Data classes

Data tidying and importing

Data Science with R

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 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

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

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

typeof(x)

[1] "integer"

class(x)

[1] "factor"

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

Dates

y <- as.Date("2025-01-01")
y

[1] "2025-01-01"

typeof(y)

[1] "double"

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

as.integer(y)

[1] 20089

as.integer(y) / 365 # roughly 55 yrs

[1] 55.03836

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

typeof(df)

[1] "list"

class(df)

[1] "data.frame"

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

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

Working with factors

Read data in as character strings

handedness <- read_csv("data/handedness.csv")
glimpse(handedness)

Rows: 60
Columns: 2
$ name       <chr> "Abdiel Camacho", "Abram Sanders", "Ady…
$ preference <chr> "left", "ambidextrous", "right", "right…

But coerce when plotting

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

Use forcats to manipulate factors

handedness |>
  mutate(preference = fct_infreq(preference)) |>
  ggplot(mapping = aes(x = preference)) +
  geom_bar()

Come for the functionality

… stay for the logo

• The forcats package provides a suite of useful tools that solve common problems with factors
• 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