Data types and classes

Lecture 9

Author

Affiliation

Dr. Mine Çetinkaya-Rundel

Duke University
STA 199 - Fall 2025

Published

September 23, 2025

Warm-up

While you wait: Participate 📱💻

Fill in the blanks:

I’m a _____ (first-year, sophomore, junior, senior)

and on Tuesdays I have _____ class(es).

Scan the QR code or go to app.wooclap.com/sta199. Log in with your Duke NetID.

Announcements

Survey: Confidence in STEM courses at Duke

Exam 1:

In class on Thu, Oct 2
Take home Thu, Oct 2 after class until Sat, Oct 4 at noon
Covers lectures 1-10, labs 1-4, and homeworks 1-3
Practice exam to be posted on Friday, exam review on Tue, Sep 30

Recap: The tidyverse package

When you load the tidyverse package, you get access to a suite of packages that work well together for data manipulation and visualization:

library(tidyverse)

── Attaching core tidyverse packages ────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4          ✔ readr     2.1.5     
✔ forcats   1.0.0          ✔ stringr   1.5.1     
✔ ggplot2   3.5.2          ✔ tibble    3.3.0.9004
✔ lubridate 1.9.4          ✔ tidyr     1.3.1     
✔ purrr     1.1.0          
── Conflicts ──────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

You never need to load one of these packages individually after you load the tidyverse, e.g.,

library(dplyr) # not necessary if you already loaded tidyverse in your document/session

Recap: Loading packages

You only need to load a package once per R session or Quarto document.
It’s good practice to load all the packages you need at the start of your document, that’s why the templates I give you usually has a load-packages code cell at the top.

```{r}
#| label: load-packages
#| message: false
library(tidyverse)
library(ggthemes)
library(scales)
# etc.
```

You never need to load these packages again further down in the same document.
If you need a new package further down in the document, go back and add it to the load-packages code cell.

Recap: Pipes

This is not a pipe.

Recap: Pipes

This is not our pipe [operator].

Recap: Pipes

This is our a pipe [operator].

Data types

How many classes do you have on Tuesdays?

survey

# A tibble: 86 × 2
   year       tue_classes
   <chr>      <chr>      
 1 Senior     3          
 2 Sophomore  4          
 3 Sophomore  3          
 4 Junior     4          
 5 Sophomore  2          
 6 First-year 2          
 7 Junior     2          
 8 Sophomore  3          
 9 First-year 2          
10 Senior     3          
# ℹ 76 more rows

Variable types

What type of variable is tue_classes?

survey

# A tibble: 86 × 2
   year       tue_classes
   <chr>      <chr>      
 1 Senior     3          
 2 Sophomore  4          
 3 Sophomore  3          
 4 Junior     4          
 5 Sophomore  2          
 6 First-year 2          
 7 Junior     2          
 8 Sophomore  3          
 9 First-year 2          
10 Senior     3          
# ℹ 76 more rows

Let’s (attempt to) clean it up…

survey <- survey |>
  mutate(
    tue_classes = case_when(
      tue_classes == "one" ~ "1",
      tue_classes == "two" ~ "2",
      tue_classes == "Two" ~ "2",
      .default = tue_classes
    ),
    tue_classes = as.numeric(tue_classes),
    year = case_when(
      year == "Sophmore" ~ "Sophomore",
      year == "Freshman" ~ "First-year",
      .default = year
    )
  ) |>
  filter(year != "29.32%")

survey

# A tibble: 85 × 2
   year       tue_classes
   <chr>            <dbl>
 1 Senior               3
 2 Sophomore            4
 3 Sophomore            3
 4 Junior               4
 5 Sophomore            2
 6 First-year           2
 7 Junior               2
 8 Sophomore            3
 9 First-year           2
10 Senior               3
# ℹ 75 more rows

Data types

Data types in R

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

Logical & character

logical - Boolean values TRUE and FALSE

typeof(TRUE)

[1] "logical"

character - character strings

typeof("First-year")

[1] "character"

Double & integer

double - floating point numerical values (default numerical type)

typeof(2.5)

[1] "double"

typeof(3)

[1] "double"

integer - integer numerical values (indicated with an L)

typeof(3L)

[1] "integer"

typeof(1:3)

[1] "integer"

Concatenation

Vectors can be constructed using the c() function.

Numeric vector:

c(1, 2, 3)

[1] 1 2 3

. . .

Character vector:

c("Hello", "World!")

[1] "Hello"  "World!"

. . .

Vector made of vectors:

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

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

Converting between types

with intention…

x <- 1:3
x

[1] 1 2 3

typeof(x)

[1] "integer"

y <- as.character(x)
y

[1] "1" "2" "3"

typeof(y)

[1] "character"

Converting between types

with intention…

x <- c(TRUE, FALSE)
x

[1]  TRUE FALSE

typeof(x)

[1] "logical"

y <- as.numeric(x)
y

[1] 1 0

typeof(y)

[1] "double"

Converting between types

without intention…

c(2, "Just this one!")

[1] "2"              "Just this one!"

. . .

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!

Converting between types

without intention…

c(FALSE, 3L)

[1] 0 3

. . .

c(FALSE, 1.2)

[1] 0.0 1.2

. . .

c(2L, "two")

[1] "2"   "two"

. . .

c(TRUE, "two")

[1] "TRUE" "two"

Participate 📱💻

What is the output of typeof(c(1.2, 3L))?

"character"
"double"
"integer"
"logical"

Scan the QR code or go to app.wooclap.com/sta199. Log in with your Duke NetID.

Explicit vs. implicit coercion

Explicit coercion:

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.

Data 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

class_years <- factor(
  c(
    "First-year",
    "Sophomore",
    "Sophomore",
    "Senior",
    "Junior"
  )
)
class_years

[1] First-year Sophomore  Sophomore  Senior     Junior    
Levels: First-year Junior Senior Sophomore

typeof(class_years)

[1] "integer"

class(class_years)

[1] "factor"

More on factors

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

glimpse(class_years)

 Factor w/ 4 levels "First-year","Junior",..: 1 4 4 3 2

as.integer(class_years)

[1] 1 4 4 3 2

Dates

today <- as.Date("2025-09-23")
today

[1] "2025-09-23"

typeof(today)

[1] "double"

class(today)

[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(today)

[1] 20354

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

[1] 55.76438

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

df

  x y
1 1 3
2 2 4

When we use the pull() function, we extract a vector from the data frame

df |>
  pull(y)

[1] 3 4

Working with factors

Read data in as character strings

survey

# A tibble: 85 × 2
   year       tue_classes
   <chr>            <dbl>
 1 Senior               3
 2 Sophomore            4
 3 Sophomore            3
 4 Junior               4
 5 Sophomore            2
 6 First-year           2
 7 Junior               2
 8 Sophomore            3
 9 First-year           2
10 Senior               3
# ℹ 75 more rows

But coerce when plotting

ggplot(survey, mapping = aes(x = year)) +
  geom_bar()

Use forcats to reorder levels

survey |>
  mutate(
    year = fct_relevel(year, "First-year", "Sophomore", "Junior", "Senior")
  ) |>
  ggplot(mapping = aes(x = year)) +
  geom_bar()

A peek into forcats

Reordering levels by:

fct_relevel(): hand
fct_infreq(): frequency
fct_reorder(): sorting along another variable
fct_rev(): reversing

…

Changing level values by:

fct_lump(): lumping uncommon levels together into “other”
fct_other(): manually replacing some levels with “other”

…

Application exercise

ae-08-durham-climate-factors

Go to your ae project in RStudio.
If you haven’t yet done so, make sure all of your changes up to this point are committed and pushed, i.e., there’s nothing left in your Git pane.
If you haven’t yet done so, click Pull to get today’s application exercise file: ae-08-durham-climate-factors.qmd.
Work through the application exercise in class, and render, commit, and push your edits by the end of class.