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

#S4 - Lecture notes (SpSe23)

 Lecture notes and solutions to the exercises of session 2 in the spring semester 2023

S4-ExerciseSolutions-I.R

library(tibble)

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

level_vector <- c("still", "medium", "sparkling")
level_vector_reversed <- c("sparkling", "medium", "still")

t1_factor <- factor(
  x = c(rep("still", 4), rep("medium", 3), rep("sparkling", 2)), 
  levels=level_vector_reversed, 
  ordered = TRUE)

# Get the relative frequencies for "medium" of this factor
table(t1_factor) # absolute freqs
table(t1_factor)/length(t1_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

col_1 <- as.double(seq(1, 5))
col_2 <- as.character(seq(6, 10))

df_t2 <- data.frame(
  "nb" = col_1,
  "char" = col_2
)
df_t2

# Transform this data frame into a tibble!
dt_t2 <- tibble::as_tibble(df_t2)
dt_t2
tibble::is_tibble(dt_t2)

# Extract the second column of this tibble such that you have a vector
dt_t2[[2]]
dt_t2[["char"]]

S4-LectureNotes.R

# Advanced object types================

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