Data classes

.title[
# Data classes
]
.subtitle[
## <br><br> Data Science in a Box
]
.author[
### <a href="https://datasciencebox.org/">datasciencebox.org</a>
]

---

layout: true
  
<div class="my-footer">
<span>
<a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a>
</span>
</div>

---

# 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
```

.pull-left\[\] .pull-right\[\]

------------------------------------------------------------------------

## 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 integer (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
```

.pull-left\[\] .pull-right\[\]

------------------------------------------------------------------------

## 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
```

------------------------------------------------------------------------

# Working with factors

------------------------------------------------------------------------

## Read data in as character strings

```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", …
```

------------------------------------------------------------------------

## 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

.pull-left\[ ... stay for the logo\] .pull-right\[\]

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

------------------------------------------------------------------------

.small\[ .your-turn\[ \### .hand\[Your turn!\]
\]\]

------------------------------------------------------------------------

# Working with dates

------------------------------------------------------------------------

## Make a date

.pull-left\[\] .pull-right\[ - **lubridate** is the tidyverse-friendly package that makes dealing with dates a little easier - It's not one of the *core* tidyverse packages, hence it's installed with `install.packages("tidyverse)` but it's not loaded with it, and needs to be explicitly loaded with `library(lubridate)`\]

------------------------------------------------------------------------

.hand\[.light-blue\[ we're just going to scratch the surface of working with dates in R here...\]\]

------------------------------------------------------------------------

.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
## 6              2015 July                                     27
## # … with 119,384 more rows, and 1 more variable:
## #   arrival_date_day_of_month <dbl>
```

------------------------------------------------------------------------

## Step 1. Construct dates

.midi\[\]

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## Step 2. Count bookings per date

.midi\[\]

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## Step 3. Visualise bookings per date

.midi\[\]

------------------------------------------------------------------------

.hand\[zooming in a bit...\]

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

.midi\[\]

------------------------------------------------------------------------

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

.midi\[\]

------------------------------------------------------------------------

## Step 2. Count bookings per date

.midi\[\]

------------------------------------------------------------------------

## Step 3a. Visualise bookings per date

.midi\[\]

------------------------------------------------------------------------

## Step 3b. Visualise using a smooth curve

.midi\[\]