Lecture notes and solutions to the in-class exercises of session 4 in the spring semester 2023.

#S4 - Lecture notes

Lecture notes and solutions to the in-class exercises of session 4 in the spring semester 2023.

S4-ExerciseSolutions.R

library(tibble)

# Task 1-------------------------------
# Create a factor with the levels "still", "medium", "sparkling", 
#  and arbitrary instances of the three levels

# Get the relative frequencies for "medium" of this factor

# Task 2-------------------------------
# Create a data frame with two columns, one called "nb" containing the 
#  numbers 1 to 5 as double, the other called "char" containing the 
#  numbers 6 to 10 as character

# Transform this data frame into a tibble!

# Extract the second column of this tibble such that you have a vector

S4-LectureNotes.R

# Advanced object types================
library(tibble)
library(dplyr)

## Factors-----------------------------

# Define a factor:
f_1 <- factor(
  x = c(rep("F", 4), 
        rep("D", 5), 
        rep("M", 3)),
  levels = c("D", "F", "M")
  )
f_1
levels(f_1) # Get the levels

# What happens if we do not specify levels explicitly?

f_2 <- factor(
  x = c(rep("F", 4), 
        rep("D", 5), 
        rep("M", 3))
)
f_2
levels(f_2) # Get the levels

# What happens if the vector contains elements not pre-specified as levels?

f_3 <- factor(
  x = c(rep("F", 4), 
        rep("D", 5), 
        rep("M", 3)),
  levels = c("F", "M")
)
f_3
levels(f_3) # Get the levels

typeof(f_3)
is.factor(f_3)

## data.frames and tibbles-----------------------
l_1 <- list(
  "gender" = c(rep("male", 3), rep("female", 2)),
  "height" = c(189, 175, 180, 166, 150)
)
df_1 <- data.frame(
  "gender" = c(rep("male", 3), rep("female", 2)),
  "height" = c(189, 175, 180, 166, 150)
  )
tb_1 <- tibble::as_tibble(df_1)

# What’s the difference between [ and [[?
tb_1["gender"] # Returns the column as a one-column tibble
tb_1[["gender"]] # Returns the underlying vector

c(tb_1[["gender"]], "male") # works as expected
c(tb_1["gender"], "male") # produces weird result

# How do you think you can test for the type of a column vector?
typeof(tb_1[["gender"]]) # NOT typeof(tb_1["gender"])
dplyr::glimpse(tb_1)