Data classes

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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 if you're curious

• Examples: factors, dates, and data frames

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Factors

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

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

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

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More on factors

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

glimpse(x)

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

as.integer(x)

## [1] 1 2 3 2

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Dates

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

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

typeof(y)

## [1] "double"

class(y)

## [1] "Date"

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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

as.integer(y)

## [1] 18262

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

## [1] 50.03288

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Data frames

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

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

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

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Lists

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

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

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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

df

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

df %>%
  pull(y)

## [1] 3 4

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class: middle

Working with factors

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Read data in as character strings

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", …

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But coerce when plotting

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

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Use forcats to manipulate factors

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

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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]

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

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class: middle

Working with dates

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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)]

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class: middle

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

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

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>

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Step 1. Construct dates

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Step 2. Count bookings per date

.midi[]

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Step 3. Visualise bookings per date

.midi[]

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.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[]

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Step 1. REVISED Construct dates "as dates"

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Step 2. Count bookings per date

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Step 3a. Visualise bookings per date

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Step 3b. Visualise using a smooth curve

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