Data Types and Data Classes

---

---

## Why should you care about data types?

---

## Example: Cat lovers

A survey asked respondents their name and number of cats. The instructions said to enter the number of cats as a numerical value.

```r
cat_lovers <- read_csv("data/cat-lovers.csv")
```

```
## # A tibble: 60 × 3
##   name           number_of_cats handedness
##   <chr>          <chr>          <chr>     
## 1 Bernice Warren 0              left      
## 2 Woodrow Stone  0              left      
## 3 Willie Bass    1              left      
## 4 Tyrone Estrada 3              left      
## 5 Alex Daniels   3              left      
## # ℹ 55 more rows
```

---

## Oh why won't you work?!

```r
cat_lovers %>%
  summarise(mean_cats = mean(number_of_cats))
```

```
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `mean_cats = mean(number_of_cats)`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA
```

```
## # A tibble: 1 × 1
##   mean_cats
##       <dbl>
## 1        NA
```

---

```r
?mean
```

---

## Oh why won't you still work??!!

```r
cat_lovers %>%
  summarise(mean_cats = mean(number_of_cats, na.rm = TRUE))
```

```
## Warning: There was 1 warning in `summarise()`.
## ℹ In argument: `mean_cats = mean(number_of_cats, na.rm = TRUE)`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA
```

```
## # A tibble: 1 × 1
##   mean_cats
##       <dbl>
## 1        NA
```

---

## Take a breath and look at your data

```r
glimpse(cat_lovers)
```

```
## Rows: 60
## Columns: 3
## $ name           <chr> "Bernice Warren", "Woodrow Stone", "Will…
## $ number_of_cats <chr> "0", "0", "1", "3", "3", "2", "1", "1", …
## $ handedness     <chr> "left", "left", "left", "left", "left", …
```

---

## Let's take another look

.small[
<div class="datatables html-widget html-fill-item" id="htmlwidget-1b4ff99564eb6e8884a5" style="width:100%;height:auto;"></div>
<script type="application/json" data-for="htmlwidget-1b4ff99564eb6e8884a5">{"x":{"filter":"none","vertical":false,"data":[["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","27","28","29","30","31","32","33","34","35","36","37","38","39","40","41","42","43","44","45","46","47","48","49","50","51","52","53","54","55","56","57","58","59","60"],["Bernice Warren","Woodrow Stone","Willie Bass","Tyrone Estrada","Alex Daniels","Jane Bates","Latoya Simpson","Darin Woods","Agnes Cobb","Tabitha Grant","Perry Cross","Wanda Silva","Alicia Sims","Emily Logan","Woodrow Elliott","Brent Copeland","Pedro Carlson","Patsy Luna","Brett Robbins","Oliver George","Calvin Perry","Lora Gutierrez","Charlotte Sparks","Earl Mack","Leslie Wade","Santiago Barker","Jose Bell","Lynda Smith","Bradford Marshall","Irving Miller","Caroline Simpson","Frances Welch","Melba Jenkins","Veronica Morales","Juanita Cunningham","Maurice Howard","Teri Pierce","Phil Franklin","Jan Zimmerman","Leslie Price","Bessie Patterson","Ethel Wolfe","Naomi Wright","Sadie Frank","Lonnie Cannon","Tony Garcia","Darla Newton","Ginger Clark","Lionel Campbell","Florence Klein","Harriet Leonard","Terrence Harrington","Travis Garner","Doug Bass","Pat Norris","Dawn Young","Shari Alvarez","Tamara Robinson","Megan Morgan","Kara Obrien"],["0","0","1","3","3","2","1","1","0","0","0","0","1","3","3","2","1","1","0","0","1","1","0","0","4","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","1","3","3","2","1","1.5 - honestly I think one of my cats is half human","0","0","1","0","1","three","1","1","1","0","0","2"],["left","left","left","left","left","left","left","left","left","left","left","left","left","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","right","ambidextrous","ambidextrous","ambidextrous","ambidextrous","ambidextrous"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th> <\/th>\n      <th>name<\/th>\n      <th>number_of_cats<\/th>\n      <th>handedness<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"columnDefs":[{"orderable":false,"targets":0},{"name":" ","targets":0},{"name":"name","targets":1},{"name":"number_of_cats","targets":2},{"name":"handedness","targets":3}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
]

---

## Sometimes you might need to babysit your respondents

```r
cat_lovers %>%
  mutate(number_of_cats = case_when(
    name == "Ginger Clark" ~ 2,
    name == "Doug Bass"    ~ 3,
    TRUE                   ~ as.numeric(number_of_cats)
    )) %>%
  summarise(mean_cats = mean(number_of_cats))
```

```
## Warning: There was 1 warning in `mutate()`.
## ℹ In argument: `number_of_cats = case_when(...)`.
## Caused by warning:
## ! NAs introduced by coercion
```

```
## # A tibble: 1 × 1
##   mean_cats
##       <dbl>
## 1     0.833
```
]

---

## Always you need to respect data types

```r
cat_lovers %>%
  mutate(
    number_of_cats = case_when(
      name == "Ginger Clark" ~ "2",
      name == "Doug Bass"    ~ "3",
      TRUE                   ~ number_of_cats
      ),
    number_of_cats = as.numeric(number_of_cats)
    ) %>%
  summarise(mean_cats = mean(number_of_cats))
```

```
## # A tibble: 1 × 1
##   mean_cats
##       <dbl>
## 1     0.833
```

---

## Now that we know what we're doing...

```r
*cat_lovers <- cat_lovers %>%
  mutate(
    number_of_cats = case_when(
      name == "Ginger Clark" ~ "2",
      name == "Doug Bass"    ~ "3",
      TRUE                   ~ number_of_cats
      ),
    number_of_cats = as.numeric(number_of_cats)
    )
```

---

## Moral of the story

- If your data does not behave how you expect it to, type coercion upon reading in the data might be the reason.
- Go in and investigate your data, apply the fix, *save your data*, live happily ever after.

---

.hand[.light-blue[now that we have a good motivation for]]  
.hand[.light-blue[learning about data types in R]]

<br>

---

## Data types

---

## Data types in R

- **logical**
- **double**
- **integer**
- **character**
- and some more, but we won't be focusing on those

---

## Logical & character

```r
typeof(TRUE)
```

```
## [1] "logical"
```
]
.pull-right[
**character** - character strings

```r
typeof("hello")
```

```
## [1] "character"
```
]

---

## Double & integer

```r
typeof(1.335)
```

```
## [1] "double"
```

```r
typeof(7)
```

```
## [1] "double"
```
]
.pull-right[
**integer** - integer numerical values (indicated with an `L`)

```r
typeof(7L)
```

```
## [1] "integer"
```

```r
typeof(1:3)
```

```
## [1] "integer"
```
]

---

## Concatenation

Vectors can be constructed using the `c()` function.

```r
c(1, 2, 3)
```

```
## [1] 1 2 3
```

```r
c("Hello", "World!")
```

```
## [1] "Hello"  "World!"
```

```r
c(c("hi", "hello"), c("bye", "jello"))
```

```
## [1] "hi"    "hello" "bye"   "jello"
```

---

## Converting between types

```r
x <- 1:3
x
```

```
## [1] 1 2 3
```

```r
typeof(x)
```

```
## [1] "integer"
```
]
--
.pull-right[

```r
y <- as.character(x)
y
```

```
## [1] "1" "2" "3"
```

```r
typeof(y)
```

```
## [1] "character"
```
]

---

## Converting between types

```r
x <- c(TRUE, FALSE)
x
```

```
## [1]  TRUE FALSE
```

```r
typeof(x)
```

```
## [1] "logical"
```
]
--
.pull-right[

```r
y <- as.numeric(x)
y
```

```
## [1] 1 0
```

```r
typeof(y)
```

```
## [1] "double"
```
]

---

## Converting between types

R will happily convert between various types without complaint when different types of data are concatenated in a vector, and that's not always a great thing!

```r
c(1, "Hello")
```

```
## [1] "1"     "Hello"
```

```r
c(FALSE, 3L)
```

```
## [1] 0 3
```
]
--
.pull-right[

```r
c(1.2, 3L)
```

```
## [1] 1.2 3.0
```

```r
c(2L, "two")
```

```
## [1] "2"   "two"
```
]

---

## Explicit vs. implicit coercion

Let's give formal names to what we've seen so far:

--
- **Explicit coercion** is when you call a function like `as.logical()`, `as.numeric()`, `as.integer()`, `as.double()`, or `as.character()`

--
- **Implicit coercion** happens when you use a vector in a specific context that expects a certain type of vector

---

- Open `type-coercion.qmd`.
- What is the type of the given vectors? First, guess. Then, try it out in R. 
If your guess was correct, great! If not, discuss why they have that type.
]
]

```r
typeof(1)
```

```
## [1] "double"
```
]
.pull-right[

```r
typeof("a")
```

```
## [1] "character"
```
]

and make a guess based on these. Then finally I'd check:
.pull-left[

```r
typeof(c(1, "a"))
```

```
## [1] "character"
```
]
]

---

## Special values

---

## Special values

- `NA`: Not available
- `NaN`: Not a number
- `Inf`: Positive infinity
- `-Inf`: Negative infinity

```r
pi / 0
```

```
## [1] Inf
```

```r
0 / 0
```

```
## [1] NaN
```
]
.pull-right[

```r
1/0 - 1/0
```

```
## [1] NaN
```

```r
1/0 + 1/0
```

```
## [1] Inf
```
]

---

## `NA`s are special ❄️s

```r
x <- c(1, 2, 3, 4, NA)
```

```r
mean(x)
```

```
## [1] NA
```

```r
mean(x, na.rm = TRUE)
```

```
## [1] 2.5
```

```r
summary(x)
```

```
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00    1.75    2.50    2.50    3.25    4.00       1
```

---

## `NA`s are logical

R uses `NA` to represent missing values in its data structures.

```r
typeof(NA)
```

```
## [1] "logical"
```

---

## Mental model for `NA`s

- Unlike `NaN`, `NA`s are genuinely unknown values
- But that doesn't mean they can't function in a logical way
- Let's think about why `NA`s are logical...

```r
# TRUE or NA
TRUE | NA
```

```
## [1] TRUE
```
]
.pull-right[

```r
# FALSE or NA
FALSE | NA
```

```
## [1] NA
```
]

`$\rightarrow$` See next slide for answers...

---

- `NA` is unknown, so it could be `TRUE` or `FALSE`

```r
TRUE | TRUE  # if NA was TRUE
```

```
## [1] TRUE
```

```r
TRUE | FALSE # if NA was FALSE
```

```
## [1] TRUE
```
]
]
.pull-right[
.midi[
- `FALSE | NA`

```r
FALSE | TRUE  # if NA was TRUE
```

```
## [1] TRUE
```

```r
FALSE | FALSE # if NA was FALSE
```

```
## [1] FALSE
```
]
]

- Doesn't make sense for mathematical operations 
- Makes sense in the context of missing data

---
class: middle

## Data classes

---

## Data classes

We talked about *types* so far, next we'll introduce the concept of *classes*

- Vectors are like Lego building blocks
 - We stick them together to build more complicated constructs, e.g. *representations of data* 
 
- The **class** attribute relates to the S3 class of an object which determines its behaviour  
 - You don't need to worry about what S3 classes really mean, but you can read more about it [here](https://adv-r.hadley.nz/s3.html#s3-classes) if you're curious

-  Examples: factors, dates, and data frames

---
## Factors

R uses factors to handle categorical variables, variables that have a fixed and known set of possible values

```r
x <- factor(c("BS", "MS", "PhD", "MS"))
x
```

```
## [1] BS  MS  PhD MS 
## Levels: BS MS PhD
```

---
## More on factors

We can think of factors like character (level labels) and an integer (level numbers) glued together

```r
glimpse(x)
```

```
##  Factor w/ 3 levels "BS","MS","PhD": 1 2 3 2
```

```r
as.integer(x)
```

```
## [1] 1 2 3 2
```

---
## Dates

```r
y <- as.Date("2020-01-01")
y
```

```
## [1] "2020-01-01"
```

```r
typeof(y)
```

```
## [1] "double"
```

```r
class(y)
```

```
## [1] "Date"
```

---
## More on dates

We can think of dates like an inte
ger (the number of days since the origin, 1 Jan 1970) and an integer (the origin) glued together

```r
as.integer(y)
```

```
## [1] 18262
```

```r
as.integer(y) / 365 # roughly 50 yrs
```

```
## [1] 50.03288
```

---
## Data frames

We can think of data frames like like vectors of equal length glued together

```r
df <- data.frame(x = 1:2, y = 3:4)
df
```

```
##   x y
## 1 1 3
## 2 2 4
```

---
## Lists

Lists are a generic vector container vectors of any type can go in them

```r
l <- list(
  x = 1:4,
  y = c("hi", "hello", "jello"),
  z = c(TRUE, FALSE)
)
l
```

```
## $x
## [1] 1 2 3 4
## 
## $y
## [1] "hi"    "hello" "jello"
## 
## $z
## [1]  TRUE FALSE
```

---
## Lists and data frames

-   A data frame is a special list containing vectors of equal length
-   When we use the `pull()` function, we extract a vector from the data frame

```r
df
```

```
##   x y
## 1 1 3
## 2 2 4
```

```r
df %>%
  pull(y)
```

```
## [1] 3 4
```

---
class: middle

## Working with factors

---

## Read data in as character strings

```r
glimpse(cat_lovers)
```

---
## But coerce when plotting

```r
ggplot(cat_lovers, mapping = aes(x = handedness)) +
  geom_bar()
```

---
## Use forcats to manipulate factors

```r
cat_lovers %>%
* mutate(handedness = fct_infreq(handedness)) %>%
  ggplot(mapping = aes(x = handedness)) +
  geom_bar()
```

---
## Come for the functionality

... stay for the logo
<img src="img/forcats-part-of-tidyverse.png" width="40%" style="display: block; margin: auto;" />

.pull-left-wide[
- Factors are useful when you have true categorical data and you want to override the ordering of character vectors to improve display 
- They are also useful in modeling scenarios 
- The **forcats** package provides a suite of useful tools that solve common problems with factors
]

---
.midi[
.your-turn[
### .hand[Your turn!]

- `Hotels + Data types` > `hotels-forcats.qmd`.
- Recreate the x-axis of the following plot.
- **Stretch goal**: Recreate the y-axis.

<img src="07-data-types-classes_files/figure-html/unnamed-chunk-47-1.png" width="90%" style="display: block; margin: auto;" />
]
]

---
class: middle

## Working with dates

---
## Make a date

.pull-left[
<img src="img/lubridate-part-of-tidyverse.png" width="60%" style="display: block; margin: auto;" />
]
.pull-right-[
 - **lubridate** is the tidyverse package that makes dealing with dates a little easier 
]

---
class: middle

---
.question[ Calculate and visualise the number of bookings on any given arrival date.]

```r
hotels %>%
  select(starts_with("arrival_"))
```

```
## # A tibble: 119,390 × 4
##   arrival_date_year arrival_date_month arrival_date_week_number
##               <dbl> <chr>                                 <dbl>
## 1              2015 July                                     27
## 2              2015 July                                     27
## 3              2015 July                                     27
## 4              2015 July                                     27
## 5              2015 July                                     27
## # ℹ 119,385 more rows
## # ℹ 1 more variable: arrival_date_day_of_month <dbl>
```

---
## Step 1. Construct dates

```r
library(glue)
hotels %>%
  mutate(arrival_date = glue("{arrival_date_year} {arrival_date_month} {arrival_date_day_of_month}")
  ) %>% 
  relocate(arrival_date)
```

```
## # A tibble: 119,390 × 33
##   arrival_date hotel      is_canceled lead_time arrival_date_year
##   <glue>       <chr>            <dbl>     <dbl>             <dbl>
## 1 2015 July 1  Resort Ho…           0       342              2015
## 2 2015 July 1  Resort Ho…           0       737              2015
## 3 2015 July 1  Resort Ho…           0         7              2015
## 4 2015 July 1  Resort Ho…           0        13              2015
## 5 2015 July 1  Resort Ho…           0        14              2015
## # ℹ 119,385 more rows
## # ℹ 28 more variables: arrival_date_month <chr>,
## #   arrival_date_week_number <dbl>,
## #   arrival_date_day_of_month <dbl>,
## #   stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>,
## #   adults <dbl>, children <dbl>, babies <dbl>, meal <chr>,
## #   country <chr>, market_segment <chr>, …
```

---
## Step 2. Count bookings per date

```r
hotels %>%
  mutate(arrival_date = glue("{arrival_date_year} {arrival_date_month} {arrival_date_day_of_month}")
  ) %>% 
  count(arrival_date)
```

```
## # A tibble: 793 × 2
##   arrival_date       n
##   <glue>         <int>
## 1 2015 August 1    110
## 2 2015 August 10   207
## 3 2015 August 11   117
## 4 2015 August 12   133
## 5 2015 August 13   107
## # ℹ 788 more rows
```

---
## Step 3. Visualise bookings per date

```r
hotels %>%
  mutate(arrival_date = glue("{arrival_date_year} {arrival_date_month} {arrival_date_day_of_month}")
  ) %>% 
  count(arrival_date) %>% 
  ggplot(aes(x = arrival_date,  y = n, group = 1)) +
  geom_line()
```

<img src="07-data-types-classes_files/figure-html/unnamed-chunk-51-1.png" width="60%" style="display: block; margin: auto;" />
---

.hand[zooming in a bit...]
.question[ Why does the plot start with August when we know our data start in July? 
And why does August 10 come after August 1?]

.midi[]
<img src="07-data-types-classes_files/figure-html/unnamed-chunk-52-1.png" width="60%" style="display: block; margin: auto;" />

---
## Step 1. *REVISED* Construct dates "as dates"

```r
library(glue)
hotels %>%
  mutate(arrival_date = ymd(glue("{arrival_date_year} {arrival_date_month} {arrival_date_day_of_month}"))
  ) %>% 
  relocate(arrival_date)
```

```
## # A tibble: 119,390 × 33
##   arrival_date hotel      is_canceled lead_time arrival_date_year
##   <date>       <chr>            <dbl>     <dbl>             <dbl>
## 1 2015-07-01   Resort Ho…           0       342              2015
## 2 2015-07-01   Resort Ho…           0       737              2015
## 3 2015-07-01   Resort Ho…           0         7              2015
## 4 2015-07-01   Resort Ho…           0        13              2015
## 5 2015-07-01   Resort Ho…           0        14              2015
## # ℹ 119,385 more rows
## # ℹ 28 more variables: arrival_date_month <chr>,
## #   arrival_date_week_number <dbl>,
## #   arrival_date_day_of_month <dbl>,
## #   stays_in_weekend_nights <dbl>, stays_in_week_nights <dbl>,
## #   adults <dbl>, children <dbl>, babies <dbl>, meal <chr>,
## #   country <chr>, market_segment <chr>, …
```

---
## Step 2. Count bookings per date

```r
hotels %>%
  mutate(arrival_date = ymd(glue("{arrival_date_year} {arrival_date_month} {arrival_date_day_of_month}"))
  ) %>% 
  count(arrival_date)
```

```
## # A tibble: 793 × 2
##   arrival_date     n
##   <date>       <int>
## 1 2015-07-01     122
## 2 2015-07-02      93
## 3 2015-07-03      56
## 4 2015-07-04      88
## 5 2015-07-05      53
## # ℹ 788 more rows
```
.midi[]

---
## Step 3a. Visualise bookings per date

```r
hotels %>%
  mutate(arrival_date = ymd(glue("{arrival_date_year} {arrival_date_month} {arrival_date_day_of_month}"))
  ) %>% 
  count(arrival_date) %>% 
  ggplot(aes(x = arrival_date,  y = n, group = 1)) +
  geom_line() 
```

---
## Step 3b. Visualise using a smooth curve