# Funktion merge() -----
example("merge")

plot(Petal.Width ~ Petal.Length, data = iris,
     pch = 16, col = iris$Species)

unique(iris$Species)

Farben <- data.frame(
  Farbe = c("orange", "darkgreen", "blue"),
  Art = c("setosa", "versicolor", "virginica"))

iris <- merge(iris, Farben,
              by.x = "Species",
              by.y = "Art")

plot(Petal.Width ~ Petal.Length, data = iris,
     pch = 16, col = iris$Farbe)
legend("bottomright", legend = Farben$Art,
       pch = 16, col = Farben$Farbe)

text(2, 2, labels = "Farbe in RGB",
     col = rgb(220, 76, 24, maxColorValue = 255))

data(iris)
Farben <- data.frame(
  Farbe = c("orange", "darkgreen"),
  Art = c("setosa", "versicolor"))
iris <- merge(iris, Farben,
              by.x = "Species",
              by.y = "Art")

data(iris)
iris <- merge(iris, Farben,
              by.x = "Species",
              by.y = "Art",
              all = TRUE)

# Entscheidungsbaum -----

download.file(url = "https://rstats.kamapu.net/Ressourcen/Dateien/KursDateien.zip",
              destfile = "KursDateien.zip")
unzip("KursDateien.zip", overwrite = TRUE)
unlink("KursDateien.zip")

titanic <- readRDS("titanic.rds")
head(titanic)

boxplot(fare ~ pclass, data = titanic)
boxplot(sibsp ~ sex, data = titanic)

summary(titanic)

# Ein Paket muss installiert werden
install.packages("rpart.plot")

library(rpart)
library(rpart.plot)

survival <- rpart(survived ~ pclass +
          sex + age + parch + sibsp + fare,
          data = titanic, method = "class")
survival

rpart.plot(survival)
prp(survival)

# https://fderyckel.github.io/machinelearningwithr/trees-and-classification.html
browseURL("https://fderyckel.github.io/machinelearningwithr/trees-and-classification.html")

sample(letters, 3)

set.seed(42)
sample(letters, 3)

set.seed(123)
sample(letters, 3)
sample(letters, 3)

# HILFE! ----

# Ich rufe einen Freund/eine Freundin an
?mean
?ggplot
??ggplot