Data classes

# Data classes
## <br><br> College of the Atlantic

---

# 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
```

```r
typeof(x)
```

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

```r
class(x)
```

```
## [1] "factor"
```
]

---

## 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
```

```r
typeof(df)
```

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

```r
class(df)
```

```
## [1] "data.frame"
```
]

---

## 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[
<img src="img/forcats-part-of-tidyverse.png" width="70%" 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
]

---

- [RStudio Cloud](http://rstd.io/dsbox-cloud) > `AE 05 - Hotels + Data types` > `hotels-forcats.Rmd` > knit
- Recreate the x-axis of the following plot. 
- **Stertch goal:** Recreate the y-axis.
]
]

---

# Working with dates

---

## Make a date

.pull-left[
<img src="img/lubridate-not-part-of-tidyverse.png" width="65%" style="display: block; margin: auto;" />
]
.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)`
]

---

---

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

```
## # A tibble: 119,390 x 4
##   arrival_date_year arrival_date_month arrival_date_wee~1 arriv~2
##               <dbl> <chr>                           <dbl>   <dbl>
## 1              2015 July                               27       1
## 2              2015 July                               27       1
## 3              2015 July                               27       1
## 4              2015 July                               27       1
## 5              2015 July                               27       1
## 6              2015 July                               27       1
## # ... with 119,384 more rows, and abbreviated variable names
## #   1: arrival_date_week_number, 2: arrival_date_day_of_month
```

---

## 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 x 33
##   arrival~1 hotel is_ca~2 lead_~3 arriv~4 arriv~5 arriv~6 arriv~7
##   <glue>    <chr>   <dbl>   <dbl>   <dbl> <chr>     <dbl>   <dbl>
## 1 2015 Jul~ Reso~       0     342    2015 July         27       1
## 2 2015 Jul~ Reso~       0     737    2015 July         27       1
## 3 2015 Jul~ Reso~       0       7    2015 July         27       1
## 4 2015 Jul~ Reso~       0      13    2015 July         27       1
...
```
]

---

## 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 x 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
## 6 2015 August 14   329
## # ... with 787 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="u2-d11-data-classes_files/figure-html/unnamed-chunk-23-1.png" width="80%" style="display: block; margin: auto;" />
]

---

.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[
<img src="u2-d11-data-classes_files/figure-html/unnamed-chunk-24-1.png" width="80%" style="display: block; margin: auto;" />
]

---

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

```r
library(lubridate)

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

```
## # A tibble: 119,390 x 33
##   arrival_date hotel      is_ca~1 lead_~2 arriv~3 arriv~4 arriv~5
##   <date>       <chr>        <dbl>   <dbl>   <dbl> <chr>     <dbl>
## 1 2015-07-01   Resort Ho~       0     342    2015 July         27
## 2 2015-07-01   Resort Ho~       0     737    2015 July         27
## 3 2015-07-01   Resort Ho~       0       7    2015 July         27
## 4 2015-07-01   Resort Ho~       0      13    2015 July         27
...
```
]

---

## 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 x 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
## 6 2015-07-06      75
## # ... with 787 more rows
```
]

---

## 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()
```

<img src="u2-d11-data-classes_files/figure-html/unnamed-chunk-27-1.png" width="80%" style="display: block; margin: auto;" />
]

---

## Step 3b. Visualise using a smooth curve

<img src="u2-d11-data-classes_files/figure-html/unnamed-chunk-28-1.png" width="80%" style="display: block; margin: auto;" />
]

---
## Acknowledgements

* This course builds on the materials from [Data Science in a Box](https://datasciencebox.org/) developed by Mine Çetinkaya-Rundel and are adapted under the [Creative Commons Attribution Share Alike 4.0 International](https://github.com/rstudio-education/datascience-box/blob/master/LICENSE.md)