> 
 #> └─[3:0x7fcd93b41eb0] <dbl>
 ```
 
 
 * Subsetting with out-of-bounds and NA values leads to different output. For example, [ returns NA for atomics and NULL for lists. (This is described in more detail within the subsetting chapter.)
 
 ```{r, eval = FALSE}
 # Subsetting atomic vectors
 (1:2)[3]
 #> [1] NA
 (1:2)[NA]
 #> [1] NA NA
 
 # Subsetting lists
 as.list(1:2)[3]
 #> [[1]]
 #> NULL
 as.list(1:2)[NA]
 #> [[1]]
 #> NULL
 #> 
 #> [[2]]
 #> NULL
 ```
 
 
 </details>
 
 ## Exercises 2/3
 
 2. Why do you need to use `unlist()` to convert a list to an atomic vector? Why doesn’t `as.vector()` work?
 
 <details><summary>Answer(s)</summary>
 A list is already a vector, though not an atomic one! Note that as.vector() and is.vector() use different definitions of “vector!”
 
 ```{r, eval = FALSE}
 is.vector(as.vector(mtcars))
 #> [1] FALSE
 ```
 
 </details>
 
 ## Exercises 3/3
 
 3. Compare and contrast `c()` and `unlist()` when combining a date and date-time into a single vector.
 
 <details><summary>Answer(s)</summary>
 Date and date-time objects are both built upon doubles. While dates store the number of days since the reference date 1970-01-01 (also known as “the Epoch”) in days, date-time-objects (POSIXct) store the time difference to this date in seconds.
 
 ```{r, eval = FALSE}
 date    <- as.Date("1970-01-02")
 dttm_ct <- as.POSIXct("1970-01-01 01:00", tz = "UTC")
 
 # Internal representations
 unclass(date)
 #> [1] 1
 unclass(dttm_ct)
 #> [1] 3600
 #> attr(,"tzone")
 #> [1] "UTC"
 ```
 
 As the c() generic only dispatches on its first argument, combining date and date-time objects via c() could lead to surprising results in older R versions (pre R 4.0.0):
 
 ```{r, eval = FALSE}
 # Output in R version 3.6.2
 c(date, dttm_ct)  # equal to c.Date(date, dttm_ct) 
 #> [1] "1970-01-02" "1979-11-10"
 c(dttm_ct, date)  # equal to c.POSIXct(date, dttm_ct)
 #> [1] "1970-01-01 02:00:00 CET" "1970-01-01 01:00:01 CET"
 ```
 
 In the first statement above c.Date() is executed, which incorrectly treats the underlying double of dttm_ct (3600) as days instead of seconds. Conversely, when c.POSIXct() is called on a date, one day is counted as one second only.
 
 We can highlight these mechanics by the following code:
 
 ```{r, eval = FALSE}
 # Output in R version 3.6.2
 unclass(c(date, dttm_ct))  # internal representation
 #> [1] 1 3600
 date + 3599
 #> "1979-11-10"
 ```
 
 As of R 4.0.0 these issues have been resolved and both methods now convert their input first into POSIXct and Date, respectively.
 
 ```{r, eval = FALSE}
 c(dttm_ct, date)
 #> [1] "1970-01-01 01:00:00 UTC" "1970-01-02 00:00:00 UTC"
 unclass(c(dttm_ct, date))
 #> [1]  3600 86400
 
 c(date, dttm_ct)
 #> [1] "1970-01-02" "1970-01-01"
 unclass(c(date, dttm_ct))
 #> [1] 1 0
 ```
 
 However, as c() strips the time zone (and other attributes) of POSIXct objects, some caution is still recommended.
 
 ```{r, eval = FALSE}
 (dttm_ct <- as.POSIXct("1970-01-01 01:00", tz = "HST"))
 #> [1] "1970-01-01 01:00:00 HST"
 attributes(c(dttm_ct))
 #> $class
 #> [1] "POSIXct" "POSIXt"
 ```
 
 A package that deals with these kinds of problems in more depth and provides a structural solution for them is the {vctrs} package9 which is also used throughout the tidyverse.10
 
 Let’s look at unlist(), which operates on list input.
 
 ```{r, eval = FALSE}
 # Attributes are stripped
 unlist(list(date, dttm_ct))  
 #> [1]     1 39600
 ```
 
 We see again that dates and date-times are internally stored as doubles. Unfortunately, this is all we are left with, when unlist strips the attributes of the list.
 
 To summarise: c() coerces types and strips time zones. Errors may have occurred in older R versions because of inappropriate method dispatch/immature methods. unlist() strips attributes.
 </details>
 
 
 ## Data frames and tibbles
 
 ![](images/vectors/summary-tree-s3-2.png) 
 
 Credit: [Advanced R](https://adv-r.hadley.nz/index.html) by Hadley Wickham
 
 ## Data frame
 
 A data frame is a:
 
 -   Named list of vectors (i.e., column names)
 -   Attributes:
     -   (column) `names`
     -   `row.names`
     -   Class: "data frame"
 
 ## Data frame, examples 1/2:
 
 ```{r data_frame}
 # Construct
 df <- data.frame(
   col1 = c(1, 2, 3),              # named atomic vector
   col2 = c("un", "deux", "trois") # another named atomic vector
   # ,stringsAsFactors = FALSE # default for versions after R 4.1
 )
 # Inspect
 df
 
 # Deconstruct
 # - type
 typeof(df)
 # - attributes
 attributes(df)
 ```
 
 
 ## Data frame, examples 2/2:
 
 ```{r df_functions}
 rownames(df)
 colnames(df)
 names(df) # Same as colnames(df)
 
 nrow(df) 
 ncol(df)
 length(df) # Same as ncol(df)
 ```
 
 Unlike other lists, the length of each vector must be the same (i.e. as many vector elements as rows in the data frame).
 
 ## Tibble
 
 Created to relieve some of the frustrations and pain points created by data frames, tibbles are data frames that are:
 
 -   Lazy (do less)
 -   Surly (complain more)
 
 ## Lazy
 
 Tibbles do not:
 
 -   Coerce strings
 -   Transform non-syntactic names
 -   Recycle vectors of length greater than 1
 
 ## ! Coerce strings
 
 ```{r tbl_no_coerce}
 chr_col <- c("don't", "factor", "me", "bro")
 
 # data frame
 df <- data.frame(
   a = chr_col,
   # in R 4.1 and earlier, this was the default
   stringsAsFactors = TRUE
 )
 
 # tibble
 tbl <- tibble::tibble(
   a = chr_col
 )
 
 # contrast the structure
 str(df$a)
 str(tbl$a)
 
 ```
 
 ## ! Transform non-syntactic names
 
 ```{r tbl_col_name}
 # data frame
 df <- data.frame(
   `1` = c(1, 2, 3)
 )
 
 # tibble
 tbl <- tibble::tibble(
   `1` = c(1, 2, 3)
 )
 
 # contrast the names
 names(df)
 names(tbl)
 ```
 
 ## ! Recycle vectors of length greater than 1
 
 ```{r tbl_recycle, error=TRUE}
 # data frame
 df <- data.frame(
   col1 = c(1, 2, 3, 4),
   col2 = c(1, 2)
 )
 
 # tibble
 tbl <- tibble::tibble(
   col1 = c(1, 2, 3, 4),
   col2 = c(1, 2)
 )
 ```
 
 ## Surly
 
 Tibbles do only what they're asked and complain if what they're asked doesn't make sense:
 
 -   Subsetting always yields a tibble
 -   Complains if cannot find column
 
 ## Subsetting always yields a tibble
 
 ```{r tbl_subset}
 # data frame
 df <- data.frame(
   col1 = c(1, 2, 3, 4)
 )
 
 # tibble
 tbl <- tibble::tibble(
   col1 = c(1, 2, 3, 4)
 )
 
 # contrast
 df_col <- df[, "col1"]
 str(df_col)
 tbl_col <- tbl[, "col1"]
 str(tbl_col)
 
 # to select a vector, do one of these instead
 tbl_col_1 <- tbl[["col1"]]
 str(tbl_col_1)
 tbl_col_2 <- dplyr::pull(tbl, col1)
 str(tbl_col_2)
 ```
 
 ## Complains if cannot find column
 
 ```{r tbl_col_match, warning=TRUE}
 names(df)
 df$col
 
 names(tbl)
 tbl$col
 ```
 
 ## One more difference
 
 **`tibble()` allows you to refer to variables created during construction**
 
 ```{r df_tibble_diff}
 tibble::tibble(
   x = 1:3,
   y = x * 2 # x refers to the line above
 )
 ```
 
 <details>
 <summary>Side Quest: Row Names</summary>
 
 - character vector containing only unique values
 - get and set with `rownames()`
 - can use them to subset rows
 
 ```{r row_names}
 df3 <- data.frame(
   age = c(35, 27, 18),
   hair = c("blond", "brown", "black"),
   row.names = c("Bob", "Susan", "Sam")
 )
 df3
 
 rownames(df3)
 df3["Bob", ]
 
 rownames(df3) <- c("Susan", "Bob", "Sam")
 rownames(df3)
 df3["Bob", ]
 ```
 
 There are three reasons why row names are undesirable:
 
 3. Metadata is data, so storing it in a different way to the rest of the data is fundamentally a bad idea. 
 2. Row names are a poor abstraction for labelling rows because they only work when a row can be identified by a single string. This fails in many cases.
 3. Row names must be unique, so any duplication of rows (e.g. from bootstrapping) will create new row names.
 
 </details>
 
 
 ## Tibles: Printing
 
 Data frames and tibbles print differently
 
 ```{r df_tibble_print}
 df3
 tibble::as_tibble(df3)
 ```
 
 
 ## Tibles: Subsetting
 
 Two undesirable subsetting behaviours:
 
 1. When you subset columns with `df[, vars]`, you will get a vector if vars selects one variable, otherwise you’ll get a data frame, unless you always remember to use `df[, vars, drop = FALSE]`.
 2. When you attempt to extract a single column with `df$x` and there is no column `x`, a data frame will instead select any variable that starts with `x`. If no variable starts with `x`, `df$x` will return NULL.
 
 Tibbles tweak these behaviours so that a [ always returns a tibble, and a $ doesn’t do partial matching and warns if it can’t find a variable (*this is what makes tibbles surly*).
 
 ## Tibles: Testing
 
 Whether data frame: `is.data.frame()`. Note: both data frame and tibble are data frames.
 
 Whether tibble: `tibble::is_tibble`. Note: only tibbles are tibbles. Vanilla data frames are not.
 
 ## Tibles: Coercion
 
 -   To data frame: `as.data.frame()`
 -   To tibble: `tibble::as_tibble()`
 
 ## Tibles: List Columns
 
 List-columns are allowed in data frames but you have to do a little extra work by either adding the list-column after creation or wrapping the list in `I()`
 
 ```{r list_columns}
 df4 <- data.frame(x = 1:3)
 df4$y <- list(1:2, 1:3, 1:4)
 df4
 
 df5 <- data.frame(
   x = 1:3, 
   y = I(list(1:2, 1:3, 1:4))
 )
 df5
 ```
 
 ## Tibbles: Matrix and data frame columns
 
 - As long as the number of rows matches the data frame, it’s also possible to have a matrix or data frame as a column of a data frame.
 - same as list-columns, must either addi the list-column after creation or wrapping the list in `I()`
 
 ```{r matrix_df_columns}
 dfm <- data.frame(
   x = 1:3 * 10,
   y = I(matrix(1:9, nrow = 3))
 )
 
 dfm$z <- data.frame(a = 3:1, b = letters[1:3], stringsAsFactors = FALSE)
 
 str(dfm)
 dfm$y
 dfm$z
 ```
 
 
 ## Exercises 1/4
 
 1. Can you have a data frame with zero rows? What about zero columns?
 
 <details><summary>Answer(s)</summary>
 Yes, you can create these data frames easily; either during creation or via subsetting. Even both dimensions can be zero. Create a 0-row, 0-column, or an empty data frame directly:
 
 ```{r, eval = FALSE}
 data.frame(a = integer(), b = logical())
 #> [1] a b
 #> <0 rows> (or 0-length row.names)
 
 data.frame(row.names = 1:3)  # or data.frame()[1:3, ]
 #> data frame with 0 columns and 3 rows
 
 data.frame()
 #> data frame with 0 columns and 0 rows
 ```
 
 Create similar data frames via subsetting the respective dimension with either 0, `NULL`, `FALSE` or a valid 0-length atomic (`logical(0)`, `character(0)`, `integer(0)`, `double(0)`). Negative integer sequences would also work. The following example uses a zero:
 
 ```{r, eval = FALSE}
 mtcars[0, ]
 #>  [1] mpg  cyl  disp hp   drat wt   qsec vs   am   gear carb
 #> <0 rows> (or 0-length row.names)
 
 mtcars[ , 0]  # or mtcars[0]
 #> data frame with 0 columns and 32 rows
 
 mtcars[0, 0]
 #> data frame with 0 columns and 0 rows
 ```
 
 
 </details>
 
 ## Exercises 2/4
 
 2. What happens if you attempt to set rownames that are not unique?
 
 <details><summary>Answer(s)</summary>
 Matrices can have duplicated row names, so this does not cause problems.
 
 Data frames, however, require unique rownames and you get different results depending on how you attempt to set them. If you set them directly or via `row.names()`, you get an error:
 
 ```{r, eval = FALSE}
 data.frame(row.names = c("x", "y", "y"))
 #> Error in data.frame(row.names = c("x", "y", "y")): duplicate row.names: y
 
 df <- data.frame(x = 1:3)
 row.names(df) <- c("x", "y", "y")
 #> Warning: non-unique value when setting 'row.names': 'y'
 #> Error in `.rowNamesDF<-`(x, value = value): duplicate 'row.names' are not allowed
 ```
 
 If you use subsetting, `[` automatically deduplicates:
 
 ```{r, eval = FALSE}
 row.names(df) <- c("x", "y", "z")
 df[c(1, 1, 1), , drop = FALSE]
 #>     x
 #> x   1
 #> x.1 1
 #> x.2 1
 ```
 
 </details>
 
 ## Exercises 3/4
 
 3. If `df` is a data frame, what can you say about `t(df)`, and `t(t(df))`? Perform some experiments, making sure to try different column types.
 
 <details><summary>Answer(s)</summary>
 Both of `t(df)` and `t(t(df))` will return matrices:
 
 ```{r, eval = FALSE}
 df <- data.frame(x = 1:3, y = letters[1:3])
 is.matrix(df)
 #> [1] FALSE
 is.matrix(t(df))
 #> [1] TRUE
 is.matrix(t(t(df)))
 #> [1] TRUE
 ```
 
 The dimensions will respect the typical transposition rules:
 
 ```{r, eval = FALSE}
 dim(df)
 #> [1] 3 2
 dim(t(df))
 #> [1] 2 3
 dim(t(t(df)))
 #> [1] 3 2
 ```
 
 Because the output is a matrix, every column is coerced to the same type. (It is implemented within `t.data.frame()` via `as.matrix()` which is described below).
 
 ```{r, eval = FALSE}
 df
 #>   x y
 #> 1 1 a
 #> 2 2 b
 #> 3 3 c
 t(df)
 #>   [,1] [,2] [,3]
 #> x "1"  "2"  "3" 
 #> y "a"  "b"  "c"
 ```
 
 </details>
 
 ## Exercises 4/4
 
 4. What does `as.matrix()` do when applied to a data frame with columns of different types? How does it differ from `data.matrix()`?
 
 <details><summary>Answer(s)</summary>
 The type of the result of as.matrix depends on the types of the input columns (see `?as.matrix`):
 
 > The method for data frames will return a character matrix if there is only atomic columns and any non-(numeric/logical/complex) column, applying as.vector to factors and format to other non-character columns. Otherwise the usual coercion hierarchy (logical < integer < double < complex) will be used, e.g. all-logical data frames will be coerced to a logical matrix, mixed logical-integer will give an integer matrix, etc.
 
 On the other hand, `data.matrix` will always return a numeric matrix (see `?data.matrix()`).
 
 > Return the matrix obtained by converting all the variables in a data frame to numeric mode and then binding them together as the columns of a matrix. Factors and ordered factors are replaced by their internal codes. […] Character columns are first converted to factors and then to integers.
 
 We can illustrate and compare the mechanics of these functions using a concrete example. `as.matrix()` makes it possible to retrieve most of the original information from the data frame but leaves us with characters. To retrieve all information from `data.matrix()`’s output, we would need a lookup table for each column.
 
 ```{r, eval = FALSE}
 df_coltypes <- data.frame(
   a = c("a", "b"),
   b = c(TRUE, FALSE),
   c = c(1L, 0L),
   d = c(1.5, 2),
   e = factor(c("f1", "f2"))
 )
 
 as.matrix(df_coltypes)
 #>      a   b       c   d     e   
 #> [1,] "a" "TRUE"  "1" "1.5" "f1"
 #> [2,] "b" "FALSE" "0" "2.0" "f2"
 data.matrix(df_coltypes)
 #>      a b c   d e
 #> [1,] 1 1 1 1.5 1
 #> [2,] 2 0 0 2.0 2
 ```
 
 </details>
 
 
 ## `NULL`
 
 Special type of object that:
 
 -   Length 0
 -   Cannot have attributes
 
 ```{r null, results = 'hide'}
 typeof(NULL)
 #> [1] "NULL"
 
 length(NULL)
 #> [1] 0
 ```
 
 ```{r null_attr, error=TRUE}
 x <- NULL
 attr(x, "y") <- 1
 ```
 
 ```{r null_check}
 is.null(NULL)
 ```
 
 
 ## Digestif
 
 Let is use some of this chapter's skills on the `penguins` data.
 
 ## Attributes
 
 ```{r}
 str(penguins_raw)
 ```
 
 ```{r}
 str(penguins_raw, give.attr = FALSE)
 ```
 
 ## Data Frames vs Tibbles
 
 ```{r}
 penguins_df <- data.frame(penguins)
 penguins_tb <- penguins #i.e. penguins was already a tibble
 ```
 
 ## Printing
 
 * Tip: print out these results in RStudio under different editor themes
 
 ```{r, eval = FALSE}
 print(penguins_df) #don't run this
 ```
 
 ```{r}
 head(penguins_df)
 ```
 
 ```{r}
 penguins_tb
 ```
 
 ## Atomic Vectors
 
 ```{r}
 species_vector_df <- penguins_df |> select(species)
 species_unlist_df <- penguins_df |> select(species) |> unlist()
 species_pull_df   <- penguins_df |> select(species) |> pull()
 
 species_vector_tb <- penguins_tb |> select(species)
 species_unlist_tb <- penguins_tb |> select(species) |> unlist()
 species_pull_tb   <- penguins_tb |> select(species) |> pull()
 ```
 
 <details>
 <summary>`typeof()` and `class()`</summary>
 ```{r}
 typeof(species_vector_df)
 class(species_vector_df)
 
 typeof(species_unlist_df)
 class(species_unlist_df)
 
 typeof(species_pull_df)
 class(species_pull_df)
 
 typeof(species_vector_tb)
 class(species_vector_tb)
 
 typeof(species_unlist_tb)
 class(species_unlist_tb)
 
 typeof(species_pull_tb)
 class(species_pull_tb)
 ```
 
 </details>
 
 ## Column Names
 
 ```{r}
 colnames(penguins_tb)
 ```
 
 ```{r}
 names(penguins_tb) == colnames(penguins_tb)
 ```
 
 ```{r}
 names(penguins_df) == names(penguins_tb)
 ```
 
 ## What if we only invoke a partial name of a column of a tibble?
 
 ```{r, error = TRUE}
 penguins_tb$y 
 ```
 
 ![tibbles are surly!](images/vectors/surly_tibbles.png)
 
 * What if we only invoke a partial name of a column of a data frame?
 
 ```{r}
 head(penguins_df$y) #instead of `year`
 ```
 
 * Is this evaluation in alphabetical order or column order?
 
 ```{r}
 penguins_df_se_sp <- penguins_df |> select(sex, species)
 penguins_df_sp_se <- penguins_df |> select(species, sex)
 ```
 
 ```{r}
 head(penguins_df_se_sp$s)
 ```
 
 ```{r}
 head(penguins_df_sp_se$s)
 ```
 
 
 ## Chapter Quiz 1/5
 
 1. What are the four common types of atomic vectors? What are the two rare types?
 
 <details><summary>Answer(s)</summary>
 The four common types of atomic vector are logical, integer, double and character. The two rarer types are complex and raw.
 </details>
 
 ## Chapter Quiz 2/5
 
 2. What are attributes? How do you get them and set them?
 
 <details><summary>Answer(s)</summary>
 Attributes allow you to associate arbitrary additional metadata to any object. You can get and set individual attributes with `attr(x, "y")` and `attr(x, "y") <- value`; or you can get and set all attributes at once with `attributes()`.
 </details>
 
 ## Chapter Quiz 3/5
 
 3. How is a list different from an atomic vector? How is a matrix different from a data frame?
 
 <details><summary>Answer(s)</summary>
 The elements of a list can be any type (even a list); the elements of an atomic vector are all of the same type. Similarly, every element of a matrix must be the same type; in a data frame, different columns can have different types.
 </details>
 
 ## Chapter Quiz 4/5
 
 4. Can you have a list that is a matrix? Can a data frame have a column that is a matrix?
 
 <details><summary>Answer(s)</summary>
 You can make a list-array by assigning dimensions to a list. You can make a matrix a column of a data frame with `df$x <- matrix()`, or by using `I()` when creating a new data frame `data.frame(x = I(matrix()))`.
 </details>
 
 ## Chapter Quiz 5/5
 
 5. How do tibbles behave differently from data frames?
 
 <details><summary>Answer(s)</summary>
 Tibbles have an enhanced print method, never coerce strings to factors, and provide stricter subsetting methods.
 </details>