Data types and classes

Lecture 8

Author
Affiliation

Dr. Mine Çetinkaya-Rundel

Duke University
STA 199 - Fall 2024

Published

September 24, 2024

Warm-up

While you wait…

Prepare for today’s application exercise: ae-08-durham-climate-factors

  • Go to your ae project in RStudio.

  • Make sure all of your changes up to this point are committed and pushed, i.e., there’s nothing left in your Git pane.

  • Click Pull to get today’s application exercise file: ae-08-durham-climate-factors.qmd.

  • Wait till the you’re prompted to work on the application exercise during class before editing the file.

Lab 1 feedback

  • Describing a distribution:
    • shape (modality and skewness)
    • center (with calculated value or estimated value or range)
    • spread (with calculated value or estimated value or range)
    • unusual observations (and, where possible, explanation for why they’re unusual)
  • Describing a relationship:
    • direction (positive or negative)
    • form (linear or non-linear)
    • strength
    • unusual observations (and, where possible, explanation for why they’re unusual)

Lab 1 commits

Regrade request policy

https://sta199-f24.github.io/course-syllabus.html#regrade-requests

  • Considered for errors in grade calculation or if a correct answer was mistakenly marked as incorrect

  • Not a mechanism for:

    • disputing the number of points deducted for an incorrect response
    • asking for clarification on feedback (come to office hours instead)

  • Due on Gradescope within a week after an assignment is returned

  • The entire assignment may be regraded, which could result in an adjustment in either direction

  • No regrade requests after the final exam has been administered

Data types

How many classes do you have on Tuesdays?

survey
# A tibble: 61 × 3
   Timestamp     How many classes do you have o…¹ `What year are you?`
   <chr>         <chr>                            <chr>               
 1 9/23/24 19:57 2                                Sophomore           
 2 9/23/24 19:58 3                                First-year          
 3 9/23/24 20:06 2                                Sophomore           
 4 9/23/24 20:09 2                                Sophomore           
 5 9/23/24 21:48 0                                Senior              
 6 9/24/24 9:44  2                                First-year          
 7 9/24/24 10:15 2                                Senior              
 8 9/24/24 10:50 2                                Sophomore           
 9 9/24/24 10:54 3                                First-year          
10 9/24/24 11:08 2                                Senior              
# ℹ 51 more rows
# ℹ abbreviated name: ¹​`How many classes do you have on Tuesdays?`

rename() variables

To make them easier to work with…

survey <- survey |>
  rename(
    tue_classes = `How many classes do you have on Tuesdays?`,
    year = `What year are you?`
  )

Variable types

What type of variable is tue_classes?

survey
# A tibble: 61 × 3
   Timestamp     tue_classes year      
   <chr>         <chr>       <chr>     
 1 9/23/24 19:57 2           Sophomore 
 2 9/23/24 19:58 3           First-year
 3 9/23/24 20:06 2           Sophomore 
 4 9/23/24 20:09 2           Sophomore 
 5 9/23/24 21:48 0           Senior    
 6 9/24/24 9:44  2           First-year
 7 9/24/24 10:15 2           Senior    
 8 9/24/24 10:50 2           Sophomore 
 9 9/24/24 10:54 3           First-year
10 9/24/24 11:08 2           Senior    
# ℹ 51 more rows

Let’s (attempt to) clean it up…

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"
[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
[1] "integer"
y <- as.character(x)
y
[1] "1" "2" "3"
[1] "character"

Converting between types

with intention…

x <- c(TRUE, FALSE)
x
[1]  TRUE FALSE
[1] "logical"
y <- as.numeric(x)
y
[1] 1 0
[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(1.2, 3L)
[1] 1.2 3.0

. . .

c(2L, "two")
[1] "2"   "two"

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

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("2024-09-24")
today
[1] "2024-09-24"
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] 19990
as.integer(today) / 365 # roughly 55 yrs
[1] 54.76712

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: 61 × 3
   Timestamp     tue_classes year      
   <chr>         <chr>       <chr>     
 1 9/23/24 19:57 2           Sophomore 
 2 9/23/24 19:58 3           First-year
 3 9/23/24 20:06 2           Sophomore 
 4 9/23/24 20:09 2           Sophomore 
 5 9/23/24 21:48 0           Senior    
 6 9/24/24 9:44  2           First-year
 7 9/24/24 10:15 2           Senior    
 8 9/24/24 10:50 2           Sophomore 
 9 9/24/24 10:54 3           First-year
10 9/24/24 11:08 2           Senior    
# ℹ 51 more rows

But coerce when plotting

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

Use forcats to reorder levels

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”

…